Name: Global Biodiversity Data
Creator: World Bank Group

The BOOST initiative is a Bank-wide collaborative effort launched in 2010 to facilitate access to budget data and promote effective use for improved decision-making processes, transparency and accountability.

BOOST - Public Expenditure Database

This collection contains datasets that may be useful for analysis, response or modelling for the Coronavirus (COVID-19) pandemic.

Coronavirus (COVID-19) Related Datasets

The Data360 initiative aims to integrate high-value data and knowledge curated in the Bank’s verticals into a user-centered data platform. 

Data 360

The Development Data Partnership platform facilitates formation of public-private partnerships for solving development challenges with technology. Specifically, the Development Data Partnership Working Group organizes opportunities for WBG staff to work with technology companies who offer pro-bono data and resources.

Development Data Partnership

This collection contains discrepancy indexes for UN COMTRADE data that aims to capture both the frequency of missed reporting and its impact on overall recorded trade value

Discrepancy Index

A collection of datasets produced through partnership between the World Bank and the European Space Agency (ESA) Earth Observation for Sustainable Development (EO4SD) initiative.

Earth Observation for Sustainable Development

Open data and analytics for a sustainable energy future.

Energy Data Info

This collection features datasets to support building policies, institutions and investments for inclusive and sustainable growth.

Equitable Growth, Finance and Institutions

Project Page: https://worldbank.github.io/GEEST/README.html 

As part of the “Geospatial Assessment of Women Employment and Business Opportunities in the Energy Sector” project, open-source Gender-related spatial data was collected for 31 Small Island Developing States (SIDS) across the globe, resulting in curated and thoroughly documented geodatabases (GDBs) that are now ready to be explored! 
Fifty-nine spatial layers were identified and then researched for each country, covering the following categories: Demographics and Population | Renewable Energy | Energy Access | Education | Jobs and Finance | Digital Inclusion | Transportation | Safety | Amenities | Climate/Earth | Law/Policy/Government. 


However, not every country GDB contains all 59 data layers, as this was dependent on the availability of open-source data in each SIDS. Users are encouraged to check the accompanying metadata excel file for more information on the datasets in each GDB, the vintage, and the

Gender Spatial Data - Small Island States

A collection of datasets that contain geospatial data. These datasets have one or more resources in a geospatial data format.

Geospatial Datasets

Collection of useful datasets from IMF made available to Bank staff via <a href=http://mydatabank.worldbank.org>myDataBank</a>

IMF Dataset

A collection of datasets to help finance the global infrastructure gap.

Infrastructure Finance Datasets

Data and dataset subscriptions managed by the Library Network on agriculture, business, economics, energy, finance, health, risk, water, and more..

Library Databases

The LinkedIn-World Bank partnership offers data to help governments and researchers understand rapidly evolving labor markets with detailed and dynamic data. It allows leaders to benchmark and compare labor markets across the world; analyze skills, occupations, migration, and industries; and leverage real-time data to make policy changes.Visualizations for many of these data are available at <a href=https://linkedindata.worldbank.org/>linkedindata.worldbank.org</a>

LinkedIn Data for Development

Collection of merchandise trade data available to World Bank staff.

Merchandise Trade Data

This collection includes datasets associated with research organized under the Bank, <a href=http://prwp.worldbank.org target='_blank' title='Opens in a new window'>Policy Research Working Papers</a>.

Policy Research Working Papers

This collection contains data on disaster and climate risk assessments curated by the Global Facility for Disaster Reduction and Recovery (GFDRR).

Risk Data Library

This collection presents software tools and models developed by the World Bank to support monitoring, planning, programming and the economic evaluation of roads and highways works.

Road Software Tools

This spatial database, a product of the South Asia Region Chief Economist's (SARCE) Office, presents data collected from official sources and harmonized into different spatial levels. Data includes economic and environmental data across 12 topic areas, and includes data from four countries in South Asia (Bangladesh, Bhutan, India, and Nepal) for both 2001 and 2011.

SARCE Spatial Database

A collection of country Small Area Estimation (SAE) and Poverty Mapping that contains papers, shapefiles, and datasets produced through partnerships between the World Bank and country statistics offices.

