relation/validation.jsonl

{"input": "Recent analyses indicate a significant shift towards renewable energy sources in sub-Saharan Africa. The findings are based on the Energy Access and Renewable Transitions Report 2023, which highlights the increasing investments in solar and wind projects across the region. This report emphasizes the importance of policy frameworks in accelerating energy transitions and improving accessibility for underserved communities.", "output": {"entities": {"named_data": ["Energy Access and Renewable Transitions Report 2023"], "organization": [], "acronym": [], "year": ["2023"], "geography": ["sub-Saharan Africa"]}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
{"input": "The Global Forced Migration Database (GFMD) provides comprehensive data on the displacement of populations across various regions. Produced by the International Organization for Migration (IOM), this dataset covers trends from 2015 to 2022, which highlights the increase in refugee numbers in conflict-affected areas.", "output": {"entities": {"named_data": ["Global Forced Migration Database"], "organization": ["International Organization for Migration"], "acronym": ["GFMD"], "year": ["2015 to 2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Forced Migration Database", "tail": "International Organization for Migration"}}, {"has_acronym": {"head": "Global Forced Migration Database", "tail": "GFMD"}}, {"has_timeframe": {"head": "Global Forced Migration Database", "tail": "2015 to 2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "forced displacement, refugees, and migration"}}
{"input": "The 2022 Population Growth Report, published by the Global Demographic Institute (GDI), reveals critical insights into fertility trends across various regions. This extensive analysis utilizes data from the Regional Fertility Assessment (RFA) covering the years 2018-2022, focusing primarily on Southeast Asia and Sub-Saharan Africa. Additionally, the World Fertility Survey (WFS) 2019 provides valuable longitudinal data on birth rates, particularly for urban areas in Nigeria. The findings indicate that while some regions are experiencing declines in fertility rates, others, such as Nigeria, continue to show significant growth. The report's findings are essential for policymakers aiming to address demographic challenges and plan for future population needs.", "output": {"entities": {"named_data": ["2022 Population Growth Report", "Regional Fertility Assessment", "World Fertility Survey"], "organization": ["Global Demographic Institute", "World Bank"], "acronym": ["GDI", "RFA", "WFS"], "year": ["2022", "2018-2022", "2019"], "geography": ["Southeast Asia", "Sub-Saharan Africa", "Nigeria"]}, "relations": [{"has_organization": {"head": "2022 Population Growth Report", "tail": "Global Demographic Institute"}}, {"has_acronym": {"head": "Regional Fertility Assessment", "tail": "RFA"}}, {"has_timeframe": {"head": "Regional Fertility Assessment", "tail": "2018-2022"}}, {"has_acronym": {"head": "World Fertility Survey", "tail": "WFS"}}, {"has_timeframe": {"head": "World Fertility Survey", "tail": "2019"}}, {"has_geography": {"head": "World Fertility Survey", "tail": "Nigeria"}}, {"has_geography": {"head": "Regional Fertility Assessment", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Regional Fertility Assessment", "tail": "Sub-Saharan Africa"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
{"input": "Urban infrastructure planning requires comprehensive assessments, such as the Urban Mobility Evaluation Report, to understand the dynamics of transportation systems in metropolitan areas. These reports provide crucial insights into traffic flow, public transit efficiency, and infrastructure development needs.", "output": {"entities": {"named_data": ["Urban Mobility Evaluation Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
{"input": "The Urban Infrastructure Assessment Report 2022 (UIAR) focuses on the state of transportation systems in urban areas across multiple countries. This dataset, produced by the International Urban Planning Association, highlights trends over the last decade, specifically from 2012 to 2022. In particular, the report covers various cities in Brazil, allowing for localized analysis and comparisons. Furthermore, the Global Transport Database (GTD) provides insights into transportation patterns worldwide, used by numerous researchers and planners, particularly for the years 2019 to 2023. While the GTD serves as a significant resource, recent findings indicate that many urban centers, including those in Southeast Asia, still lack comprehensive infrastructure data, suggesting the need for improvements in data collection efforts.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report 2022", "Global Transport Database"], "organization": ["International Urban Planning Association"], "acronym": ["UIAR", "GTD"], "year": ["2022", "2012 to 2022", "2019 to 2023"], "geography": ["Brazil", "Southeast Asia"]}, "relations": [{"has_acronym": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "UIAR"}}, {"has_timeframe": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Global Transport Database", "tail": "2019 to 2023"}}, {"has_geography": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Brazil"}}, {"has_geography": {"head": "Global Transport Database", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "International Urban Planning Association"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "urban infrastructure and transportation planning"}}
{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Demographic and Health Surveys", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1010, "dataset_name": "Demographic and Health Surveys"}}
{"input": "squared. In this specification, the dummy on receiving 5 [APHRODITE’s water resources project; http://www.chikyu.ac.jp/precip.](http://www.chikyu.ac.jp/precip) 6 25° latitude equals about 27.5 kilometers. .25° longitude varies by latitude; over the range of latitudes in this survey it equals roughly 26 kilometers. 7 It is not clear if ICICI-LOMBARD used APHRODITE or another source of historical rainfall data in order to price their policies because this information is proprietary. The APHRODITE data set is based on historical data provided primarily by the Indian Meteorological Department (IMD), but similar gridded data sets provided by IMD contain vast amounts of missing data over both time and space. It is very likely that the ICICI-LOMBARD products were priced using IMD data, but it is likely the ICICI-LOMBARD used different techniques to account for missing data. 8 It makes sense to assume that the error _εt,i_ is correlated for the same person across time as well as across people in a given year. Ideally, one would like to include individual fixed effects to account for individual heterogeneity. However, in order to exploit this variation one would need to look at customers who purchased insurance in both 2005 and 2006, and received payouts in only one of those", "output": {"entities": {"named_data": ["APHRODITE data set"], "organization": ["Indian Meteorological Department", "ICICI-LOMBARD"]}, "relations": [{"has_organization": {"head": "APHRODITE data set", "tail": "Indian Meteorological Department"}}, {"used_by": {"head": "APHRODITE data set", "tail": "ICICI-LOMBARD"}}]}, "_meta": {"entry_id": 1247, "dataset_name": "APHRODITE data set"}}
{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5.", "output": {"entities": {"named_data": ["INVALSI data"], "organization": ["MoE"]}, "relations": [{"used_by": {"head": "INVALSI data", "tail": "MoE"}}]}, "_meta": {"entry_id": 569, "dataset_name": "INVALSI data"}}
{"input": "7 Gibson and Kim (2012) use a HCES with direct measures of consumption from food stocks and find an error of up to 300 KCal per person per day from ignoring destocking of one major calorie-source (rice) that is subject to bulk buying and storage.\n\nthese reports when made by people who never transact in metric units. A typical HCES consumption\n\nOnce the food reported in an HCES is converted into calories, the household's calorie intake is compared\n\nWhile the potential sources of mismeasurement in HCES are numerous, this study systematically\n\nusing a survey experiment conducted in Tanzania. There were a total of eight alternate designs, which", "output": {"entities": {"named_data": ["HCES"], "organization": ["Gibson and Kim"]}, "relations": [{"used_by": {"head": "HCES", "tail": "Gibson and Kim"}}]}, "_meta": {"entry_id": 245, "dataset_name": "HCES"}}
{"input": "The ongoing assessment of conflict impact on local communities highlights various challenges faced by citizens. Recent findings from the Community Resilience Survey have shown a significant decline in access to basic services due to the recent unrest. Additionally, the Fragility and Violence Report provides critical insights into the systemic issues affecting governance and stability in the region. These datasets underscore the urgent need for targeted interventions to support affected populations.", "output": {"entities": {"named_data": ["Community Resilience Survey", "Fragility and Violence Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "conflict, fragility, and violence"}}
{"input": "The recent analysis of urban land use across West Africa was primarily informed by the West Africa Urban Assessment (WAUA) data collected in 2022. This dataset was published by the Urban Development Institute (UDI) and has been extensively utilized by the Regional Planning Agency (RPA) for its upcoming report on sustainable urbanization strategies. Additionally, the Remote Sensing Habitat Database (RSHD), covering extensive satellite imagery from 2019 to 2021, was produced by the Global Environment Watch (GEW) and provided crucial insights into environmental changes. The findings from the RSHD have been instrumental for the Ecosystem Management Group (EMG), which is currently developing policies for biodiversity conservation in the region.", "output": {"entities": {"named_data": ["West Africa Urban Assessment", "Remote Sensing Habitat Database"], "organization": ["Urban Development Institute", "Regional Planning Agency", "Global Environment Watch", "Ecosystem Management Group"], "acronym": ["WAUA", "RSHD"], "year": ["2022", "2019 to 2021"], "geography": ["West Africa"]}, "relations": [{"has_organization": {"head": "West Africa Urban Assessment", "tail": "Urban Development Institute"}}, {"used_by": {"head": "West Africa Urban Assessment", "tail": "Regional Planning Agency"}}, {"has_acronym": {"head": "West Africa Urban Assessment", "tail": "WAUA"}}, {"has_timeframe": {"head": "West Africa Urban Assessment", "tail": "2022"}}, {"has_organization": {"head": "Remote Sensing Habitat Database", "tail": "Global Environment Watch"}}, {"used_by": {"head": "Remote Sensing Habitat Database", "tail": "Ecosystem Management Group"}}, {"has_acronym": {"head": "Remote Sensing Habitat Database", "tail": "RSHD"}}, {"has_timeframe": {"head": "Remote Sensing Habitat Database", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Remote Sensing Habitat Database", "tail": "West Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "geospatial analysis, remote sensing, and land use mapping"}}
{"input": "3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey.", "output": {"entities": {"named_data": ["ECOSIT4 survey"], "organization": ["the Government of Chad"]}, "relations": [{"used_by": {"head": "ECOSIT4 survey", "tail": "the Government of Chad"}}]}, "_meta": {"entry_id": 330, "dataset_name": "ECOSIT4 survey"}}
{"input": "The 2020 Global Energy Access Survey, published by the International Energy Agency (IEA), provides comprehensive insights into energy consumption patterns across developing countries. A recent analysis by the United Nations Development Program (UNDP) used the findings from this survey to inform strategies aimed at improving energy access in Sub-Saharan Africa. Additionally, the Renewable Energy Transition Assessment Report 2021, developed by the Climate Policy Initiative (CPI), examines the effects of renewable energy adoption on local economies and is crucial for policymakers. This report is frequently cited by organizations including the World Resources Institute (WRI), which uses it to guide their environmental initiatives.", "output": {"entities": {"named_data": ["Global Energy Access Survey", "Renewable Energy Transition Assessment Report 2021"], "organization": ["International Energy Agency", "United Nations Development Program", "Climate Policy Initiative", "World Resources Institute"], "acronym": ["IEA", "UNDP", "CPI", "WRI"], "year": ["2020", "2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_organization": {"head": "Global Energy Access Survey", "tail": "International Energy Agency"}}, {"used_by": {"head": "Global Energy Access Survey", "tail": "United Nations Development Program"}}, {"has_timeframe": {"head": "Global Energy Access Survey", "tail": "2020"}}, {"has_geography": {"head": "Global Energy Access Survey", "tail": "Sub-Saharan Africa"}}, {"has_organization": {"head": "Renewable Energy Transition Assessment Report 2021", "tail": "Climate Policy Initiative"}}, {"used_by": {"head": "Renewable Energy Transition Assessment Report 2021", "tail": "World Resources Institute"}}, {"has_timeframe": {"head": "Renewable Energy Transition Assessment Report 2021", "tail": "2021"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "energy access and renewable transitions"}}
