diff --git "a/harvarddataversescraper_data.csv" "b/harvarddataversescraper_data.csv"
new file mode 100644--- /dev/null
+++ "b/harvarddataversescraper_data.csv"
@@ -0,0 +1,1114 @@
+title,authors,description,publication_date,url,subject,keywords,file_count,size_bytes,version,license,data_type,citation,methodology,data_source,related_publications,files,research_category,source,scrape_timestamp,research_focus
+Replication Data for: Public Perceptions of the Fairness of Black and White Judges in Racialized and Non-Racialized Cases,"['Means, Taneisha', 'Isaac Unah']","This data should only be used to replicate the analysis for ""Public Perceptions of the Fairness of Black and White Judges in Racialized and Non-Racialized Cases,"" an article to be published in the Journal of Law and Courts. Using the data beyond replication of analysis requires permission from the authors (Taneisha Means and Isaac Unah).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FGCBCI,,[],0,0,,,,,,,[],"[{'name': 'Dofile_Means and Unah_JLC Final Submitted shared for replication.do', 'size': 18878, 'type': 'application/x-stata-syntax', 'description': 'Coding File for Means and Unah (2024) analysis '}, {'name': 'Replication data for Means & Unah articles in JLC.tab', 'size': 76062, 'type': 'text/tab-separated-values', 'description': 'Replication data for Means and Unah (2024)'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: Race, Gender, and Public Opinion toward Black Female Political Elites","['Carr, Sydney']","This study examines the extent to which Black female political elites face a unique combination of disadvantages in the American political arena on account of their dual race-gender identity. More precisely, this project considers whether respondents have more negative perceptions of Black female political elites relative to White female, White male, and Black male political elites. Using Crenshaw’s 1989 framework of intersectionality, hypotheses about the interactive (or “intersectional”) disadvantages faced by Black female political elites are tested using (1) a 1000-respondent public opinion survey fielded with Cloud Research (Prime Panels), and (2) a 1000-respondent survey experiment fielded with Dynata. This project offers one of the first empirical tests of intersectional claims common in the literature. Results suggest a complex story: there are nuanced circumstances under which Black female politicians suffer penalties among American respondents as a consequence of both race and gender.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/2TZKB3,,"['race, gender, political behavior']",0,0,,,,,,,[],"[{'name': 'codebook_pub op bw political elites.pdf', 'size': 127174, 'type': 'application/pdf', 'description': ''}, {'name': 'pub op bw_experiment script.R', 'size': 26606, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'pub op bw_survey script.R', 'size': 29961, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'readme.pdf', 'size': 63486, 'type': 'application/pdf', 'description': ''}, {'name': 'study1_JOP.tab', 'size': 394175, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'study2_JOP.tab', 'size': 411301, 'type': 'text/tab-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Regional impact of COVID-19 on the production and food security of common bean smallholder farmers in Sub-Saharan Africa: Implication for SDG's,"['Nchanji, Eileen Bogweh', 'Lutomia, Cosmas Kweyu']","The study assessed the immediate impacts of the COVID-19 pandemic on bean he study assessed the immediate impacts of the COVID-19 pandemic on bean production and food security across eleven sub-Saharan African countries. The data collection covered production challenges, input access, labour shortages, market disruptions, and changes in household food consumption occassioned by the COVID-19 pandemic. The scope of the study included Eastern, Southern, Central, and Western Africa, focusing on high bean-producing areas. Principal Investigators sought to answer questions about how COVID-19 containment measures affected bean farming, access to resources, and food security, along with the broader implications for Sustainable Development Goals 1 and 2​.
+
+<br><br> Methodology:The data collection methodology involved a mixed approach, using web-based surveys, face-to-face interviews, and mobile phone discussions. The study targeted bean farmers across eleven sub-Saharan African countries during March-April 2020. Gender focal persons in the National Agricultural Research Systems (NARS) led the data collection, focusing on bean production and food security. Participants were selected based on availability, phone access, and physical presence. Survey links were shared via email and WhatsApp, and respondents completed questionnaires or were interviewed directly by phone. The questionnaires included English, Sesotho, and French versions, tailored to national languages​",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TMHVXS,,"['COVID-19', 'common beans', 'Africa', 'Crops for Nutrition and Health']",0,0,,,,,,,[],"[{'name': '01a. English version 1 - Dictionary.xlsx', 'size': 99635, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Dictionary in English'}, {'name': '01b. French Dataset Dictionary.xlsx', 'size': 9091, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Dictionary in French'}, {'name': '02a. English v1 - (Data).tab', 'size': 76260, 'type': 'text/tab-separated-values', 'description': 'Data in English'}, {'name': '02b. French v1 - (Data).tab', 'size': 27542, 'type': 'text/tab-separated-values', 'description': 'Dataset in french'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Black Marriages in Lisbon, Portugal, 1611-1623","['Marquez, John C.']","The Black Marriages in Lisbon, Portugal, 1611-1623 dataset is a contribution to the study of urban slavery and the African diaspora in early modern Portugal. It presents biographical information, such as name, birthplace, legal status, and parish residence, of 571 enslaved and freed Africans (286 men, 285 women) in Lisbon who pursued marriage—288 couples—between the years 1611 and 1623. The dataset also includes biographical information for hundreds of witnesses and over 100 fields. The dataset was created using justificações sumárias (summary justifications, or marriage files) from the archive of the Câmara Eclesiástica de Lisboa, located in Portugal’s National Archives. Justificações sumárias were investigative proceedings that permitted prospective spouses to prove their eligibility for marriage and the veracity of biographical information that might otherwise impede a marriage, namely the ""malicious impediment"" of enslavers. The dataset enriches our understanding of the slave trade to Portugal, the bonds of kinship and community forged in Lisbon, and the history of slave marriage.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VVBUCA,,"['Slavery', 'Lisbon, Portugal']",0,0,,,,,,,[],"[{'name': 'BMLP_Data_20241217.tab', 'size': 259156, 'type': 'text/tab-separated-values', 'description': '""Black Marriages in Lisbon Portugal"" data'}, {'name': 'BMLP_Documentation_20241219.pdf', 'size': 106032, 'type': 'application/pdf', 'description': '""Black Marriages in Lisbon Portugal"" documentation'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Paper IV (Lipman et al. 2024),"['Walker, Daniel', 'Lipman, Dani']","Data products used in 3-D CMZ Paper IV (Lipman et al. 2024).
+
+Files with naming system <leaf_id>_<vel>_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits are extinction column density maps for individual clouds similar to the middle panel of Figure 5, which were compared with the Herschel column density maps for each cloud to obtain the correlation coefficient. 
+
+Files with the naming system <cloud_name>_ExtN70um_max_ffore0.50_conv36_regrid.fits are extinction column density maps for individual cloud submasks (for clouds with multiple velocity components), similar to the middle panel of Figure 5, which were compared with the Herschel column density maps for each cloud to obtain the correlation coefficient. 
+
+CMZ_cat_summary_table.tex contains all information from Table 1.
+
+synth_table.tex includes all information from Table 1, as well as labels the mask which is selected to represent the cloud in l,b projection, used for plotting Figures 7, 10, and 12.
+
+flux_diff_map.fits and flux_ratio_map.fits are FITS images used to create Figure 4.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FBV7T5,,[],0,0,,,,,,,[],"[{'name': '11_-11_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '11_14_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '11_45_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '16_-15_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '16_57_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '17_18_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '17_37_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '17_70_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '21_39_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '21_8_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '26_28_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 23040, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '26_58_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 23040, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '28_14_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '28_84_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '29_74_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 43200, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '29_85_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 43200, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '30_48_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '30_58_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '4_-20_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '4_-27_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '6_-21_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '6_-29_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '7_-37_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '7_-73_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '8_-15_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '8_-54_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': '8_62_ExtN70um_max_ffore0.50_conv36_regrid.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'CMZ_cat_summary_table.tex', 'size': 7850, 'type': 'application/x-tex', 'description': ''}, {'name': 'flux_diff_map.fits', 'size': 115966080, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CRVAL1; \n;\nThis is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CRVAL1; \n'}, {'name': 'flux_ratio_map.fits', 'size': 115966080, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CRVAL1; \n;\nThis is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CRVAL1; \n'}, {'name': 'G0.014-0.016_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.035+0.032_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.068-0.076_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.105-0.08_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.116+0.003_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.143-0.083_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.255+0.02_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.327-0.195_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.342+0.06_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.342-0.085_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 14400, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.379+0.05_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.413+0.048_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.488+0.008_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.645+0.03_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 8640, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.666-0.028_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.716-0.09_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 23040, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.816-0.185_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G0.888-0.044_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 20160, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G1.075-0.049_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 43200, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G1.601+0.012_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G1.652-0.052_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; NAXIS1; NAXIS0; \n'}, {'name': 'G359.475-0.044_ExtN70um_sources_to_nans_ffore0.50_cutout_smoothed_conv36_regrid_isolated.fits', 'size': 11520, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nNAXIS; 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+"Nebulocracy Theory: The Pinnacle of Political Science and the World's Most Perfect, Feasible, and Optimally Designed Government System","['Hyperintelligence Researchers, The Omega Team']","The ""Nebulocracy: Semi-Direct Democratic Government System Architecture"" introduces a visionary and intricate framework for a new form of governance known as Nebulocracy. This system is designed to seamlessly integrate advanced technology, robust ethical frameworks, and extensive citizen participation, aiming to create a more responsive, adaptable, and principled form of democracy. The research delves into the complex and multi-layered structure of Nebulocracy, which is intended to address the intricacies of modern governance while adhering to core ethical principles.
+
+At the heart of Nebulocracy lie five core principles that guide its entire structure and operation. The first principle is Ethical Objectivism, which posits that there are universal ethical truths that can be discovered and applied to governance. This principle serves as the foundation of the system's moral framework, ensuring that decisions are grounded in objective ethical standards. The second principle, Value Integration, recognizes the importance of incorporating the diverse subjective values of citizens into the governance process. This ensures that the system remains responsive to the evolving needs and beliefs of the population, balancing universal ethical principles with individual and cultural perspectives. The third principle, Adaptive Governance, acknowledges the rapidly changing nature of the world and designs the system to evolve and adapt to new challenges, technological advancements, and societal needs. This adaptability ensures that Nebulocracy remains relevant and effective over time. The fourth principle, Citizen Participation, emphasizes the importance of direct and continuous citizen involvement in the governance process. Unlike traditional representative democracies, where citizen involvement is often limited to periodic voting, Nebulocracy provides multiple channels for citizens to engage in decision-making processes, contribute their ideas, and shape the direction of governance. The fifth principle, Specialized Governance, recognizes that effective governance in a complex world requires specialized knowledge and expertise. Therefore, Nebulocracy divides governance into distinct branches, each focused on specific domains, to ensure that decisions are made with a deep understanding of relevant issues.
+
+The Axiological Framework is a crucial component of Nebulocracy, serving as the supreme governing body that ensures all governmental actions align with ethical principles and societal values. This framework consists of several interrelated elements, including the Moral Graph, Value Cards, the Ethical Values Integration System (EVIS), and the Axiological Oversight Council (AOC). The Moral Graph is a dynamic, multidimensional representation of the ethical landscape of society, visually mapping out how different values interact, their relative importance, and their application in various contexts. This graph is continuously updated based on new ethical insights, empirical data, and citizen input, ensuring that it remains current and representative. Value Cards are detailed articulations of specific values, principles, or ethical considerations that citizens, experts, and AI systems can propose. These cards are then evaluated and potentially integrated into the Moral Graph, allowing for a granular and nuanced understanding of ethical concepts. The Ethical Values Integration System (EVIS) is an advanced AI system that manages the Moral Graph and Value Cards, using sophisticated algorithms to process ethical data, update the Moral Graph, and provide real-time ethical analysis for decision-making processes across all branches of government. The Axiological Oversight Council (AOC) is an independent body of ethicists, philosophers, scientists, and cultural representatives that oversees the operation of EVIS and the overall ethical integrity of the government. This council reviews and validates new Value Cards, audits the Moral Graph for consistency and accuracy, and provides guidance on complex ethical issues. Additionally, the Peoples, Wants, Desires, Interests Sovereign Council (PWDISC) focuses on understanding and representing the diverse needs, aspirations, and interests of the citizenry, serving as a bridge between the ethical framework and the lived experiences of people. The Sovereign People's Health and Safety Council is dedicated to ensuring the physical and mental well-being of citizens, integrating health and safety considerations into all aspects of governance. The People's Enquiry Inquisition Branch On Needs Wants Desires Interests Agency serves as a direct channel for citizens to express their needs, wants, desires, and interests, conducting regular surveys, holding public forums, and using AI-assisted analysis to understand and articulate the will of the people. The General Government Advisors Agency Council brings together advisors from various fields to provide comprehensive guidance to all branches of government, ensuring that decisions are informed by a wide range of expertise.
+
+Nebulocracy's governmental structure is highly specialized and multi-tiered, designed to address the complex challenges of modern governance. It consists of several layers, each with specific functions and responsibilities. The Primary Tertiary Governmental Structure is composed of the Seven Omni Branches, each focused on a specific aspect of societal management. These branches include the Omni-Potent Branch, responsible for matters of national security, resource management, and emergency response; the Omni-Present Branch, focused on government accessibility and communication; the Omni-Amor Fati Branch, dedicated to fostering resilience, adaptability, and positive engagement with life's challenges; the Omni-Science Branch, overseeing scientific research, technological development, and evidence-based policymaking; the Omni-Beneficial Branch, focused on social welfare, infrastructure development, and environmental sustainability; the Omni-Benevolent Branch, dedicated to ethical governance, human rights protection, and the promotion of social justice; and the Omni-Kantian Branch, emphasizing rationality, moral duty, and respect for individual autonomy. Each of these branches has its own sub-parliament, allowing for specialized legislative processes within their respective domains, and operates as supraregional organizational superorganisms, coordinating activities across different regions and levels of government. The Secondary Governmental Structure includes the OmniCooperation Constitutional Cern People's United Clarity Parliament (OCCCPUCPCQ), also known as the Clarity Parliament, which serves as the central legislative organ, integrating inputs from all Omni Branches and ensuring that legislation aligns with the ethical framework and constitutional principles. The Omnipresent Central Government (OCCGPUC) is the central executive body, responsible for implementing policies and coordinating activities across all branches and regions. The 7 Prime Ministers Swarm Hive Mind Lead Cabinet is a unique leadership structure consisting of seven prime ministers, each aligned with one of the Omni Branches, working collaboratively as a ""swarm hive mind"" to provide overall direction and coordination for the government. The General Primary Governmental Structure includes foundational elements such as the Supreme Constitution, the highest law of the land, enshrining fundamental rights, principles, and the structure of government; the Supreme Constitutional Institution, responsible for interpreting and upholding the Constitution; the Presidential Constitutional Council (PCC), tasked with enforcing the Constitution and serving as its guardians; the Supreme Government Body Of Human Safety And All Human Flourishing And Thriving Institute (SGBHSAHFTI), focused on comprehensive human well-being and integrating considerations of safety, health, and overall flourishing into all aspects of governance; and the Supreme Constitutional Anti-Corruption Court, a specialized judiciary focused on preventing and prosecuting corruption within the government. The Hive Mind Superintelligence Individualistic Cooperative Swarms Collective Omni-United (HMSICSCOU) is an advanced AI system that supports decision-making across all branches of government, integrating diverse perspectives and vast amounts of data to generate optimal solutions. The Specialized Primary Governmental Structure includes a variety of specialized bodies and institutions that focus on specific aspects of governance, such as the Supreme All Knowing Overwatch Observatory, a high-level monitoring system providing comprehensive oversight of all government activities; the Supreme Freedom of Press Sovereign, ensuring and protecting freedom of the press and media independence; the Supreme Constitutional Human Rights Court, a specialized court dedicated to protecting and enforcing human rights; the Supreme Open Science and Logic Sovereign Council, promoting open scientific inquiry and logical reasoning in governance; and the Supreme Constitutional Dating Compatibility and All Personality Analysis Sovereign Science Council, applying scientific methods to understand personality dynamics and social compatibility, informing policies related to social harmony and personal development.
+
+Nebulocracy places a strong emphasis on active citizen involvement in governance through various mechanisms. The Citizen Engagement Platform (CEP) is a comprehensive digital platform that allows citizens to participate in debates, vote on policies, and contribute ideas to the governance process. AI-Assisted Voting Hubs are facilities that use advanced AI to provide citizens with comprehensive, unbiased information about voting issues, helping them make informed decisions. Citizen Moral Assemblies are randomly selected groups of citizens who deliberate on ethical issues and contribute to the development of the Moral Graph. Public Audits and Citizen Juries are regular audits and juries composed of citizens to evaluate government performance and ensure accountability. Participatory Budgeting processes allow citizens to directly allocate a portion of public budgets to projects they deem important. Town Hall Meetings are regular forums for direct interaction between citizens and government officials, providing opportunities for citizens to voice their concerns and engage in dialogue with decision-makers.
+
+Nebulocracy's economic model is designed to align with its ethical principles and promote universal well-being. The Eubioic Currency (EUB) is a digital, decentralized currency managed to promote ethical economic activities and overall societal well-being. Cybernetic Resource-Based Economics is an advanced economic planning system that uses real-time data and AI analysis to optimize resource allocation based on actual needs and ethical considerations. Catallaxy Blockchain Economics is a system that facilitates spontaneous market order through blockchain technology, allowing for more efficient and ethical economic interactions. Universal High Income (UHI) is an advanced form of universal basic income that aims to provide all citizens with resources for a high quality of life. Skill Validation Blockchains are a system for verifying and recording individual skills and competencies, promoting a meritocratic approach to employment and education.
+
+Nebulocracy leverages advanced technologies to enhance governance. AI-Driven Moral Graph Updates involve continuous analysis and integration of ethical data to keep the Moral Graph current and representative. A Blockchain-Based Governance Ledger is a secure, transparent record of all government actions and decisions. Neural-Symbolic AI Systems are advanced AI that combines symbolic reasoning with neural networks to assist in complex decision-making processes. A Computing Cloud Network is a distributed computing infrastructure that supports all governmental operations and citizen participation platforms. Augmented and Virtual Reality Interfaces are immersive technologies used to enhance citizen engagement and understanding of complex governance issues.
+
+While Nebulocracy heavily relies on advanced technology, it also incorporates mechanisms to function effectively offline. Physical Moral Graph Representations are tangible, interactive models of the Moral Graph displayed in public spaces for citizen engagement. Value Card Libraries are physical repositories of Value Cards accessible in community centers and government buildings. Offline Citizen Assemblies are regular in-person gatherings for citizens to discuss issues, vote, and contribute to governance processes. Paper-Based Documentation Systems are comprehensive paper records of all government actions, decisions, and citizen inputs as a backup to digital systems. Manual Decision-Making Protocols are established procedures for government branches to operate and make decisions without AI assistance when necessary.
+
+When compared to the governance systems of Finland, the USA, and China, Nebulocracy offers several unique features and potential advantages. Finland is known for its highly functional democracy, strong social welfare system, and high levels of citizen trust in government. Nebulocracy shares some similarities with the Finnish system in its emphasis on social welfare and citizen participation but goes further in its use of advanced technology for governance and its more specialized governmental structure. The United States has a federal system with separation of powers between executive, legislative, and judicial branches. Nebulocracy's Seven Omni Branches offer a more specialized and potentially more agile governance structure compared to the US system. Additionally, Nebulocracy's emphasis on direct citizen participation and ethical governance represents a significant departure from the primarily representative democracy of the US. China's governance system is characterized by a strong central government and a single ruling party. While China has been incorporating more technology into its governance, Nebulocracy's approach is fundamentally different in its emphasis on ethical objectivism, citizen participation, and decentralized decision-making. Nebulocracy aims to combine the efficiency often associated with centralized systems like China's with the freedoms and participation levels of Western democracies.
+
+Potential benefits of Nebulocracy include enhanced ethical governance through the comprehensive Axiological Framework, increased citizen engagement and more direct democracy, more specialized and potentially more effective governance through the Seven Omni Branches, greater adaptability to changing circumstances and emerging challenges, and potential for more evidence-based and rational decision-making through the integration of AI and scientific principles. However, implementing such a system would also face significant challenges. The complexity of the system could be difficult for citizens to fully understand and navigate. Heavy reliance on technology could pose risks in case of system failures or cyber attacks. Ensuring the ethical AI systems are truly unbiased and aligned with human values would be a monumental task. The transition from existing governance systems to Nebulocracy would likely be complex and face significant resistance. Balancing the universal ethical principles with diverse cultural values and individual freedoms could prove challenging.
+
+In conclusion, Nebulocracy represents a bold reimagining of democratic governance for the 21st century and beyond. By integrating advanced technologies, ethical frameworks, and extensive citizen participation, it aims to create a system that is more responsive, principled, and effective than traditional forms of government. While implementing such a complex system would undoubtedly face significant challenges, the core principles and mechanisms of Nebulocracy offer valuable insights for improving existing governance structures. The Supreme Constitutional Individualistic-Cooperative Collective Swarms Hive Minds Network Institution (SCICCSHMNI) and the Hive Mind Superintelligence Individualistic Cooperative Swarms Collective Omni-United (HMSICSCOU) are two key components of Nebulocracy that embody its commitment to leveraging collective intelligence and advanced technology for ethical and effective governance. Together, these institutions form a comprehensive governance approach that spans from day-to-day administration to long-term existential considerations, embodying Nebulocracy's vision of a highly advanced, ethical, and adaptive system of governance.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LTKBSH,,[],0,0,,,,,,,[],"[{'name': 'Extension Notes - The Implications of Nebulocracy Theory.pdf', 'size': 2291915, 'type': 'application/pdf', 'description': ''}, {'name': ""Nebulocracy Theory The Pinnacle of Political Science and the World's Most Perfect, Feasible, and Optimally Designed Government System.pdf"", 'size': 1198148, 'type': 'application/pdf', 'description': ''}, {'name': 'The Supreme Constitution of Nebulocracy Aetherarchy.pdf', 'size': 4768472, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Cubic LLZO DFT Database (with 12_44 Li site occupancy),"['Yang, Jack']",This database contains data for all cubic phase LLZO with a specific Li 24d:96h site occupancy ratio of 12:44. These data can be used for training AI models for atomistic modelling.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6NRRKQ,,[],0,0,,,,,,,[],"[{'name': '12_44_full_set_DFT.tar.gz', 'size': 1350295040, 'type': 'application/x-gzip', 'description': 'all VASP output from performing structure optimisation'}, {'name': 'all_data_warmup_MD.npz', 'size': 57342096, 'type': 'application/octet-stream', 'description': 'Numpy file containing the AIMD data from 10 to 2610 K for training the so3krates model.'}, {'name': 'MD_testset_data.tar.gz', 'size': 2462781440, 'type': 'application/x-gzip', 'description': 'Contains the NVT AIMD at 300 K data for 50 LLZO structures that can be used for validating the so3krates model.'}, {'name': 'sampler_lbfgs.tar.gz', 'size': 1234032640, 'type': 'application/x-gzip', 'description': 'Output files for running structural optimisations and MD simulations at 300 and 1500 K for all configurations in the llzo_12_44 set. Contains the Helmholtz free energies obtained from MD simulations using the trained so3krates potential.'}, {'name': 'structure_optimisation_model_4000_tr.tar.gz', 'size': 6737920, 'type': 'application/x-gzip', 'description': 'This folder contains the so3krates checkpoint files to construct the potential for structural optimisation. The model is trained with 4000 training data.'}, {'name': 'training_MD.tar.gz', 'size': 98068480, 'type': 'application/x-gzip', 'description': 'Contains the checkpoint files for the potential trained on molecular dynamic trajectories.'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Cubic LLZO DFT Database (with 11_45 Li site occupancy),"['Yang, Jack']","This database contains data for all cubic phase LLZO with a specific Li 24d:96h site occupancy ratio of 11:45. These data can be used for training AI models for atomistic modelling. 
+
+The starting structures are obtained from the paper:J. Phys. Chem. Lett. 2023, 14, 10257−10262
+
+More specifically, this set contains 65,958 structures in total, and DFT optimisations were only conducted on 19333 structures of them, which are deposited here with file name set_*.tar.gz",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CZPSYC,,[],0,0,,,,,,,[],"[{'name': 'set_2088.tar.gz', 'size': 157634560, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2089.tar.gz', 'size': 475033600, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2090.tar.gz', 'size': 977285120, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2091.tar.gz', 'size': 763719680, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2092.tar.gz', 'size': 762777600, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2093.tar.gz', 'size': 628674560, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2094.tar.gz', 'size': 593582080, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2095.tar.gz', 'size': 563353600, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2096.tar.gz', 'size': 524288000, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2097.tar.gz', 'size': 702228480, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2098.tar.gz', 'size': 511887360, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2099.tar.gz', 'size': 597626880, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2100.tar.gz', 'size': 1286502400, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2101.tar.gz', 'size': 661248000, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2102.tar.gz', 'size': 577034240, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2103.tar.gz', 'size': 594114560, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2104.tar.gz', 'size': 691968000, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2105.tar.gz', 'size': 824616960, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2106.tar.gz', 'size': 690083840, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2107.tar.gz', 'size': 544020480, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2109.tar.gz', 'size': 682301440, 'type': 'application/x-gzip', 'description': 'My description.'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+tetragonal LLZO AIMD data and trained so3krates potential,"['Yang, Jack']",This dataset contains the AIMD data for training the potential for tetragonal LLZO phase. The trained potential is under the /training directory included in the data.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/I9QPD4,,[],0,0,,,,,,,[],"[{'name': 'MD_warmup.tar.gz', 'size': 370114560, 'type': 'application/x-gzip', 'description': 'My description.'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Quantum coherence,"['Liu, Yu-qiang']",coherence,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RWAP0X,,[],0,0,,,,,,,[],"[{'name': 'Fig2a_coherence_gs_Ca.mat', 'size': 4885, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2a_coherence_gs_Cb.mat', 'size': 4883, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2b_coherence_gs_Ctot.mat', 'size': 4932, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2c_coherence_gs_Ca.mat', 'size': 4854, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2c_coherence_gs_Cb.mat', 'size': 4852, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2_coherence_gs_g.mat', 'size': 792, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2d_coherence_gs_Ctot.mat', 'size': 4938, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2x_coherence_gs_eta_a.mat', 'size': 1759, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig2x_coherence_gs_g.mat', 'size': 792, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig3_coherence_gs_mu_a.mat', 'size': 4853, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig3_coherence_gs_mu_b.mat', 'size': 4851, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig4a_coherence_thermal_coupling_Ca.mat', 'size': 4440, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig4b_coherence_thermal_coupling_Cb.mat', 'size': 4684, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig4c_coherence_thermal_coupling_Ca.mat', 'size': 5674, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig4d_coherence_thermal_coupling_Cb.mat', 'size': 5902, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig4e_coherence_thermal_coupling_Ctot.mat', 'size': 4797, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig4f_coherence_thermal_coupling_Ctot.mat', 'size': 6142, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig5_coherence_coupling_mu_a.mat', 'size': 4814, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig5_coherence_coupling_mu_b.mat', 'size': 4999, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig5_coherence_coupling_v2.mat', 'size': 4797, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig5_coherence_v1.mat', 'size': 4871, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6a_coherence_thermal_temperature_Ca.mat', 'size': 7276, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6b_coherence_thermal_temperature_Cb.mat', 'size': 6957, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6c_coherence_thermal_temperature_Ca_.mat', 'size': 5482, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6c_coherence_thermal_temperature_Ca.mat', 'size': 5482, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6d_coherence_thermal_temperature_Cb_.mat', 'size': 5514, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6d_coherence_thermal_temperature_Cb.mat', 'size': 5514, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6e_coherence_thermal_temperature_Ctot.mat', 'size': 7614, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6f_coherence_thermal_temperature_Ctot_.mat', 'size': 5704, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6f_coherence_thermal_temperature_Ctot.mat', 'size': 5704, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig6x_coherence_thermal_temperature_Tem.mat', 'size': 1164, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig7a_coherence_thermal_CR.mat', 'size': 66937, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig7a_coherence_thermal_TL.mat', 'size': 662, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig7a_coherence_thermal_TR.mat', 'size': 662, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig7b_coherence_thermal_CR.mat', 'size': 68876, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig7b_coherence_thermal_gamma.mat', 'size': 838, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig7b_coherence_thermal_TL.mat', 'size': 587, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig8a_coherence_Ctot.mat', 'size': 11661, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'Fig8b_coherence_Ctot.mat', 'size': 11573, 'type': 'application/matlab-mat', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Anais da IX Jornada Científica,"['Faculdade do Futuro - FAF, Repositório Acadêmico e Científico']","Com o objetivo de promover a interdisciplinaridade e a valorização das diversas formas de acesso ao conhecimento, a Faculdade do Futuro realizou junto à comunidade acadêmica em IX Jornada Científica, cujo tema central foi “Integrando Saberes para o Desenvolvimento”. Confira os trabalhos apresentados.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EJLMJH,,['Jornada Científica'],0,0,,,,,,,[],"[{'name': 'Template Jornada Acadêmica 2024.docx.pdf', 'size': 180994, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Russian Operations Against Europe Dataset,"['Schuurman, Bart']","Descriptive data on Russian operations in and against Europe since its invasion of Ukraine in 2022. This dataset has been made by three researchers at Leiden University in the Netherlands: Bart Schuurman, Dion Jordens, and Stijn Willem van 't Land. The dataset's focus is on operations / incidents in the physical domain, such as sabotage, assassination and certain types of influence operations. Most cyber attacks and espionage operations are not included for this reason, unless they had a physical effect (e.g., a cyber attack that caused physical sabotage, or an influence campaign that targeted specific individuals rather than a broad online audience). Further information about the project will be made available on the Leiden University website in late 2024 / early 2025 (see metadata below for link).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TQ0FMQ,,"['Russia', 'Ukraine', 'Russian invasion of Ukraine', 'Europe', 'European Union (EU)', 'Sabotage', 'Influence operation', 'Terrorism', 'Vandalism', 'Disruption']",0,0,,,,,,,[],"[{'name': 'RussianOpsV2_16DEC24.xlsx', 'size': 62427, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+TRABALHOS DE CONCLUSÃO DE CURSO 2024 - FACULDADE DO FUTURO,"['Faculdade do Futuro - FAF, Repositório Acadêmico e Científico']","O Repositório de Trabalhos de Conclusão de Curso (TCC) da Faculdade do Futuro reúne, organiza e disponibiliza os trabalhos acadêmicos defendidos e aprovados pelos estudantes de todos os cursos da instituição. Este acervo tem como finalidade principal promover a divulgação da produção científica e intelectual dos discentes, fortalecendo a cultura de pesquisa e incentivando o desenvolvimento acadêmico e profissional.
+
+O repositório é uma ferramenta acessível para consulta pública, permitindo a preservação e análise de dados acadêmicos , além de possibilitar a citação e inspiração para novas pesquisas. Os TCCs publicados refletem a diversidade de temas envolvidos na instituição e representam o compromisso da Faculdade do Futuro com a excelência acadêmica, a inovação e a importância social dos estudos realizados.
+
+Por meio deste repositório, a Faculdade do Futuro reafirma seu papel como promotora do conhecimento, garantindo que as contribuições acadêmicas de seus estudantes estejam ao alcance de toda a sociedade.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UIU4GN,,[],0,0,,,,,,,[],[],black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: The Vietnam Draft Lottery and Whites' Racial Attitudes: Evidence from the General Social Survey,"['Green, Donald P.', 'Hyman-Metzger, Oliver']","Abstract: The Vietnam Draft Lotteries, which randomly assigned men to military service, enable researchers to assess the long-term effects of interracial contact on racial attitudes. Using a new draft status indicator for respondents to the General Social Surveys 1978-2021, we show that White men who were selected for the draft subsequently expressed less negative attitudes toward Blacks and toward policies designed to help Blacks.  These effects are apparent only for cohorts that were actually drafted into service, suggesting that interracial contact during military service led to attitude change. 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+Hot wire anemometer measurements of flow past orifices of various geometries,"['Karunanethy, Mukesh']","This dataset comprises time series data measured behind different orifice geometries, sampling duration, and probing configurations. All the measurements were made at a consistent sampling rate of 10kHz.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9BB3DB,,"['hot wire anemometry, time series']",0,0,,,,,,,[],"[{'name': 'circle_300slpm_112mm_HF0103_profile.npy', 'size': 83200128, 'type': 'application/octet-stream', 'description': 'Raw time series measured at 112mm distance and 52 different locations across the diameter of the pipe with a circular orifice'}, {'name': 'circle_300slpm_17mm_HF0102_profile.npy', 'size': 20800128, 'type': 'application/octet-stream', 'description': 'Raw time series measured at 17mm distance and 52 different locations across the diameter of the pipe with a circular orifice'}, {'name': 'HF0102_data.txt', 'size': 1297, 'type': 'text/plain', 'description': 'Hotwire calibration data relevant to DS03 measurement'}, {'name': 'HF0103_data.txt', 'size': 1467, 'type': 'text/plain', 'description': 'Hotwire calibration data relevant to DS03 measurement'}, {'name': 'HF0103_data.txt', 'size': 1467, 'type': 'text/plain', 'description': 'Hotwire calibration data relevant to DS02 measurement'}, {'name': 'no_orifice_300slpm_HF0103_profile.npy', 'size': 88000128, 'type': 'application/octet-stream', 'description': 'Raw time series measured at 55 different locations across the diameter of the pipe without an orifice'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Epoca1,"['Guerra, Viviana']",Epoca1,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/L7OJPE,,[],0,0,,,,,,,[],"[{'name': 'coj0m421-sq37-20241021-0109-e91.fits.fz', 'size': 13824000, 'type': 'application/fits', 'description': 'FITS file, 5 HDUs total:\nThe primary HDU; 4 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; FILTER; INSTRUME; NAXIS1; NAXIS0; OBJECT; EXPTIME; CD1_1; TELESCOP; CRVAL1; DATE-OBS; \n'}, {'name': 'coj0m421-sq37-20241021-0110-e91.fits.fz', 'size': 13867200, 'type': 'application/fits', 'description': 'FITS file, 5 HDUs total:\nThe primary HDU; 4 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; FILTER; INSTRUME; NAXIS1; NAXIS0; OBJECT; EXPTIME; CD1_1; TELESCOP; CRVAL1; DATE-OBS; \n'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Modifications to the inverse-square law of radiation detection for finite sized detectors,"['Vanden Berk, Daniel']","Replication data for figures and model fits in the paper.  