Small area estimates / Poverty mapping

This collection consists of datasets compiled and created by the World Bank Data Lab in response to a request by the Syria Economic Monitor Team to explore alternative data sources to better understand changing trends in trade and economic activity, focusing on: surface cross-border transport routes, changes in observed agricultural production, observed nighttime lights, and reported conflict.

Syria Economic Monitor

A collection of relevant datasets to support operations in Ukraine.

Ukraine datasets

Catalogue lists all the data produced under the partnership contracts between the World Bank Group and the European Space Agency

World Bank (WBG) - European Space Agency (ESA) Database

A collection of datasets to help achieve universal access to reliable and fast internet services for all.

World Bank Digital Development Data

A collection of datasets to help achieve sustainable energy for all.

World Bank Energy Datasets

A collection of datasets to help achieve sustainable mobility for all.

World Bank Transport Data

From Data to a Water-Secure World for All. World Bank Water Data surrounding the three pillars of Sustain Water Resources, Deliver Services and Build Resilience.

World Bank Water Data

The World Development Report 2021: Data for Better Lives explores the tremendous potential of the changing data landscape to improve the lives of poor people, while also acknowledging its potential to open back doors that can harm individuals, businesses, and societies. To address this tension between the helpful and harmful potential of data, this Report calls for a new social contract that enables the use and reuse of data to create economic and social value, ensures equitable access to that value, and fosters trust that data will not be misused in harmful ways. The data used in the WDR2021 are curated as a collection here.

World Development Report 2021

The World Development Report 2022: Finance for an Equitable Recovery examines the central role of finance in the economic recovery from the COVID-19 pandemic. In doing so, it looks into the consequences of the crisis most likely to affect emerging economies and proposes a set of policies to mitigate the interconnected financial risks stemming from the pandemic and to steer economies toward a sustainable and equitable recovery. The data used in the WDR2022 are curated as a collection here.

World Development Report 2022

The World Settlement Footprint Collection includes country datasets describing the evolution of the built-area across the globe at a ~30m resolution.

World Settlement Footprint

The biodiversity data species occurrence region data are overlaid with international transboundary river basins available from the TFDD version 2022-02-24.&nbsp;The result is biodiversity information summarized at a basin level dataset with the following indicators:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Continent : continent of international river basin area [source : TFDD]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Continent_Code : continent code of international river basin area [source : TFDD]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Basin_Name : international river TFDD basin name [source : TFDD]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;BCODE : Four-letter TFDD basin code [source : TFDD]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Area_km2 : Area of the river basin in kilometers squared (km2). The area was calculated in ESRI ArcMap using the World Cylindrical Equal Area Projection. [source : TFDD]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemics at 100% : count of endemic species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with small occurrence regions (50x50) : count of small area (50 km2 x 50 km2) species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with extinction threat probability gte 80 : count of species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with small occurrence regions (50x50) : count of endemic small area (50 km2 x 50 km2) species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with small occurrence regions (50x50) and extinction threat probability gte 80 : count of small area (50 km2 x 50 km2) species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with extinction threat probability gte 80 : count of endemic species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with small occurrence regions (50x50) and extinction threat probability gte 80 : count of endemic small area (50 km2 x 50 km2) species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of total species : count of species occurrence region(s) [source : Dasgupta et al. 2024 a,b]References:Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. (2024a).&nbsp;Revisiting Global Biodiversity: A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility&nbsp;(No. 10821). The World Bank.Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. (2024b).&nbsp;Estimating Extinction Threats with Species Occurrence Data from the Global Biodiversity Information Facility&nbsp;(No. 10822). The World Bank.McCracken, Melissa, and Aaron T. Wolf. 2019. “Updating the Register of International River Basins of the World.” International Journal of Water Resources Development 35 (5): 732–82. <a href="https://doi.org/10.1080/07900627.2019.1572497" rel="noopener noreferrer" target="_blank">https://doi.org/10.1080/07900627.2019.1572497</a>. “Product of the Transboundary Freshwater Dispute Database, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University. Additional information about the TFDD can be found at: http://transboundarywaters.science.oregonstate.edu.”

