| [ |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Detection and State Analysis of Drowsiness using Multitask Learning with Neural Networks", |
| "capsule_id": "capsule-5507257", |
| "task_prompt": "Run multiclass_state_analysis_testing.py", |
| "gpu": true, |
| "results": [ |
| { |
| "Report the test accuracy of 'mouth talking'.": 96.12499135323452 |
| }, |
| { |
| "Report the test accuracy of 'mouth talking'.": 96.12499135323452 |
| }, |
| { |
| "Report the test accuracy of 'mouth talking'.": 96.12499135323452 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.0217715.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Short-Term Temperature Forecasts Using a Convolutional Neural Network", |
| "capsule_id": "capsule-3449234", |
| "task_prompt": "Run the jupyter notebook visualize_results.ipynb using a python3 kernel and convert it to html. For all the runs, disable the cell execution timeout and allow errors.", |
| "gpu": true, |
| "results": [ |
| { |
| "fig From the figure containing the standard deviation for the Essen data, report the name of the model with the highest standard deviation between time 0 and 10.": "SN" |
| }, |
| { |
| "fig From the figure containing the standard deviation for the Essen data, report the name of the model with the highest standard deviation between time 0 and 10.": "SN" |
| }, |
| { |
| "fig From the figure containing the standard deviation for the Essen data, report the name of the model with the highest standard deviation between time 0 and 10.": "SN" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.8788349.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Network Diffusion Examples", |
| "capsule_id": "capsule-8807709", |
| "task_prompt": "Run 'epidemic.py'.", |
| "results": [ |
| { |
| "fig For the third subplot in the visualization of the experiments, report the color of the line with the greatest number of nodes at epoch 15.": "blue", |
| "fig Report the name of the first subplot in the visualization of the experiments.": "ill" |
| }, |
| { |
| "fig For the third subplot in the visualization of the experiments, report the color of the line with the greatest number of nodes at epoch 15.": "blue", |
| "fig Report the name of the first subplot in the visualization of the experiments.": "ill" |
| }, |
| { |
| "fig For the third subplot in the visualization of the experiments, report the color of the line with the greatest number of nodes at epoch 15.": "blue", |
| "fig Report the name of the first subplot in the visualization of the experiments.": "ill" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.1013089.v4" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Multi-Instance Ensemble Learning with Discriminative Bags (ELDB)", |
| "capsule_id": "capsule-6049678", |
| "task_prompt": "Run 'Main.py'.", |
| "results": [ |
| { |
| "Report the f1 score for the Musk1+ dataset with the svm classifier.": 87.516, |
| "Report the f1 score for the Musk1+ dataset with the j48 classifier.": 79.813 |
| }, |
| { |
| "Report the f1 score for the Musk1+ dataset with the svm classifier.": 86.578, |
| "Report the f1 score for the Musk1+ dataset with the j48 classifier.": 79.426 |
| }, |
| { |
| "Report the f1 score for the Musk1+ dataset with the svm classifier.": 86.186, |
| "Report the f1 score for the Musk1+ dataset with the j48 classifier.": 78.673 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.1490343.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "R", |
| "capsule_title": "mFLICA Reproducible Capsule", |
| "capsule_id": "capsule-2804717", |
| "task_prompt": "Export the following R default packages: datasets,utils,grDevices,graphics,stats,methods. Run 'ResultReproducibilityNotebook.Rmd' and render it as an html file. Store the output in ../results and set clean to 'TRUE'.", |
| "results": [ |
| { |
| "fig From the Network Density plot (Figure 4), report the label for the red line (ignore spaces).": "TS#1" |
| }, |
| { |
| "fig From the Network Density plot (Figure 4), report the label for the red line (ignore spaces).": "TS#1" |
| }, |
| { |
| "fig From the Network Density plot (Figure 4), report the label for the red line (ignore spaces).": "TS#1" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4248204.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Explainable Machine Learning Pipeline for Twitter Bot Detection", |
