The World Bank’s ESG Data Draft dataset provides information on 17 key sustainability themes spanning environmental, social, and governance categories.

In order to shift financial flows so that they are better aligned with global goals, the World Bank Group (WBG) is working to provide financial markets with improved data and analytics that shed light on countries’ sustainability performance. Along with new information and tools, the World Bank will also develop research on the correlation between countries’ sustainability performance and the risk and return profiles of relevant investments.

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

This data archive contains datasets developed for the purpose of training and applying random forest models to the Mississippi Embayment Regional Aquifer. The random forest models are designed to predict total stream flow and baseflow as a function of a combination of watershed characteristics and monthly weather data. These datasets are associated with a report (SIR 2022-xxxx) and code contained in a USGS GitLab repository. The GitLab repository (https://code.usgs.gov/map/maprandomforest/) contains much more information about how these data may be used to supply predictions of stream flow and baseflow.

Data repository for measurements from 3 wind masts in Papua New Guinea. Data transmits daily reports for wind speed, wind direction, air pressure, relative humidity and temperature. Please refer to the country project page for additional outputs and reports, including installation reports: http://esmap.org/re_mapping_png For access to maps and GIS layers, please visit the Global Wind Atlas: https://globalwindatlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP).

This file (wymt_ffa_2019Teton_WATSTORE.txt) contains peak flow data for peak-flow frequency analyses for selected streamgages in and near Teton County, Montana, based on data through water year 2019. The file is in a text format called WATSTORE (National Water Data Storage and Retrieval System) available from NWISWeb (http://nwis.waterdata.usgs.gov/usa/nwis/peak).

Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum observed lake depth, land-cover based estimates of surrounding canopy height and observed water temperature profiles (used here for validation). This unique dataset offers landscape-level insight into the future impact of climate change on lakes. This data set contains the following parameters: site_id, Prmnn_I, GNIS_ID, GNIS_Nm, ReachCd, FType, FCode, which are defined below.

Gender Disaggregated Labor Database (GDLD) is a global micro labor force database based on World Bank household survey collection and other public resources. This database has harmonized the economic activities and occupation categories from local classification to international comparable classifications. It fills an important information gap in global sex statistics by providing detailed accounts on education, employment levels, wages, labor income, and employment status at very disaggregated economic activity level and occupation category than is usually available.

First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. This dataset is comprised of two different zip files. Zip File 1: The data within this zip file are composed of two wildland fire datasets. (1) A merged dataset consisting of 40 different wildfire and prescribed fire layers. The original 40 layers were all freely obtained from the internet or provided to the authors free of charge with permission to use them. The merged layers were altered to contain a consistent set of attributes including names, IDs, and dates. This raw merged dataset contains all original polygons many of which are duplicates of the same fire. This dataset also contains all the errors, inconsistencies, and other issues that caused some of the data to be excluded from the combined dataset. Care should be used when working with this dataset as individual records may contain errors that can be more easily identified in the combined dataset. (2) A combined wildland fire polygon dataset composed of both wildfires and prescribed fires ranging in years from mid 1800s to the present that was created by merging and dissolving fire information from 40 different original wildfire datasets to create one of the most comprehensive wildfire datasets available. Attributes describing fires that were reported in the various sources are also merged, including fire names, fire codes, fire IDs, fire dates, fire causes. Zip File 2: The fire polygons were turned into 30 meter rasters representing various summary counts: (a) count of all wildland fires that burned a pixel, (b) count of wildfires that burned a pixel, (c) the first year a wildfire burned a pixel, (d) the most recent year a wildfire burned a pixel, and (e) count of prescribed fires that burned a pixel.

To determine if invasive annual grasses increased around energy developments after the construction phase, we calculated an invasives index using Landsat TM and ETM+ imagery for a 34-year time period (1985-2018) and assessed trends for 1,755 wind turbines (from the U.S. Wind Turbine Database) installed between 1988 and 2013 in the southern California desert. The index uses the maximum normalized difference vegetation index (NDVI) for early season greenness (January-June), and mean NDVI (July-October) for the later dry season. We estimated the relative cover of invasive annuals each year at turbine locations and control sites and tested for changes before and after each turbine was installed. These data were used to make final conclusions in the larger work described above. The GIS shapefile included in this USGS data release includes unique turbine IDs, as well as early season invasive (ESI) values for turbines and corresponding control sites summarized before and after the turbine installation date.

This map shows the USGS (United States Geologic Survey), NWIS (National Water Inventory System) Hydrologic Data Sites for Weber County, Utah. The scope and purpose of NWIS is defined on the web site: https://water.usgs.gov/public/pubs/FS/FS-027-98/

GeoTIFF raster data with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). This link provides access to the following layers: (1) Wind speed (WS): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file (2) Power Density (PD): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file. (3) Elevation (ELEV): at ground level (4) Air Density (RHO): at ground level (5) Ruggedness Index (RIX): at ground level All layers have 250m resolution.

These are terrestrial laser scanner datasets collected in forested areas west of Flagstaff, Arizona in 2015 and 2016. For each of the two scanners, six treatment areas were scanned, with four of them overlapping one another (Figure 1). These data are composed of individual scans referenced to one another using reflective targets, and geolocated using differentially corrected GPS and RTK locations of scan locations (Figure 3). There were overall large differences in point density among the two scanners (Figure 2).

The rainfall data are obtained from Vietnam's HydroMeteorological Data Center, and cover daily observations from 172 weather stations. Most of them were actively operated through out the period 1975-2006. The list of weather stations with GIS coordinates is also provided.