Species Richness and Count of Species at Risk in Transboundary River Basins

The biodiversity data species occurrence region data are overlaid with administrative and Exclusive Economic boundaries.&nbsp; The result is biodiversity information summarized at the national economy level dataset with the following indicators:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;administrative entity : name of administrative entity·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;admin_code : 3 letter administrative entity code·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;land and/or EEZ : area represents land or merged area of land and Exclusive Economic Zone (EEZ)·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemics at 100% : count of endemic species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with small occurrence regions (50x50) : count of small area (50 km2 x 50 km2) species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with extinction threat probability gte 80 : count of species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with small occurrence regions (50x50) : count of endemic small area (50 km2 x 50 km2) species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with small occurrence regions (50x50) and extinction threat probability gte 80 : count of small area (50 km2 x 50 km2) species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with extinction threat probability gte 80 : count of endemic species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with small occurrence regions (50x50) and extinction threat probability gte 80 : count of endemic small area (50 km2 x 50 km2) species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of total species : count of species occurrence region(s) [source : Dasgupta et al. 2024 a,b]References:Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. (2024a).&nbsp;Revisiting Global Biodiversity: A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility&nbsp;(No. 10821). The World Bank.Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. (2024b).&nbsp;Estimating Extinction Threats with Species Occurrence Data from the Global Biodiversity Information Facility&nbsp;(No. 10822). The World Bank.Flanders Marine Institute. 2019. Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Available online at htps://www.marineregions.org/. htps://doi.org/10.14284/386

National level dataset of Species Endemism, Small Occurrence and Species at Risk data

We describe below the data sources and provide an overview of the specific variables that are constructed for the analysis in this paper entitled, “Revisiting Global Biodiversity:&nbsp;A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility” by Susmita Dasgupta, Brian Blankespoor, and David Wheeler (2024). Data are in Comma-Separated Values format. Species·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;species = species with occurrence data. GBIF and authors’ calculation.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;grid_id = unique grid identifier at 0.25 decimal degrees.

Gridded level Database of Global Biodiversity

Surveyed fish species in coastal BangladeshThe 29 fish species included in a study by Blankespoor et al. (2025) have been drawn from Dasgupta et al. (2024), who use recent advances in machine-based pattern recognition to estimate occurrence maps for over 600,000 species reported by the Global Biodiversity Information Facility. During the period August – September 2024, the study team surveyed catch and price records for these species in five wholesale markets located near our salinity monitoring sites. The dataset displays the species’ scientific names, along with their common names, mean prices and total catch volumes during the recorded period, and maximum water salinity tolerances drawn from river monitoring stations spread across the southwest coastal region of Bangladesh (see Blankespoor et al. 2025 for more details). Variables:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Species = species scientific names from Dasgupta et al. (2024) considered in the survey ·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;english_name = common name of species·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Mean Wholesale Price (taka/kg) = mean wholesale price in local currency (taka/kg) for the 29 species during the months of March, May, July and October 2023·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Total Catch Quantity (kg) = total catch quantity (kg) during the recorded period·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Maximum Salinity (ppt) = maximum salinity (ppt) References:Blankespoor, B., Dasgupta, S., Huq, M., Khan, Z. H., Mustafa, M. G., &amp; Wheeler, D. (2025). Fishing and Climate Change in Coastal Bangladesh. (No. 11048). The World Bank. &nbsp;Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. J. (2024). Revisiting Global Biodiversity: A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility (No. 10821). The World Bank &nbsp;

Surveyed Fish Species in Bangladesh : Price, Catch Quantity and Maximum Salinity Tolerance

