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225 results for “Global database”
Data from: A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium
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ASHRAE global database of thermal comfort field measurements
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Synthesizing community changes to global change treatments using the CoRRE (Community Responses to Resource Experiments) database - Data to accompany Avolio et al. 2021
These are the required data to do all subsequent analyses in Avolio et al. 2021 in Ecology Letters using the Community Responses to Resource Experiments database (corredata.weebly.com). Paper abstract: Global change is impacting plant community composition, but the mechanisms underlying these changes are unclear. Using a dataset of 58 global change experiments, we tested the five fundamental mechanisms of community change: changes in evenness and richness, re-ordering, species gains and losses. We found 71% communities were impacted by global change treatments, and 88% of communities that were exposed to two or more global change drivers were impacted. Further, all mechanisms of change were equally likely to be affected by global change treatments – species losses and changes in richness were just as common as species gains and re-ordering. We also found no evidence of a progression of community changes, e.g., re-ordering and changes in evenness did not precede species gains and losses. We demonstrate that all processes underlying plant community composition changes are equally affected by treatments and often occur simultaneously, necessitating a wholistic approach to quantifying community changes.
Global flow of earth science scientific articles based on several databases
<p>Global flow of earth science scientific articles based on several databases contains the summary of our findings from our search of earth science articles in ten databases:</p> <ol> <li>Google Scholar</li> <li>Lens</li> <li>Dimensions</li> <li>Korean Citation Index</li> <li>Russian Scientific Citation Index</li> <li>Garuda Ristekbrin</li> <li>HAL</li> <li>Scielo</li> <li>Scopus</li> <li>Web of Science</li> </ol> <p>The data are visualized using Datawrapper in the following links. All graphs contain links to the data sources (click "Get the data" under each graph):</p> <ol> <li><a href="https://www.datawrapper.de/_/YxTc9/">https://www.datawrapper.de/_/YxTc9/ (Scielo)</a></li> <li><a href="https://www.datawrapper.de/_/blOXM/">https://www.datawrapper.de/_/blOXM/ (Dimensions)</a></li> <li><a href="https://www.datawrapper.de/_/0fyDw/">https://www.datawrapper.de/_/0fyDw/ (Lens)</a></li> <li><a href="https://www.datawrapper.de/_/ad6c8/">https://www.datawrapper.de/_/ad6c8/ (Scopus)</a></li> <li><a href="https://www.datawrapper.de/_/NyypS/">https://www.datawrapper.de/_/NyypS/ (Maximum score for documents in rank promotion regulation of Indonesia)</a></li> <li><a href="https://www.datawrapper.de/_/s1KMD/">https://www.datawrapper.de/_/s1KMD/ (Percentage of OA earth sciences documents in several databases)</a></li> <li><a href="https://www.datawrapper.de/_/2nBnP/">https://www.datawrapper.de/_/2nBnP/ (Sum of earth sciences documents in several databases)</a></li> <li><a href="https://www.datawrapper.de/_/CgpLO/">https://www.datawrapper.de/_/CgpLO/ (Distribution of earth sciences documents by year in log scale)</a></li> </ol>
A simple global river bankfull width and depth database
<p>A simple global database of river widths and depths was derived using the HydroSHEDS river topology data set and simple geomorphic relationships among area, discharge, width, and depth. This database can be useful to provide initial estimates for hydraulic or hydrologic modeling where other suitable measurements are unavailable. The purpose of this database is not to replace more detailed estimates of river width and depth, but it is a first attempt at mapping these river characteristics with near-global coverage. </p>
MESI: a database of terrestrial global change experiments
<p>effects of experimental eCO2, warming, nutrient addition and/or water addition/removal on carbon and nutrient cycle related variables</p> <p> </p> <p>New in v1.0.3:</p> <p>Improved accuracy and further completion of the following variables:</p> <p>• longitude (lon) of the experiment site (site)</p> <p>• latitude (lat) of the experiment site (site)</p> <p>• elevation (elevation) of the experiment site (site)</p> <p>• ecosystem type (ecosystem_type)</p> <p>• experiment type: field/FACE, open-top chamber, pot (experiment_type and fumigation_type)</p> <p>• treatment level (particularly c_c, c_t, n_c, n_t, p_c, p_t, k_c, k_t)</p> <p>• sampling year of the experiment (sampling_year and duration)</p> <p>• warming type (w_t1)</p> <p>• some site (site), study (study) and experiment (exp) names</p> <p> </p> <p>Response variable (response) 'leaf_area' replaced by leaf_area_leaf, leaf_area_plant, leaf_area_eco</p> <p>Response variable (response) 'leaf_biomass' replaced by leaf_biomass_leaf, leaf_biomass_plant, leaf_biomass_eco</p>
gdho: Global Database of Humanitarian Organizations
A dataset of global humanitarian organizations collected by Humanitarian Outcomes.
