Find research datasets worth reusing

openlgpl-3.0Nov 2023View details →

D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'

Cite the source of the dataset as: <blockquote> Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html </blockquote>

opencc-by-4.0Oct 2020View details →

A Synthetic Global Spatiotemporal Sampled River Discharge Database for Different Satellite Altimetry Mission Orbits

Corresponding peer-reviewed publication This dataset corresponds to all the RRR input and output files that were used in the study reported in: <ul> <li> Sikder, Md. S., Bonnema, M., Emery, C. M., David, C. H., Lin, P., Pan, M., et al. (2021). A Synthetic Data Set Inspired by Satellite Altimetry and Impacts of Sampling on Global Spaceborne Discharge Characterization. Water Resources Research, 57(2), e2020WR029035. <a href="https://doi.org/10.1029/2020WR029035">https://doi.org/10.1029/2020WR029035</a> </li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.  Note that this dataset makes extensive use of the river network and RAPID simulations that were produced in the following study, and the paper is gratefully acknowledged here: <ul> <li> Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. <a href="https://doi.org/10.1029/2019WR025287">https://doi.org/10.1029/2019WR025287</a> </li> </ul> Version of record and details of this version The version of record for this dataset (i.e. the one used in the aforementioned paper) is version V1.1 available at <a href="https://doi.org/10.5281/zenodo.4064188">https://doi.org/10.5281/zenodo.4064188</a>. This version V2.1 was produced to facilitate testing of the RRR software (<a href="https://github.com/c-h-david/rrr">https://github.com/c-h-david/rrr</a>). Notable details regarding this version compared to V2.0 are as follows: <ul> <li>The temporal sequence files (seq_TIM*.csv) of observations for regular temporal sampling now all have a sampling mean time of 0 second for every river reach instead of the previous value which corresponded to the cycle of observations (e.g. 259,200 seconds for a three-day regular temporal sampling). This allows to start sampling at the onset of each simulation instead of at the end of the first cycle. This change does impact the findings of the study.</li> <li>The sampled discharge files (Qout*.nc) where produced with an updated version of rrr_anl_spl_mod.py which now selects the time step at which a sample is retained using a slightly different approach. The update only impacts sampling results when the sampling time matches the river model output time step exactly, and is more accurate now. This change does impact the findings of the study.</li> </ul>

opencc-by-4.0May 2021View details →

Global Tax Expenditures Database (GTED)