Human coexistence indicators for terrestrial species encompass several critical factors that reflect the impact of human activities on terrestrial ecosystems related to population dynamics, infrastructure, and land use patterns. Specifically, these indicators include metrics on population density, the percentage of land use influenced by humans, and agricultural GDP. Additional indicators involve nighttime light intensity, the presence of roads, and travel times to cities and ports.The Human Coexistence Index is based on global indicators of terrestrial influence.&nbsp;The aim is to gain insight into the distribution of human influenced areas across the world, which can provide insight to the distribution of species occurrence regions.&nbsp;The indicators are constructed to measure areas of human existence considering the following steps:1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Global grids were built from raw source data, where values were harmonized by aggregation or resampling as necessary to achieve a uniform cell size of 0.25 decimal degrees across the world.2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Each indicator was normalized to a scale of 1 to 100 by dividing its values by the maximum value found within each grid (carefully avoiding division by zero). The value of 100 represents the highest value of human influence for that indicator.3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The construction of the composite indicators for marine is summarized with the geometric mean at the harmonized cell size of 0.25 decimal degrees. The main advantage of the geometric mean incorporates information from each indicator or if the values have large fluctuations.&nbsp;Indicator data derived from sources below:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Population density : Local estimates of population density (WorldPop 2018). The variable name is popden2010.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Local estimates of Agricultural GDP : Local estimates of Agricultural Gross Domestic Product (Ru and Blankespoor et al., 2024). The variable name is aggdp2010.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Percent human land use : HYBMAP: Global hybrid land-cover maps, v5, (Zhu et al. 2024) landcover classes reclassified into human dominated landcover. The variable name is hmnlc2020.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Mining density : Mining density (Maus et al. 2020). The variable name is mineden.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Road density : Global Roads Inventory Project (Meijer et al. 2018). The variable name is roadden.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Minimum travel time to cities : Travel time to cities of sizes (50K, 100K, 200K, 500K, 1M, and 5M) (Nelson 2019). The variable name is tt_cities_over_5k.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Minimum travel time to ports : Travel time to ports (large, medium, small, very small and any) (Nelson 2019). The variable name is tt_ports_large.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Nighttime lights : Harmonized Nighttime Lights, v8 (Li et al. 2020). The variable name is ntlharm2020.Composite indicator:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Terrestrial Human Coexistence Index (HCI): terrestrial_hci is the geometric mean of the following indicators: population density, local estimates of Agricultural GDP, Percent human land use, mining density, road density, miimu travel time to cities, and nighttime lights. References:Li, X., Zhou, Y., Zhao, M., &amp; Zhao, X. (2020). Harmonization of DMSP and VIIRS nighttime light data from 1992-2020 at the global scale. figshare. Dataset.Maus, V., Giljum, S., Gutschlhofer, J., da Silva, D. M., Probst, M., Gass, S. L., ... &amp; McCallum, I. (2020). A global-scale data set of mining areas.&nbsp;Scientific data,&nbsp;7(1), 289.Meijer, J. R., Huijbregts, M. A., Schotten, K. C., &amp; Schipper, A. M. (2018). Global patterns of current and future road infrastructure. Environmental Research Letters, 13(6), 064006.Nelson, Andy (2019). Travel time to cities and ports in the year 2015. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7638134.v4Sonter, L. J., Dade, M. C., Watson, J. E., &amp; Valenta, R. K. (2020). Renewable energy production will exacerbate mining threats to biodiversity.&nbsp;Nature communications,&nbsp;11(1), 4174.Ru, Y. and Blankespoor, B., Wood-Sichra, U., Thomas, T. S., You, L., &amp; Kalvelagen, E. (2022). Estimating local agricultural GDP across the world. Earth System Science Data Discussions, 2022, 1-36.Weiss, D. J., Nelson, A., Gibson, H. S., Temperley, W., Peedell, S., Lieber, A., &amp; Gething, P. W. (2018). A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688), 333-336.WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University. (2018). Global high resolution population denominators project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). <a href="https://dx.doi.org/10.5258/SOTON/WP00647" rel="noopener noreferrer" target="_blank">https://dx.doi.org/10.5258/SOTON/WP00647</a>Zaichun Zhu, Yuhang Luo, Weiqing Zhao, Muyi Li, Jun Chen, &amp; Pengjun Zhao. (2024). HYBMAP: Global hybrid land-cover maps from 2000 to 2020 with a resolution of approximately 1 km.&nbsp;Version 5. <a href="https://doi.org/10.5281/zenodo.10488191" rel="noopener noreferrer" target="_blank">https://doi.org/10.5281/zenodo.10488191</a>

Gridded level database of composite Terrestrial Human coexistence Index and individual indicators