+
+Columns are:
+
+dist -- distance between source and detector [centimeters]
+rate -- detector count rate corrected for deadtime [counts/s]
+rateErr -- detector count rate uncertainty [counts/s]",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCVKZX,,['inverse-square law'],0,0,,,,,,,[],"[{'name': 'RateVsDistance.tab', 'size': 990, 'type': 'text/tab-separated-values', 'description': 'Replication Data for: Modifications to the inverse-square law of radiation detection for finite sized detectors'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Comment on 'Evolution Equationsof Nonlinearly Permissible, Coherent Hole Structures Propagating Persistently in Collisionless Plasmas'","['Hutchinson, I.H.']","Recent critical remarks, published in ``Annalen der Physik'', about the present author's analysis of electron and ion holes and their stability are addressed and shown to be misunderstandings and misrepresentations",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4BD7OS,,"['electron holes', 'ion hole', 'plasmas', 'Poisson Equaption', 'Vlasov equation']",0,0,,,,,,,[],"[{'name': '24ja017_archival_manuscript.pdf', 'size': 74483, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Manumissions on Maryland’s Western Shore, 1775-1785","['Van Morgan, Sydney', 'Pasternak, Bailey', 'Wan, Lydia', 'Wang, Qingxi', 'Williams, Hardy']","The “Manumissions on Maryland’s Western Shore, 1775-1785” dataset identifies 618 people manumitted as the result of 83 private land deeds recorded between the years 1775 and 1785 in five Maryland counties: Anne Arundel, Baltimore, Harford, Montgomery and Prince George's. An ongoing project, this dataset is the first effort to compile and make public extant eighteenth-century slave manumission data for Maryland in a single location. Each record includes (a) the name, age, sex and deed type (immediate or delayed) for the enslaved individual manumitted; (b) the name, location and religion (if known) of the slave owner; and (c) the year the deed was
+recorded by the county court. Two-thirds of the enslaved people manumitted during the period under study were freed by Quakers or Methodists. Of the total 618 enslaved people, slightly more than two-fifths were manumitted immediately; others were required to serve further terms of service ranging in length from a few months to several years. Overall, this project departs from economic explanations of Maryland's relatively large free Black population, suggesting that during the Revolutionary Era private manumissions undertaken for moral and religious reasons were at least as important, and possibly more, as those undertaken for other motives.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8FTGTM,,"['Slavery', 'Maryland']",0,0,,,,,,,[],"[{'name': 'MMWS_Data_20240917.tab', 'size': 67599, 'type': 'text/tab-separated-values', 'description': 'Manumissions on Maryland’s Western Shore data'}, {'name': 'MMWS_Documentation_20241210.pdf', 'size': 83733, 'type': 'application/pdf', 'description': 'Manumissions on Maryland’s Western Shore documentation'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Enslaved & Free African Americans in Antebellum Frederick, Maryland",[],"This dataset contains information about 2,280 people who lived in Frederick, Maryland (including Frederick City) between 1785 to 1841. The information about enslaved, freed, or liminal status persons is drawn from the family records, deeds of manumission, bills of sale, and runaway advertisements of three prominent slave owning families: the McPhersons, the Potts, and the Murdochs. Information about free persons is drawn from the 1832 Census of Free Negroes in Frederick County conducted by the Maryland Colonization Society (hereafter referred to as “the 1832 Census”). Data pulled from the 1832 Census constitutes the majority of this dataset. This preeminence of free African Americans, whose freedom status before and after 1832 is unknown, documents Frederick County’s significant free Black population. By including enslaved, liminal status, and free people in one place, this dataset spotlights Frederick County’s diverse African American population and demonstrates the fluidity of Black freedom in antebellum Maryland.",2024-12-13 19:25:45+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TVLIMU,,"['Slavery', 'Frederick, MD']",0,0,,,,"McCoy, Gabrielle, 2024, ""Enslaved & Free African Americans in Antebellum Frederick, Maryland"", https://doi.org/10.7910/DVN/TVLIMU, Harvard Dataverse, V1, UNF:6:s8AgCz5RWBzCHvNdLNN+TA== [fileUNF]",,,,,black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Anglican Baptisms of People of African Descent in Jamaica, 1669-1800","['Strickland, Matthew Black', 'Roberts, Addy', 'Summers, Ashley', 'Cintron, Alex', 'Rudnick, Grace', 'Siracusa, Katherine']","""Anglican Baptisms of People of African Descent in Jamaica, 1669-1800"" is a dataset comprising records of 21,776 individuals baptized in the Church of England in colonial Jamaica between 1669 and 1800. This dataset offers a detailed look into the intersection of race, religion, and society during the peak of the British Atlantic slave trade. It provides invaluable data for historians, genealogists, and educators, particularly those focused on Atlantic World studies, Caribbean history, and the history of slavery. By cataloging names, dates, gender, freedom status, and racial classifications, the dataset allows for an in-depth analysis of the social dynamics within the Anglican Church and its non-white congregants. The dataset also raises significant questions for future research, such as the role of Anglicanism in the social mobility of non-white individuals, and how baptism may have influenced their legal status. It serves as a crucial resource for understanding the complex racial hierarchy in colonial Jamaica and the broader implications of religious practices in the context of slavery.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YNB3AA,,"['Slavery', 'Jamaica']",0,0,,,,,,,[],"[{'name': 'ABADJ_Data_20241015.tab', 'size': 6574516, 'type': 'text/tab-separated-values', 'description': '""Anglican Baptisms of People of African Descent in Jamaica"" data'}, {'name': 'ABADJ_Documentation_20241015.pdf', 'size': 54599, 'type': 'application/pdf', 'description': '""Anglican Baptisms of People of African Descent in Jamaica"" documentation'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"The Nashville Enslaved and Free People of Color Database, 1780-1865","['Sirko, Kelley']","The Nashville Enslaved and Free People of Color Database (NEFPCD) contains information regarding the movements of more than 14,700 Black people and their enslavers in Nashville and Davidson County, Tennessee before Emancipation. Information for this database was compiled using the information found in a total of seventeen Davidson County record books containing wills, inventories, and other probate records dating between 1780 and 1865, all of which are housed at the Metropolitan Government Archives of Nashville-Davidson County.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6K3E3X,,"['Slavery', 'Nashville, TN', 'Davidson County, TN']",0,0,,,,,,,[],"[{'name': 'NEFPCD_Data_20240917.tab', 'size': 6908646, 'type': 'text/tab-separated-values', 'description': '""Nashville Enslaved and Free People of Color Database""'}, {'name': 'NEFPCD_Documentation_20240917.tab', 'size': 1179, 'type': 'text/tab-separated-values', 'description': '""Nashville Enslaved and Free People of Color Database"" documentation'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Black People, Resistance, and the Legal Archive of Slavery in Britain, 1677-1824","['Littlefield, Marci Bounds']","The Black People, Resistance, and the Legal Archive of Slavery in Britain (LASB) dataset contains extracted information about the lives of enslaved people identified in the British judicial record involving enslaved persons from1677 to1824. The data was extracted from the British and Scottish archives and from the Judicial Cases Concerning American Slavery and the Negro, a five-volume three-hundred-page anthology of court transcripts from the local, state, and federal levels and includes English, Canadian, and Jamaican cases. The dataset only includes the British cases concerning slavery and the status of enslaved persons in this time period. While the persons in this story appear in the British legal record, they were trafficked from the early English and Caribbean colonies; thus this research connects the diasporic experiences of enslaved persons and the way Black people navigated the geography of slavery in the British Empire. The dataset calls attention to their lives, not just their bondage, and recognizes what they endured, accomplished, and how they navigated the social, legal, and political landscape of Britain.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EJMHGQ,,"['Slavery', 'Great Britain']",0,0,,,,,,,[],"[{'name': 'LASB_Data_20241119.csv', 'size': 30639, 'type': 'text/csv', 'description': '""Black People, Resistance, and the Legal Archive of Slavery in Britain"" data'}, {'name': 'LASB_Documentation_20241119.pdf', 'size': 80797, 'type': 'application/pdf', 'description': '""Black People, Resistance, and the Legal Archive of Slavery in Britain"" documentation'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Quantum Computing Perspective for Electromagnetic Wave Propagation in Cold Magnetized Plasma,"['Efstratios Koukoutsis, Kyriakos Hizanidis, George Vahala, Min Soe, Linda Vahala, Abhay K. Ram']","Electromagnetic waves are an inherent part of all plasmas - laboratory fusion plasmas or astrophysical plasmas. The conventional methods for studying properties of electromagnetic waves rely on discretization of Maxwell equations suitable for implementing on classical, present day, computers. The traditional methodology is not efficient for quantum computing implementation - a future computational source offering a tantalizing possibility of enormous speed up and a significant reduction in computational cost. This paper addresses two topics relevant to implementing Maxwell equations on a quantum computer. The first is on formulating a quantum Schrödinger representation of Maxwell equations for wave propagation in a cold, inhomogeneous, and magnetized plasma. This representation admits unitary, energy preserving, evolution and conveniently lends itself to appropriate discretization for a quantum computer. Riding on the coattails of these results, the second topic is on developing a sequence of unitary operators which form the basis for a qubit lattice algorithm (QLA). The QLA, suitable for quantum computers, can be implemented and tested on existing classical computers for accuracy as well as scaling of computational time with the number of available processors. In order to illustrate the QLA for Maxwell equations, results are presented from a time evolving, full wave simulation of propagation and scattering of an electromagnetic wave packet by non-dispersive dielectric medium localized in space.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OCSBSY,,"['maxwell equations', 'plasma waves', 'Quantum Computing', 'Quantum Information Science', 'Qubit lattice algorithm', 'Schrodinger equation']",0,0,,,,,,,[],"[{'name': '23ja018_archival_manuscript.pdf', 'size': 1548255, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Iran Uranium Enrichment Post-JCPOA (2019-2024),[],"This project developed an open-source dataset compiling, estimating, and standardizing Iranian uranium enrichment and centrifuge development measurements from the International Atomic Energy Agency (IAEA)’s Verification and monitoring in the Islamic Republic of Iran in light of United Nations Security Council resolution 2231 (2015) reports. The dataset follows enrichment activity at the Natanz Fuel Enrichment Plant (FEP), Fordow Fuel Enrichment Plan (FFEP), and Pilot Fuel Enrichment Plant (PFEP). It begins on March 3, 2019, the last set of IAEA visits that Iran complied with JCPOA requirements, and continues until the most recent report on November 17, 2024.",2024-12-12 15:45:23+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TQH1DN,,[],0,0,,,,"Schiff, Bailey, 2024, ""Iran Uranium Enrichment Post-JCPOA (2019-2024)"", https://doi.org/10.7910/DVN/TQH1DN, Harvard Dataverse, V1",,,,,black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: A Ticking Time Bomb: Restrictions on Abortion Rights and Physical Integrity Rights Abuses,"['Avdan, Nazli', 'Murdie, Amanda', 'Asal, Victor']","Included here are replication materials for A Ticking Time Bomb: Restrictions on Abortion Rights and Physical Integrity Rights, recently accepted at APSR.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BS3DF7,,"['human rights', 'abortion']",0,0,,,,,,,[],"[{'name': '2009clean.xlsx', 'size': 28631, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2009.tab', 'size': 7423, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2009.xlsx', 'size': 53712, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2012clean.xlsx', 'size': 21019, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2012.tab', 'size': 5971, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2012.xlsx', 'size': 35742, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2014clean.xlsx', 'size': 20576, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2014.tab', 'size': 7776, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2014.xlsx', 'size': 23934, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2014.xml', 'size': 153207, 'type': 'text/xml', 'description': ''}, {'name': '2019clean.xlsx', 'size': 22108, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2019.tab', 'size': 7034, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2019.xlsx', 'size': 23035, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2023clean.xlsx', 'size': 21302, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': '2023.tab', 'size': 7245, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2023.xlsx', 'size': 23690, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'Additional Supplemental Material.docx', 'size': 162, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': 'this includes all robustness analyses that did not fit within the page limit for APSR Online Appendix'}, {'name': 'Additional Supplemental Material.pdf', 'size': 1424146, 'type': 'application/pdf', 'description': 'this includes all robustness analyses that did not fit within the page limit for APSR Online Appendix'}, {'name': 'CIRIGHTS_2021.01.21.csv', 'size': 671122, 'type': 'text/csv', 'description': ''}, {'name': 'CIRIGHTS Codebook 2021.01.21.pdf', 'size': 883766, 'type': 'application/pdf', 'description': ''}, {'name': 'ci rights data project about 2-20-21.docx-2.pdf', 'size': 51227, 'type': 'application/pdf', 'description': ''}, {'name': 'CYVT.tab', 'size': 76404, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'GEO.tab', 'size': 354775, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'HumanRightsProtectionScores_v4.01.tab', 'size': 1195267, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'ITT dataset.tab', 'size': 281486, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Log File of Replication Do File for A Ticking Time Bomb.smcl', 'size': 1177194, 'type': 'application/x-stata-smcl', 'description': 'this is a Stata log file of us running the do file to replicate all analyses '}, {'name': 'Log File of Replication Do File for A Ticking Time Bomb - To Make Maps in Manuscript.smcl', 'size': 9720, 'type': 'application/x-stata-smcl', 'description': 'this is a Stata log file of us running the do file to replicate all analyses '}, {'name': 'mapcai1.htm', 'size': 3833, 'type': 'text/html', 'description': ''}, {'name': 'Merging and Cleaning log file for A Ticking Time Bomb.smcl', 'size': 151389, 'type': 'application/x-stata-smcl', 'description': ''}, {'name': 'Merging Do File to create final dataset for A Ticking Time Bomb.do', 'size': 27426, 'type': 'application/x-stata-syntax', 'description': ''}, {'name': 'ne_10m_admin_0_countries.README.html', 'size': 24083, 'type': 'text/html', 'description': ''}, {'name': 'ne_10m_admin_0_countries.VERSION.txt', 'size': 7, 'type': 'text/plain', 'description': ''}, {'name': 'ne_10m_admin_0_countries.zip', 'size': 4877006, 'type': 'application/zipped-shapefile', 'description': ' these are the shape files used with the maps do file to create the maps in the manuscript '}, {'name': 'Online Appendix.pdf', 'size': 1402420, 'type': 'application/pdf', 'description': 'this is a copy of the APSR CUP-available online appendix'}, {'name': 'p5v2018.xls', 'size': 4526592, 'type': 'application/vnd.ms-excel', 'description': ''}, {'name': 'PTS-2022.tab', 'size': 667814, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'qog_std_ts_jan22.dta', 'size': 167384418, 'type': 'application/x-stata-14', 'description': ''}, {'name': 'README.txt', 'size': 1560, 'type': 'text/plain', 'description': 'Readme file'}, {'name': 'replication data from JHR for Dicklitch Nelson LGBT rights paper.tab', 'size': 18173, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'ReplicationDataset.tab', 'size': 7453045, 'type': 'text/tab-separated-values', 'description': 'this is a Stata data file of all variables needed to run all analyses'}, {'name': 'Replication Do File for A Ticking Time Bomb.do', 'size': 90013, 'type': 'application/x-stata-syntax', 'description': 'this is the Stata do file with all necessary commands to replicate the tables and figures in the manuscript, online apendix, and additional supplemental materials'}, {'name': 'Replication Do File for A Ticking Time Bomb - To Make Maps in Manuscript.do', 'size': 5034, 'type': 'application/x-stata-syntax', 'description': 'this is the Stata do file to make the maps in the manuscript'}, {'name': 'Short Variable Descriptions for Indicators in THE CIRIGHTS HUMAN RIGHTS DATASET_2021.01.21.pdf', 'size': 154138, 'type': 'application/pdf', 'description': ''}, {'name': 'SSQ RR Dec 21.do', 'size': 16029, 'type': 'application/x-stata-syntax', 'description': ''}, {'name': 'ucdp-prio-acd-221.tab', 'size': 481338, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'V-Dem Cautionary Notes v12.pdf', 'size': 105545, 'type': 'application/pdf', 'description': ''}, {'name': 'V-Dem Codebook v12.pdf', 'size': 2471388, 'type': 'application/pdf', 'description': ''}, {'name': 'V-Dem-CY-Core-v12.dta', 'size': 403280718, 'type': 'application/x-stata-13', 'description': ''}, {'name': 'V-Dem Suggested Citation v12.pdf', 'size': 151649, 'type': 'application/pdf', 'description': ''}, {'name': 'WBL1971-2023.tab', 'size': 2076865, 'type': 'text/tab-separated-values', 'description': ''}, {'name': ""What's New v12.pdf"", 'size': 139225, 'type': 'application/pdf', 'description': ''}, {'name': 'Working Dataset 8 11 2017.tab', 'size': 2900248, 'type': 'text/tab-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Table 3 (Properties of Nearby Strömgren Spheres),"['Zucker, Catherine']","Summary of the Spatial and Dynamical Properties of Significant Strömgren Spheres (<i>R<sub>s</sub> > 5 pc</i>) in the Solar Vicinity. Columns are as follows:
+<ul>
+    <li>(1) Name of the star</li>
+    <li>(2) Right Ascension</li>
+    <li>(3) Declination</li>
+    <li>(4) Parallax</li>
+    <li>(5) Proper motion in the right ascension direction</li>
+    <li>(6) Proper motion in the declination direction</li>
+    <li>(7) Radial Velocity</li>
+    <li>(8-10) Heliocentric Galactic Cartesian coordinates</li>
+    <li>(11-13) The 3D space motions along <i>x</i>, <i>y</i>, and <i>z</i> with respect to the Local Standard of Rest (LSR)</li>
+    <li>(14) The source of the astrometry (Gaia or Hipparcos)</li>
+    <li>(15) Log of the effective temperature</li>
+    <li>(16) Log of the total number of ionizing photons emitted by the star per second</li>
+    <li>(17) Strömgren radius</li>
+</ul>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GZI7CP,,[],0,0,,,,,,,[],"[{'name': 'Table3_Stromgren.dat', 'size': 2260, 'type': 'text/x-fixed-field', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Table 2 (CLIC Modeled Ages),"['Zucker, Catherine']","Implications of our shell modeling for the ages of clouds in the CLIC and their inferred velocities at the time of formation. Columns are as follows: 
+<ul>
+    <li>(1) Name of the cloud</li>
+    <li>(2) Velocity (<i>v<sub>sh</sub> + &delta;v<sub>sh</sub></i>) that the supernova shell has when it intersects the cloud</li>
+    <li>(3) Velocity that the cloud has when it intersects the supernova shell. Since we assume the clouds formed at this time of intersection, <i>v<sub>cl,intersect</sub></i> is predicted to be the initial velocity of the clouds at birth</li>
+    <li>(4) The modeled age of the cloud, determined by the time of intersection between cloud and shell</li>
+</ul>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LK0XFS,,[],0,0,,,,,,,[],"[{'name': 'Table2_CLIC_Modeled_Ages.dat', 'size': 335, 'type': 'text/x-fixed-field', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Table 1 (Spatial and Dynamical Properties of the CLIC),"['Zucker, Catherine']","The 3D positions and 3D space motions of the CLIC, derived from data taken from Redfield & Linsky 2008 (see their Tables 16 and 18). Columns are as follows: 
+<ul>
+    <li>(1) Name of the cloud</li>
+    <li>(2-3) The central Galactic coordinates of the cloud</li>
+    <li>(4) The distance of the closest star with the cloud’s absorption velocity, equivalent to an upper limit on the cloud's distance</li>
+    <li>(5-7) The velocity magnitude <i>V<sub>0</sub></i> and the direction in Galactic coordinates <i>l<sub>0</sub></i>, <i>b<sub>0</sub></i> that best fits the set of radial velocity components detected in absorption toward each cloud</li>
+    <li>(8-10) The Heliocentric Galactic Cartesian coordinates of the cloud, derived from Columns 2–7</li>
+    <li>(11-13) The 3D space motions along <i>x</i>, <i>y</i>, and <i>z</i> with respect to the Local Standard of Rest (LSR)</li>
+    <li>(14-15) The magnitude of the 3D velocity vector of the cloud with respect to the LSR and its corresponding uncertainty</li>
+</ul>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CQJZYH,,[],0,0,,,,,,,[],"[{'name': 'Table1_CLIC_Properties.dat', 'size': 1258, 'type': 'text/x-fixed-field', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: ""Interseccionalidade da Estrutura Social das Metrópoles Brasileiras: Mudanças e Permanências em Quatro Décadas""","['Ribeiro, Marcelo Gomes', 'Ferreira, Paula Guedes Martins', 'Leandro, Bianca Borges da Silva']","Data from the article Interseccionalidade da Estrutura Social das Metrópoles Brasileiras: Mudanças e Permanências em Quatro Décadas, by Marcelo Gomes Ribeiro, Paula Guedes Martins Ferreira and Bianca Borges da Silva Leandro, published in Revista DADOS v. 68, n.3",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/AHIKPP,,"['social stratification', 'intersectionality', 'social inequality', 'metropolis']",0,0,,,,,,,[],"[{'name': 'Dados das Figuras e Tabelas - Novos Dados.xlsx', 'size': 104898, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'Passo a passo para geração de gráficos e tabelas (1).docx', 'size': 15330, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': ''}, {'name': 'SINTAXE DA PNAD 1982.docx', 'size': 34962, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': ''}, {'name': 'SINTAXE DA PNADC 2022.docx', 'size': 43344, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dataset of TEM Images for Carbon Nanomaterial Classification,"['Wang, Chen']","We employed a convolutional neural network (CNN) for a machine learning and computer vision approach to recognize and classify airborne CNT (carbon nanotubes) and CNF (carbon nanofibers) particles from transmission electron microscope (TEM) images. The model we developed creates a framework for the automatic detection and classification of complex carbon nanostructures, with potential applications in the automated structural classification of other nanomaterials. This dataset comprises 5,323 grayscale TEM images of various nanostructured carbon samples classified into five categories: Cluster, Fiber, Matrix, Matrix-Surface, and Non-CNT. For more information about the data collection and classification process, please refer to https://doi.org/10.1039/D0NA00634C.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5O0SF7,,[],0,0,,,,,,,[],"[{'name': 'CNT-Cluster-0001.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0002.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0003.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0004.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0005.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0006.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0007.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0008.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0009.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0010.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0011.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0012.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0013.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0014.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0015.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0016.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0017.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0018.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0019.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0020.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0021.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0022.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0023.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0024.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0025.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0026.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0027.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0028.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0029.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0030.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0031.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0032.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0033.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0034.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0035.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0036.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0037.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0038.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0039.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0040.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0041.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0042.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0043.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0044.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0045.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0046.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0047.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0048.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0049.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0050.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0051.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0052.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0053.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0054.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0055.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0056.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0057.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0058.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0059.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0060.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0061.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0062.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0063.tif', 'size': 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''}, {'name': 'CNT-Cluster-0085.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0086.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0087.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0088.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0089.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0090.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0091.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0092.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0093.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0094.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0095.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0096.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0097.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0098.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0099.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0100.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0101.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0102.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0103.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0104.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0105.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0106.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0107.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0108.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0109.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0110.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0111.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0112.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0113.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0114.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0115.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0116.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0117.tif', 'size': 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'CNT-Cluster-0128.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0129.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0130.tif', 'size': 350910, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0131.tif', 'size': 350910, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0132.tif', 'size': 350910, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0133.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0134.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0135.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0136.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0137.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0138.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0139.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0140.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0141.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0142.tif', 'size': 1408786, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0143.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0144.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0145.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0146.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0147.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0148.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0149.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0150.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0151.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0152.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0153.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0154.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0155.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0156.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0157.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0158.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0159.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0160.tif', 'size': 4200862, 'type': 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'image/tiff', 'description': ''}, {'name': 'NonCNT-1163.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1164.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1165.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1166.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1167.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1168.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1169.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1170.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1171.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1172.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1173.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1174.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1175.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1176.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1177.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1178.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1179.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1180.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1181.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1182.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1183.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1184.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1185.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1186.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1187.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1188.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1189.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1190.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1191.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1192.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1193.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1194.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1195.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Southern Connecticut State University MoveSpring (TM) Steps Challenge,"['Haughton, Adrian', 'Ash, Garrett']","Step counts, physical activity minutes, physical activity distance, intake questionnaire, app engagement metrics for campus team-based step-counting challenge.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IJNAVQ,,[],0,0,,,,,,,[],"[{'name': 'Database.xlsx', 'size': 108564, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Atomic-optical interferometry in fractured loops: a general solution for Rydberg radio frequency receivers,"['Kasza, Bartosz', 'Borówka, Sebastian', 'Parniak, Michał', 'Wasilewski, Wojciech']","<h1>Simulation Data</h1>
+
+The <code>.npy</code> and <code>.npz</code> files store the results of simulations that are visualized in Figure 3 and Figures 5-12. For all plots we use Matplotlib and Numpy packages. Variable <code>path</code> is the path to de storage with data.
+
+<h3>Plotting the Fig. 3 - a4p_mode_evol</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+plot_data = np.load(path + r""\a4p_mode_evol.npy"")
+re01 = np.real(plot_data[:, 1, 0])
+im01 = np.imag(plot_data[:, 1, 0])
+p11 = plot_data[:, 1, 1]
+x = np.linspace(0, 3, p11.shape[0])
+fig, axes = plt.subplots(nrows=3, figsize=(6, 5), sharex=True)
+axes[0].set_ylabel(r""$\text{Im}{\tilde{\boldsymbol{\rho}}_{10}}$"")
+axes[0].plot(x, im01, label=r""$\text{Im}{\tilde{\boldsymbol{\rho}}_{01}}$"")
+axes[1].set_ylabel(r""$\text{Re}{\tilde{\boldsymbol{\rho}}_{10}}$"")
+axes[1].plot(x, re01, label=r""$\text{Re}{\tilde{\boldsymbol{\rho}}_{01}}$"")
+axes[2].set_ylabel(r""$\tilde{\boldsymbol{\rho}}_{11}$"")
+axes[2].plot(x, p11.real, label=r""$\tilde{\boldsymbol{\rho}}_{11}$"")
+axes[2].set_xlabel(r""$\delta t$ [$2\pi$ rad]"")
+fig.tight_layout()
+plt.show()
+</pre></code>
+
+
+<h3>Plotting the Fig. 5 - mod_transfer_gamma_transit_lo_delta</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+plot_data = np.load(path + r""\mod_transfer_gamma_transit_lo_delta.npy"")
+fig, ax = plt.subplots(figsize=(6, 6))
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+obj = ax.imshow(
+    plot_data,
+    cmap=""YlOrRd"",
+    interpolation=""none"",
+    extent=[omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    aspect=""equal"",
+)
+ax.set_xlabel(""$\Omega_{LO}$ [$2 \pi \cdot$MHz]"")
+ax.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax.set_title(""$\Omega_{LS} = 0.05 \cdot$$2 \pi \cdot$MHz"")
+cbar = fig.colorbar(obj, ax=ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(1)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 6  - mod_transfer_gamma_transit_ls_compare_1_2</h3>
+
+<code><pre>
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+
+plot_data_1, plot_data_2 = np.load(
+    path + r""\mod_transfer_gamma_transit_ls_compare_1_2.npy""
+)
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+fig = plt.figure(figsize=(14, 6))
+gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0)
+kwargs = {
+    ""cmap"": ""YlOrRd"",
+    ""interpolation"": ""none"",
+    ""extent"": [omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    ""aspect"": ""equal"",
+    ""vmin"": min(np.abs(plot_data_1).min(), np.abs(plot_data_2).min()),
+    ""vmax"": max(np.abs(plot_data_1).max(), np.abs(plot_data_2).max()),
+}
+ax1 = fig.add_subplot(gs[0])
+ax2 = fig.add_subplot(gs[1])
+
+im1 = ax1.imshow(np.abs(plot_data_1), **kwargs)
+ax1.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax1.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax1.set_title(r""$d = 1$"")
+im2 = ax2.imshow(np.abs(plot_data_2), **kwargs)
+ax2.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax2.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax2.set_title(r""$d = 2$"")
+ax2.get_yaxis().set_tick_params(labelleft=False)
+ax2.set_ylabel("""")
+
+cbar_ax = fig.add_subplot(gs[2])
+cbar = fig.colorbar(im1, cax=cbar_ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(d)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 7 - bandwidth_gamma_transit_ls</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+color0, color1, color2 = (""#FF4500"", ""#FFB6C1"", ""#800080"")
+plot_data_bandwidth = np.load(
+    path + r""\bandwidth_gamma_transit_ls_plot_data_bandwidth.npy""
+)
+plot_data_mod_transfer = np.load(
+    path + r""\bandwidth_gamma_transit_ls_plot_data_mod_transfer.npy""
+)
+fig, ax1 = plt.subplots()
+ax1.set_xlabel(r""$\Omega_{LS}~[2\pi \cdot$MHz]"")
+ax1.set_ylabel(r""Bandwidth [$2\pi \cdot$MHz]"")
+ax1.plot(*plot_data_bandwidth, color=""blue"", label=""Bandwidth"")
+ax1.tick_params(axis=""y"")
+move = 0.1
+ax1.set_ylim(plot_data_bandwidth[1].min() - move, plot_data_bandwidth[1].max() + move)
+ax2 = ax1.twinx()
+ax2.set_ylabel(r""$|\alpha_{01}^{(1)}|$"")
+ax2.plot(
+    *plot_data_mod_transfer,
+    color=color2,
+    label=r""$|\alpha_{01}^{(1)}(\delta = 0)|$"",
+)
+ax2.tick_params(axis=""y"")
+fig.legend(loc=""lower left"", bbox_to_anchor=(0.17, 0.15))
+fig.tight_layout()
+</pre></code>
+
+<h3>Plotting the Fig. 8 - calculated detuned smaller peak heights</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+color0, color1, color2 = (""#FF4500"", ""#FFB6C1"", ""#800080"")
+plot_data_bandwidth = np.load(
+    path + r""\bandwidth_gamma_transit_lo_plot_data_bandwidth.npy""
+)
+plot_data_mod_transfer = np.load(
+    path + r""\bandwidth_gamma_transit_lo_plot_data_mod_transfer.npy""
+)
+fig, ax1 = plt.subplots()
+ax1.set_xlabel(r""$\Omega_{LO}~[2\pi \cdot$MHz]"")
+ax1.set_ylabel(r""Bandwidth [$2\pi \cdot$MHz]"")
+ax1.plot(*plot_data_bandwidth, color=""blue"", label=""Bandwidth"")
+ax1.tick_params(axis=""y"")
+move = 0.1
+ax1.set_ylim(plot_data_bandwidth[1].min() - move, plot_data_bandwidth[1].max() + move)
+ax2 = ax1.twinx()
+ax2.set_ylabel(r""$|\alpha_{01}^{(1)}|$"")
+ax2.plot(
+    *plot_data_mod_transfer,
+    color=color2,
+    label=r""$|\alpha_{01}^{(1)}(\delta = 0)|$"",
+)
+ax2.tick_params(axis=""y"")
+fig.legend(loc=""lower left"", bbox_to_anchor=(0.17, 0.15))
+fig.tight_layout()
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 9 - mod_transfer_cold_lo_delta</h3>
+<code><pre>
+plot_data = np.load(path + r""\mod_transfer_cold_lo_delta.npy"")
+fig, ax = plt.subplots(figsize=(6, 6))
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+obj = ax.imshow(
+    plot_data,
+    cmap=""YlOrRd"",
+    interpolation=""none"",
+    extent=[omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    aspect=""equal"",
+)
+ax.set_xlabel(""$\Omega_{LO}$ [$2 \pi \cdot$MHz]"")
+ax.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax.set_title(""$\Omega_{LS} = 0.05 \cdot$$2 \pi \cdot$MHz"")
+cbar = fig.colorbar(obj, ax=ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(1)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 10 - mod_transfer_cold_ls_compare_1_2_3</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+
+plot_data_1, plot_data_2, plot_data_3 = np.load(
+    path + r""\mod_transfer_cold_ls_compare_1_2_3.npy""
+)
+fig = plt.figure(figsize=(18, 6))
+gs = gridspec.GridSpec(1, 4, width_ratios=[1, 1, 1, 0.05], wspace=0.3)
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+kwargs = {
+    ""cmap"": ""YlOrRd"",
+    ""interpolation"": ""none"",
+    ""extent"": [omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    ""aspect"": ""equal"",
+    ""vmin"": min(
+        np.abs(plot_data_1).min(), np.abs(plot_data_2).min(), np.abs(plot_data_3).min()
+    ),  # tripple
+    ""vmax"": max(
+        np.abs(plot_data_1).max(), np.abs(plot_data_2).max(), np.abs(plot_data_3).max()
+    ),  # tripple
+}
+ax1 = fig.add_subplot(gs[0])
+ax2 = fig.add_subplot(gs[1])
+ax3 = fig.add_subplot(gs[2])
+
+
+im1 = ax1.imshow(np.abs(plot_data_1), **kwargs)
+ax1.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax1.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax1.set_title(r""$d = 1$"")
+im2 = ax2.imshow(np.abs(plot_data_2), **kwargs)
+ax2.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax2.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax2.set_title(r""$d = 2$"")
+ax2.get_yaxis().set_tick_params(labelleft=False)
+ax2.set_ylabel("""")
+im3 = ax3.imshow(np.abs(plot_data_3), **kwargs)
+ax3.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax3.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax3.get_yaxis().set_tick_params(labelleft=False)
+ax3.set_ylabel("""")
+ax3.set_title(r""$d = 3$"")
+
+cbar_ax = fig.add_subplot(gs[3])
+cbar = fig.colorbar(im1, cax=cbar_ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(d)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 11 - mod_compare_delta_demod</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+plot_data = np.load(path + r""\mod_compare_delta_demod.npy"")
+omega_seq = np.linspace(0.05, 15, 500)
+for m in [1, 2, 3]:
+    plt.plot(
+        omega_seq,
+        plot_data[m - 1, :],
+        label=rf""$m = {m}$"",
+    )
+plt.legend()
+plt.ylabel(r""$|\tilde{\boldsymbol{\rho}}_{01}^{m}$|"")
+plt.xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+plt.yscale(""log"")
+</pre></code>
+
+<h3>Plotting the Fig. 12 - mode_comparison_lattice</h3>
+<code><pre>
+import numpy as np
+import matplotlib.pyplot as plt
+
+data = np.load(path + r""\mode_comparison_lattice.npz"")
+lst = data.files
+plot_data = [data[item] for item in lst]
+fig, axes = plt.subplots(2, 2, figsize=(10, 8))
+axes = axes.ravel()
+full_N_seq = list(range(3, 16, 2))
+mode_to_check = [0, 1, 2, 3]
+for i, pd in enumerate(plot_data):
+    ax = axes[i]
+    ax.scatter(full_N_seq[-len(pd) :], pd)
+    ax.set_ylabel(
+        f""|$\\tilde{{\\boldsymbol{{\\rho}}}}_{{10}}^{{({mode_to_check[i]})}}$|""
+    )
+    ax.set_xlabel(""N"")
+    ax.set_xticks(np.arange(1, max(full_N_seq) + 1, 2))
+    ax.set_title(f""$m$ = {mode_to_check[i]}"")
+plt.tight_layout()
+</pre></code>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TLKOJT,,[],0,0,,,,,,,[],"[{'name': 'a4p_mode_evol.npy', 'size': 154496, 'type': 'application/octet-stream', 'description': 'Fig. 3 data'}, {'name': 'bandwidth_gamma_transit_lo_plot_data_bandwidth.npy', 'size': 6368, 'type': 'application/octet-stream', 'description': 'Fig. 8 data 1'}, {'name': 'bandwidth_gamma_transit_lo_plot_data_mod_transfer.npy', 'size': 8128, 'type': 'application/octet-stream', 'description': 'Fig. 8 data 2'}, {'name': 'bandwidth_gamma_transit_ls_plot_data_bandwidth.npy', 'size': 5696, 'type': 'application/octet-stream', 'description': 'Fig. 7 data 1'}, {'name': 'bandwidth_gamma_transit_ls_plot_data_mod_transfer.npy', 'size': 8128, 'type': 'application/octet-stream', 'description': 'Fig. 7 data 2'}, {'name': 'mod_compare_delta_demod.npy', 'size': 6128, 'type': 'application/octet-stream', 'description': 'Fig. 11 data'}, {'name': 'mode_comparison_lattice.npz', 'size': 1090, 'type': 'application/octet-stream', 'description': 'Fig. 12 data'}, {'name': 'mod_transfer_cold_lo_delta.npy', 'size': 1000128, 'type': 'application/octet-stream', 'description': 'Fig. 9 data'}, {'name': 'mod_transfer_cold_ls_compare_1_2_3.npy', 'size': 6000128, 'type': 'application/octet-stream', 'description': 'Fig. 10 data'}, {'name': 'mod_transfer_gamma_transit_lo_delta.npy', 'size': 1000128, 'type': 'application/octet-stream', 'description': 'Fig. 5 data'}, {'name': 'mod_transfer_gamma_transit_ls_compare_1_2.npy', 'size': 4000128, 'type': 'application/octet-stream', 'description': 'Fig. 6 data'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Code for: Mechanical memory of neurons induced by mechanosensitive channel inactivation,"['Yang, Yuehua']","code for the paper ""Mechanical memory of neurons induced by mechanosensitive channel inactivation""",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0VHADN,,[],0,0,,,,,,,[],"[{'name': 'Code-2024.12.10.rar', 'size': 2295968, 'type': 'application/x-rar-compressed', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+E1,['Bolivian Virtual Observatory'],Epoca 1,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BRORU6,,['E1'],0,0,,,,,,,[],"[{'name': 'lsc0m476-sq34-20240904-0209-e91.fits', 'size': 51989760, 'type': 'application/fits', 'description': 'FITS file, 4 HDUs total:\nThe primary HDU; 1 Table HDU(s) 3 Image HDU(s); \nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; FILTER; NAXIS; INSTRUME; NAXIS1; NAXIS0; OBJECT; EXPTIME; CD1_1; TELESCOP; CRVAL1; DATE-OBS; \n'}, {'name': 'lsc0m476-sq34-20240904-0210-e91.fits', 'size': 51992640, 'type': 'application/fits', 'description': 'FITS file, 4 HDUs total:\nThe primary HDU; 1 Table HDU(s) 3 Image HDU(s); \nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; FILTER; NAXIS; INSTRUME; NAXIS1; NAXIS0; OBJECT; EXPTIME; CD1_1; TELESCOP; CRVAL1; DATE-OBS; \n'}, {'name': 'lsc0m476-sq34-20240904-0211-e91.fits', 'size': 51972480, 'type': 'application/fits', 'description': 'FITS file, 4 HDUs total:\nThe primary HDU; 1 Table HDU(s) 3 Image HDU(s); \nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; FILTER; NAXIS; INSTRUME; NAXIS1; NAXIS0; OBJECT; EXPTIME; CD1_1; TELESCOP; CRVAL1; DATE-OBS; \n'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Digitized Images of the best Palomar 5m (200 inch) photographic plates of Cassiopeia A taken between October 1951 and September 1989.,"['Fesen, Robert', 'Patnaude, Daniel']","Palomar Observatory 's 5m (200 inch) telescope images represent the best ground-based photographs taken of the Cassiopeia A supernova remnant (Cas A) during the period from 1951 to 1989. The first Palomar image was taken in September 1951 by  Walter Baade less than a month after F. Graham Smith announced the first accurate
+position of the Cas A remnant based on radio measurements. <br>
+
+Most of these plates were obtained by Walter Baade, Rudolph Minkowski and Sidney van den Bergh, with a few addition ones taken by Halton Arp, Joseph Miller and Donald Shane at the request of Baade and/or Minkowski. These Palomar images formed the basis of the first investigations of the Cas A's optical emission structure and expansion properties. <br>
+
+The images were obtained at the prime focus of the 5m telescope on photographic plates 5 x 7 inches in size with various Kodak photographic emulsions and optical filters.  They were often taken with the prime focus Ross corrector and have a plate scale of $0.1''$ per millimeter.  <br>
+
+This 12 image collection is those good to fair quality images deemed to be among the best taken. Photographic plates were first cleaned of surface dust and then digitized using the high- speed digitizing scanning machine DASCH  (Simcoe 2006, Laycock 2010) at the Harvard–Smithsonian Center for Astrophysics. Only the central $7.7' \times 5.9'$ region of these plates were scanned and then digitized at a pixel scale of 0.42$''$ pixel$^{-1}$. However, three of some of the best images were separately scanned at a higher resolution ($0.141''$ pixel$^{-1}$) using the Yale Astronomy Department's PDS microdensitometer. <br>
+
+Most images are in FITS format with WCS coordinates and are typically 3.7 MB in size where as three ""HiRes"" versions of plates PH3033S, PH563vB, and PH7252vB are 26 MB in size and cover a somewhat larger scanned FOV thus covering the remnant's NE ejecta jet more completely.
+<br>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OBHZB8,,"['optical', 'supernova remnant']",0,0,,,,,,,[],"[{'name': '1951Nov03_PH563B_103aE_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1951Nov03_PH563B_HiRes.fits', 'size': 27063360, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; \n'}, {'name': '1951Oct31_PH553B_103aE_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1957Sept21_PH1732B_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1958Aug11_PH3033S_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1958Aug11_PH3033S_HiRes.fits', 'size': 27063360, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; \n'}, {'name': '1971Aug29_PH5946A_103aF_RG630.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1972Sept10_PH6249vB_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1976July02_PH7252vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1976July02_PH7252vB_HiRes.fits', 'size': 27063360, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; \n'}, {'name': '1983July12_PH8192vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1989Sept29_PH8206_098_RG645.fits', 'size': 2623680, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Digitized Images of Palomar 5m photographic plates of Cassiopeia A taken between 1951 thru 1989,"['Fesen, Robert', 'Patnaude, Daniel']","Palomar Observatory 's 5m (200 inch) telescope images represent the best ground-based photographs taken of the Cassiopeia A supernova remnant (Cas A) during the period from 1951 to 1989. The first Palomar image was taken in September 1951 by  Walter Baade less than a month after F. Graham Smith announced the first accurate
+position of the Cas A remnant based on radio measurements. <br>
+
+Most of these plates were obtained by Walter Baade, Rudolph Minkowski and Sidney van den Bergh, with a few addition ones taken by Halton Arp, Joseph Miller and Donald Shane at the request of Baade and/or Minkowski. These Palomar 5m images formed the basis of the first investigations of the Cas A's optical emission structure and expansion properties. <br>
+
+The images were obtained at the prime focus of the 5m telescope on photographic plates 5 x 7 inches in size with various Kodak photographic emulsions and optical filters.  They were often taken with the prime focus Ross corrector and have a plate scale of $0.1''$ per millimeter.  <br>
+
+This collection consists of 42 good to fair quality images taken between 1951 and 1989 along with jpg images of the plate envelops which give exposure details. The photographic plates were first cleaned of surface dust and then digitized using the high- speed digitizing scanning machine DASCH  (Simcoe2006, Laycock2010) at the Harvard–Smithsonian Center for Astrophysics. Only the central $7.7' \times 5.9'$ region of these plates were scanned and then digitized at a pixel scale of 0.42$''$ pixel$^{-1}$. However, three of the best images were separately scanned at a higher resolution ($0.141''$ pixel$^{-1}$) using the Yale Astronomy Department's PDS microdensitometer by John Thorstensen. <br>
+
+The images are in FITS format with WCS coordinates and are typically 3.7 MB in size.