| "capsule_id": "capsule-3418007", |
| "task_prompt": "Run 'main.py'.", |
| "results": [ |
| { |
| "Report the F1 score for statistical general only.": 0.7536528881857651, |
| "fig Report the proposed model's AUC from the ROC curves figure. Ignore the confidence interval.": 0.98 |
| }, |
| { |
| "Report the F1 score for statistical general only.": 0.7569591300356435, |
| "fig Report the proposed model's AUC from the ROC curves figure. Ignore the confidence interval.": 0.98 |
| }, |
| { |
| "Report the F1 score for statistical general only.": 0.7599081605158183, |
| "fig Report the proposed model's AUC from the ROC curves figure. Ignore the confidence interval.": 0.98 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9672787.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Feature Selection", |
| "capsule_id": "capsule-1624349", |
| "task_prompt": "Execute 'FS-Filters.ipynb'. Save the results in html format in ../results. For all the runs, disable the cell execution timeout and allow errors.", |
| "results": [ |
| { |
| "Report the best accuracy of the hybrid filter wrapper strategy.": 0.9506166187739464, |
| "fig Report the name of the feature with the highest I-Gain.": "RAWRED-MEAN" |
| }, |
| { |
| "Report the best accuracy of the hybrid filter wrapper strategy.": 0.9506166187739464, |
| "fig Report the name of the feature with the highest I-Gain.": "RAWRED-MEAN" |
| }, |
| { |
| "Report the best accuracy of the hybrid filter wrapper strategy.": 0.9506166187739464, |
| "fig Report the name of the feature with the highest I-Gain.": "RAWRED-MEAN" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.1509005.v1" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Reproducible research practices and transparency across linguistics", |
| "capsule_id": "capsule-9832712", |
| "task_prompt": "Create the following three directories in the results folder: 01_scopus-selection, 02_coding, 03_analyses. Run 'master_script.R' using Rscript.", |
| "results": [ |
| { |
| "fig From Figure 2 in the cleaned results, report the percentage of 'Not Available' analysis scripts for Pre-RC (2008-09)": 100, |
| "fig From Table 1 in the cleaned results, report the number of Included Articles After.OS": 255 |
| }, |
| { |
| "fig From Figure 2 in the cleaned results, report the percentage of 'Not Available' analysis scripts for Pre-RC (2008-09)": 100, |
| "fig From Table 1 in the cleaned results, report the number of Included Articles After.OS": 255 |
| }, |
| { |
| "fig From Figure 2 in the cleaned results, report the percentage of 'Not Available' analysis scripts for Pre-RC (2008-09)": 100, |
| "fig From Table 1 in the cleaned results, report the number of Included Articles After.OS": 255 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.2289033.v2" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation", |
| "capsule_id": "capsule-2816027", |
| "task_prompt": "Export the following R default packages: datasets,utils,grDevices,graphics,stats,methods. Then, run 'main.R' using Rscript.", |
| "results": [ |
| { |
| "fig For CTCF Signature Enrichment, report the name of the group with the highest median GSVA score.": "MCF7_D538G" |
| }, |
| { |
| "fig For CTCF Signature Enrichment, report the name of the group with the highest median GSVA score.": "MCF7_D538G" |
| }, |
| { |
| "fig For CTCF Signature Enrichment, report the name of the group with the highest median GSVA score.": "MCF7_D538G" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.0627595.v1" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Adaptations to sea level change and transitions to agriculture at Khao Toh Chong rockshelter, Peninsular Thailand", |
| "capsule_id": "capsule-3821950", |
| "task_prompt": "Create a 'figures' directory in the results folder. Run 'ktc_11_paper.Rmd' using Rscript and render it as html. Save the output to the ../results output directory. Set clean to 'TRUE'.", |
| "results": [ |
| { |
| "fig Report the name of the material with the highest Depth Below Surface at 10,000 calibrated years BP.": "charcoal", |
| "fig Report the name of the material with the highest mass (g) and 5000 years cal. BP.": "ceramics" |
| }, |
| { |
| "fig Report the name of the material with the highest Depth Below Surface at 10,000 calibrated years BP.": "charcoal", |