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for nuclear power in current and future electricity systems
<p><span><span>Here, we </span><span>present a</span><span> database </span><span>which collates </span><span>historical, </span><span>current</span><span>,</span><span> and future </span><span>cost and performance</span><span> data</span> <span>and </span><span>assumption</span><span>s</span> <span>for </span></span><span><span>nuclear power generation</span></span><span> <span>from</span><span> the open literature</span><span>. </span></span><span><span>Nuclear energy is the second-largest source of low-carbon generation, supplying 9% of global electricity</span><span>.</span></span><span> <span>The data are </span><span>global in scope but with </span><span>regional and national</span> <span>specificity</span><span>, </span><span>cover</span><span>s</span><span> the years 2015 t</span><span>hrough </span><span>to 2050, </span><span>and </span><span>span</span> </span><span><span>604</span></span><span><span> datapoints from </span></span><span><span>19</span></span><span><span> sources</span><span>.</span> </span></p> <p><span><span>The database </span><span>enables modellers to select and justify</span> <span>model input data and </span><span>provides </span><span>a </span><span>benchmark for comparing assumptions and projections to </span><span>other source</span><span>s</span><span> across the literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is </span><span>designed to be easily updated with </span><span>new sources of</span><span> data, ensuring its utility</span><span>, comprehensiveness,</span><span> and broad applicability over time.</span></span> Technoeconomic data on nuclear power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Sahana et al. Supplementary Data for Global Transboundary River Research: Databases, Case Study Analysis, and Regional Statistics for Sustainable Management
<p><span>This dataset supports our comprehensive review article on transboundary river research, exploring its implications for sustainable management worldwide. Utilizing machine learning, we analyzed 4,237 publications and conducted an in-depth desk review of 325 selected papers, examining a total of 4,713 case studies spanning 286 river basins globally. The study provides critical insights into upstream, midstream, and downstream regions, offering a complete view of challenges and opportunities in transboundary river management. Supplementary Data 1 contains the main database used in this study, sourced from Scopus, Web of Science, and Google Scholar. Additionally, Supplementary Data 2 and 3, included in the spreadsheet, offer statistics and further resources essential for understanding regional and cross-regional dynamics in river basin governance. These supplementary resources include key statistics, case study metadata, and tools, helping to facilitate a deeper exploration of basin-specific and global trends in transboundary water management. This collection of data and resources provides a valuable foundation for researchers and policymakers in advancing sustainable transboundary river management practices.</span></p>
OctocoralTraits: a global database of trait information for octocoral species
<p>The OctocoralTraits_v2_2 ZIP file contains structured data and code to create all figures from the manuscript: “The Octocoral Trait Database: a global database of trait information for octocoral species”. The folder also contains the code used to validate the data descriptor.</p> <p>Specifically, the folder contains the following files:</p> <p>· Code.R – This is the annotated code used to create all figures in R. (v R studio; 2023.06.0+421)</p> <p>· OctocoralTraits_v2_2.csv – This is the first data realase of the octocoral trait database, used to create all figures of the manuscript.</p> <p>· sp_id.csv – This is a file containing the list of accepted octocoral species contained in the database</p> <p>· iqtree.tre – This is a tree object used to create a family-resolved tree of octocorals. It has been downloaded from McFadden et al. 2022. Revisionary systematics of Octocorallia (Cnidaria: Anthozoa) guided by phylogenomics</p> <p>· MEOW folder – A folder containing regional information from Spalding et al. 2007. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas</p> <p>· Provinces_coord.csv: A file containing the coordinates of marine provinces, also derived from Spalding et al. 2007</p> <p>· Table_ids folder - A folder containing tables with information of the corresponding ids that appear in the file database.</p> <p>· Validation folder: A folder containing a Technical_validation.R file that was used to flag outliers and extreme outliers, duplicated rows and potential structural errors in the data (e.g., a given observation_id linked to more than one resource, species or location). </p>
Lake-TopoCat: A global Lake drainage Topology and Catchment database
<p><strong>Contact</strong>: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use Lake-TopoCat, please cite the following paper:</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. <em>Earth System Science Data</em>, 15, 3483-3511, <a href="https://doi.org/10.5194/essd-15-3483-2023">https://doi.org/10.5194/essd-15-3483-2023</a>.</p> <p> </p> <p><strong>Data description and components</strong><br>This version of Lake-TopoCat was constructed using the SWOT Prior Lake Database (PLD) v106 (<em>Wang et al.</em>, 2023) lake mask and the 3-arc-second-resolution hydrography dataset MERIT Hydro v1.0.1 (<em>Yamazaki et al.</em>, 2019). The drainage type of each PLD lake, such as isolated, inflow-headwater, headwater, flow-through, terminal, and coastal, was determined with assistance of MERIT Hydro-Vector (<em>Lin et al.</em>, 2021), a high-resolution river network dataset with spatially-variable drainage densities.