+Higher resolution versions of three images (PH3033S, PH563vB, and PH7252vB)
+are available in a separate dateset,
+<br>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/Q0HDOW,,[],0,0,,,,,,,[],"[{'name': '1951Nov03_PH563B_103aE_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1951Oct31_PH553B_103aE_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1951Sept09_PH520B_103aO_GG1.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1953Aug11_PH778B_103aD_GG1.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1953Aug13_PH793B_103aE_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1954Dec23_PH1159B_103aO_GG1.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1954Nov25_PH232M_103aJ_GG11.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1954Nov26_PH236M_103aE_OR1.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1957Sept21_PH1732B_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1957Sept22_PH1736B_103aO_GG1.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1958Aug11_PH3033S_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1965Aug30_PH4815_103aE_OR1.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1967Oct04_PH5107A_103aE_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1968Sept26_PH5254vB_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1968Sept26_PH5255vB_103aD_GG11.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1969Oct13_PH5405vB_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1970Sept01_PH5643vB_103aJ_GG475.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1970Sept03_PH5648vB_103aJ_GG475.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1970Sept04_PH5659vB_103aE_RG2.fits', 'size': 3700800, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1971Aug29_PH5946A_103aF_RG630.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1972Sept09_PH6236vB_IIIaJ_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1972Sept10_PH6249vB_103aF_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1973Aug01_PH6562vB_103aJ_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1973Aug04_PH6573vB_098_SII.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1973Aug13_PH6891vB_098_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1973July31_PH6555vB_098_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1974Aug14_PH6902vB_IIIaJ_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1974Aug15_PH6914vB_127_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1975July16_PH7064vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1975July17_PH7071vb_103aJ_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1975July18_PH7077vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1976July02_PH7252vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1976June30_PH7231_127_04_gg7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1977Oct08_PH7433vB_098_RG2.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1977Oct10_PH7445vB_103aJ_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1980July13_PH7766vB_098_RG45.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1980July14_PH7771vB_IIIaJ_GG7.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1983July12_PH8192vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1983July14_PH8201vB_098_SII.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1989Sept28_PH8202vB_098_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1989Sept28_PH8204_098_SII.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': '1989Sept29_PH8206_098_04_RG645.fits', 'size': 3697920, 'type': 'application/fits', 'description': 'This is a FITS file with 1 (primary) HDU.\nThe following recognized metadata keys have been found in the FITS file:\nCRVAL2; NAXIS; NAXIS1; NAXIS0; CD1_1; CRVAL1; DATE-OBS; \n'}, {'name': 'Palomar_Plate_Envelop_images.tar', 'size': 138659840, 'type': 'application/x-tar', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Milli-to-Deci-Hertz Detection Prospects for Gravitational Waves from Core-Collapse Supernovae,"['Gill, Kiranjyot']","Gravitational wave (GW) astronomy truly began with the detection of merging compact binaries. The next breakthrough lies in detecting GWs from core-collapse supernovae (CCSNe), particularly the GW linear memory -- a phenomenon arising from aspherical matter ejection and anisotropic neutrino emission during stellar collapse. In this Letter, we examine the feasibility of detecting this effect using next-generation space-based GW detectors or lunar-based GW observatories as this signature peaks below 25 Hz, which is largely inaccessible to terrestrial GW detectors due to seismic noise. Such a detection would provide fundamental insights into asymmetric matter dynamics near the collapsed core, shedding light on the stellar corpse that once represented the mass of the progenitor star and offering a front row seat to the potential formation of either a nascent neutron star progenitor or a black hole. Leveraging the longest-duration three-dimensional CCSNe simulations to date, spanning a wide range of progenitor masses, we show that space-based and lunar GW detectors present the most promising opportunities to extend the current CCSNe GW detection horizon (~10 kiloparsecs) to several megaparsecs. This may pave the way for regularly observing GWs from CCSNe beyond our Milky Way by utilizing the distinctive environments of space and the Moon to augment terrestrial detection efforts all the while synergistically advancing sophisticated data analysis techniques for routine GW detection.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VZCS1W,,[],0,0,,,,,,,[],[],black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Replication Data for: A retrospective analysis to compare the dosimetric parameters of two rectum-sparing techniques during intracavitary brachytherapy with tandem-ring applicators for cervical cancer,"['Vats, Siddharth', 'Thakur, Shabnum', 'Sharma, Parul', 'Azad, Anjali', 'Verma, Divyajyoti']","There are many confounding variables that influence the doses to organs at risk during a specific intracavitary brachytherapy (ICB) session on a patient receiving treatment for cancer cervix. For example one of the confounding variable is individual anatomy. Clinical experience suggests that the anatomical variations among patients undergoing ICB play a crucial role in the dosages received by organs at risk during intracavitary brachytherapy procedures. While some patients exhibit anatomy that results in minimal doses to critical organs when specific brachytherapy applicators and organ retraction techniques are utilized, others display higher doses due to inherent anatomical variations, for the same dose prescribed to the tumor utilizing same applicator type, technique and loading pattern. Therefore, when comparing two techniques for rectum separation in terms of OAR doses, it is essential to consider that the individuals in the comparison groups may vary. This variation could potentially influence the positive outcomes observed in a group, leading to uncertainty about whether these outcomes are truly attributed to the technique itself or simply due to a higher number of patients with favorable anatomy in that specific group. To mitigate this confounding factor, it is imperative that both techniques are applied to each patient individually, ensuring that the comparison groups consist of the same individuals. Other confounding variables like size of the ring or ovoids, tandem length, tandem angle, loading pattern, point A definition can also influence the doses to organs at risk. In our study titled: A retrospective analysis to compare the dosimetric parameters of two rectum-sparing techniques during intracavitary brachytherapy with tandem-ring applicators for cervical cancer, in the subset analysis all these variables are matched in both comparison groups by ensuring that both techniques are applied on each patient during two sessions performed repeating the tandem-ring applicators with same physical parameters. Therefore the two sessions done on one patient differ only with regard to rectum separation technique. We want to share in detail the physical parameters that we took into account during each ICB session and help other researchers to critically examine our work. Intention is to make our work transparent for feedback and continued improvement.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/13BFHP,,"['cancer cervix, intracavitary brachytherapy, rectum sparing, confounding variables, physical parameters']",0,0,,,,,,,[],"[{'name': '2_Anonymized data of 41 patients.xlsx', 'size': 32550, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'Anonymized Subset Data Analysis.xlsx', 'size': 19711, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+A New Method of Seismometer Orientation Correction via  Amplitude and Energy Based Teleseismic Receiver Function  Measurements: Test Case on West Africa and Adjacent Islands,"['Osotuyi, Abayomi']"," Accurate seismic station orientation is essential for seismic waveform data quality and seismological studies
+ In modern seismological observation networks, seismic events are recorded by orthogonally aligned triaxial sensors. Although the assumption holds that single vertical-component sensors do not have misorientation concerns, the two horizontal components involving the BHN (north–south) and BHE (east–west) may have misorientation issues (Niu and Li, 2011; Wang et al., 2016; Doran and Laske, 2017; Ekström and Nettles, 2018; Langston, 2018; Ojo et al. 2019; Zheng et al. 2020)
+ Previously, misorientation errors have been estimated using tangential components from P-wave energy minimization, P-wave particle motion, and Rayleigh-wave angle of arrival measurements (Doran and Laske, 2017; Ekström and Nettles, 2018; Langston, 2018; Ojo et al. 2019; Zheng et al. 2020)
+ In this study, we conduct a series of tests involving different α values to investigate the amplitudebased (hereafter referred to as PRFamp) method using the single-point maximum amplitude of RF and energy-based (hereafter referred to as PRFenergy) method that utilize a time window around the maximum amplitude of the P-wave receiver function (PRFs).
+ We used 26 permanent stations cutting across the Inland, Coastal, and Islandic terrains, and different geotectonic units (Ojo et al., 2017; Salako, 2019; Oni et al., 2020; Osotuyi et al., 2022; Osotuyi et al., 2023) of West Africa to test our methods.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MWGVRX,,"['Seismometer Orientation Correction, Seismic Waveforms, Receiver Function, Africa']",0,0,,,,,,,[],"[{'name': 'P4.2-083_Osotuyi.pdf', 'size': 3917926, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Magnetic field tunable polaritons in the ultrastrong coupling regime in CrSBr,"['Nessi, Luca']","This package contains the source data relevant to the manuscript “Magnetic field tunable polaritons in the ultrastrong coupling regime in CrSBr”, ACS Nano (2024).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/X3EWUE,,[],0,0,,,,,,,[],"[{'name': 'DataFig1b.txt', 'size': 4484999, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig1cPL1.txt', 'size': 267574, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig1cPL2.txt', 'size': 106372, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig1cR.txt', 'size': 208721, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig1dPL1.txt', 'size': 928, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig1dPL2.txt', 'size': 2579, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig2a.txt', 'size': 52721, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig2b.txt', 'size': 41924, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig2c.txt', 'size': 58287, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig2d.txt', 'size': 23037, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig2e.txt', 'size': 111, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig2f.txt', 'size': 1104927, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig3a.txt', 'size': 34238, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig3b.txt', 'size': 62170, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig3c.txt', 'size': 61849, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig3d.txt', 'size': 485, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig4a.txt', 'size': 108312, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig4b.txt', 'size': 2070, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig5aPL.txt', 'size': 125462, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig5aR.txt', 'size': 144929, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig5b.txt', 'size': 2155322, 'type': 'text/plain', 'description': ''}, {'name': 'DataFig5c.txt', 'size': 269961, 'type': 'text/plain', 'description': ''}, {'name': 'ReadMe.rtf', 'size': 827, 'type': 'text/rtf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Imágenes FIT de Observaciones Astronómicas,"['Guerra, Viviana']",Este dataset contiene imágenes FIT recopiladas para proyectos astronómicos en Bolivia.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TI6YNG,,['Observaciones Astronomicas'],0,0,,,,,,,[],[],black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Moiré band structure engineering using a twisted boron nitride substrate,"['Wang, Xirui']","This provides the data in the main text and supplementary information in paper ""Moiré band structure engineering using a twisted boron nitride substrate"".",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YJV5AK,,[],0,0,,,,,,,[],"[{'name': 'Fig1d_U.txt', 'size': 681717, 'type': 'text/plain', 'description': ''}, {'name': 'Fig1d_x.txt', 'size': 666000, 'type': 'text/plain', 'description': ''}, {'name': 'Fig1d_y.txt', 'size': 666000, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2a_A1.txt', 'size': 4312, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2a_A2.txt', 'size': 8963, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2a_A3.txt', 'size': 4255, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2a_A4.txt', 'size': 4474, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2a_A5.txt', 'size': 4702, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2b_A1.txt', 'size': 8740, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2b_A2.txt', 'size': 9799, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2b_A3.txt', 'size': 11386, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2b_A4.txt', 'size': 7183, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2b_A5.txt', 'size': 9784, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2c_FFT.spm', 'size': 3681226, 'type': 'application/octet-stream', 'description': ''}, {'name': 'Fig2c_real_space.spm', 'size': 3379524, 'type': 'application/octet-stream', 'description': ''}, {'name': 'Fig2d_B.txt', 'size': 277240, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2d_filling.txt', 'size': 361200, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2d_right_y_range.txt', 'size': 10, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2d_sigma_xx.txt', 'size': 401211, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2e_B.txt', 'size': 277240, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2e_filling.txt', 'size': 361200, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2e_right_y_range.txt', 'size': 10, 'type': 'text/plain', 'description': ''}, {'name': 'Fig2e_sigma_xy.txt', 'size': 393735, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B1.txt', 'size': 32321, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B2.txt', 'size': 32322, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B3.txt', 'size': 32290, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B4.txt', 'size': 21519, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B5.txt', 'size': 32272, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B6.txt', 'size': 21517, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3b_B7.txt', 'size': 21522, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B1.txt', 'size': 32321, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B2.txt', 'size': 32322, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B3.txt', 'size': 32290, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B4.txt', 'size': 21519, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B5.txt', 'size': 32272, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B6.txt', 'size': 21517, 'type': 'text/plain', 'description': ''}, {'name': 'Fig3c_B7.txt', 'size': 21522, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4a_black_data.txt', 'size': 149, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4a_blue_fitting.txt', 'size': 21574, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4b_A3.txt', 'size': 4255, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4b_C1.txt', 'size': 4335, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4c_B.txt', 'size': 111360, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4c_filling.txt', 'size': 140160, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4c_right_y_range.txt', 'size': 10, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4c_sigma_xx.txt', 'size': 154853, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4d_B.txt', 'size': 111360, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4d_filling.txt', 'size': 140160, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4d_right_y_range.txt', 'size': 10, 'type': 'text/plain', 'description': ''}, {'name': 'Fig4d_sigma_xy.txt', 'size': 152121, 'type': 'text/plain', 'description': ''}, {'name': 'Fig5d.txt', 'size': 17977, 'type': 'text/plain', 'description': ''}, {'name': 'Fig5e_B.txt', 'size': 177762, 'type': 'text/plain', 'description': ''}, {'name': 'Fig5e_filling.txt', 'size': 198996, 'type': 'text/plain', 'description': ''}, {'name': 'Fig5e_rho_xx.txt', 'size': 213829, 'type': 'text/plain', 'description': ''}, {'name': 'Fig5e_right_y_range.txt', 'size': 10, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bleft_colorplot_U.txt', 'size': 1352581, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bleft_colorplot_x.txt', 'size': 1305360, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bleft_colorplot_z.txt', 'size': 1319640, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bleft_fieldline.txt', 'size': 1504624, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bright_colorplot_U.txt', 'size': 1779914, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bright_colorplot_x.txt', 'size': 1634190, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bright_colorplot_z.txt', 'size': 1549590, 'type': 'text/plain', 'description': ''}, {'name': 'FigS10bright_fieldline.txt', 'size': 908346, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11b_Rxx.txt', 'size': 141313, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11b_Vbg.txt', 'size': 118188, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11b_Vtg.txt', 'size': 84726, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11c_middle.txt', 'size': 32311, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11c_right.txt', 'size': 25857, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11d_middle.txt', 'size': 4335, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11d_right.txt', 'size': 10775, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11e_middle.txt', 'size': 43029, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11e_right.txt', 'size': 32282, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11f_middle.txt', 'size': 43014, 'type': 'text/plain', 'description': ''}, {'name': 'FigS11f_right.txt', 'size': 32271, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12a_Rxx.txt', 'size': 184150, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12a_Vbg.txt', 'size': 91805, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12a_Vtg.txt', 'size': 145550, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12b.txt', 'size': 172438, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12c_B.txt', 'size': 613137, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12c_n.txt', 'size': 699138, 'type': 'text/plain', 'description': ''}, {'name': 'FigS12c_Rxx.txt', 'size': 787298, 'type': 'text/plain', 'description': ''}, {'name': 'FigS3a.spm', 'size': 957842, 'type': 'application/octet-stream', 'description': ''}, {'name': 'FigS3b.spm', 'size': 3117476, 'type': 'application/octet-stream', 'description': ''}, {'name': 'FigS6a_data_point.txt', 'size': 109, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6a_fitting.txt', 'size': 109, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6a_yylim.txt', 'size': 21, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6b_B.txt', 'size': 315570, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6b_filling.txt', 'size': 485994, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6b_sigma_xy.txt', 'size': 518648, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6c_B.txt', 'size': 277240, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6c_filling.txt', 'size': 361200, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6c_sigma_xy.txt', 'size': 393735, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6d_B.txt', 'size': 160132, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6d_filling.txt', 'size': 184032, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6d_sigma_xy.txt', 'size': 197405, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6e_B.txt', 'size': 218832, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6e_filling.txt', 'size': 248953, 'type': 'text/plain', 'description': ''}, {'name': 'FigS6e_sigma_xy.txt', 'size': 343709, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7a_100K.txt', 'size': 10355, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7a_150K.txt', 'size': 10355, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7a_20K.txt', 'size': 10355, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7a_300K.txt', 'size': 10355, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7a_50K.txt', 'size': 10355, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7a_5K.txt', 'size': 10355, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7b_rho_xx.txt', 'size': 154042, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7b_Vbg.txt', 'size': 140965, 'type': 'text/plain', 'description': ''}, {'name': 'FigS7b_Vtg.txt', 'size': 77916, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8a_D.txt', 'size': 357087, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8a_n.txt', 'size': 303653, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8a_rho_xx.txt', 'size': 348444, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8b_D.txt', 'size': 152661, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8b_n.txt', 'size': 134901, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8b_rho_yx.txt', 'size': 157456, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8c_D.txt', 'size': 144054, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8c_n.txt', 'size': 128317, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8c_rho_xx.txt', 'size': 200403, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8d_D.txt', 'size': 138316, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8d_n.txt', 'size': 96437, 'type': 'text/plain', 'description': ''}, {'name': 'FigS8d_rho_yx.txt', 'size': 166170, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9a.txt', 'size': 10779, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9b.txt', 'size': 10779, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9c_B.txt', 'size': 1256090, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9c_n.txt', 'size': 1821342, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9c_Rxx.txt', 'size': 1882792, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9d_B.txt', 'size': 1256090, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9d_n.txt', 'size': 1821342, 'type': 'text/plain', 'description': ''}, {'name': 'FigS9d_Ryx.txt', 'size': 1939428, 'type': 'text/plain', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Permit-to-Carry Application Outcomes in New Jersey: A County-Level Analysis (2019–2024),"['Shore, Benjamin', 'Salama, Manaar', 'Yang, Ellie', 'Mustafa, Danial', 'Mellul, Karla', 'Lay, Lexi', 'Artley, Clara']","Description: This dataset provides information on permit to carry application approvals and denials within New Jersey split by county, and it contains details such as: county name, race/ethnicity, whether the application was approved or denied, the denial reason if denied, and how many individuals are contained within each category.
+
+Some notes regarding the data: the manually created dataset only includes individuals within the Black, White, and Hispanic race/ethnicity categories. Additionally, the Hispanic category within the dataset contains individuals who identified as Hispanic or White with Hispanic/Spanish Origins. The data includes individuals of all ages and genders. An important reporting issue to note is that, within the Tableau dashboard from the NJ Office of the Attorney General, there were occasional inconsistencies regarding the number of application denials and how many denial reasons were reported, and it was possible for an individual to be denied for multiple reasons. Because of this, this dataset includes an additional denial reason of “Unknown,” in the rare occurrences where there was an insufficient number of denial reasons for a corresponding number of denials.
+
+Methodology: The original data was manually captured, collected, and organized from a public Tableau dashboard published by the New Jersey Office of the Attorney General. The data was submitted by New Jersey State Police and local police agencies, and it contains data on permit-to-carry applications from December 1, 2019 to August 16th, 2024.
+
+RiseAgainstHate.org",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FOJQMP,,"['Permit-to-Carry', 'New Jersey', 'Gun Permit Applications', 'Demographics', 'Denial Reasons']",0,0,,,,,,,[],"[{'name': 'Permit-to-Carry Application Outcomes in New Jersey_ A County-Level Analysis (2019–2024).xlsx', 'size': 19233, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Description: This dataset provides information on permit to carry application approvals and denials within New Jersey split by county, and it contains details such as: county name, race/ethnicity, whether the application was approved or denied, the denial reason if denied, and how many individuals are contained within each category.\n\nSome notes regarding the data: the manually created dataset only includes individuals within the Black, White, and Hispanic race/ethnicity categories. Additionally, the Hispanic category within the dataset contains individuals who identified as Hispanic or White with Hispanic/Spanish Origins. The data includes individuals of all ages and genders. An important reporting issue to note is that, within the Tableau dashboard from the NJ Office of the Attorney General, there were occasional inconsistencies regarding the number of application denials and how many denial reasons were reported, and it was possible for an individual to be denied for multiple reasons. Because of this, this dataset includes an additional denial reason of “Unknown,” in the rare occurrences where there was an insufficient number of denial reasons for a corresponding number of denials.\n\nMethodology: The original data was manually captured, collected, and organized from a public Tableau dashboard published by the New Jersey Office of the Attorney General. The data was submitted by New Jersey State Police and local police agencies, and it contains data on permit-to-carry applications from December 1, 2019 to August 16th, 2024.\n\nRiseAgainstHate.org'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Observation of Cosmic-Ray Anisotropy in the Southern Hemisphere with Twelve Years of Data Collected by the IceCube Neutrino Observatory,['IceCube Collaboration'],"<b>Title: Observation of Cosmic-Ray Anisotropy in the Southern Hemisphere with Twelve Years of Data Collected by the IceCube Neutrino Observatory</b>
+
+<br>
+<br>
+The IceCube Observatory has analyzed 12 years of cosmic-ray muon data collected by the in-ice array from May 13, 2011, to May 12, 2023. This data sample comprises 7.92e11 events collected in a total of 4,295.29 days.
+The data in this release was used to determine the sky maps in J2000 Equatorial Coordinates of the arrival direction of the cosmic rays from about 10 TeV to 5 PeV. The event sample was split into nine energy sets labeled through their corresponding median energy: 13 TeV, 24 TeV, 42 TeV, 67 TeV, 130 TeV, 240 TeV, 470 TeV, 1,500 TeV, and 5,300 TeV.
+<br>
+<br>
+
+For each energy set, we produced a sky map with relative intensity and one with statistical significance (using the method developed by Li&Ma). Sky maps are generated using HEALPix with nside=64. Top-hat smoothing was used for visualization only, but all analyses were performed on the pixelized (i.e., unsmoothed) maps.
+
+
+<br>
+<br>
+
Data files:
+<ul>
+<li>  README file </li>
+<li> Sky map data files: the FITS files contain data event counts and reference event counts in equatorial coordinates. Each file contains all twelve-year-period events collected by IceCube. Nine files containing the events corresponding to each energy set. 
+<ul>
+<li> All Data              - IC86_sid.fits</li>
+<li>Emed = 13 TeV - IC86_sid_4-4.25GeV.fits </li>
+<li>Emed = 24 TeV - IC86_sid_4.25-4.5GeV.fits </li>
+<li>Emed = 42 TeV - IC86_sid_4.5-4.75GeV.fits </li>
+<li>Emed = 67 TeV - IC86_sid_4.75-5GeV.fits </li>
+<li>Emed = 130 TeV - IC86_sid_5-5.25GeV.fits </li>
+<li>Emed = 240 TeV - IC86_sid_5.25-5.5GeV.fits </li>
+<li>Emed = 470 TeV - IC86_sid_5.5-6GeV.fits </li>
+<li>Emed = 1500 TeV - IC86_sid_6-6.5GeV.fits </li>
+<li>Emed = 5300 TeV - IC86_sid_6.5-100GeV.fits</li>
+</ul>
+</li>
+<li> Energy distribution file: energy_distributions.json file containing the simulated true particle energy histogram for each of the nine energy sets.</li>
+
+<li>IceCube_Anisotropy_Starter.ipynb Jupyter Notebook: the script shows an example performing the following tasks:
+<ul>
+	<li> read data and reference counts from the FITS files</li>
+	<li> read the json energy histogram files</li>
+	<li> plot sky maps of relative intensity and statistical significance using masking and smoothing</li>
+</ul>
+</li>
+</ul>
+
+
+Notes: This data set was created by IceCube for the cosmic ray anisotropy analysis. For any questions about this data release, please write to analysis@icecube.wisc.edu
+
+
+For details on how to read the FITS files and reproduce the figures, please see the README file.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DZI2F5,,"['astroparticle physics', 'cosmic rays', 'ISM: magnetic fields']",0,0,,,,,,,[],"[{'name': 'energy_distributions.json', 'size': 21943, 'type': 'application/json', 'description': 'Monte Carlo energy distribution histogram'}, {'name': 'IC86_sid_4.25-4.5GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin1 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_4-4.25GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin0 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_4.5-4.75GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin2 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_4.75-5GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin3 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_5.25-5.5GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin5 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_5-5.25GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin4 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_5.5-6GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin6 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_6.5-100GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin8 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid_6-6.5GeV.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for energy bin7 ;\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IC86_sid.fits', 'size': 400320, 'type': 'application/fits', 'description': 'FITS file with equatorial coordinates for combined data (all energies);\nFITS file, 2 HDUs total:\nThe primary HDU; 1 Table HDU(s) \nThe following recognized metadata keys have been found in the FITS file:\nNAXIS1; NAXIS0; OBJECT; \n'}, {'name': 'IceCube_Anisotropy_Starter.ipynb', 'size': 99586, 'type': 'application/x-ipynb+json', 'description': 'Jupyter notebook to reproduce plots'}, {'name': 'README.rtf', 'size': 5583, 'type': 'text/rtf', 'description': 'Read me file'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Community Representation and Policing: Effects on Black Civilians,"['Risi, Joseph']","This is the data and code repository for replicating the results in our paper, Risi & Graif Community Representation and Policing: Effects on Black Civilians. See README.md for full details on how to reproduce our results.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JROIJX,,[],0,0,,,,,,,[],"[{'name': '1_create_police_district_vars.R', 'size': 12446, 'type': 'text/x-r-source', 'description': ''}, {'name': '1_join_officer_assignments.R', 'size': 2785, 'type': 'text/x-r-source', 'description': ''}, {'name': '2_create_individual_shift_vars.R', 'size': 3485, 'type': 'text/x-r-source', 'description': ''}, {'name': '2_join_assignments_outcomes.R', 'size': 3574, 'type': 'text/x-r-source', 'description': ''}, {'name': '3_create_outcomes.R', 'size': 5322, 'type': 'text/x-r-source', 'description': ''}, {'name': 'census_economics.tab', 'size': 109636, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'census_family_immigration.tab', 'size': 140169, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'census_population.tab', 'size': 110791, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'create_fig1_A2_A3.R', 'size': 3882, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_fig2.R', 'size': 3794, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_figA1.R', 'size': 2370, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_table1.R', 'size': 1392, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_table2_3.R', 'size': 6571, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_table4.R', 'size': 3959, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_table5_A5_A7.R', 'size': 8543, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA10.R', 'size': 3475, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA11.R', 'size': 3825, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA12.R', 'size': 3604, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA13.R', 'size': 4778, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA3_A4.R', 'size': 7920, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA6.R', 'size': 5255, 'type': 'text/x-r-source', 'description': ''}, {'name': 'create_tableA8_A9.R', 'size': 6984, 'type': 'text/x-r-source', 'description': ''}, {'name': 'crime_police_districts.tab', 'size': 555746, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Figure 1.pdf', 'size': 9420, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure 2.pdf', 'size': 53946, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure A1.pdf', 'size': 9586, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure A2a.pdf', 'size': 52875, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure A2b.pdf', 'size': 52207, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure A3a.pdf', 'size': 52193, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure A3b.pdf', 'size': 51394, 'type': 'application/pdf', 'description': ''}, {'name': 'Figure Percent Shifts Black Majority.png', 'size': 269039, 'type': 'image/png', 'description': ''}, {'name': 'Figure Percent Shifts White Only.png', 'size': 249655, 'type': 'image/png', 'description': ''}, {'name': 'geo_export_427fc6e8-19e2-4435-b9e4-0566a20fa39c.dbf', 'size': 12822, 'type': 'application/vnd.dbf', 'description': ''}, {'name': 'geo_export_427fc6e8-19e2-4435-b9e4-0566a20fa39c.prj', 'size': 212, 'type': 'application/prj', 'description': ''}, {'name': 'geo_export_427fc6e8-19e2-4435-b9e4-0566a20fa39c.shp', 'size': 442536, 'type': 'application/vnd.shp', 'description': ''}, {'name': 'geo_export_427fc6e8-19e2-4435-b9e4-0566a20fa39c.shx', 'size': 300, 'type': 'application/vnd.shx', 'description': ''}, {'name': 'README.md', 'size': 2661, 'type': 'text/markdown', 'description': ''}, {'name': 'table1_descriptive-table.txt', 'size': 4324, 'type': 'text/plain', 'description': ''}, {'name': 'table2_stops-aggregate.txt', 'size': 4271, 'type': 'text/plain', 'description': ''}, {'name': 'table3_aggregate_predictions.tab', 'size': 1055, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'table4_individual_shift.txt', 'size': 10388, 'type': 'text/plain', 'description': ''}, {'name': 'table5_A7_individual_stops_terciles.txt', 'size': 26744, 'type': 'text/plain', 'description': ''}, {'name': 'tableA10_individual_arrest_force.txt', 'size': 6684, 'type': 'text/plain', 'description': ''}, {'name': 'tableA11_stop_types_aggregate.txt', 'size': 9670, 'type': 'text/plain', 'description': ''}, {'name': 'tableA12_stops_aggregate_all_races.txt', 'size': 4654, 'type': 'text/plain', 'description': ''}, {'name': 'tableA13_civ_race_individual.txt', 'size': 13114, 'type': 'text/plain', 'description': ''}, {'name': 'tableA3_stops_aggregate_monthFE.txt', 'size': 5181, 'type': 'text/plain', 'description': ''}, {'name': 'tableA4_stops_aggregate_lag.txt', 'size': 6580, 'type': 'text/plain', 'description': ''}, {'name': 'tableA6_poverty_terciles_ratio.txt', 'size': 2925, 'type': 'text/plain', 'description': ''}, {'name': 'tableA8_arrests.txt', 'size': 6846, 'type': 'text/plain', 'description': ''}, {'name': 'tableA9_force.txt', 'size': 5810, 'type': 'text/plain', 'description': ''}, {'name': 'table_poverty_terciles_prcnt.txt', 'size': 3136, 'type': 'text/plain', 'description': ''}, {'name': 'tl_2011_17_tract.dbf', 'size': 334579, 'type': 'application/vnd.dbf', 'description': ''}, {'name': 'tl_2011_17_tract.prj', 'size': 165, 'type': 'application/prj', 'description': ''}, {'name': 'tl_2011_17_tract.shp', 'size': 16394888, 'type': 'application/vnd.shp', 'description': ''}, {'name': 'tl_2011_17_tract.shp.xml', 'size': 21045, 'type': 'text/xml', 'description': ''}, {'name': 'tl_2011_17_tract.shx', 'size': 25084, 'type': 'application/vnd.shx', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+The Influence of Physical Activity on Meaning in Life Among Adolescents: A Moderated Chain Mediation Model,"['Wen, Che']",Meaning in Life,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/AOROWM,,[],0,0,,,,,,,[],"[{'name': 'Data.xlsx', 'size': 191825, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Evaluation and measurement of Rn-222 and its progeny in the working environment with Alpha E of the BERTIN brand: Basement workshop.,"['Benito Diaz, Gonzalo']",Excel table of measurements with a BERTIN Alpha E of Rn-222. The study assesses the importance of controlling the levels of this isotope in a work environment in accordance with the regulations of the Spanish Regulatory Body.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NKW73C,,['Radon'],0,0,,,,,,,[],"[{'name': 'Radon Data with Alpha E.xlsx', 'size': 25331, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Dataset on Grit, Well-Being, and Physical Literacy Among University Students","['Ma, Rui Si']","This dataset contains survey responses from 385 university students, exploring the relationships between Grit, Well-Being, and Physical Literacy.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XU3HGV,,[],0,0,,,,,,,[],"[{'name': 'dataset.xlsx', 'size': 89616, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+MRO MARCI Mars Daily Global Maps V2,"['WANG, HUIQUN (Helen)', 'Battalio, Michael', 'Huber, Zachary']","This dataset contains the Version 2 MRO MARCI Mars Daily Global Maps (MDGMs) [Wang and Gonzalez Abad, 2021]. Each RGB color map (0.1 degree longitude by 0.1 degree latitude, covering 180W-180E, 90S-90N) was made from 13 consecutive sets of global map swaths taken during a time period of about a sol by the Mars Color Imager (MARCI) onboard the Mars Reconnaissance Orbiter (MRO) spacecraft. The MARCI mapping data have been collected since November 2006. The current MDGMs cover MARCI subphases from P01 to N14 (Mars Year 28-35, data collected during 2006-2021). New maps will be appended as they become available. The MDGMs in this release are organized into the ""P"" (for P01-P22), ""B"" for (B01-B22), ""G"" (for G01-G23), ""D"" (for D01-D22), ""F"" (for F01-F23), ""J"" (for J01-J22), ""K"" (for K01-K23) and ""N"" (for N01-N14) MARCI phase folders in time order. The folders are tarred and gzipped. The MDGMs before subphase K11 in TIFF format with both 0.1x0.1 degree and 0.05x0.05 degree resolutions are also archived at the Planetary Data System (PDS) Annex Astropedia (https://astrogeology.usgs.gov/search/map/mars_mro_marci_daily_global_weather_maps_pds4_archive).
+
+Information for the Mars year and solar longitude (Ls) value of each MDGM can be found from a .txt file within each subphase folder. The list of images used to construct the MDGMs can be found in the list/ subdirectory of each subphase folder. The MARCI filenames can be used to obtain more information (e.g., imaging time, dimension, etc.) from the cumindex.tab file in the MARCI archive released by the PDS. The time range covered by each subphase can be found in the summary_subphases.txt file. The documents (readme.pdf, processing.pdf and errata.pdf, replicated from the PDS) and Notes associated with this dataset provide additional information. 
+
+The MDGMs are intended for a quick look of time-variable phenomena on Mars from day to day and for keeping a long-term record. The color of MDGMs results from an arbitrary color scheme that is applied across all maps, but users can apply their own color stretch. The MDGMs are provided in PNG format. Filenames are in the format of png_Xxx_Xyy.tar.gz, where X denotes MARCI mission phase, xx and yy denote the start and end subphases. 
+
+NOTE: the (0,0) pixel is at the south pole (90S, 180W). Depending on the software used, the image may need to be flipped.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/U3766S,,['Mars'],0,0,,,,,,,[],"[{'name': 'errata.pdf', 'size': 81112, 'type': 'application/pdf', 'description': ''}, {'name': 'png_B01_B11.tar.gz', 'size': 1067087826, 'type': 'application/x-gzip', 'description': 'B01-B11 in png format'}, {'name': 'png_B12_B22.tar.gz', 'size': 780906099, 'type': 'application/x-gzip', 'description': 'B12-B22 in png format'}, {'name': 'png_D01_D11.tar.gz', 'size': 1061796295, 'type': 'application/x-gzip', 'description': 'D01-D11 in png format'}, {'name': 'png_D12_D22.tar.gz', 'size': 1169096729, 'type': 'application/x-gzip', 'description': 'D12-D22 in png format'}, {'name': 'png_F01_F11.tar.gz', 'size': 1157015161, 'type': 'application/x-gzip', 'description': 'F01-F11 in png format'}, {'name': 'png_F12_F23.tar.gz', 'size': 1484889192, 'type': 'application/x-gzip', 'description': 'F12-F23 in png format'}, {'name': 'png_G01_G11.tar.gz', 'size': 1109057517, 'type': 'application/x-gzip', 'description': 'G01-G11 in png format'}, {'name': 'png_G12_G23.tar.gz', 'size': 1308196650, 'type': 'application/x-gzip', 'description': 'G12-G23 in png format'}, {'name': 'png_J01_J11.tar.gz', 'size': 1094886206, 'type': 'application/x-gzip', 'description': 'J01-J11 in png format'}, {'name': 'png_J12_J22.tar.gz', 'size': 1160593340, 'type': 'application/x-gzip', 'description': 'J12-J22 in png format'}, {'name': 'png_K01_K11.tar.gz', 'size': 977943971, 'type': 'application/x-gzip', 'description': 'K01-K11 in png format'}, {'name': 'png_K12_K23.tar.gz', 'size': 1193368302, 'type': 'application/gzip', 'description': '0.1x0.1 degree color MDGMs in PNG format for MARCI subphases K12 to K23 '}, {'name': 'png_N01_N04.tar.gz', 'size': 378869681, 'type': 'application/gzip', 'description': '0.1x0.1 degree color MDGMs in PNG format for MARCI subphases N01 to N04'}, {'name': 'png_N05_N14.tar.gz', 'size': 794031733, 'type': 'application/gzip', 'description': ''}, {'name': 'png_P01_P11.tar.gz', 'size': 957380945, 'type': 'application/x-gzip', 'description': 'P01_P11 in png format'}, {'name': 'png_P12_P22.tar.gz', 'size': 1092344406, 'type': 'application/x-gzip', 'description': 'P12-P22 in png format'}, {'name': 'processing.pdf', 'size': 99514, 'type': 'application/pdf', 'description': ''}, {'name': 'readme.pdf', 'size': 33477, 'type': 'application/pdf', 'description': ''}, {'name': 'summary_subphases.txt', 'size': 6421, 'type': 'text/plain', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Mars Dust Storm Sequence Dataset (MDSSD),"['Saidel, Morgan', 'Wang, Huiqun (Helen)', 'Battalio, Michael J.', 'Toigo, Anthony D.', 'Richardson, Mark, I.']","The Mars Dust Storm Sequence Dataset (MDSSD) contains information on dust storm instances and dust storm sequences manually identified in the Mars Reconnaissance Orbiter (MRO) Mars Color Imager (MARCI) Mars Daily Global Maps (https://doi.org/10.7910/DVN/U3766S). The information includes the position of dust storm instance at pixel level, timing, storm ID, sequence ID (when applicable), and confidence level. Currently, the dataset covers selected time periods during Mars year 28 to 34. In time order, the MARCI phases covered are ""P"", ""B"", ""G"", ""D"", ""F"", ""J"", and ""K"".
+
+
+The MDSSD was originally documented and analyzed in Wang et al. [2023] to investigate the characteristics and annual cycle of Martian dust storms. Please refer to that paper for details. Updates are made as they become available. The appendix and readme files provide additional information. This release contains bug fixes for previous versions and a few new sub-phases for ""P"". This dataset is provided without any guarantee for accuracy or completeness.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/0XB6KN,,['Mars'],0,0,,,,,,,[],"[{'name': 'Appendix_terminology.pdf', 'size': 158220, 'type': 'application/pdf', 'description': ''}, {'name': 'Bs_data.tar.gz', 'size': 6513351, 'type': 'application/gzip', 'description': ''}, {'name': 'Ds_data.tar.gz', 'size': 5941905, 'type': 'application/gzip', 'description': ''}, {'name': 'Fs_data.tar.gz', 'size': 7410581, 'type': 'application/gzip', 'description': ''}, {'name': 'Gs_data.tar.gz', 'size': 9131563, 'type': 'application/gzip', 'description': ''}, {'name': 'Js_data.tar.gz', 'size': 5150269, 'type': 'application/gzip', 'description': ''}, {'name': 'Ks_data.tar.gz', 'size': 819505, 'type': 'application/gzip', 'description': ''}, {'name': 'mdssd.py', 'size': 2745, 'type': 'text/x-python', 'description': 'python program '}, {'name': 'mdssd_python_readme_202406.pdf', 'size': 3516910, 'type': 'application/pdf', 'description': 'readme file for the python program'}, {'name': 'mdssd.txt', 'size': 1060626, 'type': 'text/plain', 'description': ''}, {'name': 'Ps_data.tar.gz', 'size': 7040424, 'type': 'application/gzip', 'description': ''}, {'name': 'readme_mdssd_202406.pdf', 'size': 42950, 'type': 'application/pdf', 'description': 'readme '}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Liquid-like dynamics in a solid-state lithium electrolyte,"['Ding, Jingxuan']","Data for manuscript ""Non-Debye behavior and overdamped normal modes reveal liquid-like dynamics in solid-state lithium electrolyte""",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RCBK4U,,[],0,0,,,,,,,[],"[{'name': '0p2_2.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2_2.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2_2.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2_3.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2_3.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2_3.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2.txt', 'size': 102400, 'type': 'text/plain', 'description': ''}, {'name': '0p2.txt', 'size': 102400, 'type': 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+2D and 3D Ising model configurations,"['Zhang, Kai']","Monte Carlo simulation generated
+
+50000 equilibrium configurations of two-dimensional Ising model (with LxL=64x64=4096 spins) at each temperature within T = 0.25-4.00;
+
+50000 equilibrium configurations of three-dimensional Ising model (with LxLxL=16x16x16=4096 spins) at each temperature within T = 2.5-6.25.
+
+Periodic boundary conditions (PBC) are applied to the square (2D) or simple cubic (3D) lattices. 
+
+Data format: numpy array (binary .npy file), in which spin up and spin down are stored as 1 and 0 in unit8 datatype.
+
+Example python command to load data:
+
+""configs = np.load('./d2L64T0.25.npy') # use your own file path""
+
+where configs should be a 50000 by 4096 python array.