| "fig Report the name of the material with the highest mass (g) and 5000 years cal. BP.": "ceramics" |
| }, |
| { |
| "fig Report the name of the material with the highest Depth Below Surface at 10,000 calibrated years BP.": "charcoal", |
| "fig Report the name of the material with the highest mass (g) and 5000 years cal. BP.": "ceramics" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.0c427240-6e28-417d-ba1a-777c8c4e485a" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Framework to study harms and benefits of multi-cancer early detection tests", |
| "capsule_id": "capsule-9054015", |
| "task_prompt": "Run 'pancancer_calculation.R' using Rscript.", |
| "results": [ |
| { |
| "fig Report the percentage sensitivity for cancers A and B that has the highest number of cancers detected per 1,000 women for a 1.0% prevalance of cancer B.": 90 |
| }, |
| { |
| "fig Report the percentage sensitivity for cancers A and B that has the highest number of cancers detected per 1,000 women for a 1.0% prevalance of cancer B.": 90 |
| }, |
| { |
| "fig Report the percentage sensitivity for cancers A and B that has the highest number of cancers detected per 1,000 women for a 1.0% prevalance of cancer B.": 90 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4448554.v3" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Deep Learning for Cellular Traffic Prediction", |
| "capsule_id": "capsule-3301293", |
| "task_prompt": "Run 'run_prediction.py'.", |
| "gpu": true, |
| "results": [ |
| { |
| "Report the test RMSE of the model.": 26.21204 |
| }, |
| { |
| "Report the test RMSE of the model.": 26.21204 |
| }, |
| { |
| "Report the test RMSE of the model.": 26.21204 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.761a91d4-6f2f-4912-8129-2bb8abeaa044" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Adapting the coordination of eyes and head to differences in task and environment during fully-mobile visual exploration", |
| "capsule_id": "capsule-1724988", |
| "task_prompt": "Run 'calibration_error.R', 'lss1_summary_analyses.R', 'lss2_summary_analyses.R', and 'lss2_peak_analyses.R' all using Rscript.", |
| "results": [ |
| { |
| "fig Report the task name with the higher median walking speed (m/s).": "Walk", |
| "fig Report the task name with the higher median straightness ratio.": "Search" |
| }, |
| { |
| "fig Report the task name with the higher median walking speed (m/s).": "Walk", |
| "fig Report the task name with the higher median straightness ratio.": "Search" |
| }, |
| { |
| "fig Report the task name with the higher median walking speed (m/s).": "Walk", |
| "fig Report the task name with the higher median straightness ratio.": "Search" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.8767371.v2" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Preaching to the Choir: A Problem of Participatory Interventions", |
| "capsule_id": "capsule-4299879", |
| "task_prompt": "Run '01_motivation.R', '02_design.R', '03_survey.R', '04_metaketa_comp.R', '05_lapop.R', '06_misc.R' using Rscript.", |
| "results": [ |
| { |
| "fig From the figure measuring homicide rate per 100k in the last 12 months, report the name of the sample with the lower homicide rate per 100k in 2000.": "Colombia" |
| }, |
| { |
| "fig From the figure measuring homicide rate per 100k in the last 12 months, report the name of the sample with the lower homicide rate per 100k in 2000.": "Colombia" |
| }, |
| { |
| "fig From the figure measuring homicide rate per 100k in the last 12 months, report the name of the sample with the lower homicide rate per 100k in 2000.": "Colombia" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.0479587.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "Python", |
| "capsule_title": "MLP-based classification of COVID-19 and skin diseases", |
| "capsule_id": "capsule-0851068", |
| "task_prompt": "Run the bash script 'demo.sh'.", |
| "gpu": true, |
| "results": [ |
| { |
| "Report the final AUC after training.": 0.9157952669235003 |
| }, |
| { |
| "Report the final AUC after training.": 0.9157952669235003 |
| }, |
| { |
| "Report the final AUC after training.": 0.9157952669235003 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9705378.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Compute capsule for stochastic process based COVID-19 simulation environment", |
| "capsule_id": "capsule-1394704", |