The GTED collects all publicly available data on tax expenditures (TEs) published by national governments worldwide from 1990 onwards, covering a total of 218 jurisdictions. Based on a step-by-step search process, 116 jurisdictions are currently classified as Non-reporting Jurisdictions. The remaining 102 ones do provide some type of TE data, which was gathered by the GTED team. Wherever available, the GTED gathers revenue forgone estimates and number of beneficiaries of individual TE provisions. It also gathers metadata including the definition of the TE provision, its legal basis and duration. Each record in the GTED is classified in four main categories: Tax Type, Policy Objective, Beneficiaries and Type of TE used. In some cases, second- or third-level categories have been introduced. For instance, Fuel Tax data is categorised at the third level within Tax Type: Taxes on Good and Services  Excise Taxes  Fuel Tax. If the information for a record is not available or unclear, the respective category is classified as Not stated/unclear. When governments do not publish provision-level data but rather some kind of aggregated information, the GTED gathers this aggregate data. Likewise, if governments report on specific areas of TE only (such as tax incentives for investments, or TEs on income taxes) the GTED presents data on these areas alone. The terms TE reporting or TE report are used broadly, and refer to a large variety of public documents, ranging from annual, comprehensive reports on TEs that are part of governmental budget documentation to individual documents issued by a public body and providing some aggregate information on some specific TE mechanisms. As a minimum requirement, reports must contain some kind of information on the actual use of TE provisions. For instance, a list of available tax deductions for investments, provided by a governmental investment promotion agency, would not be considered a TE report unless they provide revenue forgone estimates or any other data that would allow users of the GTED to obtain information about the actual use of the respective TEs. The GTED distinguishes regular and irregular reporting. A sequence of reports from 1995 to 2005 would not be considered regular reporting in the GTED, since the country had reported on a yearly basis, but not anymore. Likewise, regular is not necessarily related to annual reporting. Germany, for instance, publishes federal subsidy reports including TE data every two years since 1967. A total of 16 such reports have been issued since 1990, containing data on 29 budget years (until 2021). The GTED counts this as 31 years reported, because data is provided on a year-by-year basis and can be consulted and analysed as such. The data is processed in a consistent format seeking to increase the level of longitudinal and cross-country comparability. Whereas revenue forgone estimates are provided as reported by governments (in local currency units, current prices), the GTED also provides figures converted into US dollars as well as indicators providing the revenue forgone through TE provisions as shares both of GDP and Tax Revenue – to compute these two indicators, data from the <a href="https://www.wider.unu.edu/project/government-revenue-dataset">UNU-WIDER Government Revenue Dataset</a> is used as input. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries' central governments. The share of revenue forgone as a percentage of Tax Revenue is computed using figures of total tax revenue collected by countries' central governments. Besides all the effort put into ensuring comparability, cross-country analysis of TE data needs to be done cautiously. The main issue, which is inherent to TE data, regards benchmarking. TEs are defined as departures from – usually country-specific – normal tax structures or benchmarks. On this note, the GTED uses the data published by official governmental institutions, sticking to their own definitions of benchmarks, without trying to complement official figures or challenge what different countries consider as the standard tax system or the benchmark. When it comes to the methodology used by governments to compute the fiscal cost of TE provisions, the vast majority of countries report on TEs based on the revenue forgone approach that estimates the amount by which taxpayers have their tax liabilities reduced as a result of a TE based on their actual current economic behaviour. Since the revenue forgone methodology is static, the potential interconnections between different TE provisions are not taken into account when computing the fiscal cost of TEs based on it. Hence, aggregating revenue forgone estimates of the individual provisions computed separately and without taking behavioural changes into account would not result in a figure that represents the total cost of all TEs. While providing users of the database with the opportunity to draw comparisons across countries or country groups, we want to be clear that any such comparison should be mindful of different levels of reporting, differences in national benchmark systems and methodological shortcomings of revenue forgone estimations. Country Income Groups and Regional Classifications are based on the latest World Bank classifications.

opencc-by-4.0Nov 2022View details →

Global Soil Bulk Density DataBase (GSBDDB)

We complied the Global Soil Bulk Density DataBase (GSBDDB). This database inlcudes 162,470 soil samples (35,805 sampling sites) with bulk density (BD) and soil organic cabron (SOC) for the globle.  Among them, 96,705 soil samples have soil particle size fractions (i.e. clay, silt and sand) as well. In addtion, this dataset also records spatial coordinates, elevation, mean annual precipitation, mean annual temperature, potential evapotranspiration and aridity index. This dataset is asscoated to the "Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB)" by Chen et al. (in preparation). Manuscript citation: Chen, S., Dai, L, Shuai Q., Xue, J., Zhang, X., Xiao, Y., et al. Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB). In preparation. When using the data, please cite repositories as well as the original manuscript. For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

opencc-by-4.0Nov 2022View details →

Open database on global coal and metal mine production

See also the associated Data Descriptor published in Nature Scientific Data: <a href="https://www.nature.com/articles/s41597-023-01965-y">www.nature.com/articles/s41597-023-01965-y</a> This data set covers global extraction of coal and metal ores on an individual mine level. It covers 1171 individual mines in 80 different countries, reporting mine-level production for 80 different materials in the period 2000-2021. Furthermore, also data on mining coordinates, ownership, mineral reserves, mining waste, transportation of mining products, as well as mineral processing capacities (smelters and mineral refineries) and production is included. The data was gathered manually from more than 1900 openly available sources, such as annual or sustainability reports of mining companies. All datapoints are linked to their respective source documents. After manual screening and entry of the data, automatic cleaning, harmonization and data checking was conducted. Geoinformation was obtained either from coordinates available in company reports, or by retrieving the coordinates via Google Maps API and subsequent manual checking. For mines where no coordinates could be found, other geospatial attributes such as province, region, district or municipality were recorded, and linked to the GADM data set, available at <a href="https://www.gadm.org">www.gadm.org</a>. The data set, found in the "data" sub-folder, consists of 12 tables. The table “facilities” contains descriptive and spatial information of mines and processing facilities, and is available as a GeoPackage (GPKG) file. All other tables are available in comma-separated values (CSV) format. If you are working in Excel or have problems handling the GeoPackage file, it can be converted to Excel with an online tool, such as <a href="https://mygeodata.cloud/converter/gpkg-to-xlsx">https://mygeodata.cloud/converter/gpkg-to-xlsx</a>. A schematic depiction of the database is provided in the file database_model.pdf. A description of all variables of all tables is provided in the Excel file variables_descriptions.xlsx, and all materials for which production is reported in the database are listed in the file materials_covered.xlsx. For convenience, global and national coverage shares for every material and country with recorded production in the database is provided in the file coverage_table.pdf. These coverage shares were calculated by comparing the production values of this database to official production statistics reported in the UNEP IRP Global Material Flows Database, to be found under <a href="https://www.resourcepanel.org/global-material-flows-database">https://www.resourcepanel.org/global-material-flows-database</a>. For significant raw material producing countries, these coverage shares are also visualised in the file coverage_national_area_charts.pdf.