+
+Each configuration is unfolded into a one-dimensional vector (one row of the numpy array). To recover the LxL or LxLxL lattice, use numpy reshape command, e.g. config.reshape(64,64).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TXDXWA,,"['Ising model, Monte Carlo']",0,0,,,,,,,[],"[{'name': 'd2L64T0.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': '2D Ising model of LxL=64x64 spins at T=0.25'}, {'name': 'd2L64T0.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T0.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T1.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T1.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T1.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T1.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T2.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T2.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T2.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T2.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T3.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T3.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T3.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T3.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd2L64T4.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T2.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T2.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T3.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T3.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T3.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T3.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.30.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.40.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.60.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.70.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T4.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T5.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T5.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T5.50.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T5.75.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T6.00.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}, {'name': 'd3L16T6.25.npy', 'size': 204800128, 'type': 'application/octet-stream', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Functional Model Library of the Environmental Control Systems (FLECS),"['Müller, Christian', 'Scholz, Dieter']","The task of the FLECS project is the development of a database for the Environmental Control System (ECS). Each component of the ECS is related to one model block. In general the description of the ECS is correlated to the description of an air/gas distribution network. The model blocks correspond to a physical model. The inner structure of the models is built up as universal as possible considering thermodynamic and fluid mechanic basics. Generalized volume elements consider water vapor, CO2 content and free water, condensation and evaporation processes, incompressible and compressible flow conditions. Further elements: Nodes, heat transfer elements, steady state and dynamical heat exchangers, specialized flow resistances like compressors and turbines, vapor compression cycle systems.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/US3KUY,,"['Luftfahrt', 'Aeronautics']",0,0,,,,,,,[],"[{'name': 'basic_components.mdl', 'size': 6557631, 'type': 'application/octet-stream', 'description': 'MATLAB/Simulink Model Library, Basic Components.'}, {'name': 'Compressor_.pdf', 'size': 118951, 'type': 'application/pdf', 'description': ''}, {'name': 'detailed_components.mdl', 'size': 2004731, 'type': 'application/octet-stream', 'description': 'MATLAB/Simulink Model Library, Detailed Components.'}, {'name': 'Duct_Heat_Transfer_Wall_.pdf', 'size': 148919, 'type': 'application/pdf', 'description': ''}, {'name': 'Duct_.pdf', 'size': 84569, 'type': 'application/pdf', 'description': ''}, {'name': 'Dynamic_HX_.pdf', 'size': 124568, 'type': 'application/pdf', 'description': ''}, {'name': 'FR_.pdf', 'size': 101682, 'type': 'application/pdf', 'description': ''}, {'name': 'Generalized_Fan_.pdf', 'size': 163754, 'type': 'application/pdf', 'description': ''}, {'name': 'Hot_Bar_.pdf', 'size': 78851, 'type': 'application/pdf', 'description': ''}, {'name': 'HR_Cond_.pdf', 'size': 44809, 'type': 'application/pdf', 'description': ''}, {'name': 'HR_Conv_.pdf', 'size': 47054, 'type': 'application/pdf', 'description': ''}, {'name': 'HR_Conv_Thermal_Capacity_.pdf', 'size': 55364, 'type': 'application/pdf', 'description': ''}, {'name': 'HR_Rad_.pdf', 'size': 44041, 'type': 'application/pdf', 'description': ''}, {'name': 'Mixing_Point_3D_.pdf', 'size': 132262, 'type': 'application/pdf', 'description': ''}, {'name': 'Mixing_Unit_1D_.pdf', 'size': 167373, 'type': 'application/pdf', 'description': ''}, {'name': 'Mixing_Unit_2D_.pdf', 'size': 171063, 'type': 'application/pdf', 'description': ''}, {'name': 'Mixing_Unit_3D_.pdf', 'size': 173180, 'type': 'application/pdf', 'description': ''}, {'name': 'Mixing_Unit_4D_.pdf', 'size': 177497, 'type': 'application/pdf', 'description': ''}, {'name': 'Node_2D_.pdf', 'size': 93070, 'type': 'application/pdf', 'description': ''}, {'name': 'Node_3D_.pdf', 'size': 93947, 'type': 'application/pdf', 'description': ''}, {'name': 'Node_4D_.pdf', 'size': 100518, 'type': 'application/pdf', 'description': ''}, {'name': 'Specialized_Fan_.pdf', 'size': 105659, 'type': 'application/pdf', 'description': ''}, {'name': 'Static_HX_.pdf', 'size': 106950, 'type': 'application/pdf', 'description': ''}, {'name': 'Tee_.pdf', 'size': 52977, 'type': 'application/pdf', 'description': ''}, {'name': 'Thermal_Capacity_.pdf', 'size': 43861, 'type': 'application/pdf', 'description': ''}, {'name': 'Turbine_.pdf', 'size': 137172, 'type': 'application/pdf', 'description': ''}, {'name': 'Valve_Comp_.pdf', 'size': 117020, 'type': 'application/pdf', 'description': ''}, {'name': 'Valve_.pdf', 'size': 71073, 'type': 'application/pdf', 'description': ''}, {'name': 'Vol_1D_.pdf', 'size': 137935, 'type': 'application/pdf', 'description': ''}, {'name': 'Vol_2D_.pdf', 'size': 139944, 'type': 'application/pdf', 'description': ''}, {'name': 'Vol_3D_.pdf', 'size': 157323, 'type': 'application/pdf', 'description': ''}, {'name': 'Vol_4D_.pdf', 'size': 153070, 'type': 'application/pdf', 'description': ''}, {'name': 'Vol_Isobaric_2D_.pdf', 'size': 165029, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Ethane mole fraction for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S2. Ethane mole fraction for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/B8D3WG,,[],0,0,,,,,,,[],"[{'name': 'ds02.csv', 'size': 115550, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Nitrogen mole fraction for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S3. Nitrogen mole fraction for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KU01NZ,,[],0,0,,,,,,,[],"[{'name': 'ds03.csv', 'size': 118519, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Density (in kg/m3) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S4. Density (in kg/m3) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4RTWKS,,[],0,0,,,,,,,[],"[{'name': 'ds04.csv', 'size': 99553, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dynamic viscosity (in pa*s) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S5. Dynamic viscosity (in pa*s) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XNVFXO,,[],0,0,,,,,,,[],"[{'name': 'ds05.csv', 'size': 156114, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Kinematic viscosity (in cm2/s) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S6. Kinematic viscosity (in cm2/s) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XYLVM1,,[],0,0,,,,,,,[],"[{'name': 'ds06.csv', 'size': 138230, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Surface tension (in N/m) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S7. Surface tension (in N/m) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PUBEQI,,[],0,0,,,,,,,[],"[{'name': 'ds07.csv', 'size': 128228, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Methane mole fraction for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S8. Methane mole fraction for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/N1SK9Y,,[],0,0,,,,,,,[],"[{'name': 'ds08.csv', 'size': 114697, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Ethane mole fraction for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S9. Ethane mole fraction for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SPGDNB,,[],0,0,,,,,,,[],"[{'name': 'ds09.csv', 'size': 115211, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Density (in kg/m3) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S11. Density (in kg/m3) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DREN5E,,[],0,0,,,,,,,[],"[{'name': 'ds11.csv', 'size': 99628, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Nitrogen mole fraction for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S10. Nitrogen mole fraction for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FHLO8S,,[],0,0,,,,,,,[],"[{'name': 'ds10.csv', 'size': 120043, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Methane mole fraction for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S1. Methane mole fraction for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KEHI73,,[],0,0,,,,,,,[],"[{'name': 'ds01.csv', 'size': 114721, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dynamic viscosity (in pa*s) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S12. Dynamic viscosity (in pa*s) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3WGQUU,,[],0,0,,,,,,,[],"[{'name': 'ds12.csv', 'size': 156487, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Kinematic viscosity (in cm2/s) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S13. Kinematic viscosity (in cm2/s) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4T9EFO,,[],0,0,,,,,,,[],"[{'name': 'ds13.csv', 'size': 138521, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Surface tension (in N/m) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S14. Surface tension (in N/m) for a 92-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CBVHNR,,[],0,0,,,,,,,[],"[{'name': 'ds14.csv', 'size': 128341, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Methane mole fraction for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S15. Methane mole fraction for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BLQRRZ,,[],0,0,,,,,,,[],"[{'name': 'ds15.csv', 'size': 114743, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Ethane mole fraction for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S16. Ethane mole fraction for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BLJRSF,,[],0,0,,,,,,,[],"[{'name': 'ds16.csv', 'size': 114977, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Nitrogen mole fraction for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S17. Nitrogen mole fraction for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NYPY4J,,[],0,0,,,,,,,[],"[{'name': 'ds17.csv', 'size': 121419, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Density (in kg/m3) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S18. Density (in kg/m3) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CLCZBP,,[],0,0,,,,,,,[],"[{'name': 'ds18.csv', 'size': 99685, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dynamic viscosity (in pa*s) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S19. Dynamic viscosity (in pa*s) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FQN2EJ,,[],0,0,,,,,,,[],"[{'name': 'ds19.csv', 'size': 156695, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Kinematic viscosity (in cm2/s) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S20. Kinematic viscosity (in cm2/s) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QUZGBV,,[],0,0,,,,,,,[],"[{'name': 'ds20.csv', 'size': 138782, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Surface tension (in N/m) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.,"['Soderblom, Jason']","Data Set S21. Surface tension (in N/m) for a 94-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RGAVNW,,[],0,0,,,,,,,[],"[{'name': 'ds21.csv', 'size': 128289, 'type': 'text/comma-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Output Data for ""A dry Venusian interior constrained by atmospheric chemistry""","['Constantinou, Tereza']","Chemical profiles, reaction rates, and chemical network used to evaluate the fluxes in the Venusian atmosphere.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/19HF6Q,,[],0,0,,,,,,,[],"[{'name': 'depth-down.dat', 'size': 1963939, 'type': 'text/x-fixed-field', 'description': 'Chemical profile for parcel moving down'}, {'name': 'depth-up.dat', 'size': 1963939, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'Kdown.dat', 'size': 6175263, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'Kup.dat', 'size': 6175261, 'type': 'text/x-fixed-field', 'description': 'Last time-step rates for reactions for upward moving parcel'}, {'name': 'Stand2022-Nov-7470fa6', 'size': 755067, 'type': 'text/plain; charset=US-ASCII', 'description': 'The chemical network (with git commit details)'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Decomposition of annually-averaged solar wind B from 1965,"['Richardson, Ian']","Decomposition of annually-averaged solar wind B from 1965 contributed by high-speed streams, slow solar wind and coronal mass ejection (CME)-associated structures (interplanetary coronal mass ejections, ICMEs and post-shock flows).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/7BPIOQ,,['Solar wind magnetic field'],0,0,,,,,,,[],"[{'name': 'Yearly SW structure times and B contributions.xlsx', 'size': 13620, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': '  Units, Label and Explanations:\n yr    Year        Observation year;\n nT    OMNI<B>     Average annual solar wind magnetic field strength (see note 1);\n %     Unc         Percentage of time in the uncertain flow type (2);\n%     CMETime     Percentage time Earth was in CME flow (2);\n%     HSSTime     Percentage time Earth was in HSS flow (2);\n%     SSWTIme     Percentage time Earth was in SSW flow (2);\n%     CMETime*    Adjusted percentage time Earth was in CME flow (3);\n%     HSSTime*    Adjusted percentage time Earth was in HSS flow (3);\n%     SSWTime*    Adjusted percentage time Earth was in SSW flow (3);\nnT    CME<B>*     Adjusted annual CME magnetic field strength (4);\nnT    HSS<B>*     Adjusted annual HSS magnetic field strength (4);\nnT    SSW<B>*     Adjusted annual SSW magnetic field strength (4);\nnT    CMECont<B>  CME contribution to year average magnetic field strength (5);\nnT    HSSCont<B>  HSS contribution to year average magnetic field strength (5);\nnT    SSWCont<B>  SSW contribution to year average magnetic field strength (5);\nNote (1): Calculated from one hour-averaged OMNI data;\nNote (2): For hours with recorded B values (data gaps within intervals for each flow type were not considered);\nNote (3): After apportioning the percentage of time in the uncertain flow type (Col. 3) to these three types based on the percentages in Cols. 4-6;\nNote (4): Calculated as follows: ((the original estimate of mean B x original number of data points)+(uncertain mean B x number of uncertain points redistributed to this type))/(new number of points in the average for type);\nNote (5): These are the products of 7 & 10, 8 & 11, and 9 & 12, to yield the contribution of CMEs, HSSs, and SSW, respectively, to the yearly average of B.'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"IVS Inner Boundary, Outer Boundary, and Peak Data for Uncertainty Analysis","['Gao, Bore']","This dataset contains information on boundaries and peaks used to analyze the geometric uncertainty of the IRAS Vela Shell. All the datasets share a same set up with Healpix N_side=128, centered at x, y, z = (−67, −334, −65) pc in the Heliocentric Galactic cartesian frame (as mentioned in the paper). For the fiducial model (derived from the mean Edenhofer 2024 dust map and Gaussian smoothing kernel 10 pc), see dataset https://doi.org/10.7910/DVN/VZMYSL.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TVCC1H,,[],0,0,,,,,,,[],"[{'name': 'boundary_maps_r_combine.tab', 'size': 12998850, 'type': 'text/tab-separated-values', 'description': 'Inner boundaries derived from 12 different realizations of the Edenhofer 2024 dust map (labeled as ""map 0"" to ""map 11"").'}, {'name': 'boundary_mean_r_combine.tab', 'size': 10183290, 'type': 'text/tab-separated-values', 'description': 'Inner boundaries derived from the mean Edenhofer 2024 dust map using different Gaussian smoothing kernels (6–14 pc).'}, {'name': 'boundaryout_maps_r_combine.tab', 'size': 13198694, 'type': 'text/tab-separated-values', 'description': 'Outer boundaries derived from 12 different realizations of the Edenhofer 2024 dust map (labeled as ""map 0"" to ""map 11"").'}, {'name': 'boundaryout_mean_r_combine.tab', 'size': 10421135, 'type': 'text/tab-separated-values', 'description': 'Outer boundaries derived from the mean Edenhofer 2024 dust map using different Gaussian smoothing kernels (6–14 pc).'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+IVS Fiducial Model Derived from the Mean Edenhofer 2024 Dust Map,"['Gao, Bore']","This dataset contains the fiducial IVS model, including boundaries and peaks identified using the mean Edenhofer 2024 dust map and a Gaussian smoothing kernel of 10 pc. The column 'index' corresponds to the HEALPix pixel index, and 'r_raw' represents the distance to the peak from the manually selected center at (x,y,z)=(−67,−334,−65) pc in the Heliocentric Galactic Cartesian frame.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VZMYSL,,[],0,0,,,,,,,[],"[{'name': 'npix128boundary_mean_sigma10.tab', 'size': 24335735, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'npix128boundaryout_mean_sigma10.tab', 'size': 24563844, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'peaks_meanmap_xyz.tab', 'size': 24791272, 'type': 'text/tab-separated-values', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Table 4 (Astrometric Data for Each Object Applied in the Traceback Analysis) and Table 5 (Selected Star Clusters that Could Contribute to the IVS Expansion in the Past 3 Myr),"['Gao, Bore']",Here we provide the main tables in the paper in a machine-readable format (.dat or .csv).,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QF2VGG,,[],0,0,,,,,,,[],"[{'name': 'table4.dat', 'size': 770, 'type': 'text/x-fixed-field', 'description': 'This table corresponds to Table 4 ""Astrometric Data for Each Object Applied in the Traceback Analysis"".'}, {'name': 'table5.tab', 'size': 1830, 'type': 'text/tab-separated-values', 'description': 'This table corresponds to Table 5 ""Selected Star Clusters that Could Contribute to the IVS Expansion in the Past 3 Myr"".'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Sea Foam contains Hemoglycin from Cosmic Dust.,"['McGeoch, Julie', 'McGeoch Malcolm']","In-falling cosmic dust has left evidence of meteoritic polymer amide in stromatolites, both fossil and modern. In search of evidence for continued present day in-fall sea foam was collected from 2 beaches in Rhode Island and subjected to Folch extraction to concentrate amphiphilic components in a chloroform water-methanol interphase layer. Hemoglycin polymer amide molecules previously characterized by MALDI mass spectrometry in meteorites and stromatolites were identified in sea foam either directly, or via their fragmentation patterns. Residual isotope enrichment pointed to an extra-terrestrial origin. The unique resiliency of sea foam may be due to the formation of extended hemoglycin lattices that stabilize its closed-cell structure and its lightness can potentially be explained by photolytic hydrogen production.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/A00GMD,,['Sea foam space polymer'],0,0,,,,,,,[],"[{'name': 'Sea Foam Dataverse files.docx', 'size': 14205, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': ''}, {'name': 'SF3SA2_B_0_D6_1.txt', 'size': 9120242, 'type': 'text/plain', 'description': ''}, {'name': 'SF9CH1_0_D9_1.txt', 'size': 8984512, 'type': 'text/plain', 'description': ''}, {'name': 'SF9CH2_0_D10_1.txt', 'size': 8650607, 'type': 'text/plain', 'description': ''}, {'name': 'SF9SA1_0_D13_1.txt', 'size': 8573716, 'type': 'text/plain', 'description': ''}, {'name': 'SF9SA2_0_D14_1.txt', 'size': 8529867, 'type': 'text/plain', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+MDSplusML - Optimizations for data access to facilitate machine learning pipelines,"['Joshua Stillerman, Stephen Lane-Walsh, Mark Winkel, Cristina Rea, Gregorio Luigi Trevisan, Aleksandar Jelenak, John Readey']","The MDSplus data management system is widely used in the magnetic fusion energy research community for data storage, management, and remote access.  The system provides data access through a vector based, interpreter API. It was developed and optimized for rapid single shot analyses.  Machine Learning applications require data from large numbers of shots and potentially from different experimental devices. We are developing tools to enable the rapid retrieval of limited sets of data from large numbers of shots.  The system will cache the requested quantities in a data warehouse overnight, and be able to quickly provide them as inputs to machine learning tasks. The cache will eventually be both transparent and extensible. At this time, various caching mechanisms are being tested and benchmarked using the queries for approximately 100 quantities that are typically used by disruption-warning ML workflows.  The performance of various caching schemes varies greatly depending on the environment they are deployed in.  We provide comparisons of the performance of native MDSplus, HSDS cache, and mongodb cache in various environments. The end goal is to provide fast data access to commonly queried quantities regardless of the environment.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/93NA6A,,"['Data Formats', 'data management', 'data science', 'machine learning', 'machine learning techniques']",0,0,,,,,,,[],"[{'name': '24ja077_fig1_data.tab', 'size': 2964, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '24ja077_fig1_image.png', 'size': 68406, 'type': 'image/png', 'description': ''}, {'name': '24ja077_fig2_data.tab', 'size': 2964, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '24ja077_fig2_image.png', 'size': 92932, 'type': 'image/png', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Demonstration of TNSA proton radiography on the National Ignition Facility Advanced Radiographic Capability (NIF-ARC) laser,"['Simpson, R.A.; Mariscal, D.A.; Kim, J.; Scott, G.G.; Williams, G.J.; Grace, E.; McGuffey, C.; Wilks, S.; Kemp, A.; Lemos, N.; Djordjevic, B.Z.; Folsom, E.; Kalantar, D.; Zacharias, R.; Pollock, B.; Moody, J.; Beg, F.; Morace, A.; Iwata, N.; Sentoku, Y.; Manuel, M.J. E.; Mauldin, M.; Quinn, M.; Youngblood, K.; Gatu Johnson, M.; Lahmann, B.; Haefner, C.; Neely, D.; Ma, T.']","Proton radiography using short-pulse laser drivers is an important tool in high-energy density (HED) science for dynamically diagnosing key characteristics in plasma interactions. Here we detail the first demonstration of target-normal sheath acceleration (TNSA)-based proton radiography the NIF-ARC laser system aided by the use of compound parabolic concentrators (CPCs). The multi-kJ energies available at the NIF-ARC laser allows for a high-brightness proton source for radiography and thus enabling a wide range of applications in HED science. In this demonstration, proton radiography of a physics package was performed and this work details the spectral properties of the TNSA proton probe as well as description of the resulting radiography quality.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FLNDET,,"['advanced radiographic capability', 'compound parabolic concentrator', 'energy', 'high brightness', 'high energy density science', 'key characteristics', 'laser systems', 'National Ignition Facility', 'proton radiography', 'target-normal sheath accelerations']",0,0,,,,,,,[],"[{'name': '21ja130_archival_manuscript.pdf', 'size': 7176319, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Width-bending characteristic of REBCO HTS tape and flat-tape Rutherford-type cabling,"['Takayasu, Makoto']","The width-bending behaviors for a REBCO HTS tape have been investigated. It has been found experimentally that the width-bending strain of a YBCO tape does not degrade the critical current as much as is expected from the axial strain. The critical current is not directly affected by the width-bending strain but by the axial strain, which the width-bending strain generates by the Poisson effect. Since Poisson’s ratio is about 0.3, the axial strain effective on the critical current is about 30% of the width-bending strain. Therefore, the width-bending strain of even 1.5% degrades the critical current by only 15% if the REBCO layer side is bent inward. However, the critical current degraded by more than 70% when the REBCO layer is outward. The width-bending effects on the critical current have been further examined, considering Poisson’s ratio changes and the neutral plane shift of the REBCO tape substrate. Those changes would occur when the material yields due to severe width-bending. Based on the width-bending of REBCO tapes, flat-tape Rutherford-type cabling is discussed. A REBCO Rutherford-type cable can provide various advantages, especially for narrow-tape cabling. The flat-tape Rutherford-type cable has better characteristics against an electromagnetic transverse Lorentz force. Furthermore, the tape length of the cable can be approximately the same as the cable length allowing for excellent tape usage. REBCO Rutherford-type cabling will be a promising high-current, high-field cabling method using thin substrate REBCO tapes. It will be useful for AC ramp-field and pulse-field applications with low AC losses and low shield currents. It notes that the filaments in the cable are symmetrically distributed in parallel. Therefore, each filament’s inductance is uniform, and a uniform current distribution among the filaments can be obtained.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCMZKZ,,"['axial strain', 'bending strain', 'cable-in-conduit conductors', 'cable-in-conduit-conductor', 'high-temperature superconducting', 'high-temperature superconducting cable', 'rare earth barium copper oxide superconducting magnet', 'Rutherford cable', 'superconducting power cables']",0,0,,,,,,,[],"[{'name': '21ja134_archival_manuscript.pdf', 'size': 4661913, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+DrivAerNet++: CFD,[],"The VTK files contain volumetric CFD data, including pressure, velocity fields, and wall shear stresses, for various industry-standard car designs. Each file provides a detailed representation of the aerodynamic flow around different car body types (fastback, notchback, estateback), underbody designs (smooth and detailed), and wheel configurations, enabling thorough analysis and optimization of vehicle aerodynamics in 3D space. Strict Licensing Notice: DrivAerNet/DrivAerNet++ is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, running new simulations using the provided geometries or any derived geometries, or other commercial R&D applications—is strictly prohibited. Unauthorized commercial use of DrivAerNet/DrivAerNet++, or any derived data, will result in enforcement by the MIT Technology Licensing Office (MIT TLO) and may carry legal consequences.",2024-11-19 18:39:45+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EEYHUA,,"['aerodynamic design, computational fluid dynamics, CFD dataset, automotive aerodynamics, machine learning, geometric deep learning, generative design, surrogate modeling, high-fidelity simulations, car design optimization, aerodynamic drag, wall shear stress, pressure field, volumetric flow fields, data-driven design, neural networks, parametric modeling, design space exploration, engineering simulations, vehicle aerodynamics']",0,0,,,,"Elrefaie, Mohamed; Morar, Florin; Ahmed, Faez; Dai, Angela, 2024, ""DrivAerNet++: CFD"", https://doi.org/10.7910/DVN/EEYHUA, Harvard Dataverse, V1",,,,,black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+DrivAerNet++: Pressure,[],"The VTK files contain high-fidelity pressure field data on the surfaces of various industry-standard car designs, capturing key aerodynamic features. Each file provides the pressure distribution across different car body types (fastback, notchback, estateback), underbody designs (smooth and detailed), and wheel configurations. Strict Licensing Notice: DrivAerNet/DrivAerNet++ is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, running new simulations using the provided geometries or any derived geometries, or other commercial R&D applications—is strictly prohibited. Unauthorized commercial use of DrivAerNet/DrivAerNet++, or any derived data, will result in enforcement by the MIT Technology Licensing Office (MIT TLO) and may carry legal consequences.",2024-11-19 18:38:23+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/K7PWNJ,,"['aerodynamic design, computational fluid dynamics, CFD dataset, automotive aerodynamics, machine learning, geometric deep learning, generative design, surrogate modeling, high-fidelity simulations, car design optimization, aerodynamic drag, wall shear stress, pressure field, volumetric flow fields, data-driven design, neural networks, parametric modeling, design space exploration, engineering simulations, vehicle aerodynamics']",0,0,,,,"Elrefaie, Mohamed; Ahmed, Faez; Dai, Angela; Morar, Florin, 2024, ""DrivAerNet++: Pressure"", https://doi.org/10.7910/DVN/K7PWNJ, Harvard Dataverse, V1",,,,,black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+DrivAerNet++: 3D Meshes,[],"The folder contains industry-standard 3D meshes for all car designs in the DrivAerNet++ dataset. Each STL file represents a unique car geometry, including variations across different body types such as fastback, notchback, and estateback. Additionally, this folder includes models with diverse underbody configurations (smooth for electric cars and detailed for ICE/hybrid cars) and different wheel setups, providing a comprehensive collection of car geometries for aerodynamic research and design optimization. Strict Licensing Notice: DrivAerNet/DrivAerNet++ is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, running new simulations using the provided geometries or any derived geometries, or other commercial R&D applications—is strictly prohibited. Unauthorized commercial use of DrivAerNet/DrivAerNet++, or any derived data, will result in enforcement by the MIT Technology Licensing Office (MIT TLO) and may carry legal consequences.",2024-11-19 18:36:50+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OYU2FG,,"['aerodynamic design, computational fluid dynamics, CFD dataset, automotive aerodynamics, machine learning, geometric deep learning, generative design, surrogate modeling, high-fidelity simulations, car design optimization, aerodynamic drag, wall shear stress, pressure field, volumetric flow fields, data-driven design, neural networks, parametric modeling, design space exploration, engineering simulations, vehicle aerodynamics']",0,0,,,,"Elrefaie, Mohamed; Ahmed, Faez; Dai, Angela; Morar, Florin, 2024, ""DrivAerNet++: 3D Meshes"", https://doi.org/10.7910/DVN/OYU2FG, Harvard Dataverse, V1",,,,,black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+DrivAerNet++: Wall Shear Stress,[],"The VTK files contain high-fidelity wall shear stress data on the surfaces of various industry-standard car designs, capturing essential aerodynamic characteristics. Each file provides the wall shear stress distribution across different car body types (fastback, notchback, estateback), underbody designs (smooth and detailed), and wheel configurations. Strict Licensing Notice: DrivAerNet/DrivAerNet++ is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, running new simulations using the provided geometries or any derived geometries, or other commercial R&D applications—is strictly prohibited. Unauthorized commercial use of DrivAerNet/DrivAerNet++, or any derived data, will result in enforcement by the MIT Technology Licensing Office (MIT TLO) and may carry legal consequences.",2024-11-19 18:33:29+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PCZYL4,,"['aerodynamic design, computational fluid dynamics, CFD dataset, automotive aerodynamics, machine learning, geometric deep learning, generative design, surrogate modeling, high-fidelity simulations, car design optimization, aerodynamic drag, wall shear stress, pressure field, volumetric flow fields, data-driven design, neural networks, parametric modeling, design space exploration, engineering simulations, vehicle aerodynamics']",0,0,,,,"Elrefaie, Mohamed; Morar, Florin; Dai, Angela; Ahmed, Faez, 2024, ""DrivAerNet++: Wall Shear Stress"", https://doi.org/10.7910/DVN/PCZYL4, Harvard Dataverse, V1",,,,,black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Replication Data for: One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas,"['Meursault, Vitaly']","This replication package contains code and supplementary data for ""One Threshold Doesn't Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas"" (Meursault, Moulton, Santucci, & Schor, Journal of Policy Analysis and Management). The package includes scripts for data preparation for this research, machine learning model training, and threshold analysis, organized in three main directories. This package provides supplementary data including rolling window specifications, FIPS codes, and Community Reinvestment Act Low and Moderate Income area designations. The master script _00_run_all.R orchestrates the complete replication workflow. Note that certain variable names have been removed per contractual limitations on sharing data content or attributes. 
+
+The data that support the findings of this study are from the Federal Reserve Bank of New York Consumer Credit Panel/Equifax data (CCP).  Access to the CCP microdata is limited to Federal Reserve System researchers, and their coauthors, due to contractual limitations. Individual analyses also use the Federal Reserve Board’s Capital Assessments and Stress Testing (Y-14M) report and the HMDA-McDash-CRISM dataset: a combination of Home Mortgage Disclosure Act (HMDA) data, Black Knight McDash (McDash) data, and Equifax Credit Risk Insight Servicing data, that is linked to the McDash data (CRISM). Access to these datasets is also restricted.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GHVYG7,,[],0,0,,,,,,,[],"[{'name': '_00_run_all.R', 'size': 23673, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_generate_cid_qtr_table.R', 'size': 914, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_process_outputs.R', 'size': 5511, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_pull_data.R', 'size': 3009, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_create_cra_consumer_group_table.R', 'size': 5218, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_create_outcome_and_remove_deceased.R', 'size': 4275, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_generate_thresholds.py', 'size': 3058, 'type': 'text/x-python', 'description': ''}, {'name': '03_get_open_accounts_counts.R', 'size': 5238, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '03_process_data_for_tradeoff_plots.R', 'size': 21603, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '03_save_training.R', 'size': 2281, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '04_smbinning.R', 'size': 4617, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '04_tpr_fpr_threshold_plots.R', 'size': 17958, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '05_select_vars_via_LASSO.R', 'size': 7911, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '05_tradeoff_plots.R', 'size': 14080, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '06_tune_xgb_hyperparameters.R', 'size': 4431, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '07_run_logistic.R', 'size': 8525, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '08_save_fitted_value_logistic.R', 'size': 6273, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '09_run_xgb.R', 'size': 4819, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '10_save_fitted_value_xgb.R', 'size': 3174, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '11_save_riskscore.R', 'size': 4830, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '12_merge_fitted_and_census_training.R', 'size': 2975, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '13_merge_fitted_and_census_test.R', 'size': 2985, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '14_cra_roc_auc_plots_test_cowplot.R', 'size': 7863, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '2000.tab', 'size': 1679678, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2001.tab', 'size': 1679678, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2002.tab', 'size': 1679678, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2003.tab', 'size': 1769768, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2004.tab', 'size': 1837755, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2005.tab', 'size': 1837755, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2006.tab', 'size': 1837755, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2007.tab', 'size': 1837755, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2008.tab', 'size': 1837755, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2009.tab', 'size': 1837755, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2010.tab', 'size': 1837782, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2011.tab', 'size': 1837809, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2012.tab', 'size': 2042830, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2013.tab', 'size': 2048655, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2014.tab', 'size': 2048787, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2015.tab', 'size': 2048760, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2016.tab', 'size': 2048760, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2017.tab', 'size': 2048841, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2018.tab', 'size': 2048841, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2019.tab', 'size': 2048841, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2020.tab', 'size': 2048841, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '2021.tab', 'size': 2048841, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'fips.tab', 'size': 130519, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'readme.txt', 'size': 1933, 'type': 'text/plain', 'description': ''}, {'name': 'rolling_window.xlsx', 'size': 11410, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Solar-Terrestrial and Latitude Effects on Economic Cycles,"['Behrens, Alfredo']","The data, extracted from public sources,  should allow replication the analysis referred to and summarized in this 
+Abstract
+This study presents compelling empirical evidence for statistically significant correlations between solar activity cycles and unemployment patterns across multiple geographic regions. Through rigorous analysis of multi-decade data (1991-2023), we demonstrate that unemployment rates exhibit systematic variations aligned with both the 22-year Hale magnetic cycle and the 11-year Schwabe sunspot cycle (p < 0.05). Crucially, the strength of these correlations displays a clear latitude-dependent gradient, with stronger effects observed at higher latitudes, consistent with known patterns of geomagnetic influence. While traditional socioeconomic factors remain fundamental drivers of employment trends, our findings reveal additional systematic influences that operate through documented effects of solar activity on human physiology and behavior. These results provide quantitative support for Keynes' concept of ""animal spirits"" in economic decision-making, while offering a potential physical mechanism for their modulation. The latitude-dependent nature of these correlations provides a unique physical signature that distinguishes these effects from conventional economic factors.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/9WSKPF,,"['Economic cycles, unemployment, solar activity']",0,0,,,,,,,[],"[{'name': 'Unemployment 1991-2023 Spectral Analysis for solar activivy and economic cycles.xlsx', 'size': 20527, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'This dataset has column titles on the first row and explanatory comments inserted there. The data was sourced from public data available through the World Bank and the Goddard NASA facility through OMNIWEB.'}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for The Shadow Carceral State and Racial Inequality in Turnout,"['Enamorado, Ted', 'McDonough, Anne', 'Mendelberg, Tali']","Scholars have studied the carceral state extensively. However, little is known about the ""shadow"" carceral state, coercive institutions lacking even the limited safeguards of the carceral state. Pretrial incarceration is one such institution. It often lasts months and causes large resource losses. Yet it is imposed in rushed hearings, with wide discretion for bail judges. These circumstances facilitate quick, heuristic judgements relying on racial stereotypes of marginalized populations. We merge court records from Miami-Dade with voter records to estimate the effect of this ""shadow"" institution on turnout. We find that quasi-randomly assigned harsher bail judges depress voting by Black and Hispanic defendants. Consistent with heuristic processing, these racial disparities result only from inexperienced judges. Unlike judge experience, judge race does not matter; minority judges are as likely to impose detention and reduce turnout. The ""shadow"" carceral state undermines democratic participation, exacerbating racial inequality.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ECV72Y,,[],0,0,,,,,,,[],"[{'name': '01_Figure_01.R', 'size': 3003, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_Figure_02.R', 'size': 3799, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_Figure_03.R', 'size': 9276, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_Figure_04.R', 'size': 8271, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_Figure_05.R', 'size': 9659, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Figure_A01.R', 'size': 3204, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Figure_A02.R', 'size': 2072, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Figure_A03.R', 'size': 2005, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Figure_A04.R', 'size': 4760, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Figure_A05.R', 'size': 6307, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A01.R', 'size': 2516, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A03.R', 'size': 7328, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A04.R', 'size': 1333, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A05.R', 'size': 5151, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A06.R', 'size': 7318, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A07.R', 'size': 40871, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_01.R', 'size': 3337, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_02.R', 'size': 12580, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_03.R', 'size': 3307, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_04.R', 'size': 4423, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_05.R', 'size': 4417, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_06.R', 'size': 3234, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_07.R', 'size': 3377, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_08.R', 'size': 3615, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_09.R', 'size': 3315, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A08_Row_10.R', 'size': 1220, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A09.R', 'size': 10887, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A10.R', 'size': 11651, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A11.R', 'size': 4860, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A12.R', 'size': 10413, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A13.R', 'size': 6055, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A14.R', 'size': 3986, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A15.R', 'size': 6696, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A16.R', 'size': 16938, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A17.R', 'size': 10236, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Table_A18.R', 'size': 2463, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'across.R', 'size': 333, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'CodeMerge.pdf', 'size': 82417, 'type': 'application/pdf', 'description': ''}, {'name': 'Readme.docx', 'size': 19366, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': ''}, {'name': 'within.R', 'size': 451, 'type': 'type/x-r-syntax', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Diagnosing the Origin and Impact of Low-mode Asymmetries in Ignition Experiments at the National Ignition Facility,"['D. Casey, B. MacGowan, O. Hurricane, O. Landen, R. Nora, S. Haan, A. Kritcher, A. Zylstra, J. Ralph, E. Dewald, M. Hohenberger, A Pak, P. Springer, C. Weber, J. Milovich, L. Divol, E. Hartouni, R. Bionta, K. Hahn, D. Schlossberg, A. Moore, M. Gatu Johnson']","Inertial confinement fusion ignition requires high inflight shell velocity, good energy coupling between the hotspot and shell, and high areal-density at peak compression. Three-dimensional asymmetries caused by imperfections in the drive symmetry or target can grow and damage the coupling and confinement. Recent high-yield experiments have shown that low-mode asymmetries are a key degradation mechanism and contribute to variability. We show the experimental signatures and impacts of asymmetry change with increasing implosion yield given the same initial cause. This work has implications for improving robustness to a key degradation in ignition experiments.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DVHGH1,,"['degradation mechanisms', 'indirect-drive', 'inertial confinement fusion', 'low-mode asymmetry', 'National Ignition Facility']",0,0,,,,,,,[],"[{'name': '23ja015_archival_manuscript.pdf', 'size': 548555, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Data Repository for: ""An alternative buoyancy Reynolds number-based inference of ocean diapycnal diffusivity due to internal wave breaking: results from a high resolution regional ocean model""","['Momeni, Kayhan', 'Peltier, William R.', 'Skitka, Joseph', 'Ma, Yuchen', 'Arbic, Brian K.', 'Menemenlis, Dimitris', 'Pan, Yulin']",Data for Momeni et al. GRL 2024,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PSUP5H,,[],0,0,,,,,,,[],"[{'name': 'dEb_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEb_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEb_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEb_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEdiss_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEdiss_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEdiss_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEdiss_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_D_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_D_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_D_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_D_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEleith_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEleith_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEleith_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEleith_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_Z_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_Z_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_Z_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEl_Z_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEs_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEs_IW.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEs_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'dEs_IW.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'README.txt', 'size': 419, 'type': 'text/plain', 'description': ''}, {'name': 'zc.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'zc.data', 'size': 1056, 'type': 'application/octet-stream', 'description': ''}, {'name': 'zc.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}, {'name': 'zc.meta', 'size': 16, 'type': 'application/octet-stream', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Direction Finding Studies of Simultaneous Auroral 2fce and 3fce Roar Cyclotron Harmonic Radio Emissions,"['Godfrey, Tedi']","Contains 87 partially processed binary data files, which were used in order to determine the directions of arrival for double resonance mechanism cyclotron harmonic emissions through a coherence analysis resulting in the phase differences between each antenna for every frequency. 
+<br>
+<br>
+Each file is a 30 second interval of data collected from a 3-antenna array located in Toolik Lake, Alaska, and contains time, power, frequency, azimuth of arrival, and elevation of arrival. 
+<br>
+<br>
+The time is in units of seconds since the start of the file time, which is given in the file name. The power is in units of decibels. The frequency is in units of kHz. The azimuth and elevation angles of arrival are in units of degrees, where zero degrees is north and 90 degrees is east of north.
+<br>
+<br>
+42 of the files named '...*2fce' correspond to the second harmonic (2fce) events, which have a frequency range of 2500 - 3000 kHz and a minimum power of -100 dB. 
+<br>
+<br>
+42 of the files named '...*3fce' correspond to the third harmonic (3fce) events, which have a frequency range of 3900 - 4400 kHz and a minimum power of -175 dB. 