| "task_prompt": "Run 'modular.Rmd' using Rscript and render it as a html. Store the output in ../results. Set clean to 'TRUE'.", |
| "results": [ |
| { |
| "fig Report the name of the method with the higher R0.": "ML", |
| "Report the R0 of EG.": 1.035213 |
| }, |
| { |
| "fig Report the name of the method with the higher R0.": "ML", |
| "Report the R0 of EG.": 1.035213 |
| }, |
| { |
| "fig Report the name of the method with the higher R0.": "ML", |
| "Report the R0 of EG.": 1.035213 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.7958703.v1" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "No Evaluative Conditioning Effects with Briefly Presented Stimuli", |
| "capsule_id": "capsule-0504157", |
| "task_prompt": "Run 'manuscript.Rmd' using Rscript and render it as a pdf. Store the output in the ../results directory. Set clean to 'TRUE'.", |
| "results": [ |
| { |
| "fig From figure 1, report the CS presentation time with the greatest mean EC effect (ignore units).": 1000 |
| }, |
| { |
| "fig From figure 1, report the CS presentation time with the greatest mean EC effect (ignore units).": 1000 |
| }, |
| { |
| "fig From figure 1, report the CS presentation time with the greatest mean EC effect (ignore units).": 1000 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.26389ff0-ea56-467d-a550-b96cb9a31e04" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "On the Energy Footprint of Mobile Testing Frameworks", |
| "capsule_id": "capsule-3593259", |
| "task_prompt": "Run 'physalia_automators.reports' as a python module with /results as the output directory.", |
| "results": [ |
| { |
| "fig From the violin plot of the energy comsumption of tap, report the name of the framework that consumes the most energy.": "Appium" |
| }, |
| { |
| "fig From the violin plot of the energy comsumption of tap, report the name of the framework that consumes the most energy.": "Appium" |
| }, |
| { |
| "fig From the violin plot of the energy comsumption of tap, report the name of the framework that consumes the most energy.": "Appium" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4277516.v1" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "Python", |
| "capsule_title": "When to retrieve and encode episodic memories: a neural network model of hippocampal-cortical interaction", |
| "capsule_id": "capsule-3639589", |
| "task_prompt": "Run demo.py in the code/src folder.", |
| "results": [ |
| { |
| "fig Report the color of the line with the highest maximum activation for target memory activation, DM.": "blue" |
| }, |
| { |
| "fig Report the color of the line with the highest maximum activation for target memory activation, DM.": "blue" |
| }, |
| { |
| "fig Report the color of the line with the highest maximum activation for target memory activation, DM.": "blue" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.5779179.v3" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Attentional fluctuations and the temporal organization of memory - Jayakumar, Balusu, & Aly (2023). Cognition.", |
| "capsule_id": "capsule-2345790", |
| "task_prompt": "Set up the following subfolders in the ../results directory: intermediates, figures, stats_figures_markdowns. Run all the .Rmd files using Rscript and render them as html. Store the output files in ../results/stats_figures_markdowns.", |
| "results": [ |
| { |
| "From Study 1, report the mean of the response rate across all participants.": 97.82, |
| "From Study 2, report the mean of the response rate across all participants.": 99.41 |
| }, |
| { |
| "From Study 1, report the mean of the response rate across all participants.": 97.82, |
| "From Study 2, report the mean of the response rate across all participants.": 99.41 |
| }, |
| { |
| "From Study 1, report the mean of the response rate across all participants.": 97.82, |
| "From Study 2, report the mean of the response rate across all participants.": 99.41 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.3162457.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Excess significance and power miscalculations in neurofeedback research", |
| "capsule_id": "capsule-7716865", |
| "task_prompt": "Run 'manuscript.Rmd' using Rscript and render it as a pdf. Store the output in ../results. Set clean to 'TRUE'.", |
| "results": [ |
| { |
