The experimental data were taken from Simon et al., who compiled potency data for effects related to neurotoxicity from four experimental datasets, Stenberg et al. [18] and Wigestrand et al. The measures of potency were EC50 (µM) or IC50 values for all the effects except Stenberg data, which were expressed as a percentage of the control uptake for different concentrations measured. This dataset is associated with the following publication: Pradeep, P., L. Carlson, R. Judson, G. Lehmann, and G. Patlewicz. Integrating data gap filling techniques: A case study predicting TEFs for neurotoxicity TEQs to facilitate the hazard assessment of polychlorinated biphenyls. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 101: 12-23, (2019).

The Gap Analysis Program (GAP) produces data and tools that help meet critical national challenges such as biodiversity conservation, renewable energy development, climate change adaptation, and infrastructure investment. The GAP national land cover includes data on the vegetation and land-use patterns of the United States, including Alaska, Hawaii, and Puerto Rico. This national dataset combines land cover data generated by regional GAP projects with Landscape Fire and Resource Management Planning Tools (LANDFIRE) data (http://www.landfire.gov/). LANDFIRE is an interagency vegetation, fire, and fuel characteristics mapping program, sponsored by the U.S. Department of the Interior and the U.S. Department of Agriculture Forest Service.

The gender employment gap is defined as the difference between the employment rates of men and women aged 20-64. The employment rate is calculated by dividing the number of persons aged 20 to 64 in employment by the total population of the same age group. The indicator is based on the EU Labour Force Survey.

NREL, as part of the Play Fairway Analysis Retrospective, compiled and mapped publicly available geologic and geophysical data in relation to the 2008 USGS geothermal potential analysis. Included in this submission are maps displaying the publicly available data for LIDAR coverage, aeromagnetic coverage, gravity station locations, and geologic map coverage over the Western United States.

The GAP/LANDFIRE National Terrestrial Ecosystems represents a highly thematically detailed land cover map of the U.S. The GAP/LANDFIRE National Terrestrial Ecosystems dataset is produced by the U.S. Geological Survey in collaboration with the LANDFIRE Program. The GAP and LANDFIRE produce data and tools that help meet critical national challenges such as biodiversity conservation, fire and fuels modeling, renewable energy development, climate change adaptation, and infrastructure investment. The GAP National Terrestrial Ecosystems - Ver 3.0 is a 2011 update of the National Gap Analysis Program Land Cover Data - Version 2.2 for the conterminous U.S. The map legend includes types described by NatureServe's Ecological Systems Classification (Comer et al. 2002) as well as land use classes described in the National Land Cover Dataset 2011 (Homer et al. 2015). These data cover the entire continental U.S. and are a spatially continuous data layer. These raster data have a 30 m x 30 m cell resolution. National GAP Land Cover combines ecological system data from previous GAP projects in the Southwest , Southeast, and Northwest United States with recently updated California data. For Alaska and areas of the continental United States where ecological system-level GAP data has not yet been developed, data from the LANDFIRE project were used. This approach allowed GAP mappers to construct a seamless representation of ecological system distributions across the conterminous United States. Currently LANDFIRE is leading a remap effort based on 2016 Landsat imagery as well as new field data. In addition to the Ecological Systems Classification maps can be rendered using the Federal Geographic Data Committee’s National Vegetation Classification System at the Group level and higher.

Dataset Card for "gap-analysis-data"

More Information needed

Seattle Parks and Recreation 2023 Walkability Gap Analysis. SPR’s intent is to gain a more accurate picture of access, by measuring how people walk to a park or recreation facility. We are calling this "walkability".This map shows what a 5-minute and a 10-minute walking distance (or walkability area) looks like around park lands that are greater than 10,000 square feet in size.