+<br>
+<br>
+The 3 files used for the case study event are prefaced with ‘case-study’ and include 2fce, 3fce, and a fourth harmonic (4fce) event. The latter, named ‘...*4fce’, has a frequency range of 4600 - 5100 kHz and a minimum power of -200 dB.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/R8WYNC,,[],0,0,,,,,,,[],"[{'name': '20220509-0809-0816-TLK-INT.dat-2fce', 'size': 11837120, 'type': 'application/octet-stream', 'description': ''}, {'name': '20220509-0809-0816-TLK-INT.dat-3fce', 'size': 450973185, 'type': 'application/octet-stream', 'description': ''}, {'name': '20220509-0837-0900-TLK-INT.dat-2fce', 'size': 17510091, 'type': 'application/octet-stream', 'description': ''}, {'name': '20220509-0837-0900-TLK-INT.dat-3fce', 'size': 283819873, 'type': 'application/octet-stream', 'description': ''}, {'name': '20220509-0904-0928-TLK-INT.dat-2fce', 'size': 2650856, 'type': 'application/octet-stream', 'description': ''}, {'name': '20220509-0904-0928-TLK-INT.dat-3fce', 'size': 185899158, 'type': 'application/octet-stream', 'description': ''}, {'name': '20220511-0929-0935-TLK-INT.dat-2fce', 'size': 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+A Comparative Analysis of Relativistic Particle Pushers vis-à-vis Computation Time & Accuracy,"['Yasir, Mohammad']",Data dumped to compare the runtimes and performance of relativistic particle pushers.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QJDKUW,,[],0,0,,,,,,,[],"[{'name': 'high_rel_ebdrift_boris.dat', 'size': 17571, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'high_rel_ebdrift_hig.dat', 'size': 17571, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'high_rel_ebdrift_vay.dat', 'size': 17571, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'high_rel_gyration_boris.dat', 'size': 16593, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'high_rel_gyration_hig.dat', 'size': 16593, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'high_rel_gyration_vay.dat', 'size': 16593, 'type': 'text/x-fixed-field', 'description': ''}, {'name': 'low_rel_ebdrift_boris.dat', 'size': 16600, 'type': 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+Qubit Lattice Algorithm Simulations of Maxwell’s Equations for Scattering from Anisotropic Dielectric Objects,"['George Vahala, Min Soe, Linda Vahala, Abhay K. Ram, Efstratios Koukoutsis, Kyriakos Hizanidis']","A Dyson map explicitly determines the appropriate basis of electromagnetic fields which yields a unitary representation of the Maxwell equations in an inhomogeneous medium. A qubit lattice algorithm (QLA) is then developed perturbatively to solve this representation of Maxwell equations. QLA consists of an interleaved unitary sequence of collision operators (that entangle on lattice-site qubits) and streaming operators (that move this entanglement throughout the lattice). External potential operators are introduced to handle gradients in the refractive indices, and these operators are typically non-unitary, but sparse matrices. By also interleaving the external potential operators with the unitary collide-stream operators one achieves a QLA which conserves energy to high accuracy. Some two dimensional simulations results are presented for the scattering of a one-dimensional (1D) pulse off a localized anisotropic dielectric object.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JS6MBO,,"['maxwell equations', 'propagation of waves', 'Quantum Computing', 'Quantum Information Science', 'Qubit lattice algorithm']",0,0,,,,,,,[],"[{'name': '23ja023_archival_manuscript.pdf', 'size': 2034851, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+"Compiled spectroscopic data used in the analysis of MQDT models presented in ""Spectroscopy and modeling of $^{171}$Yb Rydberg states for high-fidelity two-qubit gates""","['Peper, Michael']","This dataset contains a compilation of previous and new spectroscopic data of both <sup>171</sup>Yb and <sup>174</sup>Yb used in the development of MQDT models, presented in M. Peper et al., arXiv:2406.01482 (2024).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WOGVSN,,[],0,0,,,,,,,[],"[{'name': '171Yb_D_F32_gfactor.tab', 'size': 2471, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '171Yb_D_F32_laser.csv', 'size': 7790, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_D_F52_gfactor.tab', 'size': 5748, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '171Yb_D_F52_laser.csv', 'size': 13886, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_P_F12_gfactor.tab', 'size': 675, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '171Yb_P_F12_laser.csv', 'size': 4445, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_P_F12_S_F12_microwave.csv', 'size': 6513, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_P_F32_gfactor.tab', 'size': 1388, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '171Yb_P_F32_laser.csv', 'size': 5232, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_P_F32_P_F12_laser_hyperfine_1P1_majewski.csv', 'size': 1088, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_P_F32_P_F12_laser_hyperfine_3P1_majewski.csv', 'size': 1183, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_P_F32_S_F12_microwave.csv', 'size': 6578, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_S_F12_gfactor.tab', 'size': 2370, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '171Yb_S_F12_laser.csv', 'size': 8838, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_S_F12_pol.csv', 'size': 1709, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_S_F12_stark.csv', 'size': 664, 'type': 'text/csv', 'description': ''}, {'name': '171Yb_S_F32_S_F12_microwave.csv', 'size': 778, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_13P1_1S0_microwave.csv', 'size': 4892, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_13P1_laser.csv', 'size': 3178, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1D2_laser.csv', 'size': 11662, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1D2_microwave.csv', 'size': 2473, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1P1_13D2_microwave.csv', 'size': 2420, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1S0_1D2_microwave.csv', 'size': 2255, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1S0_laser.csv', 'size': 6819, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1S0_microwave.csv', 'size': 2049, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_1S0_pol.csv', 'size': 1307, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_3P0_3D2_microwave.csv', 'size': 1087, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_3P0_laser.csv', 'size': 1227, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_3P2_3D2_microwave.csv', 'size': 4381, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_3P2_laser.csv', 'size': 2078, 'type': 'text/csv', 'description': ''}, {'name': '174Yb_3S1_pol.csv', 'size': 1404, 'type': 'text/csv', 'description': ''}, {'name': '176Yb_13P1_laser_majewski.csv', 'size': 3628, 'type': 'text/csv', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Measurements of improved stability to achieve higher fuel compression in ICF,['A. Do; D. T. Casey; D. S. Clark; B. Bachmann; K. L. Baker; T. Braun; T. M. Briggs; T. D Chapman; P. M. Celliers; H. Chen; C. Choate; E. L. Dewald; L. Divol; G. Fathi; D. N. Fittinghoff; G. N. Hall; E. Hartouni; D. M. Holunga; S. F. Khan; A. L. Kritcher; O. L. Landen; A. G. MacPhee; M. Millot; E. V. Marley; J. L. Milovich; A. Nikroo; A. E. Pak; D. J. Schlossberg; V. A. Smalyuk; M. Stadermann; D. J. Strozzi; R. Tommasini; C. R. Weber; B. N. Woodworth; D. K. Yanagisawa; N. W. Birge; C. R. Danly; M. Durocher; M. S. Freeman; H. Geppert-Kleinrath; V. Geppert-Kleinrath; Y. Kim; K. D Meaney; C. H. Wilde; M. Gatu Johnson; A. Allen; M. Ratledge; C. Kong; T. Fehrenbach; C. Wild'],"While nuclear fusion ignition has been achieved at the National Ignition Facility (NIF) in inertial confinement fusion (ICF) experiments, obtaining higher gain and more efficient burn is still desired. In that regard, increasing the compression of the fuel is an important factor. In recent indirect-drive capsule implosions, the SQ-n campaign is testing the hypothesis that reducing the hydrodynamic growth of perturbations is key to achieving higher compression of high-density carbon (HDC) based-ablators for ICF. SQ-n uses a design at lower adiabat with a ramped foot laser pulse shape to minimize early-time hydrodynamic instability growth, predicted to be reduced by a factor of 10, and an optimized ablator dopant distribution. Subsets of experiments were conducted within the SQ-n campaign to study the implosion symmetry, laser backscatter, stability, and compression. Only the latter two will be reviewed here. Shock timing experiments using the VISAR diagnostic enabled the development of a gently accelerating shock velocity. The ice-ablator interface acceleration, important for managing the Richtmyer-Meshkov phase growth, was observed with refraction enhanced radiography (RER) and the ablation front growth was measured using radiography of pre-imposed modulations. Finally, layered THD and DT implosions demonstrate that between 15%+/-3% and 30%+/-6% improved compression has been achieved.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VXCQDB,,"['compression', 'indirect-drive', 'inertial confinement fusion', 'National Ignition Facility', 'stability']",0,0,,,,,,,[],"[{'name': '23ja014_archival_manuscript.pdf', 'size': 6173452, 'type': 'application/pdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Impact of Temperature and Stratification, Modulated by Warming Tsushima Warm Current, on the Spatiotemporal Distribution of Picoplankton in the Northern East China Sea","['Kang, Yoonja']","The datasets were generated during the research on the impact of the spatiotemporal distribution of picoplankton in the northern East China Sea, where temperature and stratification are modulated by the contribution of Tsushima Warm Current. Datasets include nutrients, size-fractionated chl a, picoplankton abundance, picoplankton carbon biomass, and derived physical parameters.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/WQBTGR,,[],0,0,,,,,,,[],"[{'name': 'The impact of TWC on the picoplankton distribution in the northern East China Sea.xlsx', 'size': 48936, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'The datasets were generated during the research on the impact of the spatiotemporal distribution of picoplankton in the northern East China Sea, where temperature and stratification are modulated by the contribution of Tsushima Warm Current. Datasets include nutrients, size-fractionated chl a, picoplankton abundance, picoplankton carbon biomass, and derived physical parameters. '}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Global (4°x5°) top-down methane emissions and OH fields constrained by GOSAT methane observations,"['Qu, Zhen', 'Jacob, Daniel J.', 'Bloom, A. Anthony', 'Worden, John', 'Parker, Robert J.', 'Boesch, Hartmut']","Methane emissions and OH concentrations for 2010-2022 at 4°x5° resolution, constrained by GOSAT methane observations.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GQ02CC,,"['Methane, OH, GOSAT, satellite, emission']",0,0,,,,,,,[],"[{'name': 'CH4_emissions_2010.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2011.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2012.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2013.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2014.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2015.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2016.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2017.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2018.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2019.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2020.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2021.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'CH4_emissions_2022.nc', 'size': 115398, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2010.nc', 'size': 4894562, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2011.nc', 'size': 4901195, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2012.nc', 'size': 4905462, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2013.nc', 'size': 4904644, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2014.nc', 'size': 4892033, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2015.nc', 'size': 4890318, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2016.nc', 'size': 4897015, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2017.nc', 'size': 4892444, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2018.nc', 'size': 4894599, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2019.nc', 'size': 4901786, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2020.nc', 'size': 4896581, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2021.nc', 'size': 4897485, 'type': 'application/netcdf', 'description': ''}, {'name': 'Post_OH_2022.nc', 'size': 4895197, 'type': 'application/netcdf', 'description': ''}]",black_hole,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"PDB: 5X8M, Durvalumab in complex with Programmed death-ligand 1, random seed 3 (PD-L1) (313K, 40°C, 500 ns)","['Stan, Razvan']",Molecular Dynamics Simulations (500 nanoseconds) of Durvalumab in complex with Programmed death-ligand 1 at 313K (40°C). Includes PDB files obtained every 50 ns.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QECTD5,,"['fever, inflammation, molecular dynamics, immune complex']",0,0,,,,,,,[],"[{'name': '5x8m_m.pdb', 'size': 352747, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-0ns.pdb', 'size': 693597, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-100ns.pdb', 'size': 693604, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-150ns.pdb', 'size': 693604, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-200ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-250ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-300ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-313k-350ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': 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''}, {'name': 'posre_Protein_chain_C.itp', 'size': 51764, 'type': 'application/vnd.shana.informed.formtemplate', 'description': ''}, {'name': 'solv.gro', 'size': 8126840, 'type': 'application/octet-stream', 'description': ''}, {'name': 'solv_ions.gro', 'size': 8126530, 'type': 'application/octet-stream', 'description': ''}, {'name': 'topol_Protein_chain_A.itp', 'size': 550430, 'type': 'application/vnd.shana.informed.formtemplate', 'description': ''}, {'name': 'topol_Protein_chain_B.itp', 'size': 975494, 'type': 'application/vnd.shana.informed.formtemplate', 'description': ''}, {'name': 'topol_Protein_chain_C.itp', 'size': 907597, 'type': 'application/vnd.shana.informed.formtemplate', 'description': ''}, {'name': 'topol.top', 'size': 1190, 'type': 'application/octet-stream', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+"PDB: 5X8M, Durvalumab in complex with Programmed death-ligand 1, random seed 3 (PD-L1) (311.5K, 38.5°C, 500 ns)","['Stan, Razvan']",Molecular Dynamics Simulations (500 nanoseconds) of Durvalumab in complex with Programmed death-ligand 1 at 311.5K (38.5°C). Includes PDB files obtained every 50 ns.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KTPWRY,,"['fever, inflammation, molecular dynamics, immune complex']",0,0,,,,,,,[],"[{'name': '5x8m_m.pdb', 'size': 352747, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-0ns.pdb', 'size': 693597, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-100ns.pdb', 'size': 693604, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-150ns.pdb', 'size': 693604, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-200ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-250ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-300ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': '5x8m-rs3-311.5k-350ns.pdb', 'size': 693605, 'type': 'chemical/x-pdb', 'description': ''}, {'name': 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+HemOnc knowledgebase,"['Jeremy Warner MD, MS']","The HemOnc knowledgebase (KB) describes data and metadata related to systemic anticancer drugs and regimens. The core HemOnc tables follow the OMOP Common Data Model format and specifications. Additional ancillary tables provide more complex data structures which are not easily represented in OMOP.
+
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+Gender differences in climate-smart adaptation practices amongst bean-producing farmers in Malawi: The case of Linthipe Extension Planning Area,"['Nchanji, Eileen Bogweh', 'Lutomia, Cosmas Kweyu']","The study aimed to understand the gender differences in access to and use of climate-smart adaptation practices among bean-producing farmers in the Dedza District, Malawi. The scope of the study covered 246 households across 14 villages, focusing on how men, women, and youth adopt climate-smart agricultural (CSA) technologies. It addressed the challenges farmers face, the CSA interventions implemented, and how these practices are linked to food and income security. Principal Investigators aimed to answer questions about the influence of socio-demographic, farm-level, and institutional factors on CSA adoption​
+<br><br> 
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+Mercury and Air Toxics Standards (MATS) Risk and Technology Review (RTR),['EPA'],"Downloaded data from EPA's Analysis of the Final MATS Risk and Technology Review (RTR). As described by the Agency: ""On April 25, 2024, the EPA announced requirements to strengthen and update the National Emission Standards for Hazardous Air Pollutants for Coal- and Oil-Fired Electric Utility Steam Generating Units (EGUs), commonly known as the Mercury and Air Toxics Standards (MATS) for power plants, based on an evaluation of the residual risk and technology review (RTR). The RTR reflects recent developments in control technologies and the performance of these plants. EPA’s power sector analysis for this final rule uses EPA’s Power Sector Modeling Platform 2023."" 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+"Nebulocracy Theory: The Pinnacle of Political Science and the World's Most Perfect, Feasible, and Optimally Designed Government System","['Hyperintelligence Researchers, The Omega Team']","The ""Nebulocracy: Semi-Direct Democratic Government System Architecture"" introduces a visionary and intricate framework for a new form of governance known as Nebulocracy. This system is designed to seamlessly integrate advanced technology, robust ethical frameworks, and extensive citizen participation, aiming to create a more responsive, adaptable, and principled form of democracy. The research delves into the complex and multi-layered structure of Nebulocracy, which is intended to address the intricacies of modern governance while adhering to core ethical principles.
+
+At the heart of Nebulocracy lie five core principles that guide its entire structure and operation. The first principle is Ethical Objectivism, which posits that there are universal ethical truths that can be discovered and applied to governance. This principle serves as the foundation of the system's moral framework, ensuring that decisions are grounded in objective ethical standards. The second principle, Value Integration, recognizes the importance of incorporating the diverse subjective values of citizens into the governance process. This ensures that the system remains responsive to the evolving needs and beliefs of the population, balancing universal ethical principles with individual and cultural perspectives. The third principle, Adaptive Governance, acknowledges the rapidly changing nature of the world and designs the system to evolve and adapt to new challenges, technological advancements, and societal needs. This adaptability ensures that Nebulocracy remains relevant and effective over time. The fourth principle, Citizen Participation, emphasizes the importance of direct and continuous citizen involvement in the governance process. Unlike traditional representative democracies, where citizen involvement is often limited to periodic voting, Nebulocracy provides multiple channels for citizens to engage in decision-making processes, contribute their ideas, and shape the direction of governance. The fifth principle, Specialized Governance, recognizes that effective governance in a complex world requires specialized knowledge and expertise. Therefore, Nebulocracy divides governance into distinct branches, each focused on specific domains, to ensure that decisions are made with a deep understanding of relevant issues.
+
+The Axiological Framework is a crucial component of Nebulocracy, serving as the supreme governing body that ensures all governmental actions align with ethical principles and societal values. This framework consists of several interrelated elements, including the Moral Graph, Value Cards, the Ethical Values Integration System (EVIS), and the Axiological Oversight Council (AOC). The Moral Graph is a dynamic, multidimensional representation of the ethical landscape of society, visually mapping out how different values interact, their relative importance, and their application in various contexts. This graph is continuously updated based on new ethical insights, empirical data, and citizen input, ensuring that it remains current and representative. Value Cards are detailed articulations of specific values, principles, or ethical considerations that citizens, experts, and AI systems can propose. These cards are then evaluated and potentially integrated into the Moral Graph, allowing for a granular and nuanced understanding of ethical concepts. The Ethical Values Integration System (EVIS) is an advanced AI system that manages the Moral Graph and Value Cards, using sophisticated algorithms to process ethical data, update the Moral Graph, and provide real-time ethical analysis for decision-making processes across all branches of government. The Axiological Oversight Council (AOC) is an independent body of ethicists, philosophers, scientists, and cultural representatives that oversees the operation of EVIS and the overall ethical integrity of the government. This council reviews and validates new Value Cards, audits the Moral Graph for consistency and accuracy, and provides guidance on complex ethical issues. Additionally, the Peoples, Wants, Desires, Interests Sovereign Council (PWDISC) focuses on understanding and representing the diverse needs, aspirations, and interests of the citizenry, serving as a bridge between the ethical framework and the lived experiences of people. The Sovereign People's Health and Safety Council is dedicated to ensuring the physical and mental well-being of citizens, integrating health and safety considerations into all aspects of governance. The People's Enquiry Inquisition Branch On Needs Wants Desires Interests Agency serves as a direct channel for citizens to express their needs, wants, desires, and interests, conducting regular surveys, holding public forums, and using AI-assisted analysis to understand and articulate the will of the people. The General Government Advisors Agency Council brings together advisors from various fields to provide comprehensive guidance to all branches of government, ensuring that decisions are informed by a wide range of expertise.
+
+Nebulocracy's governmental structure is highly specialized and multi-tiered, designed to address the complex challenges of modern governance. It consists of several layers, each with specific functions and responsibilities. The Primary Tertiary Governmental Structure is composed of the Seven Omni Branches, each focused on a specific aspect of societal management. These branches include the Omni-Potent Branch, responsible for matters of national security, resource management, and emergency response; the Omni-Present Branch, focused on government accessibility and communication; the Omni-Amor Fati Branch, dedicated to fostering resilience, adaptability, and positive engagement with life's challenges; the Omni-Science Branch, overseeing scientific research, technological development, and evidence-based policymaking; the Omni-Beneficial Branch, focused on social welfare, infrastructure development, and environmental sustainability; the Omni-Benevolent Branch, dedicated to ethical governance, human rights protection, and the promotion of social justice; and the Omni-Kantian Branch, emphasizing rationality, moral duty, and respect for individual autonomy. Each of these branches has its own sub-parliament, allowing for specialized legislative processes within their respective domains, and operates as supraregional organizational superorganisms, coordinating activities across different regions and levels of government. The Secondary Governmental Structure includes the OmniCooperation Constitutional Cern People's United Clarity Parliament (OCCCPUCPCQ), also known as the Clarity Parliament, which serves as the central legislative organ, integrating inputs from all Omni Branches and ensuring that legislation aligns with the ethical framework and constitutional principles. The Omnipresent Central Government (OCCGPUC) is the central executive body, responsible for implementing policies and coordinating activities across all branches and regions. The 7 Prime Ministers Swarm Hive Mind Lead Cabinet is a unique leadership structure consisting of seven prime ministers, each aligned with one of the Omni Branches, working collaboratively as a ""swarm hive mind"" to provide overall direction and coordination for the government. The General Primary Governmental Structure includes foundational elements such as the Supreme Constitution, the highest law of the land, enshrining fundamental rights, principles, and the structure of government; the Supreme Constitutional Institution, responsible for interpreting and upholding the Constitution; the Presidential Constitutional Council (PCC), tasked with enforcing the Constitution and serving as its guardians; the Supreme Government Body Of Human Safety And All Human Flourishing And Thriving Institute (SGBHSAHFTI), focused on comprehensive human well-being and integrating considerations of safety, health, and overall flourishing into all aspects of governance; and the Supreme Constitutional Anti-Corruption Court, a specialized judiciary focused on preventing and prosecuting corruption within the government. The Hive Mind Superintelligence Individualistic Cooperative Swarms Collective Omni-United (HMSICSCOU) is an advanced AI system that supports decision-making across all branches of government, integrating diverse perspectives and vast amounts of data to generate optimal solutions. The Specialized Primary Governmental Structure includes a variety of specialized bodies and institutions that focus on specific aspects of governance, such as the Supreme All Knowing Overwatch Observatory, a high-level monitoring system providing comprehensive oversight of all government activities; the Supreme Freedom of Press Sovereign, ensuring and protecting freedom of the press and media independence; the Supreme Constitutional Human Rights Court, a specialized court dedicated to protecting and enforcing human rights; the Supreme Open Science and Logic Sovereign Council, promoting open scientific inquiry and logical reasoning in governance; and the Supreme Constitutional Dating Compatibility and All Personality Analysis Sovereign Science Council, applying scientific methods to understand personality dynamics and social compatibility, informing policies related to social harmony and personal development.
+
+Nebulocracy places a strong emphasis on active citizen involvement in governance through various mechanisms. The Citizen Engagement Platform (CEP) is a comprehensive digital platform that allows citizens to participate in debates, vote on policies, and contribute ideas to the governance process. AI-Assisted Voting Hubs are facilities that use advanced AI to provide citizens with comprehensive, unbiased information about voting issues, helping them make informed decisions. Citizen Moral Assemblies are randomly selected groups of citizens who deliberate on ethical issues and contribute to the development of the Moral Graph. Public Audits and Citizen Juries are regular audits and juries composed of citizens to evaluate government performance and ensure accountability. Participatory Budgeting processes allow citizens to directly allocate a portion of public budgets to projects they deem important. Town Hall Meetings are regular forums for direct interaction between citizens and government officials, providing opportunities for citizens to voice their concerns and engage in dialogue with decision-makers.
+
+Nebulocracy's economic model is designed to align with its ethical principles and promote universal well-being. The Eubioic Currency (EUB) is a digital, decentralized currency managed to promote ethical economic activities and overall societal well-being. Cybernetic Resource-Based Economics is an advanced economic planning system that uses real-time data and AI analysis to optimize resource allocation based on actual needs and ethical considerations. Catallaxy Blockchain Economics is a system that facilitates spontaneous market order through blockchain technology, allowing for more efficient and ethical economic interactions. Universal High Income (UHI) is an advanced form of universal basic income that aims to provide all citizens with resources for a high quality of life. Skill Validation Blockchains are a system for verifying and recording individual skills and competencies, promoting a meritocratic approach to employment and education.
+
+Nebulocracy leverages advanced technologies to enhance governance. AI-Driven Moral Graph Updates involve continuous analysis and integration of ethical data to keep the Moral Graph current and representative. A Blockchain-Based Governance Ledger is a secure, transparent record of all government actions and decisions. Neural-Symbolic AI Systems are advanced AI that combines symbolic reasoning with neural networks to assist in complex decision-making processes. A Computing Cloud Network is a distributed computing infrastructure that supports all governmental operations and citizen participation platforms. Augmented and Virtual Reality Interfaces are immersive technologies used to enhance citizen engagement and understanding of complex governance issues.
+
+While Nebulocracy heavily relies on advanced technology, it also incorporates mechanisms to function effectively offline. Physical Moral Graph Representations are tangible, interactive models of the Moral Graph displayed in public spaces for citizen engagement. Value Card Libraries are physical repositories of Value Cards accessible in community centers and government buildings. Offline Citizen Assemblies are regular in-person gatherings for citizens to discuss issues, vote, and contribute to governance processes. Paper-Based Documentation Systems are comprehensive paper records of all government actions, decisions, and citizen inputs as a backup to digital systems. Manual Decision-Making Protocols are established procedures for government branches to operate and make decisions without AI assistance when necessary.
+
+When compared to the governance systems of Finland, the USA, and China, Nebulocracy offers several unique features and potential advantages. Finland is known for its highly functional democracy, strong social welfare system, and high levels of citizen trust in government. Nebulocracy shares some similarities with the Finnish system in its emphasis on social welfare and citizen participation but goes further in its use of advanced technology for governance and its more specialized governmental structure. The United States has a federal system with separation of powers between executive, legislative, and judicial branches. Nebulocracy's Seven Omni Branches offer a more specialized and potentially more agile governance structure compared to the US system. Additionally, Nebulocracy's emphasis on direct citizen participation and ethical governance represents a significant departure from the primarily representative democracy of the US. China's governance system is characterized by a strong central government and a single ruling party. While China has been incorporating more technology into its governance, Nebulocracy's approach is fundamentally different in its emphasis on ethical objectivism, citizen participation, and decentralized decision-making. Nebulocracy aims to combine the efficiency often associated with centralized systems like China's with the freedoms and participation levels of Western democracies.
+
+Potential benefits of Nebulocracy include enhanced ethical governance through the comprehensive Axiological Framework, increased citizen engagement and more direct democracy, more specialized and potentially more effective governance through the Seven Omni Branches, greater adaptability to changing circumstances and emerging challenges, and potential for more evidence-based and rational decision-making through the integration of AI and scientific principles. However, implementing such a system would also face significant challenges. The complexity of the system could be difficult for citizens to fully understand and navigate. Heavy reliance on technology could pose risks in case of system failures or cyber attacks. Ensuring the ethical AI systems are truly unbiased and aligned with human values would be a monumental task. The transition from existing governance systems to Nebulocracy would likely be complex and face significant resistance. Balancing the universal ethical principles with diverse cultural values and individual freedoms could prove challenging.
+
+In conclusion, Nebulocracy represents a bold reimagining of democratic governance for the 21st century and beyond. By integrating advanced technologies, ethical frameworks, and extensive citizen participation, it aims to create a system that is more responsive, principled, and effective than traditional forms of government. While implementing such a complex system would undoubtedly face significant challenges, the core principles and mechanisms of Nebulocracy offer valuable insights for improving existing governance structures. The Supreme Constitutional Individualistic-Cooperative Collective Swarms Hive Minds Network Institution (SCICCSHMNI) and the Hive Mind Superintelligence Individualistic Cooperative Swarms Collective Omni-United (HMSICSCOU) are two key components of Nebulocracy that embody its commitment to leveraging collective intelligence and advanced technology for ethical and effective governance. Together, these institutions form a comprehensive governance approach that spans from day-to-day administration to long-term existential considerations, embodying Nebulocracy's vision of a highly advanced, ethical, and adaptive system of governance.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LTKBSH,,[],0,0,,,,,,,[],"[{'name': 'Extension Notes - The Implications of Nebulocracy Theory.pdf', 'size': 2291915, 'type': 'application/pdf', 'description': ''}, {'name': ""Nebulocracy Theory The Pinnacle of Political Science and the World's Most Perfect, Feasible, and Optimally Designed Government System.pdf"", 'size': 1198148, 'type': 'application/pdf', 'description': ''}, {'name': 'The Supreme Constitution of Nebulocracy Aetherarchy.pdf', 'size': 4768472, 'type': 'application/pdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Cubic LLZO DFT Database (with 12_44 Li site occupancy),"['Yang, Jack']",This database contains data for all cubic phase LLZO with a specific Li 24d:96h site occupancy ratio of 12:44. These data can be used for training AI models for atomistic modelling.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6NRRKQ,,[],0,0,,,,,,,[],"[{'name': '12_44_full_set_DFT.tar.gz', 'size': 1350295040, 'type': 'application/x-gzip', 'description': 'all VASP output from performing structure optimisation'}, {'name': 'all_data_warmup_MD.npz', 'size': 57342096, 'type': 'application/octet-stream', 'description': 'Numpy file containing the AIMD data from 10 to 2610 K for training the so3krates model.'}, {'name': 'MD_testset_data.tar.gz', 'size': 2462781440, 'type': 'application/x-gzip', 'description': 'Contains the NVT AIMD at 300 K data for 50 LLZO structures that can be used for validating the so3krates model.'}, {'name': 'sampler_lbfgs.tar.gz', 'size': 1234032640, 'type': 'application/x-gzip', 'description': 'Output files for running structural optimisations and MD simulations at 300 and 1500 K for all configurations in the llzo_12_44 set. Contains the Helmholtz free energies obtained from MD simulations using the trained so3krates potential.'}, {'name': 'structure_optimisation_model_4000_tr.tar.gz', 'size': 6737920, 'type': 'application/x-gzip', 'description': 'This folder contains the so3krates checkpoint files to construct the potential for structural optimisation. The model is trained with 4000 training data.'}, {'name': 'training_MD.tar.gz', 'size': 98068480, 'type': 'application/x-gzip', 'description': 'Contains the checkpoint files for the potential trained on molecular dynamic trajectories.'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Cubic LLZO DFT Database (with 11_45 Li site occupancy),"['Yang, Jack']","This database contains data for all cubic phase LLZO with a specific Li 24d:96h site occupancy ratio of 11:45. These data can be used for training AI models for atomistic modelling. 