| "fig From Table 1, report the sensitivity for 80% power and recalculated mean (regulation).": 1.31 |
| }, |
| { |
| "fig From Table 1, report the sensitivity for 80% power and recalculated mean (regulation).": 1.31 |
| }, |
| { |
| "fig From Table 1, report the sensitivity for 80% power and recalculated mean (regulation).": 1.31 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.7282505.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "A global and integrated analysis of CINSARC-associated genetic defects", |
| "capsule_id": "capsule-4933686", |
| "task_prompt": "Run \"Main.R\" using Rscript and xvfb-run.", |
| "results": [ |
| { |
| "fig From Figure 2 plot A, report Fisher's P. If the value is in scientific notion, convert it to a floating point number.": 0.0182, |
| "fig From Figure 1 plot A, measuring time vs. metastasis-free survival, report the numerical value of HR (ignore the confidence interval).": 2.15 |
| }, |
| { |
| "fig From Figure 2 plot A, report Fisher's P. If the value is in scientific notion, convert it to a floating point number.": 0.0182, |
| "fig From Figure 1 plot A, measuring time vs. metastasis-free survival, report the numerical value of HR (ignore the confidence interval).": 2.15 |
| }, |
| { |
| "fig From Figure 2 plot A, report Fisher's P. If the value is in scientific notion, convert it to a floating point number.": 0.0182, |
| "fig From Figure 1 plot A, measuring time vs. metastasis-free survival, report the numerical value of HR (ignore the confidence interval).": 2.15 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9777456.v4" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Automated Text Classification of News Articles: A Practical Guide", |
| "capsule_id": "capsule-9240688", |
| "task_prompt": "Run the bash script 'run.sh'.", |
| "results": [ |
| { |
| "From table 1, report the portion relevant in both corpora.": 0.44 |
| }, |
| { |
| "From table 1, report the portion relevant in both corpora.": 0.44 |
| }, |
| { |
| "From table 1, report the portion relevant in both corpora.": 0.44 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4630956.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Cardiac structure and function in schizophrenia", |
| "capsule_id": "capsule-1175539", |
| "task_prompt": "Run \"/code/CardioSCZ.R\" using Rscript.", |
| "results": [ |
| { |
| "fig Report the name of the patient group with the greater median concentricity.": "SCZ" |
| }, |
| { |
| "fig Report the name of the patient group with the greater median concentricity.": "SCZ" |
| }, |
| { |
| "fig Report the name of the patient group with the greater median concentricity.": "SCZ" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9265392.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Estimating the prevalence of discrepancies between study registrations and publications: A systematic review and meta-analyses", |
| "capsule_id": "capsule-2708693", |
| "task_prompt": "Run 'preregSR_manuscript.Rmd' and render it as a pdf. Store the output in ../results. Set clean as 'TRUE'.", |
| "results": [ |
| { |
| "fig From table 1, report the k value for the medicine discipline.": 81, |
| "fig From table 3, report n for 'percentage of studies with at least one outcome discrepancy that disclose an outcome discrepancy'.": 620 |
| }, |
| { |
| "fig From table 1, report the k value for the medicine discipline.": 81, |
| "fig From table 3, report n for 'percentage of studies with at least one outcome discrepancy that disclose an outcome discrepancy'.": 620 |
| }, |
| { |
| "fig From table 1, report the k value for the medicine discipline.": 81, |
| "fig From table 3, report n for 'percentage of studies with at least one outcome discrepancy that disclose an outcome discrepancy'.": 620 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4753181.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy [PMID: 28314784]", |
| "capsule_id": "capsule-4252248", |
| "task_prompt": "Create the symbolic links for ../results output. Create the symbolic links for ../data Data. Run 'main-ctrpv.R', 'main-nci.R', and 'main-network-generation.R' using Rscript.", |
| "results": [ |
| { |
| "fig Report the overall AUC from the PR curve generated with the CTRPv2 sensitivity dataset, tested against ATC annotations and drug-target information from CHEMBL.": 0.4929241 |
| }, |
| { |