This table shows mRNA (grey rows) and protein (white rows) boundary locations through developmental time in percent A—P position (where 0% is the anterior pole). A: indicates anterior, P: posterior boundary of a domain. T1—8 indicate time classes subdividing C14A. Boundary positions for mRNA domains correspond to the starting points of approximating splines as described in Materials and Methods. Boundary positions for protein domains are taken from [43], and correspond to the position where the level of gene expression reaches a threshold of 50% maximum fluorescence intensity. Single dashes indicate boundaries that are not present at a give time point. Double dashes indicate boundaries that are observable, but were not measured in [43].

Abstract:

The Ivy Glioblastoma Atlas Project represents a fundamental tool for investigating the cellular and molecular underpinnings of glioblastoma. It offers an accessible online atlas and database containing valuable clinical and genomic information, which will undoubtedly facilitate future studies on glioblastoma pathogenesis, diagnosis, and therapeutic approaches. Glioblastoma is a highly aggressive brain tumor with a bleak prognosis, and its intricate molecular and cellular characteristics have not been fully elucidated in relation to conventional diagnostic histologic features. The dataset provided is comprised of de-identified clinical data pertaining to both patients and tumors.

Inspiration:

This dataset was uploaded to UBRITE for GTKB project.

Acknowledgments:

Puchalski RB, Shah N, Miller J, et al. An anatomic transcriptional atlas of human glioblastoma. Science. 2018;360(6389):660-663. doi:10.1126/science.aaf2666

U-BRITE last update: 07/28/2023

Accurate water accounting plays a vital role in enabling policymakers to make informed decisions regarding the allocation and management of water resources. By tracking water availability and usage over time, the National Water Account (NWA) offers valuable insights into regions at risk of water scarcity, guiding investments in infrastructure and conservation. The NWA supports effective water policy, encourages sustainable agriculture, and expands access to clean water—key pillars of water security and resilience amid growing demand and climate variability. In this context, the International Water Management Institute (IWMI) has developed national-scale water accounts for Ethiopia at a 1 km spatial resolution and monthly timescale for the period 2003–2021.

Yearly citation counts for the publication titled "Geometry-based cycle slip and data gap repair for multi-GNSS and multi-frequency observations".

The Measurement Infrastructure Gap Analysis in Utah’s Great Salt Lake Basin was a comprehensive inventory and analysis of existing diversion and stream measurement infrastructure along 19 primary river systems, as well as a preliminary investigation of measurement infrastructure gaps around Great Salt Lake proper. The purpose of this “Gap Analysis” was to develop methods to identify and prioritize areas throughout the Great Salt Lake basin where new or updated measurement infrastructure is needed to serve diverse objectives. The following gaps were identified: (1) existing measurement infrastructure quality and completeness gaps, (2) stakeholder identified gaps, and (3) potential spatial gaps in hydrologic understanding. By adapting the prioritization schema originally presented in the Colorado River Metering and Gaging and Gap Analysis to equally weight these three gap types at the HUC12 scale, a flexible framework for prioritizing new or updated measurement infrastructure in areas with large cumulative measurement gaps was developed, and high, medium, and low priority HUC12s were identified.

Results showed that 250 diversion and 28 stream measurement devices along primary systems had at least one quality and/or completeness gap. The most common quality and completeness gaps were insufficient device types, lack of telemetry, and data record interval. Stakeholders suggested 50 instances of new or updated diversion measurement infrastructure, 95 instances of new or updated stream measurement infrastructure, and 39 recommendations for continued funding of existing measurement infrastructure—totaling 185 stakeholder-identified gaps. To provide a spatially consistent approach to identifying potential gaps in hydrologic understanding, geospatial datasets describing features or attributes that can impact local hydrology were used to identify measurement gaps at the HUC12 scale. Among HUC12s that overlapped with the river systems included in this analysis, HUC12s with the greatest number of potential spatial gaps were in the Bear River sub-basin and near reservoirs in the Weber River sub-basin.