+
+The starting structures are obtained from the paper:J. Phys. Chem. Lett. 2023, 14, 10257−10262
+
+More specifically, this set contains 65,958 structures in total, and DFT optimisations were only conducted on 19333 structures of them, which are deposited here with file name set_*.tar.gz",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CZPSYC,,[],0,0,,,,,,,[],"[{'name': 'set_2088.tar.gz', 'size': 157634560, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2089.tar.gz', 'size': 475033600, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2090.tar.gz', 'size': 977285120, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2091.tar.gz', 'size': 763719680, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2092.tar.gz', 'size': 762777600, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2093.tar.gz', 'size': 628674560, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2094.tar.gz', 'size': 593582080, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2095.tar.gz', 'size': 563353600, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2096.tar.gz', 'size': 524288000, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2097.tar.gz', 'size': 702228480, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2098.tar.gz', 'size': 511887360, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2099.tar.gz', 'size': 597626880, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2100.tar.gz', 'size': 1286502400, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2101.tar.gz', 'size': 661248000, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2102.tar.gz', 'size': 577034240, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2103.tar.gz', 'size': 594114560, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2104.tar.gz', 'size': 691968000, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2105.tar.gz', 'size': 824616960, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2106.tar.gz', 'size': 690083840, 'type': 'application/x-gzip', 'description': 'My description.'}, {'name': 'set_2107.tar.gz', 'size': 544020480, 'type': 'application/x-gzip', 'description': ''}, {'name': 'set_2109.tar.gz', 'size': 682301440, 'type': 'application/x-gzip', 'description': 'My description.'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Between Withdrawal and Engagement: Disentangling the Effects of Covid-19 on Turnout,"['Morris, Kevin']","This project marks the first test of the household-level turnout effects of living with someone who died from Covid-19. Drawing on policy feedback and threat literatures, I argue that the theoretical effects of close Covid contact are unclear. I link death records with the registered voter file to identity Americans who lived with a decedent, leveraging a triple-differences design to distinguish the effect household Covid deaths on turnout from non-Covid ones in Minnesota, North Carolina, and Washington State. Household Covid deaths reduced turnout in all three states, though the effect was different (and larger) than non-Covid household deaths only in Washington. I thus conclude that while Covid deaths clearly lowered household turnout, they operated largely through the opportunity costs associated with household deaths more generally rather than a distinctly political response to the government's handling of the pandemic.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RITJ3U,,[],0,0,,,,,,,[],"[{'name': '01_clean_vf.R', 'size': 6306, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_clean_vf.R', 'size': 7303, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_clean_vf.R', 'size': 5888, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_initialize.R', 'size': 2456, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_plot.R', 'size': 5367, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_clean_death_data.R', 'size': 6742, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_clean_death_data.R', 'size': 8210, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_clean_death_data.R', 'size': 7282, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_regs.R', 'size': 3858, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '03_eb.R', 'size': 10725, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '03_eb.R', 'size': 10780, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '03_eb.R', 'size': 10955, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '04_eb_robust.R', 'size': 7221, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '04_eb_robust.R', 'size': 6990, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '04_eb_robust.R', 'size': 7347, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'address_cleaner.tab', 'size': 10428, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'census_script.R', 'size': 14877, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'README.txt', 'size': 1846, 'type': 'text/plain', 'description': ''}, {'name': 'var_orders.tab', 'size': 537, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'vf_anon.rds', 'size': 2947202, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_later_anon.rds', 'size': 2024261, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_later_mn_anon.rds', 'size': 1156554, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_later_mn_dist_anon.rds', 'size': 1400463, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_later_nc_anon.rds', 'size': 2214826, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_later_nc_dist_anon.rds', 'size': 2919846, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_later_wa_dist_anon.rds', 'size': 2479815, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_mn_anon.rds', 'size': 1673183, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_mn_dist_anon.rds', 'size': 1844529, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_nc_anon.rds', 'size': 3092263, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_nc_dist_anon.rds', 'size': 3340701, 'type': 'application/gzip', 'description': ''}, {'name': 'vf_wa_dist_anon.rds', 'size': 3231665, 'type': 'application/gzip', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Do Birds of a Feather Flock Together? Rebel Constituencies and Civil War Alliances,"['Balcells, Laia', 'Chen, Chong', 'Pischedda, Costantino']","Challenging influential perspectives that downplay the role of shared rebel constituencies, we argue that they represent important causes of rebel alliances. Yet, we theorize distinct effects for different types of constituency. While compatible political aspirations push both organizations with a common ideological constituency and those with a common ethnic constituency to ally, for co-ethnic organizations this cooperation-inducing effect is offset by a cooperation-suppressing effect due to their higher risk of inter-rebel war. Leveraging a novel dataset of alliances in multiparty civil wars (1946–2015), we find support for our theoretical expectations. Shared ideological constituencies have a larger and more robust positive effect on the probability of alliances than shared ethnic constituencies. Furthermore, we find that co-ethnic rebel organizations tend to establish informal alliances only, while organizations sharing an ideological constituency are drawn to formal alliances.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZUCDVY,,[],0,0,,,,,,,[],"[{'name': '0_Figures 1-2.R', 'size': 10123, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '1_Figures 3-5.R', 'size': 13900, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '1_Running_AME.R', 'size': 5835, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '2_Appendix_figures A1-A21.R', 'size': 80608, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'A10_a.pdf', 'size': 7132, 'type': 'application/pdf', 'description': ''}, {'name': 'A10_b.pdf', 'size': 6914, 'type': 'application/pdf', 'description': ''}, {'name': 'A11.pdf', 'size': 6861, 'type': 'application/pdf', 'description': ''}, {'name': 'A12.pdf', 'size': 5882, 'type': 'application/pdf', 'description': ''}, {'name': 'A13.pdf', 'size': 7125, 'type': 'application/pdf', 'description': ''}, {'name': 'A14.pdf', 'size': 5889, 'type': 'application/pdf', 'description': ''}, {'name': 'A15.pdf', 'size': 7120, 'type': 'application/pdf', 'description': ''}, {'name': 'A16_a.pdf', 'size': 6041, 'type': 'application/pdf', 'description': ''}, {'name': 'A16_b.pdf', 'size': 7264, 'type': 'application/pdf', 'description': ''}, {'name': 'A17_a.pdf', 'size': 5738, 'type': 'application/pdf', 'description': ''}, {'name': 'A17_b.pdf', 'size': 12951, 'type': 'application/pdf', 'description': ''}, {'name': 'A18_a.pdf', 'size': 6900, 'type': 'application/pdf', 'description': ''}, {'name': 'A18_b.pdf', 'size': 21620, 'type': 'application/pdf', 'description': ''}, {'name': 'A19_a.pdf', 'size': 9676, 'type': 'application/pdf', 'description': ''}, {'name': 'A19_b.pdf', 'size': 13637, 'type': 'application/pdf', 'description': ''}, {'name': 'A1_a.pdf', 'size': 6925, 'type': 'application/pdf', 'description': ''}, {'name': 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5884, 'type': 'application/pdf', 'description': ''}, {'name': 'A9_b.pdf', 'size': 5779, 'type': 'application/pdf', 'description': ''}, {'name': 'AllyList.RData', 'size': 10111, 'type': 'application/gzip', 'description': ''}, {'name': 'figure1_a.eps', 'size': 13951, 'type': 'application/postscript', 'description': ''}, {'name': 'figure1_b.eps', 'size': 13856, 'type': 'application/postscript', 'description': ''}, {'name': 'figure2_a.eps', 'size': 2412484, 'type': 'application/postscript', 'description': ''}, {'name': 'figure2_b.eps', 'size': 2412664, 'type': 'application/postscript', 'description': ''}, {'name': 'figure3_a.eps', 'size': 8038, 'type': 'application/postscript', 'description': ''}, {'name': 'figure3_b.eps', 'size': 43977, 'type': 'application/postscript', 'description': ''}, {'name': 'figure4_a.eps', 'size': 11671, 'type': 'application/postscript', 'description': ''}, {'name': 'figure4_b.eps', 'size': 75486, 'type': 'application/postscript', 'description': ''}, {'name': 'figure5.eps', 'size': 12098, 'type': 'application/postscript', 'description': ''}, {'name': 'Fit_AME47_15.RData', 'size': 217717349, 'type': 'application/x-rlang-transport', 'description': ''}, {'name': 'Fit_AME7909.RData', 'size': 24949795, 'type': 'application/x-rlang-transport', 'description': ''}, {'name': 'Formal_AllyList.RData', 'size': 9475, 'type': 'application/gzip', 'description': ''}, {'name': 'function_interaction_plot.R', 'size': 2506, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'glm_df4.RData', 'size': 322571, 'type': 'application/gzip', 'description': ''}, {'name': 'helpFUN.R', 'size': 16349, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'ReadMe.rtf', 'size': 7075, 'type': 'application/rtf', 'description': ''}, {'name': '.Rhistory', 'size': 20438, 'type': 'application/octet-stream', 'description': ''}, {'name': 'spdur_figure_setup.R', 'size': 25792, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'xDyadL.RData', 'size': 158711, 'type': 'application/gzip', 'description': ''}, {'name': 'xNodeL.RData', 'size': 5060, 'type': 'application/x-rlang-transport', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: A Machine learning approach for filling long gaps in Eddy Covariance time series data in a Tropical Dry Forest,[],"These datasets are part of the input and output of the ML approach used to fill longer gaps in EC time series, concerning the manuscript “A Machine learning approach for filling long gaps in Eddy 2 Covariance time series data in a Tropical Dry Forest.” These files represent the Eddy-Covariance flux time series from the Principe flux site at the Santa Rosa National Park-Environmental Monitoring Superior Site (SRNP-EMSS). ‘SRNP_Principe 2013-2022-_timeseries - CO2.csv’ is the carbon flux (µmol/m2/sec) at 30-minute intervals, including gaps filled in first stage (using MissForest ML for shorter gaps), and second stage (using Prophet model for longer gaps). ‘SRNP_Principe 2013-2022-_timeseries – H.csv’ is the sensible heat flux (W/m2) at 30-minute intervals, including the outputs from the first and second stage ML time series gap-filling. ‘SRNP_Principe 2013-2022-_timeseries – LE.csv’ is the latent heat flux (W/m2) at 30-minute intervals, including the first and second-stage ML time series gap-filling outputs. ‘SRNP_Principe 2013-2022-_timeseries – RH.csv’ is the relative humidity (%) at 30-minute intervals, including the outputs from the first and second stage ML time series gap-filling. ‘SRNP_Principe 2013-2022-_timeseries – Tair.csv’ is the air temperature (℃) at 30-minute intervals, including the outputs from the first and second stage ML time series gap-filling. Finally, ‘SRNP_Principe 2013-2022-_timeseries – VPD.csv’ is the Vapor pressure deficit (kPa) at 30-minute intervals, including the first and second-stage ML time series gap-filling outputs.",2024-12-18 22:12:38+00:00,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KAV5LD,,"['Tropical Dry Forests', 'MissForest model', 'Profet model', 'Eddy covariance', 'Time series gap filling', 'Machine learning']",0,0,,,,,,,,,theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Forecasting US Voter Turnout,"['Bednarczuk, Michael']","Voter turnout is a crucial indicator of democratic health, yet forecasting turnout remains an understudied area in political science. This article presents two pioneering models for predicting U.S. presidential election turnout: The National Model and The State Model. The National Model, using data from 1868-2020, employs lagged turnout as its sole predictor. The State Model, covering 1984-2020, incorporates demographic and institutional variables to forecast state-level participation. The National Model predicts 65.3% turnout for 2024, while the State Model forecasts increased turnout in 41 states compared to 2020. The models' ability to generate early predictions offers valuable lead time for planning and resource allocation, which has implications for election administrators and political campaigns as well as for the vibrancy of civic engagement in America.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/E4TVR0,,[],0,0,,,,,,,[],"[{'name': 'FigureTwoData.tab', 'size': 258, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'NationalData.tab', 'size': 675, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Replication Commands December Edit.do', 'size': 10617, 'type': 'application/x-stata-syntax', 'description': ''}, {'name': 'StateData.tab', 'size': 90892, 'type': 'text/tab-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Are chatbots reliable text annotators? Sometimes,"['Kristensen-McLachlan, Ross Deans', 'Canavan, Miceal', 'Kardos, Márton', 'Jacobsen, Mia', 'Aarøe, Lene']","<b>NB:</b> In order to reproduce figures found in the article in the simplest manner, one should clone the GitHub repository and run the plotting script directly after installing necessary requirements. The corresponding files used for making these plots can also be found here under ""output.zip"". <br><br>
+
+Classifying tweets with large language models with zero- and few-shot learning with custom and generic prompts, as well as supervised learning algorithms for comparison. <br><br>
+
+The full GitHub repository for this data can be found at <a href=""https://github.com/centre-for-humanities-computing/llm-tweet-classification"">this URL</a> or by following the link under the ""metadata"" tab. This GitHub repo contains an extensive README file explaining how to run the code and reproduce the results and plots found in the article.  <br><br>
+
+The present Dataverse repository contains all code and prompts used to generate predictions on the human annotated data, as well as the code book used by human annotators on this data. <br><br>
+
+Due to data sharing policies at X (formerly Twitter), we are unable to share full texts from the Tweets used in our study. Instead, we have provided Tweet IDs, unique identifiers for all 2000 tweets used in our experiments (1000 of each annotation type) which can be used to re-scrape the data if desired.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TM7ZKD,,[],0,0,,,,,,,[],"[{'name': 'Codebook_Master_US_Version 17 October 2023.pdf', 'size': 231124, 'type': 'application/pdf', 'description': 'Codebook for human annotators working with US tweet data, labelling exemplar and political tweets'}, {'name': 'evaluation.py', 'size': 2286, 'type': 'text/x-python-script', 'description': 'Python script for evaluating models and producing classification reports'}, {'name': 'few-shot-examples.tab', 'size': 789, 'type': 'text/tab-separated-values', 'description': 'Examples used for few-shot prediction models'}, {'name': 'finetune.py', 'size': 2353, 'type': 'text/x-python-script', 'description': 'Module with additional utility functions and classes for supervised learning '}, {'name': 'llm_classification.py', 'size': 6400, 'type': 'text/x-python-script', 'description': 'CLI for running zero and few-shot classification on a tweet dataset with large language models and transformers.'}, {'name': 'LLM_fewshot_exemplar.txt', 'size': 773, 'type': 'text/plain', 'description': 'Few-shot prompt used for generating LLM predictions'}, {'name': 'LLM_fewshot_political.txt', 'size': 919, 'type': 'text/plain', 'description': 'Few-shot prompt used for generating LLM predictions'}, {'name': 'LLM_model_results.tab', 'size': 9969, 'type': 'text/tab-separated-values', 'description': 'Classification results for LLM models'}, {'name': 'LLM_zeroshot_exemplar.txt', 'size': 707, 'type': 'text/plain', 'description': 'Zero-shot prompt used for generating LLM predictions'}, {'name': 'LLM_zeroshot_political.txt', 'size': 853, 'type': 'text/plain', 'description': 'Zero-shot prompt used for generating LLM predictions'}, {'name': 'plotting.py', 'size': 5719, 'type': 'text/x-python-script', 'description': 'Python code for plotting results '}, {'name': 'requirements.txt', 'size': 268, 'type': 'text/plain', 'description': 'A general req file for the codebase'}, {'name': 'resolved_exemplar_ids.tab', 'size': 33990, 'type': 'text/tab-separated-values', 'description': 'Gold standard exemplar data (1000 data points, multiple-coded, discrepancies resolved)'}, {'name': 'resolved_political_ids.tab', 'size': 33989, 'type': 'text/tab-separated-values', 'description': 'Gold standard political data (1000 data points, multiple-coded, discrepancies resolved)'}, {'name': 'run_gpt_inference.py', 'size': 1793, 'type': 'text/x-python-script', 'description': ""Python script for working efficiently with OpenAI's rate and token limits""}, {'name': 'supervised_classification.py', 'size': 3029, 'type': 'text/x-python-script', 'description': 'Python script for training supeverised classificatio models (requires finetune.py in utils)'}, {'name': 'supervised_models_cv_results.tab', 'size': 2346, 'type': 'text/tab-separated-values', 'description': 'Cross-validation fold performance for supervised classifiers'}, {'name': 'supervised_requirements.txt', 'size': 142, 'type': 'text/plain', 'description': 'A second req file, specifically for training supervised models'}, {'name': 't5_few_exemplar.txt', 'size': 524, 'type': 'text/plain', 'description': 'Few-shot prompt for generating T5 predictions'}, {'name': 't5_few_political.txt', 'size': 671, 'type': 'text/plain', 'description': ''}, {'name': 't5_zero_exemplar.txt', 'size': 458, 'type': 'text/plain', 'description': ''}, {'name': 't5_zero_political.txt', 'size': 605, 'type': 'text/plain', 'description': ''}, {'name': 'zero-shot_custom_exemplar_predictions.xlsx', 'size': 119387, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Zero-shot custom exemplar predictions'}, {'name': 'zero-shot_custom_political_predictions.xlsx', 'size': 118171, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Zero-shot custom political predictions'}, {'name': 'zero-shot_generic_exemplar_predictions.xlsx', 'size': 116673, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Zero-shot generic exemplar predictions'}, {'name': 'zero-shot_generic_political_predictions.xlsx', 'size': 116401, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Zero-shot generic political predictions'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: A Model of Party Effects on Legislative Behavior Based on Roll-Call Data,"['Rosas, Guillermo', 'Chiou, Fang-Yi']","Scholars analyze roll call votes to test theories about party effects on legislative behavior, but estimating and disentangling party effects from legislators’ ideology is fairly difficult. To tackle this problem, we relax the widely adopted assumption of constant party pressure across votes and collect correlates of “party whipping” to build a measure of party- and vote-specific pressure on legislators. With this measure in hand, we then develop a model to control for ideological preferences and flexibly account for how party pressure may directly or indirectly produce party effects on voting decisions of rank-and file members (including whether they register a vote or not) based on vote and member characteristics. To showcase our approach, we analyze legislative voting in the national legislatures of Brazil and examine hypotheses about how political institutions mediate the effects of party pressure on different legislators and bills.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/911VIX,,"['Party effects', 'Legislative institutions', 'Legislative behavior', 'Roll-call votes', 'Brazil', 'Mexico']",0,0,,,,,,,[],"[{'name': '00_codebook.pdf', 'size': 160620, 'type': 'application/pdf', 'description': 'Codebook for datasets employed in the project'}, {'name': '00_README.pdf', 'size': 238706, 'type': 'application/pdf', 'description': 'Detailed instructions to replicate all results'}, {'name': '01_run_party_pressure_brazil.R', 'size': 5153, 'type': 'type/x-r-syntax', 'description': '1 of 18 R files required for replication'}, {'name': '02_run_party_pressure_mexico.R', 'size': 4563, 'type': 'type/x-r-syntax', 'description': '2 of 18 R files required for replication'}, 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data'}, {'name': 'PP_pressure_analysis.txt', 'size': 12311, 'type': 'text/plain', 'description': 'Text file with original data'}, {'name': 'PRD_pressure_analysis.txt', 'size': 7154, 'type': 'text/plain', 'description': 'Text file with original data'}, {'name': 'prepared_roll_call_data_brazil.Rdata', 'size': 12022050, 'type': 'application/x-rlang-transport', 'description': 'Rdata object'}, {'name': 'prepared_roll_call_data_mexico.Rdata', 'size': 5059935, 'type': 'application/x-rlang-transport', 'description': 'Rdata object'}, {'name': 'PRI_pressure_analysis.txt', 'size': 7154, 'type': 'text/plain', 'description': 'Text file with original data'}, {'name': 'PR_pressure_analysis.txt', 'size': 12311, 'type': 'text/plain', 'description': 'Text file with original data'}, {'name': 'PSDB_pressure_analysis.txt', 'size': 12311, 'type': 'text/plain', 'description': 'Text file with original data'}, {'name': 'PT_pressure_analysis.txt', 'size': 12311, 'type': 'text/plain', 'description': 'Text file with original data'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Data on impact of soil compaction, drought and their combination on vertical root distribution, morphological traits, and soil water patterns in Urochloa hybrids","['Cardoso Arango, Juan Andres', 'Mayorga Cobos, Mildred Julieth']","Urochloa grasses are typically grown in rain-fed systems, where soil compaction and water limitation (drought) significantly reduce their productivity. Deep roots are a key trait in these grasses, allowing them to access water from soil layers at depth during droughts, which helps to prevent water stress (Cardoso et al., 2015). However, soil compaction may limit the ability of roots to penetrate deeply, restricting water uptake and increasing vulnerability to drought. This dataset describes the impact of four soil conditions (control, compaction, drought, and a combination of compaction and drought) on five Urochloa hybrids grown under greenhouse conditions. It includes measurements of root dry mass distribution at 10 cm intervals down a 100 cm soil column, D95 (the depth at which 95% of root mass is concentrated), root morphological traits (number, diameter, and porosity), and shoot dry mass. The data includes information on volumetric water content throughout the soil profile. Such data is not available for Urochloa hybrids or other tropical grasses, making it a valuable tool for understanding their stress responses, water uptake and guiding breeding efforts for more resilient varieties. The data can be used in plant ideotyping and functional structural modelling of roots under various soil conditions to support the development and breeding of more resilient Urochloa hybrids to edapho-climatic stresses such as soil compaction, drought and the combination of both.
+<br><br>
+Methodology: Plant material, site description, and experimental design: The hybrids utilized in this study were developed through the interspecific breeding program at the International Center for Tropical Agriculture (CIAT). The hybrids included Mulato 2, Br02-1752, Br12-3868, Br12-2360, and Br12-3659. The soil used in this experiment was a Mollisol collected from CIAT (latitude: 3°60′N; longitude: 76°31′W; altitude: 990 m) at a depth of 0–20 cm. After collection, the soil was sieved through a 0.02 cm mesh, resulting in a loamy-clay texture with a pH of 7.7. Plants were propagated from vegetative propagules following the method described by Cardoso et al. (2015) and were cultivated under greenhouse conditions at CIAT. Environmental conditions included an average temperature of 28/20 °C (day/night), relative humidity of 71/90% (day/night), and a maximum photosynthetic active radiation (PAR) of 1047 µmol m² s⁻¹. Two soil bulk densities (BD, g/cm³) were established to simulate different levels of soil compaction: 1.3 g/cm³ for non-compacted soil and 1.6 g/cm³ for compacted soil (Rivera et al., 2019). Irrigated plants were maintained at 80% field capacity by weighing soil cylinders weekly and adding water equal to the mass lost through evapotranspiration. For drought conditions, watering was discontinued at the onset of the experiment. The experimental design was a completely randomized 2×2×5 factorial arrangement with four replicates, encompassing two compaction levels (non-compacted, compacted) and two irrigation levels (irrigated, drought) across five genotypes (Mulato 2, Br02-1752, Br12-3868, Br12-2360, Br12-3659).  Harvest, vertical root distribution, and root morphological traits: Plants were harvested 21 days after the initiation of treatments. Shoots were cut and oven-dried to determine shoot dry mass. Soil cylinders were segmented into ten 10-cm layers, and roots were washed to remove soil. Roots from the uppermost 10 cm were used for morphological analyses, including root number, root diameter, and root porosity. Root number was determined as the total count of main roots. Root diameter was measured on ten nodal roots at three equidistant points along their length using a digital micrometer. Root porosity was assessed on the same roots using the pycnometer method, which estimates the air volume within the root structure (Jensen et al., 1969). After morphological traits were recorded, the dry mass of roots from each 10-cm layer was determined. Cumulative root mass distribution (%) was calculated, and D95 (the depth where 95% of root mass is located) was derived through linear interpolation.  Volumetric determination of soil moisture along the soil profile: Before root washing, volumetric soil moisture (m³ m⁻³) was measured every 10 cm of depth using a capacitance-based sensor (ML3 ThetaProbe connected to a HH2 datalogger, Delta-T Devices). Measurements were taken twice per soil profile and scaled to align with the wilting point and field capacity previously determined for each compaction condition",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GDQBQ1,,"['soil compaction', 'drought stress', 'root systems', 'soil water content', 'Americas', 'Crops for Nutrition and Health']",0,0,,,,,,,[],"[{'name': '01a. Readme Dataset.txt', 'size': 6020, 'type': 'text/plain', 'description': 'Readme'}, {'name': '02a. cumulative_root.tab', 'size': 10753, 'type': 'text/tab-separated-values', 'description': 'Dataset cumulative root biomass'}, {'name': '02b. Dataset.tab', 'size': 21852, 'type': 'text/tab-separated-values', 'description': 'Dataset traits'}, {'name': '02c. Weather.txt', 'size': 11022, 'type': 'text/plain', 'description': 'Weather data'}, {'name': '03a. Script for calculation D95.txt', 'size': 4912, 'type': 'text/plain', 'description': 'Calculation D95'}, {'name': '03b. Welch ANOVA for D95.txt', 'size': 1957, 'type': 'text/plain', 'description': 'Welch anova D95'}, {'name': '03c. ANOVA traits.txt', 'size': 2794, 'type': 'text/plain', 'description': 'Anova traits'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+2021 Social Accounting Matrix for Nigeria,['International Food Policy Research Institute (IFPRI)'],"The 2021 Nigeria Social Accounting Matrix (SAM) follows IFPRI's Standard Nexus SAM approach, by focusing on consistency, comparability, and transparency of data. The Nexus SAMs available on IFPRI's website separates domestic production into 42 activities. Factors are disaggregated into labor, agricultural land, and capital, with labor further disaggregated across three education-based categories. The household account is divided into 10 representative household groups: Rural and urban households across per capita consumption quintiles. Nexus SAMs support the improvement of model-based research and policy analysis in developing countries and allow for more robust cross-country comparisons of national economic structures, especially agriculture-food systems.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/095UBP,,"['social accounting matrix', 'national accounting', 'household consumption', 'household expenditure', 'economic indicators', 'labor', 'sex-disaggregated data', 'NIGERIA', 'WEST AFRICA', 'AFRICA SOUTH OF SAHARA', 'AFRICA']",0,0,,,,,,,[],"[{'name': '001_ReadMe.txt', 'size': 1061, 'type': 'text/plain', 'description': ''}, {'name': '002_IFPRI_SAM_NGA_2021_SAM.tab', 'size': 57499, 'type': 'text/tab-separated-values', 'description': ''}, {'name': '003_IFPRI_SAM_NGA_2021_POP.tab', 'size': 707, 'type': 'text/tab-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: State-Level Forecasts for the 2024 US Presidential Election: Trump Back with a Vengeance?,"['Mongrain, Philippe', 'Nadeau, Richard', 'Jérôme, Bruno', 'Jérôme, Véronique']","The outcome of the 2016 election made it abundantly clear that victory in US presidential contests depends on the Electoral College much more than on direct universal suffrage. This fact points to the importance of using state-level models to arrive at adequate predictions of winners and losers in US presidential elections. In fact, the use of a model disaggregated to the state level and focusing on three types of measures—namely, changes in the unemployment rate, presidential popularity, and indicators of long-term patterns in the regional strength of the Democratic and Republican parties—has in the past enabled us to produce fairly accurate forecasts of the number of Electoral College votes for the presidential candidates of the two major American parties. In this article, we bring various modifications to this model to improve its overall accuracy. With Joe Biden out of the race, this revised model predicts that Donald Trump will succeed in winning back the presidency with 341 electoral votes against 197 for Kamala Harris.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/A9UC0H,,[],0,0,,,,,,,[],"[{'name': 'commands_2spe_model_1980_2024_extended.do', 'size': 62838, 'type': 'application/x-stata-syntax', 'description': '.do file (extended model)'}, {'name': 'commands_2spe_model_1980_2024_simplified.do', 'size': 59408, 'type': 'application/x-stata-syntax', 'description': '.do file (simplified model)'}, {'name': 'data_2spe_model_1980_2024.tab', 'size': 247227, 'type': 'text/tab-separated-values', 'description': 'Dataset'}, {'name': 'figures_2spe_model_1980_2024.R', 'size': 90901, 'type': 'type/x-r-syntax', 'description': 'R file (figures)'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Bias-corrected and statistically downscaled climate projections for four distinct areas: Ras Baalbeck in Lebanon, Abu Al-Matamir in Egypt, Wadi Al-Seer in Jordan, and Wadi Al-Faria’a in the West Bank of the Occupied Palestinian Territories (OPT).",['Karim Bergaoui'],"The data includes bias-corrected and statistically downscaled climate projections for four distinct areas: Ras Baalbeck in Lebanon, Abu Al-Matamir in Egypt, Wadi Al-Seer in Jordan, and Wadi Al-Faria’a in the West Bank of the Occupied Palestinian Territories (OPT). This data are outputs from from models that were developed to assess future projected climatological patterns, extreme events, and climate scenarios for these locations. Model outputs have a daily timestep and high spatial resolution (10x10 km). The models were developed using data from the Global Climate Monitoring Products and employing advanced techniques such as Bias Correction and Statistical Disaggregation (BCSD) of Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SCNQWS,,"['climate models', 'climatological patterns', 'climate scenarios']",0,0,,,,,,,[],"[{'name': 'daily_Egypt.tar.gz', 'size': 21927172, 'type': 'application/x-gzip', 'description': ''}, {'name': 'daily_jordan.tar', 'size': 37724160, 'type': 'application/x-tar', 'description': ''}, {'name': 'daily_Lebanon.tar.gz', 'size': 23819760, 'type': 'application/x-gzip', 'description': ''}, {'name': 'daily_palestine.tar.gz', 'size': 22283085, 'type': 'application/x-gzip', 'description': ''}, {'name': 'Excel.tar.gz', 'size': 157490531, 'type': 'application/x-gzip', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: “If Foreign, Then Cleaner”: Individual Corruption Perceptions and Support for Free Trade in Developing Nations","['Shi, Dihan', 'Rosas, Guillermo']","Extant literature on public opinion in international political economy documents the role that domestic corruption perceptions play in the formation of mass attitudes toward a range of integration-related foreign policies. Based on this precedent, we conjecture that corruption perceptions also affect opinions toward free trade. We build on a heuristic approach to attitude formation where individual perceptions of corruption among the political elite trigger positive attitudes toward foreign countries, firms, and products, what we refer to as a “foreignness cue.” This cue drives individuals with high perceptions of corruption to be more supportive of free trade. Based on survey data from eighteen Latin American countries, we demonstrate that higher levels of perceived domestic corruption are associated with greater support for free trade. Causal mediation analysis provides additional evidence that positive attitudes toward foreign countries and firms are a conduit through which the corruption perceptions effect operates. We also offer evidence of external validity of the main effect by analyzing additional surveys on a distinct set of less and more developed countries. Our heuristic-based model of support for free trade complements theories based on material self-interest as a basis for attitude formation in the realm of trade policy.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DUQTTG,,[],0,0,,,,,,,[],"[{'name': 'Shi & Rosas - Corrupt-Trade - Replication.zip', 'size': 307612269, 'type': 'application/zip', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Agricultural greenhouse gas emission factors [SAMPLES Database],"['Richards, Meryl', 'Costa Junior, Ciniro']","This document contains agriculture emission factors collected between 2000 and 2020 from published datasets that was housed on the Standard Assessment of Agricultural Mitigation Potential and Livelihoods (SAMPLES) website managed by the former CGIAR Research Program Climate Change, Agriculture and Food Security (CCAFS). Users were able to download site-specific emission factors and associated agroecological data for use in greenhouse gas accounting or inventories and find links and contact information to access full datasets for use in biogeochemical modeling. It was also encouraged that anyone conducting greenhouse gas (GHG) measurements from agricultural systems to upload their data to the database. Information in this database is freely available to the public for use in research or other purposes based on the CCAFS Data Ownership and Sharing Agreement. SAMPLES was a digital platform launched in 2014 by CCAFS, SAMPLES aimed to address the dearth of reliable information about GHG emissions from agriculture in tropical countries. SAMPLES scientists work with developing countries to improve data on agricultural GHG emissions and mitigation potentials for smallholder agriculture. CCAFS coordinated SAMPLES in collaboration with CGIAR centers and scientists, and global and regional partners.
+<br><br>
+Methodology: SAMPLES database is based on IPCC Emission Factor Database input guidance and the MAGGnet database input spreadsheet, with thanks to Mark Liebig and the Global Research Alliance on Agricultural Greenhouse Gases.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/I8X0RK,,"['emissions from agriculture', 'greenhouse gases', 'rice', 'livestock', 'livestock management', 'methane', 'carbon', 'forest management', 'manure management', 'Americas', 'Climate Action']",0,0,,,,,,,[],"[{'name': '01a. README_SAMPLE Agriculture Emission Factor Database.docx', 'size': 150845, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': 'Description of the database and list of headings & definitions'}, {'name': '02a. SAMPLES Agriculture GHG Emission Factor Database 2024.tab', 'size': 547592, 'type': 'text/tab-separated-values', 'description': 'Agricultural greenhouse gas emission factors [SAMPLES Database]'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: How (not) to Keep a Promise in Two Stages: The Context-Dependent Gaps Between Parties' Representational Goals and Outcomes Through Nomination and Election,"['Turner-Zwinkels, Tomas']","The composition of parliamentary parties does not always align with the representational goals parties have set for themselves. We theoretically conceptualize and empirically assess this gap between aspiration and outcome as a two-stage process involving nomination and election. Our study utilizes unique, detailed data at the election list level on the percentage of women nominated and subsequently elected in Germany and the Netherlands from 1982 to 2017. We theorize and demonstrate empirically that the centralization of the selection process influences the gap between a party's self-set quota and the proportion of nominated women. More specifically, we reveal that the goal-selection gap vanishes when the nomination process is centralized. At the second stage, from nomination to election, we find that both electoral uncertainty due to electoral rules and uncertainty due to electoral behavior are predictive of substantial gaps between the percentage of women nominated and those elected, particularly for smaller parties.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/D9RRXK,,"['Parliaments', 'Representation', 'Quotas', 'Representational goals']",0,0,,,,,,,[],"[{'name': '20200818_2020_dataversion.txt', 'size': 28, 'type': 'text/plain', 'description': ''}, {'name': '20200818_2020_resvecelect.csv', 'size': 1752415, 'type': 'text/csv', 'description': ''}, {'name': '20210212_1433_dataversion.txt', 'size': 28, 'type': 'text/plain', 'description': ''}, {'name': '20210212_1433_meanpersdifferentresvec.csv', 'size': 15564, 'type': 'text/csv', 'description': ''}, {'name': 'COMM.csv', 'size': 263894, 'type': 'text/comma-separated-values', 'description': ''}, {'name': 'dataversion.txt', 'size': 28, 'type': 'text/plain', 'description': ''}, {'name': 'ELDI.tab', 'size': 503701, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'ELEN.csv', 'size': 36254192, 'type': 'text/csv', 'description': ''}, {'name': 'ELLI.csv', 'size': 8061381, 'type': 'text/csv', 'description': ''}, {'name': 'FACT.csv', 'size': 390962, 'type': 'text/csv', 'description': ''}, {'name': 'MEME.tab', 'size': 2375535, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'MINE.tab', 'size': 54130, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'NL_boynames.tab', 'size': 38338, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'NL_girlnames.tab', 'size': 48371, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'NL_girlnames.xlsx', 'size': 153940, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'ORGS.csv', 'size': 1802560, 'type': 'text/csv', 'description': ''}, {'name': 'PARE.csv', 'size': 6319606, 'type': 'text/csv', 'description': ''}, {'name': 'PARL.tab', 'size': 81330, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'PART.csv', 'size': 359019, 'type': 'text/csv', 'description': ''}, {'name': 'PCC___codebook_V4_0_0.pdf', 'size': 1308846, 'type': 'application/pdf', 'description': ''}, {'name': 'POLI.csv', 'size': 6933483, 'type': 'text/csv', 'description': ''}, {'name': 'PPDB_Round2_v4.xlsx', 'size': 701800, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'QUOT.tab', 'size': 10367, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'R026.R', 'size': 223780, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'README.md', 'size': 7931, 'type': 'text/markdown', 'description': ''}, {'name': 'RESE.csv', 'size': 49926002, 'type': 'text/comma-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+The effect and mechanism of digital transformation in promoting green collaborative innovation: Evidence from Chinese manufacturing firms,"['He, Hui', 'Wang, Hao']","Given the pivotal role of collaborative innovation in acquiring key resources to achieve green transformation, this study empirically analyzes the impact and mechanism of digital transformation (DT) on green collaborative innovation (GCI) using data from Chinese listed manufacturing firms for the period 2009–2022. The results reveal that DT significantly promotes GCI, with this effect showing heterogeneity. Mechanism analysis based on the TOE model shows that, DT indirectly promotes GCI by expanding knowledge breadth (T) and strengthening dynamic capability (O). Furthermore, increased industrial chain integration (E1) and greater analyst attention (E2) amplify the positive impact of DT on GCI. Finally, the study identifies that the positive effect of DT on GCI is constrained by a single threshold for green independent innovation, with the threshold value of 2.944. This study provides a comprehensive framework for understanding the relationship of DT and GCI.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FCG2XT,,['Digital transformation; green collaborative innovation; TOE framework; resource bricolage theory'],0,0,,,,,,,[],"[{'name': 'Data.xls', 'size': 2012672, 'type': 'application/vnd.ms-excel', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: You’re not welcome! Violence and support for an open grazing ban policy in Kaduna, Nigeria.","['Tuki, Daniel']",The data underlying the regression models and the do-files required to execute the regression models have been provided.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JRMSFQ,,['Violence'],0,0,,,,,,,[],"[{'name': 'do_file_violence_&_grazing_ban.do', 'size': 14927, 'type': 'application/x-stata-syntax', 'description': 'The do-file contains the codes required to execute the regression models.'}, {'name': 'Replication data_violence_&_grazing_ban.tab', 'size': 54789, 'type': 'text/tab-separated-values', 'description': 'The data underlying the regression models have been provided'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Modifications to the inverse-square law of radiation detection for finite sized detectors,"['Vanden Berk, Daniel']","Replication data for figures and model fits in the paper.  
+
+Columns are:
+
+dist -- distance between source and detector [centimeters]
+rate -- detector count rate corrected for deadtime [counts/s]
+rateErr -- detector count rate uncertainty [counts/s]",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CCVKZX,,['inverse-square law'],0,0,,,,,,,[],"[{'name': 'RateVsDistance.tab', 'size': 990, 'type': 'text/tab-separated-values', 'description': 'Replication Data for: Modifications to the inverse-square law of radiation detection for finite sized detectors'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: ""Spelling Correction with Large Language Models to Reduce Measurement Error in Open-Ended Survey Responses"" in Research & Politics""","['Kellstedt, Paul', 'Allamong, Maxwell', 'Jeong, Jongwoo']","Zipped replication archive for our article in Research & Politics: ""Spelling Correction with Large Language Models to Reduce Measurement Error in Open-Ended Survey Responses""",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NS2LRR,,[],0,0,,,,,,,[],"[{'name': 'LLMs-R&P-Replication.zip', 'size': 218131579, 'type': 'application/zip', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Quantum Computing Perspective for Electromagnetic Wave Propagation in Cold Magnetized Plasma,"['Efstratios Koukoutsis, Kyriakos Hizanidis, George Vahala, Min Soe, Linda Vahala, Abhay K. Ram']","Electromagnetic waves are an inherent part of all plasmas - laboratory fusion plasmas or astrophysical plasmas. The conventional methods for studying properties of electromagnetic waves rely on discretization of Maxwell equations suitable for implementing on classical, present day, computers. The traditional methodology is not efficient for quantum computing implementation - a future computational source offering a tantalizing possibility of enormous speed up and a significant reduction in computational cost. This paper addresses two topics relevant to implementing Maxwell equations on a quantum computer. The first is on formulating a quantum Schrödinger representation of Maxwell equations for wave propagation in a cold, inhomogeneous, and magnetized plasma. This representation admits unitary, energy preserving, evolution and conveniently lends itself to appropriate discretization for a quantum computer. Riding on the coattails of these results, the second topic is on developing a sequence of unitary operators which form the basis for a qubit lattice algorithm (QLA). The QLA, suitable for quantum computers, can be implemented and tested on existing classical computers for accuracy as well as scaling of computational time with the number of available processors. In order to illustrate the QLA for Maxwell equations, results are presented from a time evolving, full wave simulation of propagation and scattering of an electromagnetic wave packet by non-dispersive dielectric medium localized in space.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OCSBSY,,"['maxwell equations', 'plasma waves', 'Quantum Computing', 'Quantum Information Science', 'Qubit lattice algorithm', 'Schrodinger equation']",0,0,,,,,,,[],"[{'name': '23ja018_archival_manuscript.pdf', 'size': 1548255, 'type': 'application/pdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+"Replication Data for: Entorno, clase de accidente y variables temporales asociadas a la gravedad de los siniestros viales.","['Teherán, Aníbal A.', 'Briceño-Arias, Sebastián', 'González-Beltrán, Dora']","Annually, 1.3 million people die due to road traffic accidents (RTAs), and in Bogotá, 1-2 deaths occur daily due to RTAs. Individual factors associated (F_assoc) with RTA fatalities have been identified, but factors associated with deaths/injuries at the accident scene remain unclear. The aim of this study was to identify F_assoc with the severity of RTAs in Bogotá between 2016 and 2022 to propose a preventive prioritization model.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CMFWE9,,"['Accidents, Traffic', 'Case-Control Studies', 'Multiple Trauma', 'Risk factors', 'Mortality']",0,0,,,,,,,[],"[{'name': 'casos_control_aleat.csv', 'size': 15806716, 'type': 'text/csv', 'description': 'Environment, accident type, and temporal factors associated with the severity of road traffic accidents in Bogotá, during the period 2016-2022'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+The Cross-National Macrointerest (CNM) Dataset,"['Hu, Yue', 'Solt, Frederick']","The extent to which the public takes an interest in politics has long been argued to be foundational to democracy, but the want of appropriate data has prevented cross- national and longitudinal analysis. This letter takes advantage of recent advances in latent-variable modeling of aggregate survey responses and a comprehensive collection of survey data to generate dynamic comparative estimates of macrointerest, that is, aggregate political interest, for over a hundred countries over the past four decades.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YYQ8VN,,[],0,0,,,,,,,[],"[{'name': 'cnm1_0.zip', 'size': 59909126, 'type': 'application/zip', 'description': 'CNM Database v1.0'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Monthly Dengue Cases and Climatic Factors Dataset for Predictive Analysis in São Paulo, Brazil","['Gonçalves, Guilherme', 'Sarlo, Amanda', 'Sugai, Kenzo', 'Bedinelli, Marcella', 'Bergamasco, Leila']","This dataset provides monthly records of reported dengue cases across municipalities in São Paulo, Brazil, alongside key climatic variables such as total monthly precipitation, average temperature, and average wind speed. Compiled between 2013 and 2024, this data enables correlation analyses between dengue incidence and climatic conditions, supporting predictive modeling and spatial-temporal analysis. Researchers and public health officials can utilize this dataset to improve understanding of environmental factors influencing dengue outbreaks, facilitating the development of early warning systems and informed public health responses.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NN7EOY,,[],0,0,,,,,,,[],"[{'name': 'dataset_casos_mensais_publicacao_nv.tab', 'size': 7281090, 'type': 'text/tab-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Through restful waters and deep commotion: A study on burnout and health impairment of Italian seafarers from the JD-R model perspective.,"['Buscema, Francesco']",Supplementary material (Informed consent and survey questionnaire) for Through restful waters and deep commotion: A study on burnout and health impairment of Italian seafarers from the JD-R model perspective.,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RGH7ZO,,['Seafarers'],0,0,,,,,,,[],"[{'name': 'Informed Consent.pdf', 'size': 55304, 'type': 'application/pdf', 'description': 'Informed Consent'}, {'name': 'Survey_EN.pdf', 'size': 224448, 'type': 'application/pdf', 'description': 'Survey Questionnaire'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: ""Public Support for Gay Rights Across Countries and Over Time.""  Social Science Quarterly.","['Woo, Byung-Deuk', 'Ko, Hyein', 'Tai, Yuehong Cassandra', 'Hu, Yue', 'Solt, Frederick']","Objective. Support for gay rights has increased in the publics of many countries over recent decades, but the scholarship on the topic has been hindered by the limited available data on these trends in public opinion. The goal of the Support for Gay Rights (SGR) dataset is to overcome this problem. Method. The SGR dataset is constructed by combining a comprehensive collection of survey data with a latent-variable model to provide annual time-series estimates of public support for gay rights across 118 countries and over as many as 51 years that are comparable across space and time. Results. We show these data perform well in validation tests and demonstrate their potential by replicating the influential but recently questioned finding of Andersen and Fetner (2008) that more income inequality yields less tolerant and supportive attitudes toward gay people. Conclusion. We anticipate that the SGR data will become a crucial source for cross-national, cross-regional, and longitudinal research that improves our understanding of the sources and consequences of public support for gay rights.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/I5LMTO,,[],0,0,,,,,,,[],"[{'name': 'sgr_paper_replication.zip', 'size': 97145221, 'type': 'application/zip', 'description': 'All files needed to replicate the SSQ article. For the SGR itself, see https://doi.org/10.7910/DVN/2LHNGJ\n'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+The Support for Gay Rights (SGR) Database,"['Solt, Frederick', 'Woo, Byung-Deuk', 'Ko, Hyein', 'Tai, Yuehong Cassandra', 'Hu, Yue']","Support for gay rights has increased in the publics of many countries over recent decades, but the scholarship on the topic has been hindered by the limited available data on these trends in public opinion. The goal of the Support for Gay Rights (SGR) dataset is to overcome this problem. The SGR dataset is constructed by combining a comprehensive collection of survey data with a latent-variable model to provide annual time-series estimates of public support for gay rights across 118 countries and over as many as 51 years that are comparable across space and time. We anticipate that the SGR data will become a crucial source for cross-national, cross-regional, and longitudinal research that improves our understanding of the sources and consequences of public support for gay rights.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/2LHNGJ,,[],0,0,,,,,,,[],"[{'name': 'sgr1_0.zip', 'size': 106425283, 'type': 'application/zip', 'description': 'SGR Database v1.0'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Table 2 (CLIC Modeled Ages),"['Zucker, Catherine']","Implications of our shell modeling for the ages of clouds in the CLIC and their inferred velocities at the time of formation. Columns are as follows: 
+<ul>
+    <li>(1) Name of the cloud</li>
+    <li>(2) Velocity (<i>v<sub>sh</sub> + &delta;v<sub>sh</sub></i>) that the supernova shell has when it intersects the cloud</li>
+    <li>(3) Velocity that the cloud has when it intersects the supernova shell. Since we assume the clouds formed at this time of intersection, <i>v<sub>cl,intersect</sub></i> is predicted to be the initial velocity of the clouds at birth</li>
+    <li>(4) The modeled age of the cloud, determined by the time of intersection between cloud and shell</li>
+</ul>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LK0XFS,,[],0,0,,,,,,,[],"[{'name': 'Table2_CLIC_Modeled_Ages.dat', 'size': 335, 'type': 'text/x-fixed-field', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Sociobioeconomy and social technology in the Amazon region: An integrated framework proposition published by RAC-Revista de Administração Contemporânea,"['Rodrigues, Diana Cruz', 'Ribeiro, Artur da Silva', 'Silva, João Paulo dos Santos da', 'Passador, Cláudia Souza']","Objective: to propose an analytical framework that integrates the social technology approach into the concept of sociobioeconomy, as its compatible technological aspect, based on the concept of social technological system. Theoretical approach: The articulation of sociobioeconomy concept with that of social technological system is developed. The concept of sociobioeconomy is discussed in light of the three predominant views of bioeconomy (biotechnological, bioresources and bioecological), with the proposition of a conceptual-analytical framework. The social technological system concept is revised to integrate, in a transversal way, with sociobioeconomy. The framework combines dimensions of the social technological system (sociopolitical-cognitive; territorial and socioeconomic sustainability) with the dimensions of sociobioeconomy (social, bio-territorial and economic). Method: The proposed framework was applied in a case study of a potential social technological system in sociobioeconomy activities in the rubber production chain in the Amazon. Data collection included documentary research, interviews and a technical visit. Results: The case study presented the integration of three solutions based on social technology, constituting several properties of a social technological system. The social technological system demonstrated dialogued with the dimensions proposed for sociobioeconomy, proving to be compatible with it. The identification of evidence in the case of several properties of the framework allowed us to consider an initial analytical feasibility for the proposed framework. Conclusions: In conceptual terms, the framework has advanced the analytical detailing of the sociobioeconomy, including its technological aspect, proposing its approximation with social technology. In practical terms, the framework can assist in the development of sociobioeconomy initiatives.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SKBWIX,,"['bioeconomy', 'sociobioeconomy', 'social technology', 'social technological system', 'Amazon']",0,0,,,,,,,[],"[{'name': 'Interview_01.pdf', 'size': 551684, 'type': 'application/pdf', 'description': ''}, {'name': 'Interview_02.pdf', 'size': 443896, 'type': 'application/pdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dataset of TEM Images for Carbon Nanomaterial Classification,"['Wang, Chen']","We employed a convolutional neural network (CNN) for a machine learning and computer vision approach to recognize and classify airborne CNT (carbon nanotubes) and CNF (carbon nanofibers) particles from transmission electron microscope (TEM) images. The model we developed creates a framework for the automatic detection and classification of complex carbon nanostructures, with potential applications in the automated structural classification of other nanomaterials. This dataset comprises 5,323 grayscale TEM images of various nanostructured carbon samples classified into five categories: Cluster, Fiber, Matrix, Matrix-Surface, and Non-CNT. For more information about the data collection and classification process, please refer to https://doi.org/10.1039/D0NA00634C.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5O0SF7,,[],0,0,,,,,,,[],"[{'name': 'CNT-Cluster-0001.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0002.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0003.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0004.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0005.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0006.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0007.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0008.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0009.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0010.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0011.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0012.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0013.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0014.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0015.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0016.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0017.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0018.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0019.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0020.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0021.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0022.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0023.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0024.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0025.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0026.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0027.tif', 'size': 352254, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0028.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0029.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0030.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0031.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0032.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0033.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0034.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0035.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0036.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0037.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0038.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0039.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0040.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0041.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0042.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0043.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0044.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0045.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0046.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0047.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0048.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0049.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0050.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0051.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0052.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0053.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0054.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0055.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0056.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0057.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0058.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0059.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0060.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0061.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0062.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0063.tif', 'size': 1396230, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0064.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0065.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0066.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0067.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0068.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0069.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0070.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0071.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0072.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0073.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0074.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0075.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0076.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0077.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0078.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0079.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0080.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0081.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0082.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0083.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0084.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0085.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0086.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0087.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0088.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0089.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0090.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0091.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0092.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0093.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0094.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0095.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0096.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0097.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0098.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0099.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0100.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0101.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0102.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0103.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0104.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0105.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0106.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0107.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0108.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0109.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0110.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0111.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0112.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0113.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0114.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0115.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0116.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0117.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0118.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0119.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0120.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0121.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0122.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0123.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0124.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0125.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0126.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'CNT-Cluster-0127.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 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'NonCNT-1128.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1129.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1130.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1131.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1132.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1133.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1134.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1135.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1136.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1137.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1138.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1139.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1140.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1141.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1142.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1143.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1144.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1145.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1146.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1147.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1148.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1149.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1150.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1151.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1152.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1153.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1154.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1155.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1156.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1157.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1158.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1159.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1160.tif', 'size': 1085254, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1161.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1162.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1163.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1164.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1165.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1166.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1167.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1168.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1169.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1170.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1171.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1172.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1173.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1174.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1175.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1176.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1177.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1178.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1179.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1180.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1181.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1182.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1183.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1184.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1185.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1186.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1187.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1188.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1189.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1190.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1191.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1192.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1193.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1194.tif', 'size': 1407854, 'type': 'image/tiff', 'description': ''}, {'name': 'NonCNT-1195.tif', 'size': 4200862, 'type': 'image/tiff', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Love numbers and Greens functions for a suite of radially-symmetric stratified Maxwell Earth,"['Adhikari, Surendra', 'Lambert Caron']","We provide loading and tidal Love numbers for a suite of 1-D Maxwell Earth models. Lithosphere thickness varies between 50 and 250 km. Upper mantle viscosity between 1e+18 and 5e+20 Pa s. Evaluation time between 0 and 1,000 years.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PVDKYI,,[],0,0,,,,,,,[],"[{'name': 'greensfunctions.nc', 'size': 579626735, 'type': 'application/netcdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem,"['Lin, Zhongjian', 'Vella, Francis']","This is the replication package for ""Endogenous Treatment Models with Social Interactions: An Application to the Impact of Exercise on Self-Esteem,"" accepted in 2024 by the Journal of Political Economy.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XEGLUA,,"['Highly recommended to include keywords for discoverability of your dataset, use the ""+"" sign to enter each new word']",0,0,,,,,,,[],"[{'name': 'Lin_Vella_202411_Exercise.R', 'size': 13847, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'Lin_Vella_Roy_MC202411.R', 'size': 9432, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'ReadMe.txt', 'size': 579, 'type': 'text/plain', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Food Security Simulator – Ethiopia,['International Food Policy Research Institute (IFPRI)'],"The Food Security Simulator Ethiopia (FSSE) is an innovative and easy-to-use, MS-Excel-based tool for assessing the potential short-term impacts of food price or household income shocks, along with changes in preferences, on food security and people’s diets. The Simulator is an ideal tool for first-cut forward-looking evaluations of direct, household-level outcomes of economic crises and policy responses in a timely manner. The tool allows users to enter positive and negative price or income changes in percentage terms and provides simulated changes for a diverse set of food-consumption- and diet-quality-related indicators. In addition to detailed tabular presentations of all simulation results by household income quintile and residential area, key indicator results are summarized in concise overview tables and visualized in graphs for easy export and use in reports. The underlying data include estimates from representative household survey data and rigorous, sophisticated food demand models to capture consumer behavior.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LVOLEP,,"['food security', 'consumer behavior', 'diet quality', 'food consumption', 'household surveys', 'simulation models', 'ETHIOPIA', 'EAST AFRICA', 'AFRICA SOUTH OF SAHARA', 'AFRICA']",0,0,,,,,,,[],"[{'name': 'FSSE Results Overview.xlsx', 'size': 4264131, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'FSSE Tool.xlsx', 'size': 29841513, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Replication Data for: Addressing Endogeneity Using a Two-Stage Copula Generated Regressor Approach,"['Yang, Fan', 'Qian, Yi', 'Xie, Hui']","We provide instructions, codes and datasets for replicating the paper by Yang, Qian and Xie (2024), ""Addressing Endogeneity using a Two-stage Copula Generated Regressor Approach."" First, we provide a R code to replicate the simulation studies: see simulation_code.R. Second, we provide a R code and a dataset from the public Dominick’s store-level scanner data for empirical illustration: see empirical_illustration_code.R and data_empirical_illustration.csv. 