Human coexistence indicators for freshwater species encompass several critical factors that reflect the impact of human activities on freshwater ecosystems. Important metrics include the population density within the catchment, economic activities related to water use, and connectivity of the river network. Specifically, these indicators consist of measures related to population density from WorldPop (2018) and river related information on water consumption, road density, sedimentation, degree of fragmentation and connectivity derived from the global dataset with over 12 million Kilometers of rivers from Grill et al. (2019).The Human Coexistence Index is based on global indicators of freshwater influence.&nbsp;The aim is to gain insight into the distribution of human influenced areas across the world, which can provide insight to the distribution of species occurrence regions.&nbsp;The indicators are constructed to measure areas of human existence considering the following steps:1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Global grids were built from raw source data, where values were harmonized by aggregation or resampling as necessary to achieve a uniform cell size of 0.25 decimal degrees across the world.2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Each indicator was normalized to a scale of 1 to 100 by dividing its values by the maximum value found within each grid (carefully avoiding division by zero). The value of 100 represents the highest value of human influence for that indicator.3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The construction of the composite indicators for marine is summarized with the geometric mean at the harmonized cell size of 0.25 decimal degrees. The main advantage of the geometric mean incorporates information from each indicator or if the values have large fluctuations.&nbsp;Indicator data derived from sources below:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Population density : Local estimates of population density (WorldPop 2018). The variable name is popden2010·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Degree of Fragmentation : Maximum degree of Fragmentation assigned to the river network within the pixel (Grill, Lehner et al. 2019). The variable name is maxDOF.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Road density : Median road density assigned to the river network within the pixel (Grill, Lehner et al. 2019). The variable name is maxRDD.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Connectivity Status Index : Minimum Connectivity Status Index assigned to the river network within the pixel (Grill, Lehner et al. 2019). The variable name is minCSI.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Sediment Trapping : Maximum sediment trapping assigned to the river network within the pixel (Grill, Lehner et al. 2019). The variable name is maxSED.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Water use : Maximum water use assigned to the river network within the pixel (Grill, Lehner et al. 2019). The variable name is meanUSE. Composite indicator:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Freshwater Human Coexistence Index (HCI): freshwater_hci is the geometric mean of population density, maximum road density, mean water use and minimum connectivity status index.&nbsp;

Gridded level database of composite Freshwater Human coexistence Index and individual indicators

Human coexistence indicators for marine species encompass several critical factors that reflect the impact of human activities on marine ecosystems.&nbsp;Important metrics include the coastal population, economic activities, and the degradation of ocean quality due to plastic pollution.The Human Coexistence Index is based on global indicators of marine influence.&nbsp;The aim is to gain insight into the distribution of human influenced areas across the world, which can provide insight to the distribution of species occurrence regions.&nbsp;The indicators are constructed to measure areas of human existence considering the following steps:1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Global grids were built from raw source data, where values were harmonized by aggregation or resampling as necessary to achieve a uniform cell size of 0.25 decimal degrees across the world.2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Each indicator was normalized to a scale of 1 to 100 by dividing its values by the maximum value found within each grid (carefully avoiding division by zero). The value of 100 represents the highest value of human influence for that indicator.3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The construction of the composite indicators for marine is summarized with the geometric mean at the harmonized cell size of 0.25 decimal degrees. The main advantage of the geometric mean incorporates information from each indicator or if the values have large fluctuations.&nbsp;Indicator data derived from sources below:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fishing intensity 1: Near-real-time recording of <a href="https://globalfishingwatch.org/data-download/datasets/public-fishing-effort" rel="noopener noreferrer" target="_blank">fishing effort</a> by vessels using the Automatic Identification System maintained by Global Fishing Watch (GFW) (Kroodsma et al. 2018). The variable name is fishing_intensity1·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Fishing intensity 2: Summary <a href="https://figshare.com/articles/journal_contribution/Satellite_mapping_reveals_extensive_industrial_activity_at_sea_-_analysis_data/24309475" rel="noopener noreferrer" target="_blank">raster</a> at 0.10 decimal degree resolution using satellite imagery (Paolo et al., 2024). The variable name is fishing_intensity2·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Coastal population: <a href="https://sedac.ciesin.columbia.edu/data/collection/gpw-v4" rel="noopener noreferrer" target="_blank">Gridded Population of the World</a> (GPW), v4 (SEDAC/CIESIN, 2023). The variable name is coastal_population_shadow·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Shipping density: IMF’s World Seaborne Trade monitoring system (Cerdeiro et al. 2020). The variable name is shipping density.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Marine plastics: Ocean Microplastic Concentration V1.0 (Evans and Ruf 2021). The variable name is marine_plastics Composite indicator:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Marine Human Coexistence Index (HCI): marine_hci is the geometric mean of the following indicators: fishing intensity 1, fishing intensity 2, coastal population, shipping density and marine plastics. References:&nbsp;Cerdeiro, D. A., Cerdeiro, M. D. A., Komaromi, A., Liu, Y., &amp; Saeed, M. (2020). World seaborne trade in real time: A proof of concept for building AIS-based nowcasts from scratch (No. 20-57). International Monetary Fund.Evans, M. C., &amp; Ruf, C. S. (2021). Toward the detection and imaging of ocean microplastics with a spaceborne radar.&nbsp;IEEE Transactions on Geoscience and Remote Sensing,&nbsp;60, 1-9.Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., ... &amp; Worm, B. (2018). Tracking the global footprint of fisheries.&nbsp;Science,&nbsp;359(6378), 904-908.Paolo, F. S., Kroodsma, D., Raynor, J., Hochberg, T., Davis, P., Cleary, J., ... &amp; Halpin, P. (2024). Satellite mapping reveals extensive industrial activity at sea.&nbsp;Nature,&nbsp;625(7993), 85-91.SEDAC/CIESIN (NASA Socioeconomic Data and Applications Center/Center for International Earth Science Information Network, Columbia University). (2023). gpw_v4_population_count_adjusted_to_2015_unwpp_country_totals_rev11_2020_2pt5_min.tif.