| "fig Report the overall AUC from the PR curve generated with the CTRPv2 sensitivity dataset, tested against ATC annotations and drug-target information from CHEMBL.": 0.4929241 |
| }, |
| { |
| "fig Report the overall AUC from the PR curve generated with the CTRPv2 sensitivity dataset, tested against ATC annotations and drug-target information from CHEMBL.": 0.4929241 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.0012b3fb-3cf2-41fa-9b8d-6cc055b53ca2" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "SPT: Security Policy Translator for Network Security Functions in Cloud-Based Security Services", |
| "capsule_id": "capsule-4671827", |
| "task_prompt": "Execute 'PerformanveEval.ipynb'. Save the results in html format in ../results. For all the runs, disable the cell execution timeout and allow errors.", |
| "results": [ |
| { |
| "fig Report the name of the mapping with the higher execution time at 44 elements.": "Semantic-based" |
| }, |
| { |
| "fig Report the name of the mapping with the higher execution time at 44 elements.": "Semantic-based" |
| }, |
| { |
| "fig Report the name of the mapping with the higher execution time at 44 elements.": "Semantic-based" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.7409799.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "lab: An R package for generating analysis-ready data from laboratory records", |
| "capsule_id": "capsule-7186268", |
| "task_prompt": "Run 'SampleCode.Rmd' using Rscript and render it as html. Store the output in ../results. Set clean as 'TRUE'.", |
| "results": [ |
| { |
| "fig From laboratory test 18262-6, report the name of the method with the higher missing rate at gap 30.": "By Window", |
| "fig From laboratory test 2160-0 Creatinine, report the ID number with the highest laboratory result at window 2.": 109 |
| }, |
| { |
| "fig From laboratory test 18262-6, report the name of the method with the higher missing rate at gap 30.": "By Window", |
| "fig From laboratory test 2160-0 Creatinine, report the ID number with the highest laboratory result at window 2.": 109 |
| }, |
| { |
| "fig From laboratory test 18262-6, report the name of the method with the higher missing rate at gap 30.": "By Window", |
| "fig From laboratory test 2160-0 Creatinine, report the ID number with the highest laboratory result at window 2.": 109 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9692445.v1" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "R", |
| "capsule_title": "Partisan Enclaves and Information Bazaars: Mapping Selective Exposure to Online News", |
| "capsule_id": "capsule-5136217", |
| "task_prompt": "Make the following subfolders in the ../results directory: tables, figures, for_publication/tables, for_publication/figures. Run all the .R scripts in the ../code folder using Rscript with 'source' and set echo to 'TRUE'.", |
| "results": [ |
| { |
| "fig From figure 3 from the figures for publication, report the name of the party ID with the lowest share of political news from portals.": "Lean DEM" |
| }, |
| { |
| "fig From figure 3 from the figures for publication, report the name of the party ID with the lowest share of political news from portals.": "Lean DEM" |
| }, |
| { |
| "fig From figure 3 from the figures for publication, report the name of the party ID with the lowest share of political news from portals.": "Lean DEM" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.1889895.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "Python", |
| "capsule_title": "A Predictive Analytics Framework for Early-Stage Thyroid Cancer Using ML", |
| "capsule_id": "capsule-7800694", |
| "task_prompt": "Execute 'Thyroid Cancer Final Code.ipynb'. Save the results in html format in ../results. For all the runs, disable the cell execution timeout and allow errors.", |
| "results": [ |
| { |
| "Which model has the highest Macro F1?": "CatBoost", |
| "What is the ROC AUC (macro OvR) of the best performing model?": 0.957 |
| }, |
| { |
| "Which model has the highest Macro F1?": "CatBoost", |
| "What is the ROC AUC (macro OvR) of the best performing model?": 0.957 |
| }, |
| { |
| "Which model has the highest Macro F1?": "CatBoost", |
| "What is the ROC AUC (macro OvR) of the best performing model?": 0.957 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.2418823.v1" |
| }, |
| { |