{"input": "The recent Urban Mobility Assessment conducted in 2022 by the Global Transport Organization aims to evaluate current transportation systems in major cities worldwide. This comprehensive study provides insights into urban infrastructure challenges and is designed to guide policymakers in improving public transport services. The report highlights the importance of data-driven decision-making in urban planning and emphasizes the necessity for ongoing collaboration among various stakeholders.", "output": {"entities": {"named_data": ["Urban Mobility Assessment"], "organization": ["Global Transport Organization"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment", "tail": "Global Transport Organization"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR statistical population online dataset", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 4, "dataset_name": "UNHCR statistical population online dataset"}}
{"input": "PAGE 25 --> similar income aggregates can be obtained between POF and PNADC (Paffhausen et al., 2021), which could be exploited in further work in this direction.\n\nMyanmar Ministry of Planning and Finance & World Bank Group. (2017). Technical Poverty Estimation\nReport: Myanmar Poverty and Living Conditions Survey. World Bank, Yangon.\n\nOliveira, L. S., De Souza, D. F., Dos Santos, L. A., Antunes, M., Brendolin, N. C., & Quintaes, V. C. (2016). \"Construction of a Consumption Aggregate Based on Information from POF 2008-2009 and Its Use in the Measurement of Welfare, Poverty, Inequality and Vulnerability of Families.\" _Review of Income and_ _Wealth_, 62, 179-S210.\n\nRodrigues, C.T., & Helfand, S., & Lima, J.E. (2018). Novas linhas de pobreza para o Brasil: Uma análise a partir das Pesquisas De Orçamentos Familiares (POF) 2002-2003 e 2008-2009.", "output": {"entities": {"named_data": ["Myanmar Poverty and Living Conditions Survey"], "organization": ["World Bank", "Myanmar Ministry of Planning and Finance"]}, "relations": [{"has_organization": {"head": "Myanmar Poverty and Living Conditions Survey", "tail": "World Bank"}}, {"used_by": {"head": "Myanmar Poverty and Living Conditions Survey", "tail": "Myanmar Ministry of Planning and Finance"}}]}, "_meta": {"entry_id": 437, "dataset_name": "Myanmar Poverty and Living Conditions Survey"}}
{"input": "The Gender Empowerment and Economic Participation Survey (GEEPS) conducted in 2022 provides valuable insights into the barriers faced by women in the labor market. The dataset, which focuses on employment trends across different sectors in Nigeria, highlights significant disparities in earnings and job security. This survey aims to inform policies that enhance women's economic opportunities and is essential for stakeholders interested in gender equality. Source: Nigeria Bureau of Statistics elaboration based on GEEPS data, which is a crucial tool for understanding the socio-economic challenges women encounter.", "output": {"entities": {"named_data": ["Gender Empowerment and Economic Participation Survey", "GEEPS"], "organization": ["Nigeria Bureau of Statistics"], "acronym": ["GEEPS"], "year": ["2022"], "geography": ["Nigeria"]}, "relations": [{"has_acronym": {"head": "Gender Empowerment and Economic Participation Survey", "tail": "GEEPS"}}, {"has_timeframe": {"head": "GEEPS", "tail": "2022"}}, {"has_geography": {"head": "GEEPS", "tail": "Nigeria"}}, {"has_organization": {"head": "GEEPS", "tail": "Nigeria Bureau of Statistics"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
{"input": "The Family Dynamics Survey (FDS) conducted by the Institute for Population Studies provides critical insights into the changing patterns of family structures and reproductive health across various demographics. Further analysis has been carried out using the National Fertility Assessment (NFA) data produced by the Global Health Institute, which focuses specifically on fertility trends in developing countries. Both datasets serve as essential resources for researchers and policymakers aiming to understand population growth challenges.", "output": {"entities": {"named_data": ["Family Dynamics Survey", "National Fertility Assessment"], "organization": ["Institute for Population Studies", "Global Health Institute"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Family Dynamics Survey", "tail": "Institute for Population Studies"}}, {"has_organization": {"head": "National Fertility Assessment", "tail": "Global Health Institute"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "demographics, fertility, and population growth"}}
{"input": "fact that they are time consuming and therefore expensive to collect. At least three other less resource\nintensive alternatives to the FAO approach have been suggested to derive hunger numbers:\n\nanthropometric data, self-assessments, and direct use of HCES.\n\nquicker and cheaper to collect than full HCES efforts. However, how well they correlate with other\n\nThe third approach, and the one that we concentrate on here, is to use HCES to derive hunger statistics\n\nHCES are positioned between the single subjective hunger question and the intensive 24-hour recall.", "output": {"entities": {"named_data": ["direct use of HCES"], "organization": ["FAO", "the one that we concentrate on here"]}, "relations": [{"has_organization": {"head": "direct use of HCES", "tail": "FAO"}}, {"used_by": {"head": "direct use of HCES", "tail": "the one that we concentrate on here"}}]}, "_meta": {"entry_id": 692, "dataset_name": "direct use of HCES"}}
{"input": "The recent economic evaluations emphasize the importance of targeted social protection measures. For instance, the National Safety Nets Database provides valuable insights into the effectiveness of various interventions. Similarly, the Vulnerability Assessment Report outlines critical aspects of food security and poverty levels in different regions. These data sources are essential for policymakers to design informed strategies.", "output": {"entities": {"named_data": ["National Safety Nets Database", "Vulnerability Assessment Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "social protection and safety nets"}}
{"input": "The Economic Competitiveness Assessment Report 2022 (ECAR) provides invaluable insights into the trade dynamics of Southeast Asian countries. This dataset, produced by the Trade Policy Institute, covers various indicators from 2018 to 2022, offering a comprehensive view of economic trends and challenges in the region. Notably, the report indicates that Vietnam's trade volumes have significantly increased during this period. Additionally, the Southeast Asia Trade Database (SATD) is another vital resource, encompassing trade data from 2015 to 2021 and serving as a benchmark for researchers and policymakers alike. This database is widely cited by economists analyzing the region's growth patterns.", "output": {"entities": {"named_data": ["Economic Competitiveness Assessment Report 2022", "Southeast Asia Trade Database"], "organization": ["Trade Policy Institute"], "acronym": ["ECAR", "SATD"], "year": ["2022", "2018 to 2022", "2015 to 2021"], "geography": ["Southeast Asia", "Vietnam"]}, "relations": [{"has_acronym": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "ECAR"}}, {"has_timeframe": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "2018 to 2022"}}, {"has_geography": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_organization": {"head": "Economic Competitiveness Assessment Report 2022", "tail": "Trade Policy Institute"}}, {"has_acronym": {"head": "Southeast Asia Trade Database", "tail": "SATD"}}, {"has_timeframe": {"head": "Southeast Asia Trade Database", "tail": "2015 to 2021"}}, {"has_geography": {"head": "Southeast Asia Trade Database", "tail": "Southeast Asia"}}, {"used_by": {"head": "Southeast Asia Trade Database", "tail": "economists"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "trade, industry, and economic competitiveness"}}
{"input": "The Digital Connectivity Assessment 2023 (DCA 2023) published by the International Telecommunication Union (ITU) provides insights into internet penetration rates across various regions. This dataset, used by the United Nations Development Programme (UNDP), highlights significant disparities in digital access between urban and rural areas within Eastern Europe. Additionally, the Mobile Usage and Adoption Report 2022 (MUAR 2022), released by the World Bank, details mobile device usage trends in Southeast Asia and is extensively cited by local non-governmental organizations to inform policy decisions. The comprehensive analysis of these datasets underscores the importance of targeted interventions to enhance digital equity.", "output": {"entities": {"named_data": ["Digital Connectivity Assessment 2023", "Mobile Usage and Adoption Report 2022"], "organization": ["International Telecommunication Union", "United Nations Development Programme", "World Bank"], "acronym": ["DCA 2023", "MUAR 2022"], "year": ["2023", "2022"], "geography": ["Eastern Europe", "Southeast Asia"]}, "relations": [{"has_organization": {"head": "Digital Connectivity Assessment 2023", "tail": "International Telecommunication Union"}}, {"used_by": {"head": "Digital Connectivity Assessment 2023", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "Digital Connectivity Assessment 2023", "tail": "DCA 2023"}}, {"has_timeframe": {"head": "Digital Connectivity Assessment 2023", "tail": "2023"}}, {"has_geography": {"head": "Digital Connectivity Assessment 2023", "tail": "Eastern Europe"}}, {"has_organization": {"head": "Mobile Usage and Adoption Report 2022", "tail": "World Bank"}}, {"used_by": {"head": "Mobile Usage and Adoption Report 2022", "tail": "local non-governmental organizations"}}, {"has_acronym": {"head": "Mobile Usage and Adoption Report 2022", "tail": "MUAR 2022"}}, {"has_timeframe": {"head": "Mobile Usage and Adoption Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Mobile Usage and Adoption Report 2022", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "digital development and technology adoption"}}
{"input": "The Urban Infrastructure Assessment Report 2022, published by the Global Development Initiative, offers critical insights into urban transportation systems in Southeast Asia. This dataset has been extensively utilized by the Asian Development Bank to inform their policy recommendations and infrastructure projects. Furthermore, the Metropolitan Transit Survey (MTS) 2021, created by the Urban Planning Institute, has also played a pivotal role in shaping transportation strategies across major cities in the region. The findings from this survey have been referenced in various reports by the World Resources Institute, which aims to promote sustainable urban mobility solutions. Overall, the interplay between these datasets illustrates the importance of collaborative efforts in enhancing urban infrastructure planning.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report 2022", "Metropolitan Transit Survey (MTS) 2021"], "organization": ["Global Development Initiative", "Asian Development Bank", "Urban Planning Institute", "World Resources Institute"], "acronym": ["Metropolitan Transit Survey"], "year": ["2022", "2021"], "geography": ["Southeast Asia"]}, "relations": [{"has_organization": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Global Development Initiative"}}, {"used_by": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Asian Development Bank"}}, {"has_organization": {"head": "Metropolitan Transit Survey (MTS) 2021", "tail": "Urban Planning Institute"}}, {"used_by": {"head": "Metropolitan Transit Survey (MTS) 2021", "tail": "World Resources Institute"}}, {"has_acronym": {"head": "Metropolitan Transit Survey", "tail": "MTS"}}, {"has_timeframe": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "2022"}}, {"has_timeframe": {"head": "Metropolitan Transit Survey (MTS) 2021", "tail": "2021"}}, {"has_geography": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Metropolitan Transit Survey (MTS) 2021", "tail": "Southeast Asia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "urban infrastructure and transportation planning"}}
{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "organization": ["IISS"]}, "relations": [{"has_organization": {"head": "Armed Conflict Survey", "tail": "IISS"}}]}, "_meta": {"entry_id": 497, "dataset_name": "Armed Conflict Survey"}}
{"input": "The Gender Equality and Women’s Economic Empowerment Survey (GEWE Survey) conducted in 2022 provides crucial insights into the participation of women in the workforce across various sectors. This dataset, published by the International Institute for Gender Studies, highlights significant disparities in employment rates and wages, paving the way for informed policy-making aimed at addressing these gaps.", "output": {"entities": {"named_data": ["Gender Equality and Women’s Economic Empowerment Survey"], "organization": ["International Institute for Gender Studies"], "acronym": ["GEWE Survey"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "GEWE Survey", "tail": "International Institute for Gender Studies"}}, {"has_acronym": {"head": "Gender Equality and Women’s Economic Empowerment Survey", "tail": "GEWE Survey"}}, {"has_timeframe": {"head": "GEWE Survey", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "gender equality and women's economic empowerment"}}