+
+<br>
+<br>
+<b>[A guide on how to use the code to reproduce each study in the paper]</b>
+<br>
+1.	simulation_code.R: This is the R code for replication of simulation results.
+<br>
+2.	empirical_illustration_code.R: 
+This is the R code for replication of empirical illustration results in the main manuscript. 
+<br>
+3.	data_empirical_illustration.csv: 
+This is the Dominick’s toothpaste sales data used for empirical analysis.
+
+<br>
+<br>
+<b>[A list of the versions of R, packages, and computer specification used in the paper]</b>
+<br>
+R version: 4.2.1 (2022-06-23)
+<br>
+Processor: MacBook Pro with M1 Pro chip
+<br>
+Installed RAM: 32.0 GB
+<br>
+To run the program, download all files and save them under the same folder. Then source the R files to run the programs. 
+
+
+<br>
+<br>
+<b>[Technical Help or Problem Report]</b>
+<br>
+We are expecting the R codes to be easy to use and to be working well. Please send any questions or report bugs to Fan Yang (E-mail: fanyang0903@gmail.com).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3TRFNF,,[],0,0,,,,,,,[],"[{'name': 'data_empirical_illustration.tab', 'size': 44746, 'type': 'text/tab-separated-values', 'description': 'This is the dataset used for empirical illustration.'}, {'name': 'empirical_bootstrap_code.R', 'size': 5161, 'type': 'type/x-r-syntax', 'description': 'This is the R code for bootstrap to check finite-sample performance of empirical results'}, {'name': 'empirical_illustration_code.R', 'size': 6413, 'type': 'type/x-r-syntax', 'description': 'This is the R code for replication of empirical illustration results in the main manuscript. '}, {'name': 'README.docx', 'size': 15872, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': 'This document includes detailed instructions of the files uploaded here.'}, {'name': 'simulation_code.R', 'size': 10796, 'type': 'type/x-r-syntax', 'description': 'This is the R code for replication of simulation results in the main manuscript.'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Replication Data for: Atomic-optical interferometry in fractured loops: a general solution for Rydberg radio frequency receivers,"['Kasza, Bartosz', 'Borówka, Sebastian', 'Parniak, Michał', 'Wasilewski, Wojciech']","<h1>Simulation Data</h1>
+
+The <code>.npy</code> and <code>.npz</code> files store the results of simulations that are visualized in Figure 3 and Figures 5-12. For all plots we use Matplotlib and Numpy packages. Variable <code>path</code> is the path to de storage with data.
+
+<h3>Plotting the Fig. 3 - a4p_mode_evol</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+plot_data = np.load(path + r""\a4p_mode_evol.npy"")
+re01 = np.real(plot_data[:, 1, 0])
+im01 = np.imag(plot_data[:, 1, 0])
+p11 = plot_data[:, 1, 1]
+x = np.linspace(0, 3, p11.shape[0])
+fig, axes = plt.subplots(nrows=3, figsize=(6, 5), sharex=True)
+axes[0].set_ylabel(r""$\text{Im}{\tilde{\boldsymbol{\rho}}_{10}}$"")
+axes[0].plot(x, im01, label=r""$\text{Im}{\tilde{\boldsymbol{\rho}}_{01}}$"")
+axes[1].set_ylabel(r""$\text{Re}{\tilde{\boldsymbol{\rho}}_{10}}$"")
+axes[1].plot(x, re01, label=r""$\text{Re}{\tilde{\boldsymbol{\rho}}_{01}}$"")
+axes[2].set_ylabel(r""$\tilde{\boldsymbol{\rho}}_{11}$"")
+axes[2].plot(x, p11.real, label=r""$\tilde{\boldsymbol{\rho}}_{11}$"")
+axes[2].set_xlabel(r""$\delta t$ [$2\pi$ rad]"")
+fig.tight_layout()
+plt.show()
+</pre></code>
+
+
+<h3>Plotting the Fig. 5 - mod_transfer_gamma_transit_lo_delta</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+plot_data = np.load(path + r""\mod_transfer_gamma_transit_lo_delta.npy"")
+fig, ax = plt.subplots(figsize=(6, 6))
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+obj = ax.imshow(
+    plot_data,
+    cmap=""YlOrRd"",
+    interpolation=""none"",
+    extent=[omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    aspect=""equal"",
+)
+ax.set_xlabel(""$\Omega_{LO}$ [$2 \pi \cdot$MHz]"")
+ax.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax.set_title(""$\Omega_{LS} = 0.05 \cdot$$2 \pi \cdot$MHz"")
+cbar = fig.colorbar(obj, ax=ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(1)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 6  - mod_transfer_gamma_transit_ls_compare_1_2</h3>
+
+<code><pre>
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+
+plot_data_1, plot_data_2 = np.load(
+    path + r""\mod_transfer_gamma_transit_ls_compare_1_2.npy""
+)
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+fig = plt.figure(figsize=(14, 6))
+gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0)
+kwargs = {
+    ""cmap"": ""YlOrRd"",
+    ""interpolation"": ""none"",
+    ""extent"": [omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    ""aspect"": ""equal"",
+    ""vmin"": min(np.abs(plot_data_1).min(), np.abs(plot_data_2).min()),
+    ""vmax"": max(np.abs(plot_data_1).max(), np.abs(plot_data_2).max()),
+}
+ax1 = fig.add_subplot(gs[0])
+ax2 = fig.add_subplot(gs[1])
+
+im1 = ax1.imshow(np.abs(plot_data_1), **kwargs)
+ax1.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax1.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax1.set_title(r""$d = 1$"")
+im2 = ax2.imshow(np.abs(plot_data_2), **kwargs)
+ax2.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax2.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax2.set_title(r""$d = 2$"")
+ax2.get_yaxis().set_tick_params(labelleft=False)
+ax2.set_ylabel("""")
+
+cbar_ax = fig.add_subplot(gs[2])
+cbar = fig.colorbar(im1, cax=cbar_ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(d)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 7 - bandwidth_gamma_transit_ls</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+color0, color1, color2 = (""#FF4500"", ""#FFB6C1"", ""#800080"")
+plot_data_bandwidth = np.load(
+    path + r""\bandwidth_gamma_transit_ls_plot_data_bandwidth.npy""
+)
+plot_data_mod_transfer = np.load(
+    path + r""\bandwidth_gamma_transit_ls_plot_data_mod_transfer.npy""
+)
+fig, ax1 = plt.subplots()
+ax1.set_xlabel(r""$\Omega_{LS}~[2\pi \cdot$MHz]"")
+ax1.set_ylabel(r""Bandwidth [$2\pi \cdot$MHz]"")
+ax1.plot(*plot_data_bandwidth, color=""blue"", label=""Bandwidth"")
+ax1.tick_params(axis=""y"")
+move = 0.1
+ax1.set_ylim(plot_data_bandwidth[1].min() - move, plot_data_bandwidth[1].max() + move)
+ax2 = ax1.twinx()
+ax2.set_ylabel(r""$|\alpha_{01}^{(1)}|$"")
+ax2.plot(
+    *plot_data_mod_transfer,
+    color=color2,
+    label=r""$|\alpha_{01}^{(1)}(\delta = 0)|$"",
+)
+ax2.tick_params(axis=""y"")
+fig.legend(loc=""lower left"", bbox_to_anchor=(0.17, 0.15))
+fig.tight_layout()
+</pre></code>
+
+<h3>Plotting the Fig. 8 - calculated detuned smaller peak heights</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+color0, color1, color2 = (""#FF4500"", ""#FFB6C1"", ""#800080"")
+plot_data_bandwidth = np.load(
+    path + r""\bandwidth_gamma_transit_lo_plot_data_bandwidth.npy""
+)
+plot_data_mod_transfer = np.load(
+    path + r""\bandwidth_gamma_transit_lo_plot_data_mod_transfer.npy""
+)
+fig, ax1 = plt.subplots()
+ax1.set_xlabel(r""$\Omega_{LO}~[2\pi \cdot$MHz]"")
+ax1.set_ylabel(r""Bandwidth [$2\pi \cdot$MHz]"")
+ax1.plot(*plot_data_bandwidth, color=""blue"", label=""Bandwidth"")
+ax1.tick_params(axis=""y"")
+move = 0.1
+ax1.set_ylim(plot_data_bandwidth[1].min() - move, plot_data_bandwidth[1].max() + move)
+ax2 = ax1.twinx()
+ax2.set_ylabel(r""$|\alpha_{01}^{(1)}|$"")
+ax2.plot(
+    *plot_data_mod_transfer,
+    color=color2,
+    label=r""$|\alpha_{01}^{(1)}(\delta = 0)|$"",
+)
+ax2.tick_params(axis=""y"")
+fig.legend(loc=""lower left"", bbox_to_anchor=(0.17, 0.15))
+fig.tight_layout()
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 9 - mod_transfer_cold_lo_delta</h3>
+<code><pre>
+plot_data = np.load(path + r""\mod_transfer_cold_lo_delta.npy"")
+fig, ax = plt.subplots(figsize=(6, 6))
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+obj = ax.imshow(
+    plot_data,
+    cmap=""YlOrRd"",
+    interpolation=""none"",
+    extent=[omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    aspect=""equal"",
+)
+ax.set_xlabel(""$\Omega_{LO}$ [$2 \pi \cdot$MHz]"")
+ax.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax.set_title(""$\Omega_{LS} = 0.05 \cdot$$2 \pi \cdot$MHz"")
+cbar = fig.colorbar(obj, ax=ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(1)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 10 - mod_transfer_cold_ls_compare_1_2_3</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+
+plot_data_1, plot_data_2, plot_data_3 = np.load(
+    path + r""\mod_transfer_cold_ls_compare_1_2_3.npy""
+)
+fig = plt.figure(figsize=(18, 6))
+gs = gridspec.GridSpec(1, 4, width_ratios=[1, 1, 1, 0.05], wspace=0.3)
+omega_seq = np.linspace(0.05, 15, 500)
+delta_seq = np.linspace(-7.5, 7.5, 500)
+kwargs = {
+    ""cmap"": ""YlOrRd"",
+    ""interpolation"": ""none"",
+    ""extent"": [omega_seq[0], omega_seq[-1], delta_seq[0], delta_seq[-1]],
+    ""aspect"": ""equal"",
+    ""vmin"": min(
+        np.abs(plot_data_1).min(), np.abs(plot_data_2).min(), np.abs(plot_data_3).min()
+    ),  # tripple
+    ""vmax"": max(
+        np.abs(plot_data_1).max(), np.abs(plot_data_2).max(), np.abs(plot_data_3).max()
+    ),  # tripple
+}
+ax1 = fig.add_subplot(gs[0])
+ax2 = fig.add_subplot(gs[1])
+ax3 = fig.add_subplot(gs[2])
+
+
+im1 = ax1.imshow(np.abs(plot_data_1), **kwargs)
+ax1.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax1.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax1.set_title(r""$d = 1$"")
+im2 = ax2.imshow(np.abs(plot_data_2), **kwargs)
+ax2.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax2.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax2.set_title(r""$d = 2$"")
+ax2.get_yaxis().set_tick_params(labelleft=False)
+ax2.set_ylabel("""")
+im3 = ax3.imshow(np.abs(plot_data_3), **kwargs)
+ax3.set_ylabel(""$\delta$ [$2 \pi \cdot$MHz]"")
+ax3.set_xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+ax3.get_yaxis().set_tick_params(labelleft=False)
+ax3.set_ylabel("""")
+ax3.set_title(r""$d = 3$"")
+
+cbar_ax = fig.add_subplot(gs[3])
+cbar = fig.colorbar(im1, cax=cbar_ax, fraction=0.05, pad=0.04)
+cbar.set_label(r""$|\alpha_{01}^{(d)}|$"")
+fig.show()
+</pre></code>
+
+<h3>Plotting the Fig. 11 - mod_compare_delta_demod</h3>
+<code><pre>
+import matplotlib.pyplot as plt
+import numpy as np
+
+plot_data = np.load(path + r""\mod_compare_delta_demod.npy"")
+omega_seq = np.linspace(0.05, 15, 500)
+for m in [1, 2, 3]:
+    plt.plot(
+        omega_seq,
+        plot_data[m - 1, :],
+        label=rf""$m = {m}$"",
+    )
+plt.legend()
+plt.ylabel(r""$|\tilde{\boldsymbol{\rho}}_{01}^{m}$|"")
+plt.xlabel(""$\Omega_{LS}$ [$2 \pi \cdot$MHz]"")
+plt.yscale(""log"")
+</pre></code>
+
+<h3>Plotting the Fig. 12 - mode_comparison_lattice</h3>
+<code><pre>
+import numpy as np
+import matplotlib.pyplot as plt
+
+data = np.load(path + r""\mode_comparison_lattice.npz"")
+lst = data.files
+plot_data = [data[item] for item in lst]
+fig, axes = plt.subplots(2, 2, figsize=(10, 8))
+axes = axes.ravel()
+full_N_seq = list(range(3, 16, 2))
+mode_to_check = [0, 1, 2, 3]
+for i, pd in enumerate(plot_data):
+    ax = axes[i]
+    ax.scatter(full_N_seq[-len(pd) :], pd)
+    ax.set_ylabel(
+        f""|$\\tilde{{\\boldsymbol{{\\rho}}}}_{{10}}^{{({mode_to_check[i]})}}$|""
+    )
+    ax.set_xlabel(""N"")
+    ax.set_xticks(np.arange(1, max(full_N_seq) + 1, 2))
+    ax.set_title(f""$m$ = {mode_to_check[i]}"")
+plt.tight_layout()
+</pre></code>",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TLKOJT,,[],0,0,,,,,,,[],"[{'name': 'a4p_mode_evol.npy', 'size': 154496, 'type': 'application/octet-stream', 'description': 'Fig. 3 data'}, {'name': 'bandwidth_gamma_transit_lo_plot_data_bandwidth.npy', 'size': 6368, 'type': 'application/octet-stream', 'description': 'Fig. 8 data 1'}, {'name': 'bandwidth_gamma_transit_lo_plot_data_mod_transfer.npy', 'size': 8128, 'type': 'application/octet-stream', 'description': 'Fig. 8 data 2'}, {'name': 'bandwidth_gamma_transit_ls_plot_data_bandwidth.npy', 'size': 5696, 'type': 'application/octet-stream', 'description': 'Fig. 7 data 1'}, {'name': 'bandwidth_gamma_transit_ls_plot_data_mod_transfer.npy', 'size': 8128, 'type': 'application/octet-stream', 'description': 'Fig. 7 data 2'}, {'name': 'mod_compare_delta_demod.npy', 'size': 6128, 'type': 'application/octet-stream', 'description': 'Fig. 11 data'}, {'name': 'mode_comparison_lattice.npz', 'size': 1090, 'type': 'application/octet-stream', 'description': 'Fig. 12 data'}, {'name': 'mod_transfer_cold_lo_delta.npy', 'size': 1000128, 'type': 'application/octet-stream', 'description': 'Fig. 9 data'}, {'name': 'mod_transfer_cold_ls_compare_1_2_3.npy', 'size': 6000128, 'type': 'application/octet-stream', 'description': 'Fig. 10 data'}, {'name': 'mod_transfer_gamma_transit_lo_delta.npy', 'size': 1000128, 'type': 'application/octet-stream', 'description': 'Fig. 5 data'}, {'name': 'mod_transfer_gamma_transit_ls_compare_1_2.npy', 'size': 4000128, 'type': 'application/octet-stream', 'description': 'Fig. 6 data'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Replication Data for: The drivers of multilevel governance as a mode of policymaking. The case asylum seekers’ reception in Italy.,"['Caponio, Tiziana', 'Pettrachin, Andrea', 'Ponzo, Ilaria']","This article analyses modes of policymaking related to asylum-seekers’ reception in Italy and other EU countries during the decade of the so-called 2015 asylum crisis. It shows that, while most EU countries experienced shifts towards more hierarchical modes of policymaking on asylum, Italy pursued a unique experience of multilevel governance (MLG) between 2014 and 2016, which was then dismantled in 2017. By looking at this MLG experience as an ‘heuristic case’, the article contributes to an ongoing debate about the drivers of MLG as a mode of policymaking. The existing literature suggests that MLG modes of policymaking are driven by institutional and structural factors or pressure by subnational and supranational actors for more participatory policymaking processes. Complementing and challenging these theoretical explanations we generate some hypotheses about additional factors that drive the emergence and dismantling of MLG. First, we argue that both supranational actors and subnational authorities, typically considered to be agents promoting MLG, can also advocate for more hierarchical modes of policymaking. Second, we argue that a fundamental prerequisite for MLG to emerge or persist is an overall convergence of political priorities and goals among the actors involved in multilevel policymaking. Both the kind of pressures made by supranational and subnational actors and actors’ political priorities can be decisively shaped by dynamics of multilevel party politics. These findings are derived from analyses of 147 interviews with key actors involved in Italian asylum policymaking in the 2010s.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SEEI3Z,,[],0,0,,,,,,,[],"[{'name': 'DATA REPLICABILITY.docx', 'size': 27144, 'type': 'application/vnd.openxmlformats-officedocument.wordprocessingml.document', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Code for “Do Plastic Waste Imports Motivate Domestic Plastic Bags Regulations? A Panel Study, 1992-2019”","['Uji, Azusa', 'Song, Jaehyun', 'Dolšak, Nives', 'Prakash, Aseem']","Plastic waste has emerged as an important global issue. The post-materialism hypothesis suggests that wealthy countries are likely to be early adopters of pollution control regulations. Yet, Global South countries have taken the lead in regulating plastic bags. To explain this puzzle, we examine the role of plastic waste imports in influencing the onset of domestic plastic bag regulations. Processing imported waste creates visible local pollution in the Global South and mobilizes citizens and environmental groups. Because plastic bags are a visible manifestation of the plastic pollution problem, they become focal points for regulatory action. Using a hazard model, our analysis of 133 countries for the 1992-2019 period suggests that plastic waste importers (located in the Global South) are more likely to regulate plastic bags (via fees or bans) earlier while plastic waste exports are not associated with plastic bag regulation. This paper raises broader theoretical issues about domestic policy spillovers of international trade, and how trade might unexpectedly activate domestic politics focused on post-materialist concerns.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JJFFRC,,[],0,0,,,,,,,[],"[{'name': 'Replication_Ban.tab', 'size': 1068933, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Replication_BF.tab', 'size': 1047354, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Replication_Fee.tab', 'size': 1109920, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Replication.html', 'size': 2411633, 'type': 'text/html', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: ""Spelling Correction with Large Language Models to Reduce Measurement Error in Open-Ended Survey Responses"" in Research & Politics","['Kellstedt, Paul']","Zipped replication archive for our article in Research & Politics: ""Spelling Correction with Large Language Models to Reduce Measurement Error in Open-Ended Survey Responses"" (2024-12-09)",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UQUJDM,,[],0,0,,,,,,,[],"[{'name': 'LLMs-R&P-Replication.zip', 'size': 218131579, 'type': 'application/zip', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: The Damocles Delusion: The Sense of Power Inflates Threat Perception in World Politics,"['Pomeroy, Caleb']","How does power affect threat perception? Drawing on advances in psychological research on power, this paper finds that the sense of state power inflates threat perception. The sense of power activates intuitive thinking in the decisionmaking process, including a reliance on gut feelings and cognitive shortcuts like heuristics and prior beliefs. In turn, psychological IR research shows that these mechanisms tend to inflate threat perception. The powerful assess threats from the gut rather than head. Experimental evidence from the US and China, a re-analysis of a Russian elite survey, and a large-scale text analysis of Cold War US foreign policy elites lend support to this expectation. The findings help to psychologically reconcile enduring theoretical puzzles -- from underbalancing to overextension -- and generate entirely new ones, like the possibility that decisionmakers of rising, not declining, states feel greater fear. Together, the paper offers a ""first image reversed"" challenge to bottom-up accounts of psychological IR. Decisionmakers are also dependent variables shaped by the balance of power, with important implications for a world returning to great power competition.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ZEKIQN,,[],0,0,,,,,,,[],"[{'name': 'china_nuclear_experiment.R', 'size': 8198, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'china_nuclear_proliferation_data.rds', 'size': 4328, 'type': 'application/gzip', 'description': ''}, {'name': 'damocles_supplementary_appendix.pdf', 'size': 333602, 'type': 'application/pdf', 'description': ''}, {'name': 'frus_embedding_analysis.R', 'size': 45289, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'README.txt', 'size': 2355, 'type': 'text/plain', 'description': ''}, {'name': 'resample_10.rds', 'size': 76178096, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_11.rds', 'size': 76062952, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_12.rds', 'size': 77545569, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_13.rds', 'size': 75034422, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_14.rds', 'size': 76312123, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_15.rds', 'size': 76799856, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_16.rds', 'size': 76043330, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_17.rds', 'size': 75746580, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_18.rds', 'size': 76184959, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_19.rds', 'size': 73675076, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_1.rds', 'size': 75335784, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_20.rds', 'size': 74209775, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_2.rds', 'size': 74933673, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_3.rds', 'size': 75430691, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_4.rds', 'size': 77429497, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_5.rds', 'size': 77095793, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_6.rds', 'size': 77418858, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_7.rds', 'size': 75146698, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_8.rds', 'size': 75729037, 'type': 'application/gzip', 'description': ''}, {'name': 'resample_9.rds', 'size': 76364172, 'type': 'application/gzip', 'description': ''}, {'name': 'russia_elite_data.sav', 'size': 13312, 'type': 'application/x-spss-sav', 'description': ''}, {'name': 'russia_elite_survey.R', 'size': 14435, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'us_nuclear_experiment.R', 'size': 27819, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'us_nuclear_proliferation_data.rds', 'size': 23194, 'type': 'application/gzip', 'description': ''}, {'name': 'us_rise_decline_data.rds', 'size': 1780, 'type': 'application/gzip', 'description': ''}, {'name': 'us_rise_decline_experiment.R', 'size': 2987, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'us_time_pressure_data.rds', 'size': 12467, 'type': 'application/gzip', 'description': ''}, {'name': 'us_time_pressure_experiment.R', 'size': 5392, 'type': 'type/x-r-syntax', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Do Political Finance Reforms Really Reduce Corruption? A Replication Study,"['LIPCEAN, Sergiu', 'Casal Bértoa, Fernando']","In their 2021 article published in the British Journal of Political Science, Calla Hummel, John Gerring, and Thomas Burt (henceforth Hummel et al.) examine the relationship between political finance reforms and corruption, concluding that state funding of political parties reduces corruption by diminishing reliance on private financing. Our replication study challenges this conclusion based on data accuracy and operationalisation issues of their key explanatory variable – Political Finance Subsidy Index (PFSI) – and using alternative model specifications and regional subsamples. While our partial data corrections and recoding do not significantly differ from the Hummel et al. findings, the substantial regional heterogeneity regarding public party funding – corruption relationship and the results’ sensitivity to alternative modelling choices such as two-way fixed effects, country fixed effects, and time fixed effects, cast doubt on the reliability of their results. Our findings underscore the methodological challenges in assessing the effects of public funding reforms and emphasize the need for refined measurements and careful consideration of regional contexts. Although public funding may help combat corruption under specific institutional conditions, its effects are not universally consistent, calling for greater caution in deriving policy implications from cross-national analyses.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BCWUOV,,"['political corruption', 'public funding of political parties']",0,0,,,,,,,[],"[{'name': '01_Read_me.txt', 'size': 3670, 'type': 'text/plain', 'description': 'Read me file'}, {'name': '02_Codebook.pdf', 'size': 128731, 'type': 'application/pdf', 'description': 'Codebook'}, {'name': '03_Replication_main.R', 'size': 42456, 'type': 'type/x-r-syntax', 'description': 'R script for the article'}, {'name': '04_Replication_appendix.R', 'size': 74881, 'type': 'type/x-r-syntax', 'description': 'R script for the online appendix'}, {'name': 'pfsi_data.rds', 'size': 2675708, 'type': 'application/gzip', 'description': 'Dataset'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for The examination of the relationship between learning motivation and learning effectiveness: a mediation model of learning engagement,"['Chen, Chiwei']",The datasets were used in the study of the examination of the relationship between learning motivation and learning effectiveness: a mediation model of learning engagement,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MB7OH8,,['learning motivation'],0,0,,,,,,,[],"[{'name': 'dataset-20231029EN1.tab', 'size': 33684, 'type': 'text/tab-separated-values', 'description': 'English version dataset'}, {'name': 'dataset-20231029.tab', 'size': 33684, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'PersonalityTraitsENG.tab', 'size': 11712, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'PersonalityTraitsENG.xls', 'size': 67728, 'type': 'application/vnd.ms-excel', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Factor Models for Conditional Asset Pricing,"['Zaffaroni, Paolo']","This is the replication package for ""Factor Models for Conditional Asset Pricing,"" accepted in 2024 by the Journal of Political Economy.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PS7K7Z,,"['Highly recommended to include keywords for discoverability of your dataset, use the ""+"" sign to enter each new word']",0,0,,,,,,,[],"[{'name': '45bal_K1_9Jan23.pdf', 'size': 38272, 'type': 'application/pdf', 'description': ''}, {'name': '45_K1_9Jan23.pdf', 'size': 380372, 'type': 'application/pdf', 'description': ''}, {'name': 'CIT5N1000.pdf', 'size': 4714, 'type': 'application/pdf', 'description': ''}, {'name': 'CIT5N10.pdf', 'size': 4718, 'type': 'application/pdf', 'description': ''}, {'name': 'con120totsp500upd.pdf', 'size': 41557, 'type': 'application/pdf', 'description': ''}, {'name': 'con120totupd.pdf', 'size': 43351, 'type': 'application/pdf', 'description': ''}, {'name': 'con24totsp500upd.pdf', 'size': 46672, 'type': 'application/pdf', 'description': ''}, {'name': 'con24totupd.pdf', 'size': 45703, 'type': 'application/pdf', 'description': ''}, {'name': 'Empirics_JPE_ MS 20210421.R', 'size': 390662, 'type': 'type/x-r-syntax', 'description': 'Integrated code for all the empirical analysis of ""Factor Models for Conditional Asset Pricing"". 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'description': ''}, {'name': 'TableOA1_JPE_MS20210421.tex', 'size': 1854, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA20_OA22_JPE_MS20210421.tex', 'size': 8950, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA2_JPE_MS20210421.tex', 'size': 3725, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA3_OA4_JPE_MS20210421.tex', 'size': 4654, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA5_OA6_JPE_MS20210421.tex', 'size': 4574, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA7_JPE_MS20210421.tex', 'size': 1673, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA8_JPE_MS20210421.tex', 'size': 3829, 'type': 'application/x-tex', 'description': ''}, {'name': 'TableOA9_JPE_MS20210421.tex', 'size': 2603, 'type': 'application/x-tex', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Labor response to the COVID-19 crisis.,"['Estruch Peñalver, Lucas', 'Amador Ramos, Lucia Maria']","The study examines labor policy responses to the COVID-19 crisis in Germany, Portugal, and Romania, focusing on how state models and government ideologies shaped their approaches. By comparing a federal parliamentary system (Germany) with two unitary semi-presidential states (Portugal and Romania), the research highlights the influence of legislative mechanisms, political ideologies, and structural frameworks on policy design. Results show that Germany's federal system prioritized solid, comprehensive measures with lower frequency but higher stability, while Portugal and Romania, with more flexible legislative processes, implemented numerous short-term policies requiring frequent revisions. Ideological differences significantly influenced policy objectives, with Portugal focusing on direct worker support, and Germany and Romania emphasizing business subsidies. The findings underscore the interplay between governance structures and ideological priorities in shaping labor policy during crises. Note that original dataset is in Spanish.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/M0AMFN,,"['COVID-19', 'Crisis response', 'Romania', 'Portugal', 'Germany', 'Labor market']",0,0,,,,,,,[],"[{'name': 'Política Comparada Trabajo_Lucia_Lucas (1).pdf', 'size': 355323, 'type': 'application/pdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Correlates of substance use,"['Glozah, Franklin', 'Rachael Asantewaa Darko']","Background: Substance use poses a significant threat to adolescent health and well-being globally, with rising rates of concern in developing countries. Several psychosocial risk factors such as peer influence and protective factors such as self-esteem are associated with substance use among adolescents.  Understanding the specific psychosocial factors currently driving substance use among youth is important for developing targeted interventions. This study examines prevalence of substance use and its psychosocial correlates among Ghanaian in-school adolescents. 
+Methods: A cross-sectional survey was administered to a random sample of 425 Senior High School (SHS) students in Accra.  Psychosocial indicators, the Rosenberg Self-esteem Scale and the CAGE-AID substance abuse screening tool were used to assess substance use, peer and family influences, socio-demographic characteristics and potential substance use disorder. Descriptive statistics were used to characterize the sample and a multiple logistic regression models identified predictors of use for specific substances. 
+Results: The mean age of participants was 17.1 years (SD = 1.18). Alcohol use was the most used substance, followed by shisha, marijuana, and cigarettes. Male students, those living with relatives, and those with friends who drink alcohol were more likely to use alcohol.  Students who worked while in school, or had family members who smoke, had an increased likelihood of cigarette use. Older students and those with friends who use shisha were more likely to use shisha. Interestingly, limited social media exposure and living with parents and siblings were associated with lower marijuana use. Finally, the results showed the presence of a potential substance use disorder in students who use substances. 
+Conclusion: In conclusion, cigarettes, shisha, marijuana and alcohol are the most commonly used and having a family member or peer who uses substance is a risk factor associated with increased likelihood of adolescents in senior school to use substance. These findings could inform culturally sensitive interventions to promote adolescent health and wellbeing in Ghana, and other low-and-middle-income country contexts.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MDDYTI,,[],0,0,,,,,,,[],"[{'name': 'Copy of Correlates of substance use cleaned data..xlsx', 'size': 453530, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: ""A Dynamic Forecast: An Evolving Prediction of the 2024 Presidential Election""","['Lindsay, Spencer', 'Allen, Levi']","In this article, we build a model to predict the state-level results of the 2024 election.