opencc-by-4.0Feb 2023View details →

Global Fire Emissions Database (GFED5) Burned Area

The monthly GFED5 burned area data produced in this study are available in netCDF files. For years between 2001 and 2020, five layers of burned area (Norm: normal type, Crop: cropland burning, Defo: deforestation burning, Peat: peatland burning, Total: the sum of all burning) are provided at 0.25°×0.25° resolution. The ‘Norm’ layer contains burned areas in each grid cell (in km2) separated by 17 major land cover types. For the pre-MODIS era (1997-2000), only the ‘Total’ burned area layer with reduced spatial resolution (1°×1°) is provided. We also provide two global maps of burnable area (with water and snow/ice cover excluded) in each grid cell (0.25°×0.25° for the MODIS era and 1°×1° for the pre-MODIS era). Please refer to readme.html or readme.pdf for more detail about the dataset.

opencc-by-4.0Aug 2023View details →

Companion data for RAT 3.0: Global Database, Test data, Parameter files and Routing Script

<a href="https://depts.washington.edu/saswe/rat/">Reservoir Assessment Tool version 3.0</a> is a scalable and user-friendly software platform to mobilize the global water management community. RAT uses satellite remote sensing data to monitor water surface area and water level changes in artificial reservoirs. It uses this information, along with topographical information (either derived from satellite data, or in-situ topo maps) to estimate the Storage Change (∆S) in the reservoirs. Additionally, RAT models the Inflow (I) and the Evaporation (E) of each reservoir. Finally, RAT uses the modeled I, and E, and estimated ∆S, to estimate the Outflow (O) from reservoirs. The datasets and files provided here are used by RAT 3.0 as default inputs to make it easy to set up and execute RAT for first-time users. global_data.zip - It includes <a href="https://rat-satellitedams.readthedocs.io/en/latest/RAT_Data/GlobalDatabase/">Global Database</a> encompassing global elevation data, global reservoir and dam data, major river basins in the world and the river networks, flow direction file, and geoid model. It is used by RAT 3.0 as default input for easy execution for first-time users. global_vic_params.zip - It contains <a href="https://zenodo.org/record/3475602">global VIC soil and domain parameters</a> for executing the hydrological model within RAT 3.0. It is considered a part of the Global Database but is packaged separately. params.zip - It consists of all the default parameter files used by RAT 3.0 to execute the hydrological model within it and to execute RAT itself.  routing.zip - It consists of the Fortran code for <a href="https://vic.readthedocs.io/en/vic.4.2.d/Documentation/Routing/RunRouting/">the Routing model</a> for easy installation for users.  test_data.zip - It consists of data used by RAT 3.0 to test whether it has been installed and initialized properly in a user's system.

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OLIGOTREND, a global database of multi-decadal timeseries of chlorophyll-a and nutrient concentrations in inland and transitional waters, 1986-2023

The Oligotrend database is a collection of multi-decadal chlorophyll-a and nutrient timeseries in inland and transitional waters. The objective of this Data Package was to explore how inland and transitional aquatic ecosystems respond to oligotrophication trends. Overall, the Oligotrend L1 database is made of 4.3 million valid observations originating from 1,894 stations. There are 238, 687 and 969 stations located in estuaries, lakes and rivers, respectively. The top 3 largest sources of data are the French national water quality monitoring (775 stations), the global database of lake datasets from Naderian et al. 2024 (378 stations), and the Chesapeake Bay Program (199 stations). The data was harmonized through a reproducible data processing pathway. In this Data Package, quality-checked level L1 data is provided, together with data sources, geographical coordinates of the stations, and the output of a trend analysis of all timeseries (level L2).

openCC (other)Nov 2025View details →

opencc-by-4.0Mar 2020View details →

Database of global dam detections

GPKG package of detected global dams from global imagery.

opencc-by-4.0Nov 2023View details →

A meta-analysis on global change drivers and the risk of infectious disease database and code

Data and code associated with the manuscript "A Meta-analysis on Global change drivers and the risk of infectious disease".