{"input": "In the analysis of conflict dynamics in Eastern Africa, the East Africa Conflict Assessment Survey (EACAS) conducted in 2022 has provided significant insights into the region's instability. This dataset, published by the International Institute for Peace Studies, is extensively used by the United Nations Development Programme to inform its policies and interventions. Additionally, the World Bank's Fragility and Resilience Index (FRI) from 2021 offers a comprehensive overview of factors influencing violence across various countries. This index is cited by various NGOs, including Mercy Corps, to tailor their programs effectively in fragile states. The combination of these datasets allows for a nuanced understanding of the interactions between governance and conflict in these regions.", "output": {"entities": {"named_data": ["East Africa Conflict Assessment Survey", "Fragility and Resilience Index"], "organization": ["International Institute for Peace Studies", "United Nations Development Programme", "World Bank", "Mercy Corps"], "acronym": ["EACAS", "FRI"], "year": ["2022", "2021"], "geography": ["Eastern Africa"]}, "relations": [{"has_organization": {"head": "East Africa Conflict Assessment Survey", "tail": "International Institute for Peace Studies"}}, {"used_by": {"head": "East Africa Conflict Assessment Survey", "tail": "United Nations Development Programme"}}, {"has_organization": {"head": "Fragility and Resilience Index", "tail": "World Bank"}}, {"used_by": {"head": "Fragility and Resilience Index", "tail": "Mercy Corps"}}, {"has_acronym": {"head": "East Africa Conflict Assessment Survey", "tail": "EACAS"}}, {"has_acronym": {"head": "Fragility and Resilience Index", "tail": "fri"}}, {"has_timeframe": {"head": "East Africa Conflict Assessment Survey", "tail": "2022"}}, {"has_timeframe": {"head": "Fragility and Resilience Index", "tail": "2021"}}, {"has_geography": {"head": "East Africa Conflict Assessment Survey", "tail": "Eastern Africa"}}, {"has_geography": {"head": "Fragility and Resilience Index", "tail": "Eastern Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "conflict, fragility, and violence"}}
{"input": "Therefore, in addition we adopted another adapted calculation method which is based on travel paths simulations using OD pairs from the JICA travel survey.\n\nThe conventional approach calculates shortest paths between every possible pair of nodes within the network whereas the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey.\n\n**Acknowledgments:** We would like to thank GoMetro for leading the public transport mapping, and in\nparticular Bob Kabeya and Clayton Lane, as well as the enumerators that conducted these surveys. We thank\nShohei Nakamura, Takaaki Masaki and Mervy Ever Viboudoulou Vilpoux for their help in accessing\nKinshasa commuter and household surveys. We are grateful to Laurent Corroyer, Stephane Hallegatte and\nKirsten Homman for their inputs and feedback throughout this project. We are also grateful to Catalina\nOchoa for her review of an earlier draft of this paper and her thoughtful comments. This study was supported\nby the Global Facility for Disaster Risk Reduction and Recovery (GFDRR).\n\nThese data sets were combined with travel survey data containing travelers' socioeconomic attributes and trip parameters, as well as a high-resolution flood maps.", "output": {"entities": {"named_data": ["JICA commuter travel survey"], "organization": ["JICA", "GoMetro"]}, "relations": [{"has_organization": {"head": "JICA commuter travel survey", "tail": "JICA"}}, {"used_by": {"head": "JICA commuter travel survey", "tail": "GoMetro"}}]}, "_meta": {"entry_id": 481, "dataset_name": "JICA commuter travel survey"}}
{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor.", "output": {"entities": {"named_data": ["Barro-Lee"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Barro-Lee", "tail": "World Bank"}}]}, "_meta": {"entry_id": 676, "dataset_name": "Barro-Lee"}}
{"input": "The Global Biodiversity Assessment Report (GBAR) provides detailed insights into the state of biodiversity across various ecosystems. Produced by the International Union for Conservation of Nature (IUCN) in 2022, the report synthesizes data from numerous studies conducted worldwide, with a particular focus on regions such as Central Africa and Southeast Asia. The findings underscore the critical need for conservation efforts, especially in areas experiencing rapid habitat loss. However, some regions, like Northern Europe, also show positive trends in biodiversity recovery, challenging the notion of a universally declining global biodiversity. The comprehensive nature of the GBAR allows for a wide range of applications, from policy formulation to academic research.", "output": {"entities": {"named_data": ["Global Biodiversity Assessment Report"], "organization": ["International Union for Conservation of Nature"], "acronym": ["GBAR"], "year": ["2022"], "geography": ["Central Africa", "Southeast Asia", "Northern Europe"]}, "relations": [{"has_acronym": {"head": "Global Biodiversity Assessment Report", "tail": "GBAR"}}, {"has_timeframe": {"head": "Global Biodiversity Assessment Report", "tail": "2022"}}, {"has_geography": {"head": "Global Biodiversity Assessment Report", "tail": "Central Africa"}}, {"has_geography": {"head": "Global Biodiversity Assessment Report", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Global Biodiversity Assessment Report", "tail": "Northern Europe"}}, {"has_organization": {"head": "Global Biodiversity Assessment Report", "tail": "International Union for Conservation of Nature"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
{"input": "we have constructed the cyclone database to support research on household- and community-level adaptation to cyclone risks. These risks may affect coastal population dynamics in at least two ways. In the long run, the number and size of coastal communities may be affected by the long-run frequency and power of cyclone strikes. Other things equal, we would expect areas that have been largely cyclone-free for many years to be more populous than frequently-impacted areas. Shorter-term, if impact-intensive zones shift over time, 8", "output": {"entities": {"named_data": ["the cyclone database"], "organization": ["we"]}, "relations": [{"used_by": {"head": "the cyclone database", "tail": "we"}}]}, "_meta": {"entry_id": 1110, "dataset_name": "the cyclone database"}}
{"input": "Recent analyses of gender pay gaps have utilized the Women’s Labor Force Participation Survey conducted across various regions. This survey provides essential insights into the barriers women face in entering the workforce. Furthermore, the Gender Equality Index Report highlights progress in women's economic empowerment initiatives worldwide, shedding light on impactful policies and practices that have been adopted.", "output": {"entities": {"named_data": ["Women’s Labor Force Participation Survey", "Gender Equality Index Report"], "organization": [], "acronym": [], "year": [], "geography": []}, "relations": []}, "_meta": {"category": "no_relations", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "gender equality and women's economic empowerment"}}
{"input": "The Renewable Energy Access Survey (REAS) conducted by the Ministry of Energy in 2022 provides valuable insights into electricity access in rural communities across the country. This dataset highlights significant disparities in energy access and helps inform policy decisions aimed at promoting renewable energy solutions.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey"], "organization": ["Ministry of Energy"], "acronym": ["REAS"], "year": ["2022"], "geography": ["country"]}, "relations": [{"has_organization": {"head": "Renewable Energy Access Survey", "tail": "Ministry of Energy"}}, {"has_acronym": {"head": "Renewable Energy Access Survey", "tail": "REAS"}}, {"has_timeframe": {"head": "Renewable Energy Access Survey", "tail": "2022"}}, {"has_geography": {"head": "Renewable Energy Access Survey", "tail": "country"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
{"input": "The Renewable Energy Access Database (READ) offers comprehensive data on energy consumption patterns across various regions. The 2020 Energy Assessment Report highlights key trends in renewable energy adoption and is utilized by several international organizations to shape energy policies. Furthermore, the Africa Energy Evaluation Survey (AEES) conducted in 2019 provides insights into the effectiveness of renewable initiatives in countries like Nigeria and Kenya. Together, these datasets are crucial for understanding the dynamics of energy transitions in developing nations.", "output": {"entities": {"named_data": ["Renewable Energy Access Database", "2020 Energy Assessment Report", "Africa Energy Evaluation Survey"], "organization": ["international organizations"], "acronym": ["READ", "AEES"], "year": ["2020", "2019"], "geography": ["Nigeria", "Kenya"]}, "relations": [{"has_acronym": {"head": "Renewable Energy Access Database", "tail": "READ"}}, {"has_timeframe": {"head": "2020 Energy Assessment Report", "tail": "2020"}}, {"has_geography": {"head": "Africa Energy Evaluation Survey", "tail": "Nigeria"}}, {"has_geography": {"head": "Africa Energy Evaluation Survey", "tail": "Kenya"}}, {"has_timeframe": {"head": "Africa Energy Evaluation Survey", "tail": "2019"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "energy access and renewable transitions"}}
{"input": "3) Female teacher who is HIV�/ but not sick\nshould be allowed to continue teaching in\nschool.\n\nPEPFAR\n\n4) Would not want to keep the HIV�/ status PEPFAR\nof a family member a secret.\n\nPractices\n\nCondom use 1) Percent of men and women (aged 15 �24)\nwho used a condom at last sex with a\nnon-marital, non-cohabiting partner, of\nthose who have had sex with a non-marital,\nnon-cohabiting partner in the last 12\nmonths. [bc]\n\nUNGASS, MDG, PEPFAR\n\na prior to the UNGASS indicators in 2002, the UNAIDS stated indicators did not specify youth\n(15 �24 years).\nb the MDG indicator replaces ‘have’ with ‘transmit’.\nc the MDG indicators do not specify ‘non-marital, non-cohabiting’ but add ‘high risk’. PEFPAR\nuses 15 �49 years.\n\nwith the surrounding host population response and a sub-regional approach\nundertaken in order to take into account the displacement cycle (UNHCR 2005).\nTimely and accurate data are needed to provide targeted and effective\ninterventions in conflict and post-conflict settings. Unfortunately, due to unstable", "output": {"entities": {"named_data": ["UNGASS indicators"], "organization": ["UNGASS", "PEPFAR"]}, "relations": [{"has_organization": {"head": "UNGASS indicators", "tail": "UNGASS"}}, {"used_by": {"head": "UNGASS indicators", "tail": "PEPFAR"}}]}, "_meta": {"entry_id": 471, "dataset_name": "UNGASS indicators"}}
{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19", "output": {"entities": {"named_data": ["EPR-ER"], "organization": ["we"]}, "relations": [{"used_by": {"head": "EPR-ER", "tail": "we"}}]}, "_meta": {"entry_id": 514, "dataset_name": "EPR-ER"}}
{"input": "The Fragility Assessment Report 2022, published by the Global Development Institute, provides crucial insights into the socio-political dynamics of conflict-affected regions. It has been extensively utilized by the United Nations Development Programme (UNDP) for their ongoing projects aimed at strengthening resilience in these areas. Additionally, the African Conflict Database (ACD) encompasses data from 2018 to 2021, detailing violent incidents across the continent, and is actively used by the African Union to formulate conflict resolution strategies. This comprehensive approach to utilizing data underscores the vital role that detailed assessments play in addressing fragility and promoting peace.", "output": {"entities": {"named_data": ["Fragility Assessment Report 2022", "African Conflict Database"], "organization": ["Global Development Institute", "United Nations Development Programme", "African Union"], "acronym": ["ACD"], "year": ["2022", "2018 to 2021"], "geography": ["Africa"]}, "relations": [{"has_organization": {"head": "Fragility Assessment Report 2022", "tail": "Global Development Institute"}}, {"used_by": {"head": "Fragility Assessment Report 2022", "tail": "United Nations Development Programme"}}, {"has_acronym": {"head": "African Conflict Database", "tail": "ACD"}}, {"has_timeframe": {"head": "African Conflict Database", "tail": "2018 to 2021"}}, {"used_by": {"head": "African Conflict Database", "tail": "African Union"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "conflict, fragility, and violence"}}
{"input": "The Domestic Revenue Assessment Report 2022 (DRAR 2022) highlights the significant progress made by several countries in improving their tax collection systems. In particular, the report examines data from the Public Financial Management Index (PFM Index) covering the period 2018–2020 for East African nations, including Kenya, Uganda, and Tanzania. The analysis, conducted by the International Fiscal Policy Institute (IFPI), identifies best practices and challenges faced by these countries in enhancing revenue generation. Additionally, the report showcases the Domestic Budget Tracking Survey (DBTS) 2021, which analyzes budget allocations and expenditures across various sectors in Ghana. The comprehensive findings provide valuable insights for policymakers aiming to enhance financial sustainability.", "output": {"entities": {"named_data": ["Domestic Revenue Assessment Report 2022", "Public Financial Management Index", "Domestic Budget Tracking Survey"], "organization": ["International Fiscal Policy Institute"], "acronym": ["DRAR", "PFM Index", "DBTS"], "year": ["2022", "2018–2020", "2021"], "geography": ["East Africa", "Kenya", "Uganda", "Tanzania", "Ghana"]}, "relations": [{"has_acronym": {"head": "Domestic Revenue Assessment Report 2022", "tail": "DRAR"}}, {"has_timeframe": {"head": "Domestic Revenue Assessment Report 2022", "tail": "2022"}}, {"has_geography": {"head": "Domestic Revenue Assessment Report 2022", "tail": "Ghana"}}, {"has_acronym": {"head": "Public Financial Management Index", "tail": "PFM Index"}}, {"has_timeframe": {"head": "Public Financial Management Index", "tail": "2018–2020"}}, {"has_geography": {"head": "Public Financial Management Index", "tail": "East Africa"}}, {"has_acronym": {"head": "Domestic Budget Tracking Survey", "tail": "DBTS"}}, {"has_timeframe": {"head": "Domestic Budget Tracking Survey", "tail": "2021"}}, {"has_geography": {"head": "Domestic Budget Tracking Survey", "tail": "Ghana"}}, {"has_organization": {"head": "Domestic Revenue Assessment Report 2022", "tail": "International Fiscal Policy Institute"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "public financial management and domestic revenue"}}