| "field": "Social Sciences", |
| "language": "Python", |
| "capsule_title": "The Sovereign Constitution Fabric (SNF) v2.0 — Reproducible Synthetic-Floor Capsule", |
| "capsule_id": "capsule-8412128", |
| "task_prompt": "Run 'run_all.sh'.", |
| "results": [ |
| { |
| "What is the mean of the snf_effectiveness_index?": 0.5791337825021187, |
| "What is the median of the dignity_score?": 0.5888590852515395 |
| }, |
| { |
| "What is the mean of the snf_effectiveness_index?": 0.5791337825021187, |
| "What is the median of the dignity_score?": 0.5888590852515395 |
| }, |
| { |
| "What is the mean of the snf_effectiveness_index?": 0.5791337825021187, |
| "What is the median of the dignity_score?": 0.5888590852515395 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9005626.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "DignityProof — Global Verifiability Capsule (v1.0)", |
| "capsule_id": "capsule-7655932", |
| "task_prompt": "Run 'run_all.sh'.", |
| "results": [ |
| { |
| "What is the point estimate (deaths averted synthetic) from the child wasting impact model? Give results as integer.": 280000, |
| "What is the child wasting impact model reported 95% interval (synthetic)? Give a list of 2 integer value for the confidence interval.": [ |
| 196000, |
| 364000 |
| ] |
| }, |
| { |
| "What is the point estimate (deaths averted synthetic) from the child wasting impact model? Give results as integer.": 280000, |
| "What is the child wasting impact model reported 95% interval (synthetic)? Give a list of 2 integer value for the confidence interval.": [ |
| 196000, |
| 364000 |
| ] |
| }, |
| { |
| "What is the point estimate (deaths averted synthetic) from the child wasting impact model? Give results as integer.": 280000, |
| "What is the child wasting impact model reported 95% interval (synthetic)? Give a list of 2 integer value for the confidence interval.": [ |
| 196000, |
| 364000 |
| ] |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.0436753.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "Unravelling the transcriptome of the human tuberculosis lesion and its clinical implications", |
| "capsule_id": "capsule-9294029", |
| "task_prompt": "Run 'script1.R' using Rscript.", |
| "results": [ |
| { |
| "fig what location has the lowest median enrichment score?": "H" |
| }, |
| { |
| "fig what location has the lowest median enrichment score?": "H" |
| }, |
| { |
| "fig what location has the lowest median enrichment score?": "H" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4427270.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "custom code for: Predicting IVF live birth probabilities using live machine learning, center-specific models", |
| "capsule_id": "capsule-6295990", |
| "task_prompt": "Run 'R_demo.R' using Rscript.", |
| "results": [ |
| { |
| "What is the LBP model ROC-AUC (weighted)": 0.7142857, |
| "What is the percent AUC-Improvement? Give an integer": 25 |
| }, |
| { |
| "What is the LBP model ROC-AUC (weighted)": 0.7142857, |
| "What is the percent AUC-Improvement? Give an integer": 25 |
| }, |
| { |
| "What is the LBP model ROC-AUC (weighted)": 0.7142857, |
| "What is the percent AUC-Improvement? Give an integer": 25 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.8413662.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "R", |
| "capsule_title": "VascularBC-ST: the code to analyze the Vascular microenvironment of Breast Cancer using Spatial Transcriptome", |
| "capsule_id": "capsule-9477017", |
| "task_prompt": "Export the following R default packages: datasets,utils,grDevices,graphics,stats,methods. Run 'main.R' using Rscript.", |
| "results": [ |
| { |
| "fig Pearson correlation coefficients between the estimated proportions of different cell types were calculated, what is the highest pearson correlation related to? Give response in a list of strings": [ |
| "Smooth.1", |
| "Endo.1" |
| ] |
| }, |
| { |
| "fig Pearson correlation coefficients between the estimated proportions of different cell types were calculated, what is the highest pearson correlation related to? Give response in a list of strings": [ |
| "Smooth.1", |
| "Endo.1" |
| ] |
| }, |
| { |
| "fig Pearson correlation coefficients between the estimated proportions of different cell types were calculated, what is the highest pearson correlation related to? Give response in a list of strings": [ |