Gridded level database of composite Marine Human coexistence Index and individual indicators

We describe below the data sources and provide an overview of the specific variables that are constructed for the analysis in this paper entitled, “Revisiting Global Biodiversity:&nbsp;A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility” by Susmita Dasgupta, Brian Blankespoor, and David Wheeler (2024). Global grid·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;grid_id = unique grid identifier at 0.25 decimal degrees.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x = longitude·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;y = latitude

Global Biodiversity Species Global Grid

The biodiversity data species occurrence region data are overlaid with administrative areas with Fragile and conflict-affected situations using the World Bank’s 2024 classification.&nbsp;The result is biodiversity information summarized at an administrative area with the following indicators:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemics at 100% : count of endemic species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with small occurrence regions (50x50) : count of small area (50 km2 x 50 km2) species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with extinction threat probability gte 80 : count of species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with small occurrence regions (50x50) : count of endemic small area (50 km2 x 50 km2) species occurrence region(s) [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of species with small occurrence regions (50x50) and extinction threat probability gte 80 : count of small area (50 km2 x 50 km2) species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with extinction threat probability gte 80 : count of endemic species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of endemic species with small occurrence regions (50x50) and extinction threat probability gte 80 : count of endemic small area (50 km2 x 50 km2) species occurrence region(s) with greater than or equal to 80 extinction threat probability [source : Dasgupta et al. 2024 a,b]·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Number of total species : count of species occurrence region(s) [source : Dasgupta et al. 2024 a,b]References:Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. (2024a).&nbsp;Revisiting Global Biodiversity: A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility&nbsp;(No. 10821). The World Bank. Dasgupta, S., Blankespoor, B., &amp; Wheeler, D. (2024b).&nbsp;Estimating Extinction Threats with Species Occurrence Data from the Global Biodiversity Information Facility&nbsp;(No. 10822). The World Bank.&nbsp;

Species Richness and Count of Species at Risk in Fragile and Conflict-Affected Situations