Database for: Meta-analysis of the impact of land use intensification on earthworms in global agroecosystems

The dataset comprises a compilation of studies investigating the impact of land use intensification on earthworms across global agroecosystems. Extracted from peer-reviewed publications, the dataset includes various fields such as climate characteristics are described using the Köppen-Geiger climate classification system. Soil properties such as type, texture, pH, and organic content are documented. Additionally, details regarding experimental parameters like replicates, sampling depth, and extraction methods are provided. Furthermore, the dataset encompasses information on agricultural practices including herbicide, insecticide, pesticide usage, fertilizer type and rate, grazing, tillage methods, and days after tillage for earthworm collection. Abundance, diversity, and their associated metrics are recorded for both control and treatment sites.

opencc-by-4.0Apr 2024View details →

The conservation burden of Intact Forest Landscapes (IFLs): A global database of management units and IFLs

Introduction This dataset includes a global overview of publicly available forest Management Units (MUs), Intact Forest Landscapes (IFLs) and their overlap. This includes both the boreal forests of Canada and Russia, and the tropical forests in the Amazon, the Congo basin, South-East Asia. The dataset was developed for the paper "Feasibility and effectiveness of global Intact Forest Landscape protection through forest certification: The conservation burden of Intact Forest Landscapes" by Zwerts et al. (2024). A comprehensive list of MUs with % and absolute overlap with IFLs is presented in Table S1 of Zwerts et al. (2024). Data collection We collected and collated all publicly available MU and IFL data of Central Africa, Southeast Asia, the Amazon, and of the boreal forests in Canada and Russia. As such, we included MU data from Cameroon, Canada, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, the Republic of Congo and Russia. Together, these forests comprise the majority of all IFLs (Potapov et al., 2017). We utilized the 2020 intact forest landscape (IFL) dataset generated by Potapov et al. (2017). Both FSC-certified and non-FSC MUs were considered and FSC-certification status data was collected using the FSC public dashboard (FSC, 2023). All data was collected in March 2023. Our dataset is not exhaustive. To our knowledge, not all MU data is publicly available. For Southeast Asia no public MU data is available for Papua New Guinea and Peninsular Malaysia. For the Amazon, insufficient public MU data was available to create an accurate representation of the situation. This area was excluded from the main analysis in Zwerts et al., 2024. We included a distinction between FSC-certified and non-FSC MUs in Russia, even though the FSC has withdrawn all certificates in Russia in April 2023 following the invasion of Ukraine. We chose to retain the distinction between FSC and non-FSC MUs for the Russian data because of the uncertainty of the current situation and the significant influence of FSC-certification in the Russian management of IFLs. Overlap analysis All area was transformed to geodesic distance. Furthermore, several MU names were altered because of duplicate names. The number of hectares of MUs that overlap with IFLs was calculated in ArcGIS Pro 3.0.0, using the WGS_1984_Web_Mercator_Auxiliary_Sphere coordinate system. Using the intersect and multipart to singlepart tools every overlap fragment was isolated. For the results in Zwerts et al. (2024) the total overlap and the percentage of overlap was calculated in R.  Abstract of the related article Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders' needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of 'conservation burden' for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for hydroelectric generation from the open literature. Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1011 datapoints from 11 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.  Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for battery energy storage systems from the open literature. Battery energy storage is the fastest growing form of power system flexibility, and will be critical to integrating large shares of variable renewable energy. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 671 datapoints from 18 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for wind power generation from the open literature. Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 1506 datapoints from 28 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.   The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for coal-fired power generation from the open literature. Coal supplies 35% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 345 datapoints from 12 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar power in current and future electricity systems

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for solar power generation from the open literature. Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 753 datapoints from 31 sources. The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time. Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.