{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.", "output": {"entities": {"named_data": ["HICES"], "organization": ["Turkish Statistical Institute (TUIK)"]}, "relations": [{"has_organization": {"head": "HICES", "tail": "Turkish Statistical Institute (TUIK)"}}]}, "_meta": {"entry_id": 740, "dataset_name": "HICES"}}
{"input": "Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Health institutions (health center/hospital) Home Other Percent Figure D.10: Childbirth in health institutions (children under five years) Source: World Bank Staff based on SESRE 2023. Annexes 104 Hearing Walking or climbing steps Percent Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Hosts Seeing Difficulty with selfcare Communicating Remembering or concentrating Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts 50 40 30 20 90 100 80 70 60 10 0 Figure D.13: Types of disability Source: World Bank Staff based on SESRE 2023. - 10,000 20,000 30,000 40,000 50,000 60,000 Hosts Refugees Addis Ababa Total annual rent expenditure Per adult equivalent rent expenditure Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali", "output": {"entities": {"named_data": ["SESRE 2023"], "organization": ["World Bank Staff"]}, "relations": [{"used_by": {"head": "SESRE 2023", "tail": "World Bank Staff"}}]}, "_meta": {"entry_id": 776, "dataset_name": "SESRE 2023"}}
{"input": "The recent analysis of fertility trends in Central Asia has been significantly supported by the Central Asia Reproductive Health Survey (CARHS) conducted in 2020. This dataset, published by the Asian Development Bank (ADB), provides critical insights into reproductive health indicators across the region. Additionally, the United Nations Population Fund (UNFPA) has utilized the CARHS to inform their ongoing projects aimed at improving maternal health outcomes in countries such as Kazakhstan and Kyrgyzstan. To further complement these findings, the World Bank's Population Growth Database (PGD) offers a comprehensive overview of demographic shifts from 2015 to 2022, which has been referenced by various NGOs working in demographic research. Overall, these datasets are essential for formulating effective policies related to population growth and reproductive health in Central Asia.", "output": {"entities": {"named_data": ["Central Asia Reproductive Health Survey", "Population Growth Database"], "organization": ["Asian Development Bank", "United Nations Population Fund", "World Bank"], "acronym": ["CARHS", "PGD"], "year": ["2020", "2015 to 2022"], "geography": ["Central Asia", "Kazakhstan", "Kyrgyzstan"]}, "relations": [{"has_organization": {"head": "Central Asia Reproductive Health Survey", "tail": "Asian Development Bank"}}, {"used_by": {"head": "Central Asia Reproductive Health Survey", "tail": "United Nations Population Fund"}}, {"has_acronym": {"head": "Central Asia Reproductive Health Survey", "tail": "CARHS"}}, {"has_timeframe": {"head": "Central Asia Reproductive Health Survey", "tail": "2020"}}, {"has_organization": {"head": "Population Growth Database", "tail": "World Bank"}}, {"has_timeframe": {"head": "Population Growth Database", "tail": "2015 to 2022"}}, {"has_geography": {"head": "Central Asia Reproductive Health Survey", "tail": "Central Asia"}}, {"has_geography": {"head": "Population Growth Database", "tail": "Central Asia"}}, {"has_geography": {"head": "Population Growth Database", "tail": "Kazakhstan"}}, {"has_geography": {"head": "Population Growth Database", "tail": "Kyrgyzstan"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
{"input": "The Water Quality Assessment Report 2022, published by the Global Water Institute, provides comprehensive insights into the status of water sanitation practices across various regions. This report analyzes the effectiveness of current policies implemented in different countries and highlights areas that require urgent attention to improve public health and environmental sustainability.", "output": {"entities": {"named_data": ["Water Quality Assessment Report 2022"], "organization": ["Global Water Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Water Quality Assessment Report 2022", "tail": "Global Water Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
{"input": "The Coastal Ecosystem Monitoring Survey (CEMS) provides critical insights into the health of marine environments and is widely used for policy-making in sustainable fisheries. This dataset, which covers the period from 2015 to 2020, includes comprehensive data collected from various coastal regions of Indonesia. The CEMS is increasingly referenced by environmental organizations aiming to improve coastal management practices. Source: Developed by the Indonesian Ministry of Marine Affairs and Fisheries, this survey enhances our understanding of biodiversity changes and fisheries resources over time.", "output": {"entities": {"named_data": ["Coastal Ecosystem Monitoring Survey"], "organization": ["Indonesian Ministry of Marine Affairs and Fisheries"], "acronym": ["CEMS"], "year": ["2015 to 2020"], "geography": ["Indonesia"]}, "relations": [{"has_acronym": {"head": "Coastal Ecosystem Monitoring Survey", "tail": "CEMS"}}, {"has_timeframe": {"head": "Coastal Ecosystem Monitoring Survey", "tail": "2015 to 2020"}}, {"has_geography": {"head": "Coastal Ecosystem Monitoring Survey", "tail": "Indonesia"}}, {"has_organization": {"head": "Coastal Ecosystem Monitoring Survey", "tail": "Indonesian Ministry of Marine Affairs and Fisheries"}}, {"used_by": {"head": "Coastal Ecosystem Monitoring Survey", "tail": "environmental organizations"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "environmental sustainability and natural resources"}}
{"input": "The monthly precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association's Climate Prediction Center, is based on ground station measurements of precipitation over the period 1948-2001.\n\nSoil data was obtained from FAO (2003). The FAO data provides information about the\nmajor and minor soils in each location. Data concerning the hydrology was predicted from a\nhydrological model for Africa (Strzepek & McCluskey 2006). The model calculated the\nwater flow through each district in the surveyed countries. Data on elevation at the centroid\nof each district was obtained through GIS manipulation using data from the United States\nGeological Survey (USGS, 2004). The USGS data are derived from a global digital elevation\nmodel with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).\n\nWeng F & Grody N, 1998. Physical retrieval of land surface temperature using the Special Sensor Microwave Imager. _Journal of Geophysical Research_ 103: 8839-8848. World Bank, 2003. Africa rainfall and temperature evaluation system (ARTES). World Bank, Washington DC.", "output": {"entities": {"named_data": ["ARTES"], "organization": ["National Oceanic and Atmospheric Association's Climate Prediction Center", "World Bank"]}, "relations": [{"has_organization": {"head": "ARTES", "tail": "National Oceanic and Atmospheric Association's Climate Prediction Center"}}, {"used_by": {"head": "ARTES", "tail": "World Bank"}}]}, "_meta": {"entry_id": 43, "dataset_name": "ARTES"}}
{"input": "The Domestic Revenue Collection Assessment (DRCA) conducted in 2022 provides essential insights into the efficiency of tax systems across various countries. This dataset, published by the International Monetary Fund (IMF), has been widely used by the African Tax Administration Forum (ATAF) to enhance tax policy frameworks in member states. Furthermore, the World Bank's Public Financial Management Database (PFMD) for 2021 contains crucial information regarding public expenditure trends and is leveraged by various governmental organizations to improve fiscal governance. Additionally, the Latin America Tax Transparency Report (LATTR) published in 2023 focuses on cross-border tax regulations and is frequently cited by the Inter-American Development Bank (IDB) in their regional assessments.", "output": {"entities": {"named_data": ["Domestic Revenue Collection Assessment", "Public Financial Management Database", "Latin America Tax Transparency Report"], "organization": ["International Monetary Fund", "African Tax Administration Forum", "World Bank", "Inter-American Development Bank"], "acronym": ["DRCA", "PFMD", "LATTR"], "year": ["2022", "2021", "2023"], "geography": ["Africa", "Latin America"]}, "relations": [{"has_organization": {"head": "Domestic Revenue Collection Assessment", "tail": "International Monetary Fund"}}, {"used_by": {"head": "Domestic Revenue Collection Assessment", "tail": "African Tax Administration Forum"}}, {"has_organization": {"head": "Public Financial Management Database", "tail": "World Bank"}}, {"used_by": {"head": "Public Financial Management Database", "tail": "various governmental organizations"}}, {"has_organization": {"head": "Latin America Tax Transparency Report", "tail": "Inter-American Development Bank"}}, {"has_timeframe": {"head": "Domestic Revenue Collection Assessment", "tail": "2022"}}, {"has_timeframe": {"head": "Public Financial Management Database", "tail": "2021"}}, {"has_timeframe": {"head": "Latin America Tax Transparency Report", "tail": "2023"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "public financial management and domestic revenue"}}
{"input": "The recent Domestic Revenue Assessment Report 2022, published by the International Finance Institute (IFI), provides an in-depth analysis of public financial management practices across various countries. This report, utilized by the Ministry of Finance in Ghana, highlights key areas for improvement in tax collection and allocation. Furthermore, the African Tax Revenue Database (ATRD), compiled by the African Union, offers comparative insights and is employed by several regional governments to enhance their fiscal policies. By leveraging data from these sources, policymakers can better understand the unique challenges faced in their respective contexts and develop strategies to optimize revenue generation.", "output": {"entities": {"named_data": ["Domestic Revenue Assessment Report 2022", "African Tax Revenue Database"], "organization": ["International Finance Institute", "Ministry of Finance", "African Union", "Ghana"], "acronym": ["Domestic Revenue Assessment Report", "ATRD"], "year": ["2022"], "geography": ["Ghana"]}, "relations": [{"has_organization": {"head": "Domestic Revenue Assessment Report 2022", "tail": "International Finance Institute"}}, {"used_by": {"head": "Domestic Revenue Assessment Report 2022", "tail": "Ministry of Finance"}}, {"has_timeframe": {"head": "Domestic Revenue Assessment Report 2022", "tail": "2022"}}, {"has_organization": {"head": "African Tax Revenue Database", "tail": "African Union"}}, {"used_by": {"head": "African Tax Revenue Database", "tail": "several regional governments"}}, {"has_acronym": {"head": "African Tax Revenue Database", "tail": "ATRD"}}, {"has_geography": {"head": "Domestic Revenue Assessment Report 2022", "tail": "Ghana"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "public financial management and domestic revenue"}}
{"input": "The Water Quality Monitoring Survey (WQMS) conducted in 2022 provides critical insights into the state of water resources in urban areas of Nigeria. This survey, which focuses on the assessment of water contaminants, highlights significant trends over the past year while being utilized by various local health organizations for enhancing public health strategies. The data collected are essential for policymakers aiming to improve water safety standards across the region. The findings can also inform future initiatives aimed at sustainable water management practices. Source: Nigerian Ministry of Water Resources elaboration based on the WQMS findings.", "output": {"entities": {"named_data": ["Water Quality Monitoring Survey"], "organization": ["Nigerian Ministry of Water Resources"], "acronym": ["WQMS"], "year": ["2022"], "geography": ["Nigeria"]}, "relations": [{"has_acronym": {"head": "Water Quality Monitoring Survey", "tail": "WQMS"}}, {"has_timeframe": {"head": "Water Quality Monitoring Survey", "tail": "2022"}}, {"has_geography": {"head": "Water Quality Monitoring Survey", "tail": "Nigeria"}}, {"used_by": {"head": "Water Quality Monitoring Survey", "tail": "local health organizations"}}, {"has_organization": {"head": "Water Quality Monitoring Survey", "tail": "Nigerian Ministry of Water Resources"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "water, sanitation, and hygiene"}}
{"input": "The Food Security and Nutrition Assessment 2022 conducted by the International Food Policy Research Institute (IFPRI) has revealed critical insights into the agricultural landscape of Sub-Saharan Africa. This dataset, used extensively by various NGOs and governmental bodies, highlights the ongoing challenges in food availability and accessibility. Additionally, the 2021 National Agriculture Survey, published by the Ministry of Agriculture in Kenya, provides complementary data that can inform policy decisions. Organizations such as the World Health Organization (WHO) have relied on both datasets to shape their initiatives aimed at reducing malnutrition in the region. Given the interlinked nature of these findings, it is vital for stakeholders to utilize the comprehensive insights offered by these assessments to strategize effectively.", "output": {"entities": {"named_data": ["Food Security and Nutrition Assessment 2022", "National Agriculture Survey"], "organization": ["International Food Policy Research Institute", "Ministry of Agriculture", "World Health Organization"], "acronym": ["IFPRI"], "year": ["2022", "2021"], "geography": ["Sub-Saharan Africa", "Kenya"]}, "relations": [{"has_organization": {"head": "Food Security and Nutrition Assessment 2022", "tail": "International Food Policy Research Institute"}}, {"used_by": {"head": "Food Security and Nutrition Assessment 2022", "tail": "NGOs"}}, {"used_by": {"head": "Food Security and Nutrition Assessment 2022", "tail": "governmental bodies"}}, {"has_organization": {"head": "National Agriculture Survey", "tail": "Ministry of Agriculture"}}, {"used_by": {"head": "National Agriculture Survey", "tail": "World Health Organization"}}, {"has_timeframe": {"head": "Food Security and Nutrition Assessment 2022", "tail": "2022"}}, {"has_timeframe": {"head": "National Agriculture Survey", "tail": "2021"}}, {"has_geography": {"head": "Food Security and Nutrition Assessment 2022", "tail": "Sub-Saharan Africa"}}, {"has_geography": {"head": "National Agriculture Survey", "tail": "Kenya"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Profile Global Registration System"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "Profile Global Registration System", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 270, "dataset_name": "Profile Global Registration System"}}