We describe below the data and provide an overview of the specific variables that are constructed for the analysis in the paper entitled, “Estimating Extinction Risks with Species Occurrence Data from the Global Biodiversity Information Facility” by Susmita Dasgupta, Brian Blankespoor, and David Wheeler (2024). Variables:Species·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;species = species with occurrence data. GBIF and authors’ calculation.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d_assignedrisk = GBIF species with IUCN Red-List (Version 2022-2). Yes==1, No==0Threats·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;parkpct = formal protection index. To compute the formal protection index parkpct, we have transformed the shapefile from the World Database of Protected Areas (UNEP-WCMC 2019) (which includes 283,568 polygons) into a global raster with a resolution of .05 decimal degrees (about 5 km). Each raster cell has value 1 if it includes a protected area and 0 otherwise.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;popdens = population density. We measure density with a spatial raster at 2.5 min resolution (.042 decimal degrees) from the Gridded Population of the World (GPW), v4 (SEDAC/CIESIN, 2023). Using the previously-described population raster, we assign each terrestrial raster cell to one of 15 scaled population group Popdens.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;eezpct = percent of the total occurrence area of a species that lies within an EEZ. We employ the Extended Economic Zones (EEZ) boundary shapefile from the Maritime Regions Geodatabase maintained by the Flanders Marine Institute (2019). For each species, we construct an EEZ coverage index as the percent of its total occurrence area that lies within an EEZ : eezpct.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;popshadow = Coastal Population Influence. We have constructed a general index of population influence with a spatial kriging algorithm that replaces offshore missing values in our global terrestrial population raster with projected values from proximate onshore populations.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;totfishing = total fishing intensity [Fishing Intensity (1)]. AIS-Based (Automatic Identification System) Global Fishing Effort from Global Fishing Watch (e.g. Kroodsma et al. 2018).·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;natfishing = nature total fishing intensity [Fishing Intensity (2)]. The estimates of fishing intensity are from high-resolution satellite imagery from Global Fishing Watch along with collaboration with several research institutions (Paolo et al. 2024).·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;pthreat = predicted threat probability index (0-100).·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;popgroupmax = Max population group (1:15) References:Dasgupta, S., B. Blankespoor, and D. Wheeler. 2023. Revisiting Global Biodiversity:&nbsp;A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility.&nbsp;World Bank, September.Flanders Marine Institute. 2019. Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Available online at https://www.marineregions.org/. <a href="https://doi.org/10.14284/386" rel="noopener noreferrer" target="_blank">https://doi.org/10.14284/386</a>Global Fishing Watch. https://globalfishingwatch.org/dataset-and-code-fishing-effort/Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., ... &amp; Worm, B. (2018). Tracking the global footprint of fisheries.&nbsp;Science,&nbsp;359(6378), 904-908.&nbsp;IUCN. 2022.&nbsp;The IUCN Red List of Threatened Species. Version 2022-2. <a href="https://www.iucnredlist.org/" rel="noopener noreferrer" target="_blank">https://www.iucnredlist.org</a>. Accessed on [9 May 2023] at <a href="https://doi.org/10.15468/0qnb58" rel="noopener noreferrer" target="_blank">https://doi.org/10.15468/0qnb58</a>&nbsp;SEDAC/CIESIN (NASA Socioeconomic Data and Applications Center/Center for International Earth Science Information Network, Columbia University). 2023. gpw_v4_population_count_adjusted_to_2015_unwpp_country_totals_rev11_2020_2pt5_min.tif&nbsp;Paolo, F., D. Kroodsma, J. Raynor et al. 2024. Satellite mapping reveals extensive industrial activity at sea.&nbsp;Nature, 625: 85–91.UNEP-WCMC. 2019. User Manual for the World Database on Protected Areas and world database on other effective area-based conservation measures: 1.6. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/WDPA_Manual