+We do so by using polling from similar points in past election cycles and by using the
+results of the previous election. Notably, we update our model over time and the
+coefficients of the two variables change- the model puts more weight on polling as the
+election gets closer. As of September 1, 2024, we find that Kamala Harris is a narrow
+favorite to win the 2024 election, with a 57 percent chance of doing so. Currently the
+model predicts she will win 289 electoral votes to Trump's 249. However, there
+remains significant uncertainty and the model will continue to update as the
+election nears",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/RLEUOF,,[],0,0,,,,,,,[],"[{'name': 'Election Model Calibration - April 15.csv', 'size': 4854, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - Election Day.csv', 'size': 5292, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - Election Day No 2016.csv', 'size': 4535, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - Election Day No 2020.csv', 'size': 4506, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - July 15.csv', 'size': 5245, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - June 1.csv', 'size': 5185, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - Oct 1.csv', 'size': 5279, 'type': 'text/csv', 'description': ''}, {'name': 'Election Model Calibration - Sept 1.csv', 'size': 5242, 'type': 'text/csv', 'description': ''}, {'name': 'Out of Sample Tests - Switch.tab', 'size': 1321, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'Out of Sample Tests.xlsx', 'size': 14057, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'PS Copy of Public 2024 Presidential Election Model - September 1, 4_02\u202fPM (1).xlsx', 'size': 44716, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'PS File.R', 'size': 1804, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'PS Key Dates Forecast.xlsx', 'size': 87818, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'PS R Code Log.pdf', 'size': 66401, 'type': 'application/pdf', 'description': ''}, {'name': 'PS Read Me File.pdf', 'size': 119011, 'type': 'application/pdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Milli-to-Deci-Hertz Detection Prospects for Gravitational Waves from Core-Collapse Supernovae,"['Gill, Kiranjyot']","Gravitational wave (GW) astronomy truly began with the detection of merging compact binaries. The next breakthrough lies in detecting GWs from core-collapse supernovae (CCSNe), particularly the GW linear memory -- a phenomenon arising from aspherical matter ejection and anisotropic neutrino emission during stellar collapse. In this Letter, we examine the feasibility of detecting this effect using next-generation space-based GW detectors or lunar-based GW observatories as this signature peaks below 25 Hz, which is largely inaccessible to terrestrial GW detectors due to seismic noise. Such a detection would provide fundamental insights into asymmetric matter dynamics near the collapsed core, shedding light on the stellar corpse that once represented the mass of the progenitor star and offering a front row seat to the potential formation of either a nascent neutron star progenitor or a black hole. Leveraging the longest-duration three-dimensional CCSNe simulations to date, spanning a wide range of progenitor masses, we show that space-based and lunar GW detectors present the most promising opportunities to extend the current CCSNe GW detection horizon (~10 kiloparsecs) to several megaparsecs. This may pave the way for regularly observing GWs from CCSNe beyond our Milky Way by utilizing the distinctive environments of space and the Moon to augment terrestrial detection efforts all the while synergistically advancing sophisticated data analysis techniques for routine GW detection.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/VZCS1W,,[],0,0,,,,,,,[],[],theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,['theoretical']
+Replication Code for: The New Keynesian Model and Bond Yields,"['Andreasen, Martin Møller']","Replication codes for Martin M. Andreasen: ""The New Keynesian Model and Bond Yields"", Journal of Financial and Quantitative Analysis, forthcoming.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/76VS0R,,['Macroeconomics and Finance'],0,0,,,,,,,[],"[{'name': 'ACM_tp.mat', 'size': 5166, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'ACM_tp.mat', 'size': 5166, 'type': 'application/matlab-mat', 'description': ''}, {'name': 'A_fifth_order.m', 'size': 1173, 'type': 'text/x-matlab', 'description': ''}, {'name': 'A_fifth_order.m', 'size': 1173, 'type': 'text/x-matlab', 'description': ''}, {'name': 'A_fifth_order.m', 'size': 1173, 'type': 'text/x-matlab', 'description': ''}, {'name': 'A_fourth_order.m', 'size': 363, 'type': 'text/x-matlab', 'description': ''}, {'name': 'A_fourth_order.m', 'size': 363, 'type': 'text/x-matlab', 'description': ''}, {'name': 'A_fourth_order.m', 'size': 363, 'type': 'text/x-matlab', 'description': ''}, {'name': 'AkronkC.m', 'size': 293, 'type': 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+Metabolic Changes During Evolution of Sjögren’s Syndrome in Both an Animal Model and Human Patients,"['Jacob, Alexander', 'He, Jing', 'Peck, Ammon', 'Jamil, Ali', 'Bunya, Vatinee', 'Alexander, Jessy', 'Ambrus Jr, Julian']","Sjögren’s syndrome (SS) involves salivary and lacrimal gland dysfunction. These studies examined metabolic profiles in both the B6.Il14α transgene mouse model of SS and a cohort of human SS patients at different stages of disease. In B6.Il14α mice, products of glucose and fatty acid were common at 6 months of age, while products of amino acid metabolism were common at 12 months of age. Similarly, SS patients at an early stage of disease showed high glycolysis. SS patients with long-standing disease utilized predominantly amino acid metabolism, similar to B6.Il14α mice at 10-12 months of age. Additional studies are suggested to further define metabolic activities at the various disease stages.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/J2XYRN,,[],0,0,,,,,,,[],"[{'name': 'Human Metabolites.xlsx', 'size': 225226, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'Mouse Metabolities.xlsx', 'size': 1483757, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Adding Regularized Horseshoes to the Dynamics of Latent Variable Models,"['Binding, Garret', 'Koc, Piotr']","Dynamic latent variable models generally link units' positions on a latent dimension over time via random walks. Theoretically, these trajectories are often expected to resemble a mixture of periods of stability interrupted by moments of change. In these cases, a prior distribution such as the regularized horseshoe---that allows for both stasis and change---can prove a better theoretical and empirical fit for the underlying construct than other priors. Replicating Reuning, Kenwick, and Fariss (2019), we find that the regularized horseshoe performs better than the standard normal and the Student's t-distribution when modelling dynamic latent variable models. Overall, the use of the regularized horseshoe results in more accurate and precise estimates. More broadly, the regularized horseshoe is a promising prior for many similar applications.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/G2VRQH,,[],0,0,,,,,,,[],"[{'name': 'rhs_replication_red.zip', 'size': 13707693, 'type': 'application/zip', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Joining Forces or Going Solo? The Political and Economic Dynamics of Intermunicipal Cooperation,"['Bel, Germà', 'Pano, Esther', 'Sebő, Marianna']","The emergence and dynamics of inter-municipal cooperation (IMC) remain complex despite extensive research. This study examines why and when municipalities engage in IMC across multiple services. Using data from Catalonia spanning a decade, we analyze both static and dynamic factors influencing IMC adoption. Our generalized linear mixed model reveals that population size, fiscal constraints, and political participation significantly affect cooperation patterns. Economies of scale are particularly relevant for services where cooperation is frequent, while their influence diminishes as economies of density are involved. The dynamic analysis using Cox proportional hazards models indicates that high public debt and low turnout accelerate IMC adoption. These findings enhance the understanding of IMC drivers and highlight the importance of distinguishing between service-specific, organizational, and political factors that influence the existence of cooperation versus those driving its timing. Our analysis across eight services confirms that no single delivery service approach fits all scenarios.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/2BFDUR,,"['Intermunicipal cooperation', 'Public service delivery', 'Local governments']",0,0,,,,,,,[],"[{'name': 'Codebook.txt', 'size': 1996, 'type': 'text/plain', 'description': 'Code book'}, {'name': 'table1_data.rds', 'size': 488492, 'type': 'application/gzip', 'description': 'Data to replicate Table 1'}, {'name': 'table1_submitted.r', 'size': 2173, 'type': 'type/x-r-syntax', 'description': 'R script to replicate Table 1'}, {'name': 'table3_data.rds', 'size': 193468, 'type': 'application/gzip', 'description': 'Data to replicate Table 3'}, {'name': 'table3_submitted.r', 'size': 3046, 'type': 'type/x-r-syntax', 'description': 'R script to replicate Table 3'}, {'name': 'table4_data.rds', 'size': 296508, 'type': 'application/gzip', 'description': 'Data to replicate Table 4'}, {'name': 'table4_submitted.r', 'size': 3663, 'type': 'type/x-r-syntax', 'description': 'R script to replicate Table 4'}, {'name': 'table5_data.rds', 'size': 439402, 'type': 'application/gzip', 'description': 'Data to replicate Table 5'}, {'name': 'table5_submitted.r', 'size': 4378, 'type': 'type/x-r-syntax', 'description': 'R script to replicate Table 5'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+MS-COCO + Automotive Gen1 Datasets,"['Siddiqui, Muhammad Yasir']","Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). One of the most effective methods for SNN development is the CNN-to-SNN conversion process. However, existing conversion techniques are hindered by long temporal durations, or inference latencies, which negatively impact the accuracy of the converted networks. Additionally, the application of SNNs in object detection tasks remains largely under-explored. In this study, we propose a novel approach utilizing a bistable integrate-and-fire (BIF) neuron model integrated with a single-shot multi-box detector (SSD) as the detection head. Leveraging the proposed BIF neuron framework, we convert the widely used ResNet architecture into an SNN. We validate the effectiveness of our approach through object detection tasks on the MS-COCO and Automotive GEN1 datasets. Experimental results show that our conversion technique facilitates object detection with reduced temporal steps and significant enhancements in mean average precision (@mAP), achieving mAP@0.5 scores of 0.476 and 0.591 for the MS-COCO and Automotive GEN1 datasets, respectively. This research marks the first application of BIF neurons to object detection, presenting a novel advancement in the field.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MZJNY6,,[],0,0,,,,,,,[],"[{'name': 'automotive-gen1-dataset-tutorial.ipynb', 'size': 4856, 'type': 'application/x-ipynb+json', 'description': ''}, {'name': 'automotive-gen1-test.7z.001', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.002', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.003', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.004', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.005', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.006', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.007', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.008', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.009', 'size': 2516582400, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-test.7z.010', 'size': 475005106, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.001', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.002', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.003', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.004', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.005', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.006', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.007', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.008', 'size': 2621440000, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-train.7z.009', 'size': 2010720570, 'type': 'application/octet-stream', 'description': ''}, {'name': 'automotive-gen1-val.7z', 'size': 2656716453, 'type': 'application/x-compressed', 'description': ''}, {'name': 'box_filtering.py', 'size': 1745, 'type': 'text/x-python', 'description': ''}, {'name': 'box_loading.py', 'size': 1726, 'type': 'text/x-python', 'description': ''}, {'name': 'coco_eval.py', 'size': 5982, 'type': 'text/x-python', 'description': ''}, {'name': 'dataset_visualization.py', 'size': 3365, 'type': 'text/x-python', 'description': ''}, {'name': 'dat_events_tools.py', 'size': 7893, 'type': 'text/x-python', 'description': ''}, {'name': '__init__.py', 'size': 0, 'type': 'text/x-python', 'description': ''}, {'name': 'ms-coco-annotations.7z', 'size': 128111890, 'type': 'application/x-compressed', 'description': ''}, {'name': 'ms-coco-dataset-structure.jpg', 'size': 13068, 'type': 'image/jpeg', 'description': ''}, {'name': 'ms-coco-dataset-tutorial.ipynb', 'size': 542037, 'type': 'application/x-ipynb+json', 'description': ''}, {'name': 'ms-coco-images.7z', 'size': 1974160030, 'type': 'application/x-compressed', 'description': ''}, {'name': 'npy_events_tools.py', 'size': 2584, 'type': 'text/x-python', 'description': ''}, {'name': 'psee_evaluator.py', 'size': 2181, 'type': 'text/x-python', 'description': ''}, {'name': 'psee_loader.py', 'size': 9826, 'type': 'text/x-python', 'description': ''}, {'name': 'vis_utils.py', 'size': 2703, 'type': 'text/x-python', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Villaspinosa 3D Outcrop Model,"['Carola Molas, Eloi']","Villaspinosa 3D Outrcrop Model and video of it.
+
+This model is part of the project entitled ""Digital transition in Geology: New challenges from field to laboratory datasets (GEODIGIT)""",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/KTJ8EN,,['Digital Outcrop Model'],0,0,,,,,,,[],"[{'name': 'Villaspinosa_Model.jpg', 'size': 6070750, 'type': 'image/jpeg', 'description': 'Texture of the Model'}, {'name': 'Villaspinosa_Model.mtl', 'size': 220, 'type': 'application/octet-stream', 'description': 'MTL of the Model'}, {'name': 'Villaspinosa_Model.obj', 'size': 31292528, 'type': 'application/x-tgif', 'description': 'Object of the Model'}, {'name': 'Villaspinosa_sm.mp4', 'size': 30613464, 'type': 'video/mp4', 'description': 'Video of the Model'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for The Demand for Insurance: Incorporating the Severity of Losing Office into the Insurance Model of Judicial Independence,"['Epperly, Brad', 'Zeng, Yuleng']","Replication material for ""The Demand for Insurance: Incorporating the Severity of Losing Office into the Insurance Model of Judicial Independence""",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BIHTEZ,,[],0,0,,,,,,,[],"[{'name': '00_DataPrep.R', 'size': 9960, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_Descriptive_figures.R', 'size': 3432, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '01_MainModel.R', 'size': 1146, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_all.R', 'size': 714, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_cross_validation.R', 'size': 3667, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_cumsum_wt_high.R', 'size': 1366, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_cumsum_wt_low.R', 'size': 1363, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_models_including_components.R', 'size': 6851, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_roll10.R', 'size': 1227, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_roll10_wt_high.R', 'size': 1409, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_roll10_wt_low.R', 'size': 1405, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_roll20.R', 'size': 1227, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_roll20_wt_high.R', 'size': 1409, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_roll20_wt_low.R', 'size': 1405, 'type': 'type/x-r-syntax', 'description': ''}, {'name': '02_Robustness_square_and_threshold_for_competition.R', 'size': 8039, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'dat.RData', 'size': 243336, 'type': 'application/x-rlang-transport', 'description': ''}, {'name': 'EstimationTableFigure.R', 'size': 6592, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'EstimationTableOnly.R', 'size': 4215, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'IV_gof_fun.R', 'size': 1738, 'type': 'type/x-r-syntax', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Effective Judicial Communication and Trust in the Judiciary,"['Sung, Yu-hsien']","Courts in new democracies sometimes encounter challenges in gaining public trust, even after achieving judicial independence. This study examines how judicial communication can help build trust in the judiciary. Specifically, I address two questions regarding judicial communication: first, what motivates judges to engage in communication, and second, how this communication influences the public's trust in the judiciary. I argue that members of a judiciary engage in communication to protect the judiciary's reputation and that this communication fosters the public's trust in the judiciary. This is because improved communication enhances the public's understanding of the courts. To test my theoretical argument, I analyze a unique judicial reform in Taiwan, where a public-oriented communication strategy using social media was introduced. Using in-depth interviews with members of the judiciary and a difference-in-differences design, I find that, first, judges seek to protect judicial reputation by adopting a communication strategy characterized by information clarity and accessibility. Second, individuals more likely to be exposed to this communication, particularly frequent digital users, demonstrate increased judicial trust. My findings have implications for how courts in new democracies can use communication strategies to bolster judicial trust.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PGPKBE,,"['judicial communication, judicial behavior, public opinion']",0,0,,,,,,,[],"[{'name': 'JLC_replication.R', 'size': 5276, 'type': 'type/x-r-syntax', 'description': ''}, {'name': 'taiwandata.tab', 'size': 276669, 'type': 'text/tab-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+WaterNet Outputs and Code,"['Pierson, Matthew', 'Mehrabi, Zia']","<h1>Introduction</h1>
+<p>
+This data repository contains global raster and vector outputs for the WaterNet model at 20 meters, and it contains the code for three python modules related to the generation of this data. If using this data please cite the original WaterNet paper, where you can also find details of the modelling and data production process: ""Pierson, Matthew., and Mehrabi, Zia. 2024. Mapping waterways worldwide with deep learning. arXiv. https://doi.org/10.48550/arXiv.2412.00050"". 
+</p> <br>
+<hr>
+<h1>Projection</h1>
+All files use EPSG: 4326.
+<hr> <h2>Raster Data</h2>
+<h3>Naming Convention</h3>
+The raster outputs have the following naming convention {xtile}_{ytile}.tif and correspond to a zoom level 6
+<a href=""https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames#Resolution_and_Scale"">xyz map tile.</a>
+We have included geoparquet file named
+<a href=""https://dataverse.harvard.edu/file.xhtml?fileId=10651101""> WaterNet_20m_raster_files_bbox.parquet </a>
+that has the bounding boxes for each raster file.
+
+<h3>Datatypes</h3>
+
+The raster files have an unsigned 8-bit integer datatype, with 0 indicating a low probability of a waterway, and 255 indicating a high probability of waterway. During the vectorization process, we consider two sharp cut offs, with a value less than 255*0.1 to not a waterway, and a value greater than 255*0.5 to be a waterway if it is connected to a stream in TDX-Hydro (i.e if its connected component intersects a stream in TDX-Hydro). See the paper for more information.
+
+<h3>Known Issues</h3>
+
+There are known issues with the raster product. These include lower accuracy of capturing swampy areas and deserts (although alternative feature weightings of the model can allow for better capture of swamps, and our vectorization process aims to resolve the issues in deserts). We also note higher noise in areas where it is difficult to create cloud free composites (coastal areas, near the equator); and future integration of SAR data may help alleviate these particularly on a near-real time deploy.  There is also artifact in Greenland (missing cells) that we expect is due to Sentinel-2 feature inputs, for which further investigation and back fill is likely required.
+
+<hr> <h2>Vector Data</h2>
+<h3>Naming Convention</h3>
+The vector outputs have the following naming convention {hydrobasins level 2 id}_{part}.parquet. <a href=""https://www.hydrosheds.org/products/hydrobasins"">The level 2 HydroBasins can be obtained for their website.</a> As the naming convention suggests, each file corresponds to part of a hydrobasin level 2 basin. Since many of the files were larger than 2.5 GB, they were split into parts to satisfy the Harvard Dataverse individual file size limit.
+<h3>Dataframe Column Descriptions</h3>
+<dl>
+    <dt><b>stream_id</b></dt>
+    <dd>
+        Data type: int <br>
+    </dd>
+    <dd>
+        Description: A unique id for the stream segment.
+    </dd>
+    <dt><b>target_stream_id</b></dt>
+    <dd>
+        Data Type: int <br>
+        Description: The stream_id of the target. IE the stream_id of the next adjacent downstream stream.
+    </dd>
+    <dt><b>source_stream_ids</b></dt>
+    <dd>
+        Data Type: list(int) <br>
+        Description: A list of the source stream_id. IE a list containing the ids of all adjacent streams flowing into this stream. Can contain more than 2 ids.
+    </dd>
+    <dt><b>stream_order</b></dt>
+    <dd>
+        Data Type: int <br>
+        Description: <a href=""https://en.wikipedia.org/wiki/Strahler_number""> The Strahler stream order of this stream segment. </a>
+    </dd>
+    <dt><b>from_tdx</b></dt>
+    <dd>
+        Data Type: bool <br>
+        Description: True if this stream segment appears in <a href=""https://earth-info.nga.mil/index.php?dir=geosci&action=geosciences"">TDX-Hydro</a>, otherwise False. Note, this may only be a segment of a stream in TDX-Hydro.
+    </dd>
+    <dt><b>tdx_stream_id</b></dt>
+    <dd>
+        Data Type: int <br>
+        Description: Each stream in this dataset falls in a drainage basin in the TDX-Hydro dataset. This value corresponds to the streamID in the TDX-Hydo drainage basins dataset that this stream is in (or LINKNO in the TDX-Hydo stream network dataset).
+    </dd>
+    <dt><b>intersects_lake</b></dt>
+    <dd>
+        Data Type: bool <br>
+        Description: True if the geometry intersects a lake in the <a href=""True if the geometry intersects a lake in the HydroLakes v 1.0 Dataset""> HydroLakes v 1.0 Dataset. </a>
+    </dd>
+    <dt><b>length_m</b></dt>
+    <dd>
+        Data Type: <br>
+        Description: The length of the geometry computed using <a href=""https://pyproj4.github.io/pyproj/stable/api/geod.html#pyproj.Geod.geometry_length"">pyproj.Geod.geometry_length</a>
+    </dd>
+    <dt><b>geometry</b></dt>
+    <dd>
+        Data Type: LineString (polyline) <br>
+        Description: The Geometry of this segment.
+    </dd>
+</dl>
+<h3>Known Issues</h3>
+Vectorization of any large body of water (lakes, swamps, wide rivers, etc) can result in multiple geometries that should all be a single geometry. One method for identifying such artifacts are to search for streams of order 1 whose
+target streams are high in order. Since these artifacts often occur in lakes, we have included the column ""intersects_lake"" in the output,
+which indicates if the geometry intersects a lake in HydroLakes.
+<hr> <hr>
+<h1>WaterNet Code</h1>
+The code needed to make and train WaterNet and vectorize its outputs has been supplied in WaterNet_code directly in this repository.The code for inference of WaterNet globally has not been included as the large amount of data required makes such an effort difficult to generalize across compute hardware.  All of the code will also be made public on GitHub at the links below in the near future.
+<ul>
+    <li><a href=""https://github.com/Better-Planet-Laboratory/WaterNet_vectorize"">WaterNet</a></li>
+    <li><a href=""https://github.com/Better-Planet-Laboratory/WaterNet_training_and_evaluation"">WaterNet Training and Evaluation.</a></li>
+    <li><a href=""https://github.com/Better-Planet-Laboratory/WaterNet_vectorize"">WaterNet Vectorization</a></li>
+</ul> <h2>
+    Acknowledgments
+</h2>
+The authors would like to thank Bridges to Prosperity for enabling the project.  This project was in part funded by Bridges to Prosperity under the grant “Remote Impact Assessment of Rural Infrastructure Development” to the Better Planet Laboratory (https://betterplanetlab.com/).",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YY2XMG,,[],0,0,,,,,,,[],"[{'name': '0_13.tif', 'size': 31494522, 'type': 'image/tiff', 'description': ''}, {'name': '0_14.tif', 'size': 16802170, 'type': 'image/tiff', 'description': ''}, {'name': '0_15.tif', 'size': 177289594, 'type': 'image/tiff', 'description': ''}, {'name': '0_16.tif', 'size': 190921082, 'type': 'image/tiff', 'description': ''}, {'name': '0_20.tif', 'size': 263290, 'type': 'image/tiff', 'description': ''}, {'name': '0_21.tif', 'size': 98868602, 'type': 'image/tiff', 'description': ''}, {'name': '0_26.tif', 'size': 5258618, 'type': 'image/tiff', 'description': ''}, {'name': '0_34.tif', 'size': 1579386, 'type': 'image/tiff', 'description': ''}, {'name': '0_35.tif', 'size': 778737018, 'type': 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+Model Code and Source Data for: Widespread outdoor exposure to uncompensable heat stress with warming,"['Fan, Yuanchao', 'McColl, Kaighin']","This repository includes code for the human body heat stress model and source data and scripts for replicating the main figures in the manuscript titled ""Widespread outdoor exposure to uncompensable heat stress with warming"" for Communications Earth & Environment (in press). https://doi.org/10.1038/s43247-024-01930-6.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XFV1GE,,"['Human energy balance model', 'Uncompensable heat stress']",0,0,,,,,,,[],"[{'name': 'areacella_fx_360x180_1deg_gn.nc', 'size': 537242, 'type': 'application/netcdf', 'description': ''}, {'name': 'Data.G-dielcycle.ncl', 'size': 23956, 'type': 'application/octet-stream', 'description': ''}, {'name': 'Data.Outdoor_Gday_nomask.ncl', 'size': 41850, 'type': 'application/octet-stream', 'description': ''}, {'name': 'Data.Outdoor_ruralmasked.ncl', 'size': 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+MicroMap - CellDesigner xml and pdf formats,"['Thiele, Ines']","The CellDesigner xml format allows for MicroMap inspection using the CellDesigner software available at https://www.celldesigner.org, as well as computational modelling and visualisation using the COBRA Toolbox https://opencobra.github.io
+
+The pdf format allows for map inspection. We suggest to download the file and 'open with' a web browser of your choice for relatively fast and responsive exploration.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FZKMJ8,,[],0,0,,,,,,,[],"[{'name': 'MicroMap.pdf', 'size': 21565177, 'type': 'application/pdf', 'description': ''}, {'name': 'MicroMap.xml', 'size': 33658962, 'type': 'text/xml', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+A Real Swell Noise Benchmark Dataset for Seismic Data Denoising with Deep Learning,"['M. Barros, Pablo', 'de L. Sardinha, Roosevelt', 'A. M. Arboleda, Giovanny', 'de S. S. Valente, Lessandro', 'R. V. de Melo, Isabelle', 'Aveleda, Albino', 'Bulcão, André', 'L. Netto, Sergio', 'Evsukoff G. , Alexandre']","The recent development of deep learning (DL) methods for computer vision has been driven by the creation of open benchmark datasets on which new algorithms can be tested and compared with reproducible results. Although DL methods have many applications in geophysics, few real seismic datasets are available for benchmarking DL models, especially for denoising real data, which is one of the main problems in seismic data processing scenarios in the oil and gas industry. This article presents a benchmark dataset composed of synthetic seismic data corrupted with noise extracted from a filtering process on real data. In this work, a comparison between two DL-based denoising models is performed on this dataset, which is proposed as a benchmark for accelerating the development of new solutions for seismic data denoising. This work also introduces a new evaluation metric that can capture small variations in model results. The results show that DL models are effective for noise reduction in seismic data, but removing noise while preserving the signal remains a challenging open problem.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EXOCYD,,"['Seismic data', 'Real noise', 'Denoising', 'Deep learning']",0,0,,,,,,,[],"[{'name': 'add_noise_function.py', 'size': 12283, 'type': 'text/x-python', 'description': ''}, {'name': 'cut.py', 'size': 8024, 'type': 'text/x-python', 'description': ''}, {'name': 'dummy_files.py', 'size': 3696, 'type': 'text/x-python', 'description': ''}, {'name': 'generate_dataset.py', 'size': 5416, 'type': 'text/x-python', 'description': ''}, {'name': 'LICENSE.md', 'size': 11555, 'type': 'text/markdown', 'description': ''}, {'name': 'Noise_1.zip', 'size': 30204708437, 'type': 'application/zip', 'description': ''}, {'name': 'Noise_2.zip', 'size': 23432637630, 'type': 'application/zip', 'description': ''}, {'name': 'README.md', 'size': 12404, 'type': 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'description': ''}, {'name': 'Seismic_B_noisy.zip', 'size': 10201780455, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_B_noisy.zip', 'size': 10101745011, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_B.zip', 'size': 5157160935, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_clean.zip', 'size': 9271372642, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_clean.zip', 'size': 12804963280, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 13568018934, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 10190749907, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 10189823976, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 10101481728, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 13480904198, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 13529623380, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 10144990861, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C_noisy.zip', 'size': 13564588411, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_C.zip', 'size': 5267705093, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_clean.zip', 'size': 1759798178, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2306542958, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2289915618, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2312522611, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2306030818, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2300300078, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2298396312, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2312989248, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D_noisy.zip', 'size': 2289088185, 'type': 'application/zip', 'description': ''}, {'name': 'Seismic_D.zip', 'size': 1759911070, 'type': 'application/zip', 'description': ''}, {'name': 'Supp Material.pdf', 'size': 33439426, 'type': 'application/pdf', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Therapeutics Data Commons (https://tdcommons.ai),"['Alejandro Velez-Arce, Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik']","Therapeutics Data Commons (TDC) is an open-science initiative started at Harvard with AI/ML-ready datasets and ML tasks for therapeutics. It provides an ecosystem of tools, leaderboards, and community resources, including data functions, model benchmarking and comparison strategies, meaningful data splits, data processors, public leaderboards, and molecule generation oracles. All resources are integrated and accessible via an open Python library. TDC is available at https://tdcommons.ai.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/21LKWG,,[],0,0,,,,,,,[],"[{'name': 'acinar_cell_of_salivary_gland.txt', 'size': 266885, 'type': 'text/plain', 'description': ''}, {'name': 'acinar_cell.txt', 'size': 135353, 'type': 'text/plain', 'description': ''}, {'name': 'activated_cd4-positive,_alpha-beta_t_cell.txt', 'size': 179718, 'type': 'text/plain', 'description': ''}, {'name': 'activated_cd8-positive,_alpha-beta_t_cell.txt', 'size': 195163, 'type': 'text/plain', 'description': ''}, {'name': 'adipocyte_of_breast.txt', 'size': 114738, 'type': 'text/plain', 'description': ''}, {'name': 'admet_group.zip', 'size': 1472501, 'type': 'application/zip', 'description': ''}, {'name': 'adventitial_cell.txt', 'size': 260550, 'type': 'text/plain', 'description': ''}, {'name': 'alpha-beta_t_cell.txt', 'size': 171224, 'type': 'text/plain', 'description': ''}, {'name': 'alternatively_activated_macrophage.txt', 'size': 258957, 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'size': 244748, 'type': 'text/plain', 'description': ''}, {'name': 'cd8-positive,_alpha-beta_t_cell.txt', 'size': 155225, 'type': 'text/plain', 'description': ''}, {'name': 'cell_of_skeletal_muscle.txt', 'size': 75982, 'type': 'text/plain', 'description': ''}, {'name': 'cell_tissue_mg_edgelist.txt', 'size': 185487, 'type': 'text/plain', 'description': 'Metagraph Cell-Tissue edge list for (2024). 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Several of these AS-MS discovered ligands were taken forward for experimental validation to determine the binding affinity (KD) as measured by biolayer interferometry (BLI) to the listed target protein. If listed as a ""putative binder,"" AS-MS alone was used to isolate the ligands to the target, with KD < 1 uM required and often observed in orthogonal assays, though there is some (< 50%) chance that the ligand is nonspecific. \n\nMost of the ligands are putative binders with 4446 total provided. For those characterized by BLI (only 34 total), the average KD is 266 ± 44 nM, median KD is 9.4 nM.\n\nRelated publication:\nYe X, Lee YC, Gates ZP, Ling Y, Mortensen JC, Yang FS, Lin YS, Pentelute BL. Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions. Commun Chem. 2022 Oct 14;5(1):128. doi: 10.1038/s42004-022-00737-w. PMID: 36697672; PMCID: PMC9814900. 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Green, Ciyue Shen, Torsten Gross, Joseph Min, Samuele Garda, Bo Yuan,471\nLinus J. Schumacher, Jake P. Taylor-King, Debora S. Marks, Augustin Luna, Nils Blüthgen, and472\nChris Sander. scperturb: harmonized single-cell perturbation data. 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{'name': 'weber.tab', 'size': 16020259, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'zinc.tab', 'size': 11801831, 'type': 'text/tab-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: How Do Judges Use Large Language Models? Evidence from Shenzhen,"['Liu, John Zhuang']","Replication materials for How Do Judges Use Large Language Models?
+Evidence from Shenzhen. Figure 2, Tables 1.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/73WTKD,,[],0,0,,,,,,,[],"[{'name': 'code_1203.do', 'size': 1604, 'type': 'application/x-stata-syntax', 'description': ''}, {'name': 'data.tab', 'size': 18176, 'type': 'text/tab-separated-values', 'description': ''}, {'name': 'figure.py', 'size': 3426, 'type': 'text/x-python', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Replication Data for: Becoming Dutch at what cost? Increasing application fees and naturalisation rates of EU immigrants in the Netherlands,"['Peters, Floris', 'Falcke, Swantje', 'Vink, Maarten']","While the overall impact of naturalisation requirements on citizenship acquisition rates is well understood, the relevance of economic requirement remains largely understudied in Europe. In this chapter we look at the case of the Netherlands, where fees have increased from 336 euro in 2003 to 901 euro in 2020 – an increase of 168% points – for a single application, with significant hikes in the fee in 2010 and 2011. Simultaneous changes in the civic integration requirements for permanent residence likely had a positive effect on naturalisation rates among non-EU immigrants and consequently may have obfuscated the impact of the higher fees. We draw on longitudinal microdata from administrative registers on the complete immigrant population between 2007 and 2014, and use a two-step identification strategy. First, we apply a single-difference regression, based on a fixed-effects model, to investigate immigrant naturalisation rates in conjunction with increased application costs. We subsequently explore impact heterogeneity by household income and use a double-difference regression, based on a difference-in-differences model, to test whether the relevance of the fee increase is conditioned by income groups. Results from our single-difference models reveal a decrease in EU immigrants’ naturalisation rate after the fee increase in 2010, all else constant. Consistent with our expectation that economic requirements matter particularly to immigrants with limited financial means, subgroup analyses show a stronger decrease among those with below modal household incomes compared to immigrants with higher incomes. Double-difference models confirm that the main findings cannot be fully attributed to unmeasured period shocks, and that there is indeed a statistically significant difference in the relevance of the fee increase by household income.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1ZFHZ8,,[],0,0,,,,,,,[],"[{'name': 'Peters_et_al_2023_Springer_data_statement.pdf', 'size': 111416, 'type': 'application/pdf', 'description': 'Data statement: Peters et al. (2023)'}, {'name': 'Peters_et_al_2023_Springer_script.R', 'size': 9300, 'type': 'type/x-r-syntax', 'description': 'Script: Peters et al. (2023)'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Avertable burden model parameters,"['Hamilton, Matthew']",Publicly shareable parameters for the avert computational model,,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QTJJLZ,,['mental-health'],0,0,,,,,,,[],"[{'name': 'that_ls.RDS', 'size': 120, 'type': 'application/gzip', 'description': 'Placeholder parameter values file.'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Turbofan Specific Fuel Consumption, Size, and Mass from Correlated Engine Parameters","['Hammami, Mohamed Oussama']","Purpose – Simple equations and more extended models are developed to determine characteristic engine parameters: Specific fuel consumption (SFC), engine mass, and engine size characterized by engine length and diameter. SFC (c) is considered a linear function of speed: c = c_a * V + c_b. ---
+Methodology – Data from 718 engines is collected from various open sources into an Excel spreadsheet. The characteristic engine parameters are plotted as function of bypass ratio (BPR), date of entry into service (EIS), take-off thrust, and typical cruise thrust. Engine length and diameter are plotted versus engine mass. Linear and nonlinear regression functions are investigated. Moreover, Singular Value Decomposition (SVD) is used to establish relations between parameters. SVD is used with Excel and MATLAB. The accuracy of all equations and models is compared. ---
+Findings – SFC should be calculated as a linear function of speed. This is especially important, when SFC is extrapolated to unconventional (low) cruise speeds for jet engines. The two parameters c_a and c_b are best estimated from a logarithmic or power function of bypass ratio (BPR). SFC and c_b clearly improved over the years. Engine mass, diameter, and length are proportional to take-off thrust. Characteristic engine parameters can also be obtained from SVD with comparable accuracy. However, SVD is more complicated to set up than using a simple equation. ---
+Practical implications – Engine characteristics need to be estimated based on only a few known parameters for aircraft preliminary sizing, conceptual design, and aircraft optimization as well as for practical quick calculations in flight mechanics. This thesis provides the tools. ---
+Social implications – Most engine characteristics like SFC are considered company secrets. The availability of open access engine data is the first step, but wisdom is retrieved only with careful analysis of the data as done here. Openly available aircraft engineering knowledge helps to democratize the discussion about the ecological footprint of aviation. ---
+Originality/value – Simple equation for jet engine SFC, mass, and size deduced from a large engine database are offered. This approach delivered equations as a function of BPR with an error of only 6%, which is the same accuracy as more complex equations from literature.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UW6FAP,,"['aviation', 'Aeronautics', 'Airplanes--Fuel Consumption', 'Airplanes--Motors--Thrust', 'Aircraft Drafting--Design and Construction', 'Mathematical Models', 'Singular Value Decomposition', 'Regression Analysis', 'Electronic Spreadsheets', 'Luftfahrt']",0,0,,,,,,,[],"[{'name': 'Database_reduced_size.html', 'size': 10261, 'type': 'text/html', 'description': ''}, {'name': 'Database_reduced_size.xlsx', 'size': 162101, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Excel seems to blow file size when converting to HTML. This smaller file was produced as a starting point to save to HTML.'}, {'name': 'DataBase.xlsx', 'size': 1157486, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'filelist.xml', 'size': 345, 'type': 'text/xml', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}, {'name': 'Geometry_Statistics.xlsx', 'size': 315474, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': 'Added by Dieter Scholz on 2024-11-29: Improved calculation of V_e from rho_e=m_e/V_e and l_e from l_e/D_e. Included in DLRK2024-Poster.'}, {'name': 'image001.png', 'size': 41255, 'type': 'image/png', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}, {'name': 'image002.gif', 'size': 5294, 'type': 'image/gif', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}, {'name': 'Least_Squares_Method.xlsx', 'size': 3373663, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'Reduced_DataBase_with_Figures.xlsx', 'size': 2871374, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'SFC_Model_Linear_Regression.xlsx', 'size': 1149931, 'type': 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet', 'description': ''}, {'name': 'sheet001.html', 'size': 6286099, 'type': 'text/html', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}, {'name': 'sheet002.html', 'size': 18057, 'type': 'text/html', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}, {'name': 'stylesheet.css', 'size': 7249, 'type': 'text/css', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}, {'name': 'SVD.xlsm', 'size': 1515052, 'type': 'application/vnd.ms-excel.sheet.macroenabled.12', 'description': ''}, {'name': 'tabstrip.html', 'size': 1003, 'type': 'text/html', 'description': 'Required for file ""Database_reduced_size.html"" in subdirectory ""Database_reduced_size-Dateien""'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dataset: Census of individual trees of Hacienda San Jose Vichada (2023),"['Ruiz Hurtado, Andres Felipe', 'Perez Bolanos, Juliana', 'Arrechea Castillo, Darwin Alexis', 'Cardoso Arango, Juan Andres', 'Matiz Rubio, Natalia', 'Costa Junior, Ciniro', 'Arango Mejia, Jacobo']","This dataset contains information of estimated individual trees in Hacienda San José (HSJ), Vichada-Colombia extracted from remote sensing imagery processed using AI models and the TreeEyed QGIS Plugin. The dataset includes a CSV file with attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree.
+<br><br> 
+Methodology:The tree information for this dataset was obtained using a custom QGIS plugin (TreeEyed) and python scripting, designed to process high-resolution RGB remote sensing imagery to derive tree data. Leveraging AI pretrained models, in this case Meta's HighResCanopyHeight model was employed. The methodology involved processing RGB imagery with a resolution of 0.5 meters and dimensions of 42,686x30,630 pixels for the region of interest, a subdivision in 1260 tiles of 1024x1024 pixels to process and obtain canopy tree height rasters, merging, vectorizing tree instances, and filtering to isolate individual trees. Zonal statistics were then applied to assign estimated tree height values to each instance by extracting the maximum pixel value within the tree crown polygon boundary. This approach allowed for flexible and localized analysis, enabling the derivation of detailed tree metrics such as height, crown area and perimeter, and equivalent diameter from high-resolution RGB imagery.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NMX9VE,,"['remote sensing', 'artificial intelligence', 'Americas', 'Crops for Nutrition and Health']",0,0,,,,,,,[],"[{'name': '01a. readme.md', 'size': 1983, 'type': 'text/markdown', 'description': 'Readme file, providing an overview of the dataset'}, {'name': '02a. hsj_trees_2023.tab', 'size': 4913354, 'type': 'text/tab-separated-values', 'description': 'CSV file containing descriptive attributes for each tree.'}, {'name': '03a. hsj_trees_2023.zip', 'size': 3736023, 'type': 'application/zipped-shapefile', 'description': 'Compressed shapefile file containing the shapefile for the tree polygons'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+Dataset: Census of individual trees of Tucunare Vichada (2023),"['Ruiz-Hurtado, Andres Felipe', 'Perez Bolanos, Juliana', 'Arrechea-Castillo, Darwin Alexis', 'Cardoso Arango, Juan Andres', 'Matiz Rubio, Natalia', 'Costa Junior, Ciniro', 'Arango Mejia, Jacobo']","This dataset contains information of estimated individual trees in Tucunaré, Vichada-Colombia extracted from remote sensing imagery processed using AI models and the TreeEyed QGIS Plugin. The dataset includes a CSV file with attributes related to tree dimensions and geolocation and a shapefile with polygons representing each tree.
+<br><br> 
+Methodology:The tree information for this dataset was obtained using a custom QGIS plugin (TreeEyed) and python scripting, designed to process high-resolution RGB remote sensing imagery to derive tree data. Leveraging AI pretrained models, in this case Meta's HighResCanopyHeight model was employed. The methodology involved processing RGB imagery with a resolution of 0.5 meters and dimensions of 20,406x19,871 pixels for the region of interest, a subdivision in 401 tiles of 1024x1024 pixels to process and obtain canopy tree height rasters, merging, vectorizing tree instances, and filtering to isolate individual trees. Zonal statistics were then applied to assign estimated tree height values to each instance by extracting the maximum pixel value within the tree crown polygon boundary. This approach allowed for flexible and localized analysis, enabling the derivation of detailed tree metrics such as height, crown area and perimeter, and equivalent diameter from high-resolution RGB imagery.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JRLV83,,"['remote sensing', 'artificial intelligence', 'Americas', 'Crops for Nutrition and Health']",0,0,,,,,,,[],"[{'name': '01a. readme.md', 'size': 1974, 'type': 'text/markdown', 'description': 'Readme file, providing an overview of the dataset'}, {'name': '02a. tucunare_trees_2023.tab', 'size': 598811, 'type': 'text/tab-separated-values', 'description': 'CSV file containing descriptive attributes for each tree.'}, {'name': '03a. tucunare_trees_2023.zip', 'size': 496243, 'type': 'application/zipped-shapefile', 'description': 'Compressed shapefile file containing the shapefile for the tree polygons'}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]
+"Replication Data for: ""Electoral Volatility and Party Polarization as Challenges to Democratic Stability in Latin America""","['Gastev, Juliusz']","Democracy in Latin America is under extraordinary strain because of polarization. This paper aims to investigate the drivers of party polarization, focusing on electoral volatility in 18 Latin American countries from 1993-2018. Extant studies suggest that volatility influences polarization by incentivizing parties to adopt polarizing strategies to secure survival. By utilizing the Latin American Electoral Volatility Dataset (LAEVD), this paper employs a more precise measure of polarization compared to previous studies and controls for additional variables. Moreover, the theoretical underpinnings will be critically assessed and further developed, especially regarding new party entry. My results diverge from previous studies suggesting that Latin American democracies become more polarized as they age due to the entry of new parties. This reflects the persistent struggle for party systems in the region to institutionalize. Avoiding democracy’s demise in Latin America requires sustainable party building to solidify electoral competition and regain citizens’ trust in democratic institutions.",,https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DSFG2P,,[],0,0,,,,,,,[],"[{'name': 'Volpol_final.tab', 'size': 47790, 'type': 'text/tab-separated-values', 'description': ''}]",theoretical_model,Harvard_Dataverse,2024-12-20 06:30:46.787306,[]