{"input": "The recent report highlights the findings from the Global Refugee Survey 2022 conducted by the International Organization for Migration (IOM), which provides critical insights into the patterns of forced displacement across various regions. This data has been widely utilized by the UN High Commissioner for Refugees (UNHCR) to inform policy decisions and humanitarian responses. Additionally, the Africa Migration Trends Assessment (AMTA) 2021 offers a comprehensive overview of migration flows within the continent, published by the African Union. The AMTA data has been instrumental for local governments in East Africa, particularly in addressing the challenges faced by displaced populations.", "output": {"entities": {"named_data": ["Global Refugee Survey 2022", "Africa Migration Trends Assessment"], "organization": ["International Organization for Migration", "UN High Commissioner for Refugees", "African Union", "local governments in East Africa"], "acronym": ["AMTA"], "year": ["2022", "2021"], "geography": ["East Africa"]}, "relations": [{"has_organization": {"head": "Global Refugee Survey 2022", "tail": "International Organization for Migration"}}, {"used_by": {"head": "Global Refugee Survey 2022", "tail": "UN High Commissioner for Refugees"}}, {"has_acronym": {"head": "Africa Migration Trends Assessment", "tail": "AMTA"}}, {"has_timeframe": {"head": "Africa Migration Trends Assessment", "tail": "2021"}}, {"has_organization": {"head": "Africa Migration Trends Assessment", "tail": "African Union"}}, {"used_by": {"head": "Africa Migration Trends Assessment", "tail": "local governments in East Africa"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "forced displacement, refugees, and migration"}}
{"input": "**The World Bank**\nSouth Sudan Health Sector Transformation Project (HSTP) (P181385)\n\n|Percentage of children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)|Col2|\n|---|---|\n|Description|Proportion of surviving infants who have received 1st & 3rd dose of the combined diphtheria, tetanus toxoid,<br>pertussis, Hepatitis B and Homophiles influenza type b vaccine|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of refugee children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|**Percentage of refugee children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|\n|Description|Proportion of surviving infants who have received 1st & 3rd dose of the combined diphtheria, tetanus toxoid,<br>pertussis, Hepatitis B and Homophiles influenza type b vaccine|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of HC children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|**Percentage of HC children under one year of age who have received 1st & 3rd dose of pentavalent vaccine (Percentage)**|\n|Description|Proportion of surviving infants who have received 1st dose of the combined diphtheria, tetanus toxoid, pertussis,<br>Hepatitis B and Homophiles influenza type b vaccine|\n|Frequency|Quarterly|\n|Data source|DHIS2|\n|Methodology for Data<br>Collection|DHIS2|\n|Responsibility for Data<br>Collection|MoH and UNICEF; Measures subcomponent 1.1 Under UNICEF|\n|**Percentage of facilities reporting stock out of tracer medicines (Percentage)**|**Percentage of facilities reporting stock out of tracer medicines (Percentage)**|\n|Description|This indicator measures whether facilities experienced a stockout of one or more tracer medicines and<br>laboratory reagents at any point during the reporting period being assessed. The result is expressed as a<br>percentage of the total number of facilities.|\n|Frequency|Quarterly|\n|Data source|Pharmaceutical agency/ Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|Pharmaceutical agency, to be verified quarterly by TPM|\n|Responsibility for Data<br>Collection|TPM; PMU; pharmaceutical agency|\n|**Percentage of refugee facilities reporting stock out of tracer medicines (Percentage)**|**Percentage of refugee facilities reporting stock out of tracer medicines (Percentage)**|\n|Description|This indicator measures whether refugee facilities experienced a stockout of one or more tracer medicines and<br>laboratory reagents at any point during the reporting period being assessed. The result is expressed as a<br>percentage of the total number of facilities.|\n|Frequency|Quarterly|\n|Data source|Pharmaceutical agency/ Quarterly Health Facility Assessment|\n|Methodology for Data<br>Collection|Pharmaceutical agency, to be verified quarterly by TPM|\n|Responsibility for Data<br>Collection|TPM; PMU; pharmaceutical agency|\n|**Percentage of HC facilities reporting stock out of tracer medicines (Percentage)**|**Percentage of HC facilities reporting stock out of tracer medicines (Percentage)**|\n|Description|This indicator measures whether HC facilities experienced a stockout of one or more tracer medicines and<br>laboratory reagents at any point during the reporting period being assessed. The result is expressed as a<br>percentage of the total number of facilities.|", "output": {"entities": {"named_data": ["DHIS2"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "DHIS2", "tail": "World Bank"}}]}, "_meta": {"entry_id": 821, "dataset_name": "DHIS2"}}
{"input": "The Population Growth Analysis Report 2022 was recently released, providing essential insights into demographic trends. This report, published by the National Institute of Demographic Studies, highlights significant findings on fertility rates and population dynamics across various regions. The data included in this report is vital for policymakers aiming to address the challenges of population growth and resource allocation.", "output": {"entities": {"named_data": ["Population Growth Analysis Report 2022"], "organization": ["National Institute of Demographic Studies"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Population Growth Analysis Report 2022", "tail": "National Institute of Demographic Studies"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "demographics, fertility, and population growth"}}
{"input": "The Energy Access Assessment Report 2022 has revealed crucial insights into the progress of renewable energy initiatives across various regions. Compiled by the Global Energy Institute, this report highlights the disparities in energy access and emphasizes the need for targeted investments in sustainable energy projects. The findings are intended to inform policymakers and stakeholders about effective strategies to enhance energy availability in underserved communities.", "output": {"entities": {"named_data": ["Energy Access Assessment Report 2022"], "organization": ["Global Energy Institute"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Energy Access Assessment Report 2022", "tail": "Global Energy Institute"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
{"input": "we use a cutoff distance of 20 km, we assume there is little economic footprint beyond that distance. Of course, any such distance is arbitrarily chosen, which is why we try different specifications to explore the spatial heterogeneity by varying this distance (using 10 km, 20 km, through 50 km) as well as a spatial lag structure (using 0 to 10 km, 10 to 20 km, through 40 to 50 km distance bins). [4] Second, we collapse the DHS mining data at the district level. [5] The number of districts has changed over time in Ghana, because districts with high population growth have been split into smaller districts. To avoid endogeneity concerns, we use the baseline number of districts that existed at the start of our analysis period, which are 137. Eleven of these districts have industrial mining. Because some mines are close to district boundaries, we additionally test whether there is an effect in neighboring districts. **3.1 Resource data** The Raw Materials Data are from InterraRMG (2013). The data set contains information on past or current industrial mines. All mines have information on annual production volumes, ownership structure, and GPS coordinates on location. We complete this data with exact", "output": {"entities": {"named_data": ["Raw Materials Data"], "organization": ["InterraRMG"]}, "relations": [{"has_organization": {"head": "Raw Materials Data", "tail": "InterraRMG"}}]}, "_meta": {"entry_id": 1182, "dataset_name": "Raw Materials Data"}}
{"input": "The 2020 Labor Market Assessment for Southeast Asia provides key insights into employment trends and skill shortages across the region. This dataset, produced by the International Labor Organization (ILO), is crucial for policymakers aiming to improve labor market outcomes. It highlights significant discrepancies in job availability between urban and rural areas, and recommends targeted interventions. The findings are particularly relevant for countries such as Indonesia, Thailand, and Vietnam, where labor market dynamics are rapidly evolving. Source: ILO elaboration based on Southeast Asia Labor Market Assessment (SELMA).", "output": {"entities": {"named_data": ["Labor Market Assessment for Southeast Asia", "Southeast Asia Labor Market Assessment"], "organization": ["International Labor Organization"], "acronym": ["SELMA"], "year": ["2020"], "geography": ["Southeast Asia", "Indonesia", "Thailand", "Vietnam"]}, "relations": [{"has_acronym": {"head": "Southeast Asia Labor Market Assessment", "tail": "SELMA"}}, {"has_timeframe": {"head": "Labor Market Assessment for Southeast Asia", "tail": "2020"}}, {"has_geography": {"head": "Labor Market Assessment for Southeast Asia", "tail": "Southeast Asia"}}, {"has_geography": {"head": "Labor Market Assessment for Southeast Asia", "tail": "Indonesia"}}, {"has_geography": {"head": "Labor Market Assessment for Southeast Asia", "tail": "Thailand"}}, {"has_geography": {"head": "Labor Market Assessment for Southeast Asia", "tail": "Vietnam"}}, {"has_organization": {"head": "Labor Market Assessment for Southeast Asia", "tail": "International Labor Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "employment, labor markets, and skills development"}}
{"input": "maps developed for this study allow for this potential in future work. For the HCMC analysis, the location of the slum areas in the PUMA data set are mainly restricted to the old town. Furthermore, slum areas are often difficult to define (with PUMA only identifying potential slums) and the data we have likely does not capture all slum areas within HCMC. In terms of the hazard, the flood maps for HCMC show flood depth and extent from the river and from sea (when looking at the sea level rise scenario). Pluvial flooding and possible ‘sink’-areas in the city are not taken into account. Moreover, the lowest return period we have flood maps for is not low, compared to what is experienced in the city. Some areas of HCMC are flooded every year. Since this analysis used a flood with a 10-year return period as the flooding scenario with the highest recurrence interval we were not able to capture the relative differences in exposure to these yearly/bi-annual flooding events (and we hypothesize that poor people are relatively more exposed to these types of flooding than the general population). Despite these limitations, the analysis presented at the country and city-level can", "output": {"entities": {"named_data": ["PUMA data set"], "organization": ["PUMA"]}, "relations": [{"has_organization": {"head": "PUMA data set", "tail": "PUMA"}}]}, "_meta": {"entry_id": 1196, "dataset_name": "PUMA data set"}}
{"input": "using purchasing power parity rates. It comes from the World Development Indicators\n\nThe governance indicators come from the International Country Risk Guide (ICRG)\n\nData on the Gini coefficient were drawn from a more complete World Bank source\n\n100 km of ice-free coast) come from Gallup et al. (1999). Data on total forest area (km [2] ),\n\nprecipitation come from WDI (2010). Latitude (in absolute value), mean elevation (meters", "output": {"entities": {"named_data": ["International Country Risk Guide"], "organization": ["Gallup et al. (1999)"]}, "relations": [{"used_by": {"head": "International Country Risk Guide", "tail": "Gallup et al. (1999)"}}]}, "_meta": {"entry_id": 65, "dataset_name": "International Country Risk Guide"}}
{"input": "The recent analysis of the Community Resilience Survey conducted in 2022 has highlighted significant improvements in local disaster preparedness. The survey data, published by the Global Disaster Institute, was extensively utilized by various NGOs to develop strategies for climate adaptation. These efforts underscore the importance of collaboration among organizations aiming to enhance community resilience against climate-related hazards.", "output": {"entities": {"named_data": ["Community Resilience Survey"], "organization": ["Global Disaster Institute", "NGOs"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Community Resilience Survey", "tail": "Global Disaster Institute"}}, {"used_by": {"head": "Community Resilience Survey", "tail": "NGOs"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "climate resilience and disaster risk reduction"}}