GLORIA data for the Gulf of Alaska Exclusive Economic Zone (EEZ) were acquired during five cruises over a four year period. The first cruise conducted in 1986 (F-1-86-GA) surveyed an area of the north-central mosaic area and covered an area of approximately 40,000 square kilometers (sq km). The second two cruises (F-8-88-AA, F-9-88-WG) were conducted in 1988. One of the 1988 cruises (F-8-88-AA) focused on a survey of the Aleutian Arc. The eastern most portion of that survey extended outside of the Aleutian Arc survey area and covered an area of approximately 52,000 square kilometers (sq km) of seafloor on the western edge of the Gulf of Alaska. The final two cruises (F-6-89-GA, F-7-89-EG) were completed in 1989. As in earlier EEZ reconnaissance surveys, the USGS utilized the GLORIA (Geological LOng-Range Inclined Asdic) sidescan-sonar system to complete the geologic mapping. The collected GLORIA data were processed and digitally mosaicked to produce continuous imagery of the seafloor. Thirty digital mosaics with a 50-meter pixel resolution were completed for the region.

This USGS Data Release represents geospatial and tabular data for the Gulf Coast Vulnerability Assessment Project. The data release was produced in compliance with the new 'open data' requirements as way to make the scientific products associated with USGS research efforts and publications available to the public. The dataset consists of 2 separate items: 1. Vulnerability assessment data for habitat and species based on expert opinion (Tabular datasets) 2. Vulnerability assessment values for species across subregions in study area (Vector GIS dataset)

The text file "Piezometer_time_series.txt" contains recorded observations of water levels and temperature for 16 piezometers installed in four deconstruction lot pairs. Water levels are presented as depth below the ground surface and as altitude above the National Vertical Datum of 1988. Lot pairs consisted of a Legacy deconstruction lot and an adjacent Green program deconstruction lot. There are four piezometers in each Legacy-Green lot pair. Both Legacy and Green lots had a shallow (6 or 6.5 foot) piezometer installed within the footprint of the deconstructed structure on the lot (piezometer 1) . The Legacy lots also had two piezometers installed outside of the structure footprint and included one shallow (6 foot; Legacy site 1 piezometer 3; Legacy site 2 piezometer 2; Legacy site 3 piezometer 2; Legacy site 4 piezometer 2 ) and one deep (16 foot; Legacy site 1 piezometer 2; Legacy site 2 piezometer 3; Legacy site 3 piezometer 3; Legacy site 4 piezometer 3) piezometer. Water level and temperature data were collected between November 14, 2018 and September 17, 2020. Recorded water levels were checked against periodic tape-downs using an electric well tape and adjusted to tape readings, if necessary. The piezometer information is as follows: Piezometer Legacy1-1; (Latitude 38.67741, Longitude -90.26539); depth 6 feet Piezometer Legacy1-2; (Latitude 38.67749, Longitude -90.26558); depth 16 feet Piezometer Legacy1-3; (Latitude 38.67747, Longitude -90.26553); depth 6 feet Piezometer Green1-1; (Latitude 38.67747, Longitude -90.26553); depth 6 feet Piezometer Legacy2-1; (Latitude 38.6796, Longitude -90.27142); depth 6 feet Piezometer Legacy2-2; (Latitude 38.67942, Longitude -90.27151); depth 6.5 feet Piezometer Legacy2-3; (Latitude 38.67943, Longitude -90.27148); depth 16 feet Piezometer Green2-1; (Latitude 38.67962, Longitude -90.27151); depth 6 feet Piezometer Legacy3-1; (Latitude 38.69901, Longitude -90.25448); depth 6 feet Piezometer Legacy3-2; (Latitude 38.69913, Longitude -90.25454); depth 6 feet Piezometer Legacy3-3; (Latitude 38.69916, Longitude -90.2546); depth 16 feet Piezometer Green3-1; (Latitude 38.6991, Longitude -90.25433); depth 6 feet Piezometer Legacy4-1; (Latitude 38.69942, Longitude -90.25374); depth 6.5 feet Piezometer Legacy4-2; (Latitude 38.69951, Longitude -90.25378); depth 6.5 feet Piezometer Legacy4-3; (Latitude 38.69957, Longitude -90.25375); depth 16 feet Piezometer Green4-1; (Latitude 38.69936, Longitude -90.25388); depth 6 feet

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

The data in this layer are from an unpublished report produced by the Bigelow Laboratory. The source project was conducted primarily to examine contaminant distributions, but also produced sediment textural data. The data presented in this layer were not part of the Gulf of Maine Contaminated Sediments Database.

The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits).

The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States.

NOTE: A version of this data theme that is more throughly checked (though based on smaller-scale maps) is available here: https://water.usgs.gov/lookup/getspatial?huc2m

HUC, GIRAS, Hydrologic Units, 1:250

For the most current data and information relating to hydrologic unit codes (HUCs) please see http://water.usgs.gov/GIS/huc.html. The Watershed Boundary Dataset (WBD) is the most current data available for watershed delineation. See http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset

Malawi MW: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 49.008 NA in 2023. This stayed constant from the previous number of 49.008 NA for 2022. Malawi MW: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 48.358 NA from Dec 2015 (Median) to 2023, with 9 observations. The data reached an all-time high of 49.008 NA in 2023 and a record low of 43.008 NA in 2017. Malawi MW: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malawi – Table MW.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;