Based on the prioritization schema applied to synthesize these three gap types, there were 52 HUC12s along primary systems classified as high priority for measurement improvement. Of the 250 existing diversion and 28 stream measurement devices with at least one quality and/or completeness gap, 217 and 10 devices, respectively, were located within high priority HUC12s. Most stakeholder-identified gaps identified in the Weber and Jordan River sub-basins also fell within high-priority HUCs. Eighteen unique agencies suggested new or updated measurement infrastructure or continued funding of existing measurement infrastructure in high-priority HUC12s, demonstrating some consensus regarding measurement gaps in critical areas. There were 6 high priority HUC12s with no existing measurement infrastructure quality and completeness gaps, and 11 high priority HUC12s with no stakeholder-identified gaps. High priority HUC12s highlighted only due to potential spatial gaps may warrant additional investigation to further understand potential measurement gaps in these HUC12s.

Because the prioritization schema applied equally weighted all three gap types, it likely does not fully represent the diverse missions and priorities of different stakeholder groups. To facilitate an adaptable approach to prioritize measurement gaps within the Great Salt Lake basin, raw data for each of the three gap types are provided to allow varied prioritization schemes to be developed by weighting gap types differently or considering subsets of data. These data provide the basis for stakeholders within the Great Salt Lake basin to collectively prioritize future investments in gaging infrastructure and better manage water throughout the Great Salt Lake basin.

Yearly citation counts for the publication titled "Multi-frequency BeiDou cycle slip and data gap repair with geometry-based model".

GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). To represent these geographic limits, GAP compiled existing GAP data, where available, and NatureServe data (Patterson et al. 2003, Ridgely et al. 2007, NatureServe 2010) IUCN data (IUCN 2004), where needed. Data provided by GAP in collaboration with the Northwest Gap Analysis Project (NWGAP), the Southwest Regional Gap Analysis Project (SWReGAP), the Southeast Gap Analysis Project (SEGAP), the Alaska Gap Analysis Project (AKGAP), the Hawaii Gap Analysis Project (HIGAP), the Puerto Rico Gap Analysis Project (PRGAP), and the U.S. Virgin Islands Gap Analysis Project (USVIGAP). Web map services for species ranges can be accessed via: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/ Bird data provided by NatureServe in collaboration with Robert Ridgely, James Zook, The Nature Conservancy's Migratory Bird Program, Conservation International's Center for Applied Biodiversity Science (CABS), World Wildlife Fund US, and Environment Canada's WILDSPACE. Mammal data provided by NatureServe in collaboration with Bruce Patterson, Wes Sechrest, Marcelo Tognelli, Gerardo Ceballos, The Nature Conservancy's Migratory Bird Program, Conservation International's CABS, World Wildlife Fund US, and Environment Canada's WILDSPACE. Reptile data were provided by the International Union for Conservation of Nature and Natural Resources (IUCN). Amphibian data developed as part of the Global Amphibian Assessment and provided by IUCN-World Conservation Union, Conservation International and NatureServe. Once the needed range data were compiled it was intersected with Natural Resource Conservation Service National Watershed Boundary dataset of 12-digit hydrological units for the US (U.S. Geological Survey and U.S. Department of Agriculture, Natural Resources Conservation Service 2009). Range data were attributed with information regarding occurrence/presence, origin, reproductive use, and seasonal use from GAP regional projects (SWReGAP, SEGAP, NWGAP, AKGAP, HIGAP, PRGAP, and USVIGAP), NatureServe data, and IUCN data. GAP used the best information available to create these species ranges; however GAP seeks to improve and update these data as new information becomes available. These species range data provide the biological context within which to build our species distribution models. Recommended citation: U.S. Geological Survey Gap Analysis Program (USGS-GAP). [Year]. National Species Ranges. Available: http://gapanalysis.usgs.gov. Accessed [date].

The Global Gender Gap Index (by World Economic Forum) benchmarks the evolution of gender-based gaps among four key dimensions. The following information are visualized for each country: Economic Participation and Opportunity, Educational Attainment, Health and Survival, and Political Empowerment.