{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.", "output": {"entities": {"named_data": ["ACLED dataset"], "organization": ["ACLED"]}, "relations": [{"has_organization": {"head": "ACLED dataset", "tail": "ACLED"}}]}, "_meta": {"entry_id": 1086, "dataset_name": "ACLED dataset"}}
{"input": "Using observations from NASA's OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). For a large sample of urban areas, we compare alternative trend estimation models and conclude that the template can be constructed from a simple model that estimates trends directly from OCO-2 data that are prefiltered to isolate local concentration anomalies.\n\nIn an additional exercise, we use our regression model results to compute expected emissions from\nurban areas. The regression residuals identify the directions and relative magnitudes of departures\nfrom expected values for individual areas. We convert the residuals to their emissions equivalents\nusing high-resolution gridded information from the EDGAR global database (Crippa et al. 2020). For\n1,306 urban areas with populations greater than 500,000, we find a rough balance between cities whose\nemissions are higher and lower than their expected values. Converting deviations to percentages of\nexpected values, we find that percent deviations are typically greater in absolute value for cities with\nlower-than-expected emissions.\n\n1 Recent research has used Google Traffic to infer vehicular emissions from high-resolution traffic congestion data for\nsome cities (Heger et al. 2018; Dasgupta, Lall, and Wheeler 2021). However, no currently available technology enables\ndirect estimation of global vehicular emissions at 25 km resolution.\n2 Household air conditioning is powered by fossil-fired home generators in many hot low-income areas where utility-scale\npower is either nonexistent or unreliable. For a detailed assessment, see Lam et al. (2019).\n\nThe design of OCO-2 supports comparative exercises like our analysis. It follows a sun-synchronous\nnear-polar orbit, crossing the equator in ascending mode around 1330 hours local time. This means\nthat the OCO-2 observations for our study are collected between 1200 and 1500 local time for all", "output": {"entities": {"named_data": ["EDGAR global database"], "organization": ["Crippa et al. 2020"]}, "relations": [{"used_by": {"head": "EDGAR global database", "tail": "Crippa et al. 2020"}}]}, "_meta": {"entry_id": 116, "dataset_name": "EDGAR global database"}}
{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 696, "dataset_name": "UNHCR Statistical Online Population Database"}}
{"input": "**the generation of user‐friendly evidence for efficient service delivery.** Routine surveys will be used to\ncollate data from target facilities, which will be triangulated through the existing management\ninformation system within the Health and Secondary Education Departments. The remote monitoring\nsystem within the SED uses technology‐based data management solutions with a dashboard to display\nthe broader analysis. The project will support the Health Department to adopt a similar system and cater\nfor the monitoring needs of the project. The project will also support an innovative, technology‐based\npilot intervention to track the presence of providers at HFs and assess their knowledge to design\nappropriate trainings. In addition, the project will train district‐ and cluster‐level education and health\nteams in data collection, management, analysis, and timely course correction. Process evaluations will be\nused to measure the quality of implementation. To tackle the challenges in evidence‐based decision\nmaking and improved accountability within the Health Department, the project will support the GoB to\n(a) establish or strengthen an HRH database, a health institutional database that routinely tracks facility", "output": {"entities": {"named_data": ["HRH database"], "organization": ["GoB", "Health Department"]}, "relations": [{"has_organization": {"head": "HRH database", "tail": "GoB"}}, {"used_by": {"head": "HRH database", "tail": "Health Department"}}]}, "_meta": {"entry_id": 867, "dataset_name": "HRH database"}}
{"input": "The Maternal Health Improvement Survey (MHIS) conducted in 2021 provides critical insights into the barriers faced by women during childbirth in Sub-Saharan Africa. This comprehensive dataset, published by the African Health Organization, details maternal outcomes across various regions and highlights significant disparities in care. Additionally, the Global Maternal Health Assessment Report 2020/2021 offers a broader perspective on global maternal health trends, although it is primarily used by academic institutions for research purposes. By comparing these two datasets, we can better understand the challenges and progress made in maternal health over the last few years.", "output": {"entities": {"named_data": ["Maternal Health Improvement Survey", "Global Maternal Health Assessment Report 2020/2021"], "organization": ["African Health Organization", "academic institutions"], "acronym": ["Maternal Health Improvement Survey", "Global Maternal Health Assessment Report"], "year": ["2021", "2020/2021"], "geography": ["Sub-Saharan Africa"]}, "relations": [{"has_acronym": {"head": "Maternal Health Improvement Survey", "tail": "MHIS"}}, {"has_timeframe": {"head": "Maternal Health Improvement Survey", "tail": "2021"}}, {"has_geography": {"head": "Maternal Health Improvement Survey", "tail": "Sub-Saharan Africa"}}, {"has_acronym": {"head": "Global Maternal Health Assessment Report 2020/2021", "tail": "Global Maternal Health Assessment Report"}}, {"has_timeframe": {"head": "Global Maternal Health Assessment Report 2020/2021", "tail": "2020/2021"}}, {"used_by": {"head": "Global Maternal Health Assessment Report 2020/2021", "tail": "academic institutions"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "health systems and maternal outcomes"}}
{"input": "The 2022 Economic Inclusivity Report (EIR) highlights significant advancements in financial access across various demographics. This dataset, produced by the Global Financial Insights Organization (GFIO), encompasses data from 2010 to 2021, reflecting changes in economic participation in several countries. Notably, the East African Financial Survey (EAFS) provides additional insights into the regional trends, with a focus on Kenya and Tanzania during the 2019-2021 timeframe. These datasets underscore the importance of understanding the financial landscape and its implications for policy-making. Source: GFIO analysis based on the EIR and EAFS.", "output": {"entities": {"named_data": ["Economic Inclusivity Report", "East African Financial Survey"], "organization": ["Global Financial Insights Organization"], "acronym": ["EIR", "EAFS"], "year": ["2022", "2010 to 2021", "2019-2021"], "geography": ["Kenya", "Tanzania"]}, "relations": [{"has_acronym": {"head": "Economic Inclusivity Report", "tail": "EIR"}}, {"has_timeframe": {"head": "Economic Inclusivity Report", "tail": "2022"}}, {"has_timeframe": {"head": "Economic Inclusivity Report", "tail": "2010 to 2021"}}, {"has_acronym": {"head": "East African Financial Survey", "tail": "EAFS"}}, {"has_timeframe": {"head": "East African Financial Survey", "tail": "2019-2021"}}, {"has_geography": {"head": "East African Financial Survey", "tail": "Kenya"}}, {"has_geography": {"head": "East African Financial Survey", "tail": "Tanzania"}}, {"has_organization": {"head": "Economic Inclusivity Report", "tail": "Global Financial Insights Organization"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "macroeconomic trends and financial inclusion"}}
{"input": "The recent analysis of the 2020 Global Forests Assessment (GFA) reveals significant trends in deforestation rates across various regions. Published by the Food and Agriculture Organization (FAO), the GFA provides critical insights for policymakers. In particular, the data has been extensively used by the World Wildlife Fund (WWF) to advocate for sustainable forest management practices. The GFA is instrumental in shaping conservation strategies, especially in tropical areas where forest loss is most acute.", "output": {"entities": {"named_data": ["2020 Global Forests Assessment", "GFA"], "organization": ["Food and Agriculture Organization", "FAO", "World Wildlife Fund", "WWF"], "acronym": ["Global Forests Assessment", "GFA"], "year": ["2020"], "geography": ["tropical areas"]}, "relations": [{"has_organization": {"head": "2020 Global Forests Assessment", "tail": "Food and Agriculture Organization"}}, {"used_by": {"head": "2020 Global Forests Assessment", "tail": "World Wildlife Fund"}}, {"has_acronym": {"head": "Global Forests Assessment", "tail": "GFA"}}, {"has_timeframe": {"head": "2020 Global Forests Assessment", "tail": "2020"}}, {"has_geography": {"head": "2020 Global Forests Assessment", "tail": "tropical areas"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "environmental sustainability and natural resources"}}
{"input": "use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes. The paper contributes to the growing literature on the local effects of mining. Much of the academic interest in natural resources is focused on country-wide effects, and this research discusses whether the discovery of natural resources is a blessing or a curse to the national economy. Natural resource dependence at the national level has been linked to worsening economic and political outcomes, such as weaker institutions, and more corruption and conflict (see Frankel 2012 and van der Ploeg 2011 for an overview). While all these effects can have household-level implications, fewer analyses have, thus far, analyzed the geographic dispersion of such impacts. A recent literature on the local and subnational effects of natural resources contributes to the understanding of such effects (for example Aragón and Rud 2013, 2015; Axbard et al., 2016; Benshaul-Tolonen 2018, 2019; Caselli and Michaels 2013; Corno and de Walque 2012; Fafchamps et al. 2016; Kotsadam and Tolonen 2016; Loyaza et al 2013; Michaels 2011; von der Goltz and Barnwal 2019; Wilson", "output": {"entities": {"named_data": ["Ghana Living Standard Survey (GLSS)"], "organization": ["the paper"]}, "relations": [{"used_by": {"head": "Ghana Living Standard Survey (GLSS)", "tail": "the paper"}}]}, "_meta": {"entry_id": 1161, "dataset_name": "Ghana Living Standard Survey (GLSS)"}}
{"input": "The recent findings from the Global Agriculture Monitoring Survey (GAMS) highlight significant trends in agricultural productivity across various regions. Produced by the Food and Agriculture Organization (FAO), this dataset provides insights into the challenges faced by farmers due to climate change. Furthermore, the World Food Programme (WFP) has utilized the GAMS data to inform their strategies on food security interventions in vulnerable communities. This collaboration underscores the importance of data-driven decision-making in addressing global food challenges.", "output": {"entities": {"named_data": ["Global Agriculture Monitoring Survey (GAMS)"], "organization": ["Food and Agriculture Organization", "World Food Programme"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Global Agriculture Monitoring Survey (GAMS)", "tail": "Food and Agriculture Organization"}}, {"used_by": {"head": "Global Agriculture Monitoring Survey (GAMS)", "tail": "World Food Programme"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "agriculture, food security, and nutrition"}}
{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner", "output": {"entities": {"named_data": ["Living Standards Measurement Studies"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Living Standards Measurement Studies", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1010, "dataset_name": "Living Standards Measurement Studies"}}
{"input": "**FIGURE 1. Mekong River Basin Commission Monitoring Station Locations**\n\n_Notes:_ Map shows location of water quality monitoring stations in Mekong River Basin Commission dataset.\n\nOur time-varying NPP data comes from the moderate resolution imaging spectroradiometer (MODIS), whose data starts in 2000. We use the annual MOD17A3 measures from 2000-2013 generated by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana (Zhao _et al_ ., 2005) which corrects for cloud contamination prevalent in MODIS land products ~~.~~ [3] Our interest is in estimating NPP from cropland, as opposed to natural forests or vegetation. To do so, we make use of a new and unique land cover dataset developed by the European Space Agency’s (ESA) Salt of the Earth 5", "output": {"entities": {"named_data": ["moderate resolution imaging spectroradiometer (MODIS)"], "organization": ["Numerical Terradynamic Simulation Group (NTSG)", "we"]}, "relations": [{"has_organization": {"head": "moderate resolution imaging spectroradiometer (MODIS)", "tail": "Numerical Terradynamic Simulation Group (NTSG)"}}, {"used_by": {"head": "moderate resolution imaging spectroradiometer (MODIS)", "tail": "we"}}]}, "_meta": {"entry_id": 1399, "dataset_name": "moderate resolution imaging spectroradiometer (MODIS)"}}