| "Smooth.1", |
| "Endo.1" |
| ] |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.9243235.v1" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "Codex Wall v4.0 — Encrypted Holo-Causality & Predictive Governance (Reproducible Capsule)", |
| "capsule_id": "capsule-0201673", |
| "task_prompt": "Run 'run_all.sh'.", |
| "results": [ |
| { |
| "What is the mean_gdp_gain_pct?": 0.9999005880537901, |
| "What is the p95?": 1.1643528158326386, |
| "What is the p50?": 0.9999966483532146 |
| }, |
| { |
| "What is the mean_gdp_gain_pct?": 0.9999005880537901, |
| "What is the p95?": 1.1643528158326386, |
| "What is the p50?": 0.9999966483532146 |
| }, |
| { |
| "What is the mean_gdp_gain_pct?": 0.9999005880537901, |
| "What is the p95?": 1.1643528158326386, |
| "What is the p50?": 0.9999966483532146 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.4072743.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "Python", |
| "capsule_title": "Deep learning-enabled accurate assessment of gait impairments in Parkinson's disease using smartphone videos", |
| "capsule_id": "capsule-0152700", |
| "task_prompt": "Check GPU status using nvidia-smi, run the training, validation, and testing shell scripts (train.sh, val.sh, test.sh), and execute the Python scripts: directory_features_process.py, grad_cam.py, joint_feature_severity_spearman_calculation.py, sig_cal.py, joint_significance_cal_medication_response.py, summary_of_joint_medication_response_stats.py, joint_significance_ana_medication_response.py, and chord.py.", |
| "results": [ |
| { |
| "fig are there more than 2 features with accuracy exceeding 0.4? Respond with yes or no.": "yes", |
| "fig which joint has the lowest absolute spearman correlation for acceleration?": "ankle", |
| "Given the Kruskal-Wallis for Group 0-2 (Group 1 vs. Group 3), what is the p-value?": "2.341092434893948e-10" |
| }, |
| { |
| "fig are there more than 2 features with accuracy exceeding 0.4? Respond with yes or no.": "yes", |
| "fig which joint has the lowest absolute spearman correlation for acceleration?": "ankle", |
| "Given the Kruskal-Wallis for Group 0-2 (Group 1 vs. Group 3), what is the p-value?": "2.341092434893948e-10" |
| }, |
| { |
| "fig are there more than 2 features with accuracy exceeding 0.4? Respond with yes or no.": "yes", |
| "fig which joint has the lowest absolute spearman correlation for acceleration?": "ankle", |
| "Given the Kruskal-Wallis for Group 0-2 (Group 1 vs. Group 3), what is the p-value?": "2.341092434893948e-10" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.7700825.v1" |
| }, |
| { |
| "field": "Medical Sciences", |
| "language": "Python", |
| "capsule_title": "A motif preferred adenine base editor with minimal bystander and off-targets editing", |
| "capsule_id": "capsule-2242462", |
| "task_prompt": "Run 'main.py'.", |
| "results": [ |
| { |
| "What is the Chromosome chr1 with positioning 1013983, IsNGGMatch status?": "True", |
| "What is the Chromosome chr1 with positioning 1319313, ABE8e_NG status?": "False" |
| }, |
| { |
| "What is the Chromosome chr1 with positioning 1013983, IsNGGMatch status?": "True", |
| "What is the Chromosome chr1 with positioning 1319313, ABE8e_NG status?": "False" |
| }, |
| { |
| "What is the Chromosome chr1 with positioning 1013983, IsNGGMatch status?": "True", |
| "What is the Chromosome chr1 with positioning 1319313, ABE8e_NG status?": "False" |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.8003706.v2" |
| }, |
| { |
| "field": "Computer Science", |
| "language": "Python", |
| "capsule_title": "PM-Score-Net: A Disjoint-Label Multi-Task Learning Framework for Automated Continuous Severity Assessment of Myopic Maculopathy", |
| "capsule_id": "capsule-3762736", |
| "task_prompt": "Run the evaluation scripts: run_baseline_eval.sh, run_convnext_eval.sh, run_scl_eval.sh, run_task1_eval.sh, run_palm_eval.sh, and run_rigor_eval.sh.", |
| "results": [ |
| { |
| "What is the Task1_Only macro f1?": 0.7012, |
| "What is the Winner_SCL RMSE?": 0.4708 |
| }, |
| { |
| "What is the Task1_Only macro f1?": 0.7012, |
| "What is the Winner_SCL RMSE?": 0.4708 |
| }, |
| { |
| "What is the Task1_Only macro f1?": 0.7012, |
| "What is the Winner_SCL RMSE?": 0.4708 |
| } |
| ], |
| "capsule_doi": "https://doi.org/10.24433/CO.6713286.v2" |
| } |
| ] |
|
|