The Global Gender Gap index benchmarks 156 countries, providing a tool for cross-country comparison and to prioritize the most effective policies needed to close gender gaps. The methodology of the index has remained stable since its original conception in 2006, providing a basis for robust cross-country and time-series analysis. The Global Gender Gap Index measures scores on a 0 to 100 scale and scores can be interpreted as the distance to parity (i.e. the percentage of the gender gap that has been closed).

This dataset includes indicators from 2006 to 2021.

Yearly citation counts for the publication titled "Measuring the data gap: inclusion of sex and gender reporting in diabetes research".

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Dataset paper (public preprint)

A description of this dataset, including the methodology and validation results, is available at:

Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content. Uncertainty estimates are based on models calibrated in experiments to fill satellite-like gaps introduced to GLDAS Noah reanalysis soil moisture (Rodell et al., 2004), and consider the gap size and local vegetation conditions as parameters that affect the gapfilling performance.

Summary

• Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
• Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
• Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm, linear interpolation over periods of frozen soils. Uncertainty estimates are provided for all data points.
• More information: See Preimesberger et al. (2025) and https://doi.org/10.5281/zenodo.8320869" target="_blank" rel="noopener">ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Programmatic Download

You can use command line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/3fcxr-cde10/files"

# Loop through years 1991 to 2023 and download & extract data
for year in {1991..2023}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1r1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

• sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
• sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of the original satellite observations and of the predictions used to fill observation data gaps.
• sm_anomaly: Soil moisture anomalies (reference period 1991-2020) derived from the gap-filled values (`sm`)
• sm_smoothed: Contains DCT-PLS predictions used to fill data gaps in the original soil moisture field. These values are also provided for cases where an observation was initially available (compare `gapmask`). In this case, they provided a smoothed version of the original data.
• gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (1), and where the interpolated (smoothed) values are used instead (0) in the 'sm' field.
• frozenmask: (0 | 1) Indicates grid cells where ERA5 soil temperature is <0 °C. In this case, a linear interpolation over time is applied.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.1r1 (previous version was v09.1):

• This version uses a novel uncertainty estimation scheme as described in Preimesberger et al. (2025).

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (python)
• https://github.com/TUW-GEO/esa_cci_sm">esa_cci_sm (python)
• Similar tools exists for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArCGIS
• You can also use the GUI software Panoply to view the contents of each file

References

• Preimesberger, W., Stradiotti, P., and Dorigo, W.: ESA CCI Soil Moisture GAPFILLED: An independent global gap-free satellite climate data record with uncertainty estimates, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-610, in review, 2025.
• Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
• Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020
• Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, Bulletin of the American Meteorological Society, 85, 381 – 394, https://doi.org/10.1175/BAMS-85-3-381, 2004.

Related Records

The following records are all part of the Soil Moisture Climate Data Records from satellites community

The Commercialization Gap Fund (CGF) supports science and research-based innovations in Oregon by bridging early stage capital gaps, leading to private capital investments. The Fund targets companies in the advanced manufacturing, active lifestyle, bioscience, medical devices, and natural resource industries. This report covers fiscal years 2020-2024 For more information, please visit https://www.oregon.gov/biz/programs/CGF/Pages/default.aspx

Geothermal Data Gap Analysis identified candidate sites for geophysical studies. Assessed data availability and proximity to existing projects produced by the National Renewable Energy Laboratory. Last updated December 4, 2012. For more information, see: https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2013/Esposito.pdf

Abstract The network of weather stations in Brazil is very recent, with few stations in the country possessing data over more than 100 years. In addition, many of the series from these stations present missing data, either due to lack of measurement (in the case of conventional stations) or due to equipment failures (in the case of automatic stations). There are different methods in the literature for filling these gaps. In this work, six methods are compared and applied to series of known monthly data for mean compensated temperature and precipitation for weather stations located in the State of Rio Grande do Sul, in Southern Brazil. The results for mean compensated temperature suggest that multiple linear regression methods and regional weighing are best suited for estimating missing data, while for precipitation there was no superior method.