{"input": "The Urban Mobility Assessment Report 2022, produced by the International Transport Forum (ITF), provides comprehensive insights into the transportation dynamics affecting major cities. This dataset has been extensively used by local governments in developing strategies for sustainable urban mobility. Additionally, the Global Infrastructure Data Repository (GIDR), published by the World Bank, contains vital statistics on urban infrastructure investments from 2019 to 2021. City planners and development agencies have frequently cited this repository to inform their infrastructural developments. Both datasets are pivotal for understanding urban growth patterns and enhancing transportation planning.", "output": {"entities": {"named_data": ["Urban Mobility Assessment Report 2022", "Global Infrastructure Data Repository"], "organization": ["International Transport Forum", "World Bank", "local governments", "development agencies"], "acronym": ["ITF", "GIDR"], "year": ["2022", "2019 to 2021"], "geography": ["major cities"]}, "relations": [{"has_organization": {"head": "Urban Mobility Assessment Report 2022", "tail": "International Transport Forum"}}, {"used_by": {"head": "Urban Mobility Assessment Report 2022", "tail": "local governments"}}, {"has_acronym": {"head": "Urban Mobility Assessment Report 2022", "tail": "ITF"}}, {"has_organization": {"head": "Global Infrastructure Data Repository", "tail": "World Bank"}}, {"used_by": {"head": "Global Infrastructure Data Repository", "tail": "development agencies"}}, {"has_acronym": {"head": "Global Infrastructure Data Repository", "tail": "GIDR"}}, {"has_timeframe": {"head": "Global Infrastructure Data Repository", "tail": "2019 to 2021"}}, {"has_geography": {"head": "Urban Mobility Assessment Report 2022", "tail": "major cities"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "urban infrastructure and transportation planning"}}
{"input": "20 Note: Poverty is defined as the percent of the population living with less than $ 2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http: / / www. systemicpeace. org /). 4. 2. Lessons from Case Studies in Kenya, Tanzania, and Uganda Given the limits of cross ‐ country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014). Case Study # 1: The protracted refugee situations in Tanzania Tanzania has been known as a refugee ‐ hosting country for long due to its peaceful history and its location surrounded by conflict ‐ affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan ‐ African solidarity in the post ‐ independence periods from many African nations.", "output": {"entities": {"named_data": ["World Development Indicators database"], "organization": ["Center for Systematic Peace"]}, "relations": [{"used_by": {"head": "World Development Indicators database", "tail": "Center for Systematic Peace"}}]}, "_meta": {"entry_id": 929, "dataset_name": "World Development Indicators database"}}
{"input": "The Agricultural Productivity Survey (APS) conducted by the Food and Agriculture Organization (FAO) in 2022 provided crucial insights into crop yields across different regions. Data from this survey has been widely used by the Global Alliance for Improved Nutrition (GAIN) to analyze food security trends in East Africa, particularly in Ethiopia and Kenya. Additionally, the World Bank released the Rural Development Assessment Report 2021, which examines rural livelihoods and agricultural practices, and has been utilized by several local NGOs for policy formulation. Furthermore, the Regional Crop Statistics Database (RCSD) compiled by the African Development Bank (AfDB) covers a variety of agricultural indicators for the years 2018 to 2020, serving as an essential resource for researchers and policymakers alike.", "output": {"entities": {"named_data": ["Agricultural Productivity Survey", "Rural Development Assessment Report 2021", "Regional Crop Statistics Database"], "organization": ["Food and Agriculture Organization", "Global Alliance for Improved Nutrition", "World Bank", "African Development Bank"], "acronym": ["APS", "GAIN", "AfDB"], "year": ["2022", "2021", "2018 to 2020"], "geography": ["Ethiopia", "Kenya", "East Africa"]}, "relations": [{"has_organization": {"head": "Agricultural Productivity Survey", "tail": "Food and Agriculture Organization"}}, {"used_by": {"head": "Agricultural Productivity Survey", "tail": "Global Alliance for Improved Nutrition"}}, {"has_timeframe": {"head": "Agricultural Productivity Survey", "tail": "2022"}}, {"has_geography": {"head": "Agricultural Productivity Survey", "tail": "Ethiopia"}}, {"has_geography": {"head": "Agricultural Productivity Survey", "tail": "Kenya"}}, {"has_organization": {"head": "Rural Development Assessment Report 2021", "tail": "World Bank"}}, {"used_by": {"head": "Rural Development Assessment Report 2021", "tail": "local NGOs"}}, {"has_timeframe": {"head": "Rural Development Assessment Report 2021", "tail": "2021"}}, {"has_organization": {"head": "Regional Crop Statistics Database", "tail": "African Development Bank"}}, {"has_timeframe": {"head": "Regional Crop Statistics Database", "tail": "2018 to 2020"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 4, "domain": "agriculture, food security, and nutrition"}}
{"input": "The recent analysis of the Renewable Energy Access Survey conducted by the International Renewable Energy Agency (IRENA) highlights the significant advancements made in energy access across developing countries. This dataset, which captures comprehensive insights from various regions, is instrumental for policymakers and stakeholders aiming to implement effective energy solutions. The collaboration between IRENA and local governments has ensured that the findings reflect the real-time challenges and progress in the field of renewable energy.", "output": {"entities": {"named_data": ["Renewable Energy Access Survey"], "organization": ["International Renewable Energy Agency", "IRENA"], "acronym": [], "year": [], "geography": []}, "relations": [{"has_organization": {"head": "Renewable Energy Access Survey", "tail": "International Renewable Energy Agency"}}, {"used_by": {"head": "Renewable Energy Access Survey", "tail": "IRENA"}}]}, "_meta": {"category": "org_only", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "energy access and renewable transitions"}}
{"input": "The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.\n\nThe source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.\n\nWe additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).\n\nLastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).", "output": {"entities": {"named_data": ["Ethiopia Socioeconomic Survey (ESS 5)"], "organization": ["Ethiopia Statistical Service", "World Bank"]}, "relations": [{"has_organization": {"head": "Ethiopia Socioeconomic Survey (ESS 5)", "tail": "Ethiopia Statistical Service"}}, {"used_by": {"head": "Ethiopia Socioeconomic Survey (ESS 5)", "tail": "World Bank"}}]}, "_meta": {"entry_id": 51, "dataset_name": "Ethiopia Socioeconomic Survey (ESS 5)"}}
{"input": "The Maternal Health Assessment Report 2022 (MHAR) provides comprehensive insights into the progress of maternal health interventions across various regions. This dataset covers maternal health outcomes from 2018 to 2022 and emphasizes the disparities observed in rural versus urban settings. Analysis of the MHAR data has been conducted by numerous organizations, including local health ministries and international NGOs. The report highlights significant findings for countries such as Nigeria and Mexico, where urgent improvements in maternal healthcare systems are needed. In addition to geographical coverage, the dataset also outlines critical demographic trends relevant to policymakers working to enhance maternal health services.", "output": {"entities": {"named_data": ["Maternal Health Assessment Report 2022", "MHAR"], "organization": ["local health ministries", "international NGOs"], "acronym": ["MHAR"], "year": ["2022", "2018 to 2022"], "geography": ["Nigeria", "Mexico"]}, "relations": [{"has_acronym": {"head": "Maternal Health Assessment Report 2022", "tail": "MHAR"}}, {"has_timeframe": {"head": "MHAR", "tail": "2018 to 2022"}}, {"has_geography": {"head": "MHAR", "tail": "Nigeria"}}, {"has_geography": {"head": "MHAR", "tail": "Mexico"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "health systems and maternal outcomes"}}
{"input": "The recent Food Security Assessment Survey (FSAS) conducted by the Global Agriculture Initiative (GAI) in 2022 highlighted significant challenges faced by rural communities in Eastern Africa. This survey, which covers Kenya and Tanzania, aims to provide detailed insights into food availability and accessibility. Furthermore, the 2023 Agricultural Productivity Report (APR) has been utilized by several NGOs for policy development, focusing on sustainable practices and efficient resource management. It emphasizes the need for ongoing data collection to address food security issues in the region. Notably, the National Nutrition Database (NND) is an essential resource that compiles data from multiple sources but does not specifically cover any year-related findings.", "output": {"entities": {"named_data": ["Food Security Assessment Survey", "Agricultural Productivity Report", "National Nutrition Database"], "organization": ["Global Agriculture Initiative", "NGOs"], "acronym": ["FSAS", "APR", "NND"], "year": ["2022", "2023"], "geography": ["Eastern Africa", "Kenya", "Tanzania"]}, "relations": [{"has_acronym": {"head": "Food Security Assessment Survey", "tail": "FSAS"}}, {"has_timeframe": {"head": "Food Security Assessment Survey", "tail": "2022"}}, {"has_geography": {"head": "Food Security Assessment Survey", "tail": "Kenya"}}, {"has_geography": {"head": "Food Security Assessment Survey", "tail": "Tanzania"}}, {"has_acronym": {"head": "Agricultural Productivity Report", "tail": "APR"}}, {"has_timeframe": {"head": "Agricultural Productivity Report", "tail": "2023"}}, {"used_by": {"head": "Agricultural Productivity Report", "tail": "NGOs"}}, {"has_acronym": {"head": "National Nutrition Database", "tail": "NND"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "agriculture, food security, and nutrition"}}
{"input": "Over 11,000 indigenous Venezuelans currently residing in Brazil have been identified by UNHCR\nand partners. Compared to the overall Venezuelan population, they face compounded challenges\naccessing basic rights and services, including higher rates of food insecurity (58% vs 52%), health\ncare needs (75% vs 59%) and out of school children (21% vs 15%). [3] Language barriers and limited\nformal education of adults (indigenous refugees are 5 times more likely to have no formal\neducation when compared to the general Venezuelan population in Brazil), significantly affect their\nprospects for successful integration. [4]\n\n### Protection brief in graphics\n\nPopulation category Population category\n\nBreakdown by nationality\n\n_2 World Bank and UNHCR (2021), Integration of Venezuelan Refugees and Migrants in Brazil,_\n\n_[https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf;](https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf)_\n_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para Haitianos no Brasil,_\n_[https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf](https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf)_\n_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para pessoas refugiadas afegãs no Brasil_\nhttps://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-pessoas-afegas-no-brasil-junho-2024.pdf\n\n---\n[3] R4V (2023), Refugee and Migrant Needs Analysis, https://rmrp.r4v.info/rmna2023/](https://rmrp.r4v.info/rmna2023/) p. 85", "output": {"entities": {"named_data": ["Refugee and Migrant Needs Analysis"], "organization": ["R4V", "World Bank"]}, "relations": [{"has_organization": {"head": "Refugee and Migrant Needs Analysis", "tail": "R4V"}}, {"used_by": {"head": "Refugee and Migrant Needs Analysis", "tail": "World Bank"}}]}, "_meta": {"entry_id": 1327, "dataset_name": "Refugee and Migrant Needs Analysis"}}
{"input": "UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018).", "output": {"entities": {"named_data": ["JD ‐ HV database"], "organization": ["UNICEF"]}, "relations": [{"used_by": {"head": "JD ‐ HV database", "tail": "UNICEF"}}]}, "_meta": {"entry_id": 976, "dataset_name": "JD ‐ HV database"}}
{"input": "Data sources: UNHCR PRIMES, UNHCR Resettlement Statistics Report.   For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhc\n\n**1,729**\n\n**154**\n\n**57**\n\n**53**\n\n**34**\n\n**13**\n\n**12**\n\n**4**\n\n**3**\n\n**2**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**TUR**\n\n**ZAM**\n\n**ETH**\n\n**UGA**\n\n**ANG**\n\n**AFG**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**ETH**\n\n**PAK**\n\n**TUR**\n\n**CAR**\n\n**BOT**\n\n**ERT**\n\n**3,619**\n\n**263**\n\n**235**\n\n**220**\n\n**40**\n\n**36**\n\n**19**\n\n**14**\n\n**8**\n\n**8**", "output": {"entities": {"named_data": ["UNHCR Resettlement Statistics Report"], "organization": ["UNHCR"]}, "relations": [{"has_organization": {"head": "UNHCR Resettlement Statistics Report", "tail": "UNHCR"}}]}, "_meta": {"entry_id": 457, "dataset_name": "UNHCR Resettlement Statistics Report"}}
{"input": "The COVID-19 Panel Phone Survey of Households sample is nationally representative, representative of Bamako, and representative of both urban and rural areas.\n\nThe EHCVM sample itself covered 8,390 households across Mali and is nationally representative, representative of Bamako, and representative of both urban and rural areas. The survey relies on a multi-module instrument covering topics including a household's socio-economic characteristics, time use, production activities, and welfare indicators such as consumption expenditure and food security.\n\nuse sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates\n\n\nin the COVID-19 Panel Phone Survey of Households. These sampling weights are applied both in our\n\nIn this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest. The FIES aims to measure food insecurity based on the direct experiences of people relating to food security (Ballard _et_ _al._, 2013; Smith _et_ _al._, 2017).", "output": {"entities": {"named_data": ["Food Insecurity Experience Scale (FIES)"], "organization": ["FAO", "we"]}, "relations": [{"has_organization": {"head": "Food Insecurity Experience Scale (FIES)", "tail": "FAO"}}, {"used_by": {"head": "Food Insecurity Experience Scale (FIES)", "tail": "we"}}]}, "_meta": {"entry_id": 23, "dataset_name": "Food Insecurity Experience Scale (FIES)"}}