Global Biodiversity Species Extinction Risks Data

We describe below the data sources and provide an overview of the specific variables that are constructed for the analysis in this paper entitled, “Revisiting Global Biodiversity:&nbsp;A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility” by Susmita Dasgupta, Brian Blankespoor, and David Wheeler (2024). Variables:Species·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;species = species with occurrence data. GBIF and authors’ calculation.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;kingdom, phylum, class, order, family, genus = taxonomic categories from GBIF backbone taxonomy.&nbsp;·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;grp = group of species. The species are grouped into categories including amphibians, animals, arthropods, birds, fish, fungi, other kingdoms, mammals, plants and reptiles.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d_smallrange25 = small occurrence of 25 sq km. Yes==1, No==0·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d_smallrange100 = small occurrence of 100 sq km. Yes==1, No==0·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;area = area from Authors’ calculation.·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lcat = categorical variable of land, coast and/or ocean.&nbsp;Administrative area·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;max_sh_spp = maximum share of species occurrence area in an administrative area·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d_endemic100 = one administrative entity has 100% of the species occurrence area·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d_endemic095 = one administrative entity has more than equal to 95% of the species occurrence area·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;d_endemic090 = one administrative entity has more than equal to 90% of the species occurrence area·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;WB_DATANAM = administrative entity with the maximum species occurrence area. Administrative land areas are from the World Bank. The Extended Economic Zones (EEZ) boundary shapefile with entity names is from the Maritime Regions Geodatabase maintained by the Flanders Marine Institute (2019). The remaining area is assigned ocean.&nbsp;·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;WB_ISO = country / economy code : World Bank·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;ISO_A3 = country code 3 letters·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Type = Entity type References:Flanders Marine Institute. 2019. Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11. Available online at <a href="https://www.marineregions.org/" rel="noopener noreferrer" target="_blank">https://www.marineregions.org/</a>. <a href="https://doi.org/10.14284/386" rel="noopener noreferrer" target="_blank">https://doi.org/10.14284/386</a><a href="https://datacatalog.worldbank.org/search/dataset/0038272/world-bank-official-boundaries" rel="noopener noreferrer" target="_blank">World Bank Official Boundaries</a> : Development Data Hub, 2024. Available online at <a href="https://datacatalog.worldbank.org/search/dataset/0038272/world-bank-official-boundaries" rel="noopener noreferrer" target="_blank">https://datacatalog.worldbank.org/search/dataset/0038272/world-bank-official-boundaries</a>

Global Biodiversity Species Endemism and Small Occurrence data

Aruba

Afghanistan

Angola

Anguilla

Albania

Andorra

United Arab Emirates

Argentina

Armenia

American Samoa

Antigua and Barbuda

Australia

Austria

Azerbaijan

Burundi

Belgium

Benin

Caribbean Netherlands

Burkina Faso

Bangladesh

Bulgaria

Bahrain

Bahamas, The

Bosnia and Herzegovina

Belarus

Belize

Bermuda

Bolivia

Brazil

Barbados

Brunei Darussalam

Bhutan

Botswana

Central African Republic

Canada

Cocos (Keeling) Islands

Switzerland

Chile

China

Cote d'Ivoire

Cameroon

Congo, Republic of

Cook Islands

Colombia

Comoros

Cabo Verde

Costa Rica

Cuba

Curacao

Christmas Island

Cayman Islands

Cyprus

Czechia

Germany

Djibouti

Dominica

Denmark

Dominican Republic

Algeria

Ecuador

Egypt, Arab Republic of

Eritrea

Spain

Estonia

Ethiopia

Finland

Fiji

France

Faroe Islands

Micronesia, Federated States of

Gabon

United Kingdom

Georgia

Guernsey

Ghana

Guinea

Gambia, The

Guinea-Bissau

Equatorial Guinea

Greece

Grenada

Greenland

Guatemala

Guam

Guyana

Hong Kong SAR, China

Heard Island and McDonald Islands

Honduras

Croatia

Haiti

Hungary

Indonesia

Isle of Man

India

British Indian Ocean Territory

Ireland

Iran, Islamic Republic of

Iraq

Iceland

We describe below the data and provide an overview of the specific variables that are constructed for the analysis in the following papers:<ul><li>“Revisiting Global Biodiversity:&nbsp;A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility” by Dasgupta, Blankespoor, and Wheeler” (2024)</li><li>“Estimating Extinction Risks with Species Occurrence Data from the Global Biodiversity Information Facility” by Dasgupta, Blankespoor, and Wheeler (2024).</li><li>"Fishing and Climate Change in Coastal Bangladesh : The Economic and Health Impacts of Increasing Salinity" by Blankespoor; Dasgupta; Huq; Khan; Mustafa; Wheeler (2025).</li><li>"Implementing 30x30 Lessons from Country Case Studies" by Dasgupta, Blankespoor, and Wheeler (2025).</li><li>"Bridging Conflicts and Biodiversity Protection : The Critical Role of Reliable and Comparable Data" by Blankespoor, Dasgupta, and Wheeler (2025).</li></ul>

Global Biodiversity Data

Metadata