{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"entities": {"named_data": ["Murdock ’ s Atlas"], "organization": ["Zylkin"]}, "relations": [{"used_by": {"head": "Murdock ’ s Atlas", "tail": "Zylkin"}}]}, "_meta": {"entry_id": 747, "dataset_name": "Murdock ’ s Atlas"}}
{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51", "output": {"entities": {"named_data": ["Ethnic Power Relations Data Set Family"], "organization": ["World Bank"]}, "relations": [{"used_by": {"head": "Ethnic Power Relations Data Set Family", "tail": "World Bank"}}]}, "_meta": {"entry_id": 6, "dataset_name": "Ethnic Power Relations Data Set Family"}}
{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "organization": ["World Bank"]}, "relations": [{"has_organization": {"head": "Youth Employment Inventory database", "tail": "World Bank"}}]}, "_meta": {"entry_id": 505, "dataset_name": "Youth Employment Inventory database"}}
{"input": "The Global Education Assessment Report 2022, published by the International Institute for Educational Planning (IIEP), provides valuable insights into learning outcomes across various nations. This report, which focuses on the performance of students in primary education, has been extensively used by UNESCO to inform policy interventions aimed at improving educational standards. Additionally, the 2023 Enrollment Trends Survey from the World Education Database (WED) offers a comprehensive look at school enrollment patterns in over 50 countries. The World Bank has cited the WED survey to analyze trends in access to education during the ongoing global pandemic.", "output": {"entities": {"named_data": ["Global Education Assessment Report 2022", "Enrollment Trends Survey"], "organization": ["International Institute for Educational Planning", "UNESCO", "World Education Database", "World Bank"], "acronym": ["IIEP", "WED"], "year": ["2022", "2023"], "geography": ["50 countries"]}, "relations": [{"has_organization": {"head": "Global Education Assessment Report 2022", "tail": "International Institute for Educational Planning"}}, {"used_by": {"head": "Global Education Assessment Report 2022", "tail": "UNESCO"}}, {"has_acronym": {"head": "Global Education Assessment Report 2022", "tail": "IIEP"}}, {"has_timeframe": {"head": "Global Education Assessment Report 2022", "tail": "2022"}}, {"has_organization": {"head": "Enrollment Trends Survey", "tail": "World Education Database"}}, {"used_by": {"head": "Enrollment Trends Survey", "tail": "World Bank"}}, {"has_acronym": {"head": "Enrollment Trends Survey", "tail": "WED"}}, {"has_timeframe": {"head": "Enrollment Trends Survey", "tail": "2023"}}, {"has_geography": {"head": "Enrollment Trends Survey", "tail": "50 countries"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "education, learning achievement, and school enrollment"}}
{"input": "The Population Growth Assessment Report (PGAR) 2022, published by the Global Demographics Institute, provides insightful data on fertility trends across various regions. This report has been extensively used by UNICEF to inform its programs aimed at enhancing maternal health. Additionally, the International Population Foundation released the National Fertility Survey (NFS) 2021, which focuses on birth rates in Indonesia. The NFS has been referenced by several local NGOs, including the Indonesian Women's Association, to guide their initiatives in reproductive health education. Both datasets are crucial for understanding demographic shifts and addressing the challenges associated with population growth.", "output": {"entities": {"named_data": ["Population Growth Assessment Report", "National Fertility Survey"], "organization": ["Global Demographics Institute", "UNICEF", "International Population Foundation", "Indonesian Women's Association"], "acronym": ["PGAR", "NFS"], "year": ["2022", "2021"], "geography": ["Indonesia"]}, "relations": [{"has_organization": {"head": "Population Growth Assessment Report", "tail": "Global Demographics Institute"}}, {"used_by": {"head": "Population Growth Assessment Report", "tail": "UNICEF"}}, {"has_acronym": {"head": "Population Growth Assessment Report", "tail": "PGAR"}}, {"has_timeframe": {"head": "Population Growth Assessment Report", "tail": "2022"}}, {"has_organization": {"head": "National Fertility Survey", "tail": "International Population Foundation"}}, {"used_by": {"head": "National Fertility Survey", "tail": "Indonesian Women's Association"}}, {"has_acronym": {"head": "National Fertility Survey", "tail": "NFS"}}, {"has_timeframe": {"head": "National Fertility Survey", "tail": "2021"}}, {"has_geography": {"head": "National Fertility Survey", "tail": "Indonesia"}}]}, "_meta": {"category": "chained_multi", "source": "llm_gpt-4o-mini", "num_mentions": 3, "domain": "demographics, fertility, and population growth"}}
{"input": "The 2022 Global Food Security Assessment Report provides critical insights into the state of food insecurity across various regions. This report, published by the International Food Policy Research Institute (IFPRI), highlights the urgent need for policy interventions to address the ongoing challenges faced by vulnerable populations. It serves as a vital resource for policymakers and researchers engaged in efforts to enhance food security.", "output": {"entities": {"named_data": ["Global Food Security Assessment Report"], "organization": ["International Food Policy Research Institute"], "acronym": ["IFPRI"], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Global Food Security Assessment Report", "tail": "International Food Policy Research Institute"}}, {"has_acronym": {"head": "Global Food Security Assessment Report", "tail": "IFPRI"}}, {"has_timeframe": {"head": "Global Food Security Assessment Report", "tail": "2022"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "agriculture, food security, and nutrition"}}
{"input": "The recent analysis of the Labor Market Trends Report (LMTR) 2020 indicates a significant shift in employment patterns across various sectors in the Asia-Pacific region. The data, collected from multiple sources including national statistical agencies, sheds light on the evolving landscape of job opportunities and skills demand from 2019 to 2021. Additionally, the Skills Development Index (SDI) for 2021 highlights the urgent need for targeted training programs in response to changing labor market requirements. This index provides valuable insights into skill shortages in countries like Indonesia and Vietnam, making it a vital resource for policymakers aiming to enhance workforce capabilities. Source: Asia-Pacific Employment Network based on Labor Market Trends Report.", "output": {"entities": {"named_data": ["Labor Market Trends Report", "Skills Development Index"], "organization": ["Asia-Pacific Employment Network"], "acronym": ["LMTR", "SDI"], "year": ["2020", "2019 to 2021", "2021"], "geography": ["Asia-Pacific", "Indonesia", "Vietnam"]}, "relations": [{"has_acronym": {"head": "Labor Market Trends Report", "tail": "LMTR"}}, {"has_timeframe": {"head": "Labor Market Trends Report", "tail": "2020"}}, {"has_geography": {"head": "Labor Market Trends Report", "tail": "Asia-Pacific"}}, {"has_acronym": {"head": "Skills Development Index", "tail": "SDI"}}, {"has_timeframe": {"head": "Skills Development Index", "tail": "2021"}}, {"has_geography": {"head": "Skills Development Index", "tail": "Indonesia"}}, {"has_geography": {"head": "Skills Development Index", "tail": "Vietnam"}}, {"used_by": {"head": "Skills Development Index", "tail": "Asia-Pacific Employment Network"}}]}, "_meta": {"category": "metadata_rich", "source": "llm_gpt-4o-mini", "num_mentions": 2, "domain": "employment, labor markets, and skills development"}}
{"input": "The Urban Infrastructure Assessment Report 2022, published by the Global Development Organization, provides crucial insights into the transportation systems across various metropolitan regions. This report analyzes the current state of urban transport networks and offers recommendations for improvement based on comprehensive data collection.", "output": {"entities": {"named_data": ["Urban Infrastructure Assessment Report 2022"], "organization": ["Global Development Organization"], "acronym": [], "year": ["2022"], "geography": []}, "relations": [{"has_organization": {"head": "Urban Infrastructure Assessment Report 2022", "tail": "Global Development Organization"}}]}, "_meta": {"category": "single_simple", "source": "llm_gpt-4o-mini", "num_mentions": 1, "domain": "urban infrastructure and transportation planning"}}
{"input": "To construct a water-resource indicator, we draw on two sources of information. The first is an estimated geographic distribution of excess demand for water resources (surface and sub-surface) in Vörösmarty, et al. (2000). We use GIS to compute the total population residing in excess-demand areas identified by this research. The second information source is a database of deaths and injuries from floods maintained by the Centre for Research on the Epidemiology of Disasters (CRED, Université Catholique de Louvain). For each of the Bank's partner countries, we calculate the sum of deaths and injuries for all recorded floods since 1960. In constructing an indicator for flood damage, we weight deaths to injuries in the ratio 50:1. Using equal weights, we combine our indicators for demand pressure and floods into a composite indicator of water-related problems. [4] We derive our indicator for environmental policy and institutional problems from two sources. The first is the World Bank's Country Policy and Institutional Assessment (CPIA) database, which rates environmental policies and institutions on a numerical scale of 1 (the lowest) to 6. For this exercise, we reverse the scaling (1 becomes the highest) and normalize the ratings so that countries with the greatest problems score", "output": {"entities": {"named_data": ["Country Policy and Institutional Assessment (CPIA) database"], "organization": ["World Bank", "the Bank"]}, "relations": [{"has_organization": {"head": "Country Policy and Institutional Assessment (CPIA) database", "tail": "World Bank"}}, {"used_by": {"head": "Country Policy and Institutional Assessment (CPIA) database", "tail": "the Bank"}}]}, "_meta": {"entry_id": 1185, "dataset_name": "Country Policy and Institutional Assessment (CPIA) database"}}
{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "organization": ["The Demographic and Health Surveys"]}, "relations": [{"has_organization": {"head": "Demographic and Health Surveys", "tail": "The Demographic and Health Surveys"}}]}, "_meta": {"entry_id": 435, "dataset_name": "Demographic and Health Surveys"}}
{"input": "Using data from the Armenia Land Tenure and Area study - a study designed specifically for this analysis - we compare the implications of the use of a proxy respondent versus the recommended self-respondent approach and the use of aggregated land data versus parcel-level land data (recommended).\n\nThe ALTA study was implemented with the technical guidance of the lead author by the International Centre for Agribusiness Research and Education (ICARE) in partnership with the Statistical Committee of the Republic of Armenia (ArmStat) and with the financial support of the 50x2030 Initiative.\n\nThe paper is organized as follows: section II describes the land tenure system in Armenia, providing context for the results that follow; Section III describes the ALTA study design and data; Section IV discusses SDG indicator computation and methods; Section V presents the results; and Section VI concludes.\n\nThe sample of the ALTA study consists of 1,200 households, scientifically selected from 100 urban and rural enumeration areas (EAs) covering both agricultural and non-agricultural households.\n\nIn addition to the module on land tenure, the ALTA questionnaire included a brief set of modules capturing\nindividual socio-demographic characteristics, educational attainment, employment status, and agricultural activity. [6]\nAn additional focus of the ALTA study was the validation of measurement approaches to land area estimation. To\nthis end, both Arms 1 and 3 included a module on land area measurement that incorporated GPS and satellite\nimagery-based parcel-level area measurement. A series of cognitive interviews were conducted to ensure that the\nland tenure questions were translated as intended from English to Armenian, as well as to understand how the\ninterpretation of questions varied across individuals, with a view to understanding male versus female", "output": {"entities": {"named_data": ["Armenia Land Tenure and Area study"], "organization": ["International Centre for Agribusiness Research and Education (ICARE)", "we"]}, "relations": [{"has_organization": {"head": "Armenia Land Tenure and Area study", "tail": "International Centre for Agribusiness Research and Education (ICARE)"}}, {"used_by": {"head": "Armenia Land Tenure and Area study", "tail": "we"}}]}, "_meta": {"entry_id": 775, "dataset_name": "Armenia Land Tenure and Area study"}}
{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided.", "output": {"entities": {"named_data": ["1998 Census of Population"], "organization": ["Government of Pakistan", "our research team"]}, "relations": [{"has_organization": {"head": "1998 Census of Population", "tail": "Government of Pakistan"}}, {"used_by": {"head": "1998 Census of Population", "tail": "our research team"}}]}, "_meta": {"entry_id": 968, "dataset_name": "1998 Census of Population"}}