</p> <p><br>For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins or regions, and the Lake-TopoCat data products were also organized based on these 68 regions, with their region or basin IDs shown in the Fig. 'Pfaf2_basins.jpg', attached to this database.</p> <p><br>Lake-TopoCat consists of five feature components, each with multiple attributes depicting lake drainage relationships. The five features are:</p> <p><strong>1. Lake boundaries:</strong> polygons of 5,893,363 PLD lakes, larger than 1 ha.</p> <p> File name: <em>Lakes_pfaf_xx </em>where, 'pfaf_xx' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg')</p> <p><strong>2. Lake outlets:</strong> points representing outlet or pour points of each individual lake. There are multiple outlets from a multifurcation lake. We identified 5,983,642 outlets for 5,893,363 lakes, where 83,819 lakes (~1.4% of the global lakes) show bi/multifurcation.</p> <p> File name: <em>Outlets_pfaf_xx</em></p> <p><strong>3. Unit catchment:</strong> boundary polygons of catchment defining the drainage areas between cascading (i.e., immediately upstream and downstream) lake outlets. The count of unit catchments equal to the count of lake outlets, and bifurcation or multifurcation lakes have multiple local catchments. In total, the delineated catchments in Lake-TopoCat cover about 85.1 million km2, which is about 63% of the Earth’s land mass excluding the Antarctic.</p> <p> File name: <em>Catchments_pfaf_xx</em></p> <p><strong>4. Inter-lake reaches:</strong> line features defining the drainage networks that connect the lake outlets to the inland sinks or the ocean. About 11 million connecting reaches were generated among ~6 million outlets. The total length of these inter-lake connecting reaches is ~19 million km, which is at least 8.75 times longer than the SWOT-visible river reaches as depicted in the SWOT River Database (SWORD) v16 (<em>Altenau et al.</em>, 2021).</p> <p> File name: <em>Reaches_pfaf_xx</em></p> <p><strong>5. Lake-network basins:</strong> boundary polygons of the entire drainage area containing each inter-lake network (i.e., a complete basin from the headwater to an inland sink or the ocean for all basins containing lakes). A total of 108,985 lake-network basins were identified. Among them, endorheic basins account for 2.75% by count and 19.5% by area of all lake-network basins. These endorheic basins cover ~17.5% of global surface excluding Antarctica.</p> <p> File name: <em>Basins_pfaf_xx</em></p> <p>The attribute tables for each of the feature components are explained in Section 4 of the product description document. For user convenience, we release the preliminary Lake-TopoCat lake outlets, unit catchments, and inter-lake reaches, with the affix '_prelim' in the file names (explained in the attached product description document). We also provide the polygon boundaries of the 68 Pfafstetter basins or regions in the file named 'Pfaf2_regions'. All files of Lake-TopoCat are available in both shapefile and geodatabase formats.</p> <p> </p> <p><strong>Disclaimer</strong><br>Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of Lake-TopoCat.</p>
Global Acritarch Database
Open the record for dataset details and reuse information.
globalbioticinteractions/AEC-DBCNet: Collaborative databasing of North American bee collections within a global informatics network project archive
<p>Data in this archive are from the <em>Collaborative databasing of North American bee collections within a global informatics network project</em>. Data was originally captured using Arthropod Easy Capture software developed at the American Museum of Natural History (AMNH), New York. Project lead investigators are John Ascher (Principal Investigator) and Jerome Rozen (Co-Principal Investigator) at the AMNH, and Douglas Yanega (Principal Investigator), University of California Riverside.</p> <p><strong>Please use this citation for this archive: </strong>John Ascher, Digital Bee Collections Network data archive from the C<em>ollaborative databasing of North American bee collections within a global informatics network project</em>. Version: 08 Mar 2016. https://doi.org/10.5281/zenodo.1436853</p> <p>This project was supported by the National Science Foundation grant <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956388">DBI 0956388</a> and <a href="https://nsf.gov/awardsearch/showAward?AWD_ID=0956340">DBI 0956340</a></p> <p><strong>ABSTRACT</strong> Natural history collections contain millions of bee specimens documenting the geographic ranges, temporal occurrence patterns, and floral associations of the 20,000 described bee species. This project will digitize and consolidate specimen records from 10 bee collections across the United States. The investigators will make or verify species identifications, capture full label data, georeference and error-check localities, and upload this information to publicly accessible databases. Web-based tools will be used to capture data across collections efficiently, validate bee and plant names through automated comparison with taxonomic authority files, and synthesize data on species pages with images, digitized literature records, and other information about bees and their host plants. Data will be uploaded to the Global Biodiversity Information Facility and to Discover Life (www.discoverlife.org), a website that features customizable global maps for all global bee species and dynamic identification keys for North American species. To obtain information needed to conserve and manage pollinators, the investigators will work with ecologists to model geographic and temporal trends in bee populations in relation to environmental variables. Bees are the most important pollinators of the approximately 1/3 of crops that require animal pollination. Recent declines in honey bee populations highlight the need to understand better the roles of native bees in agricultural and natural systems. This project will help predict risks to bees and their pollination services from climate change, habitat loss, and other factors. The outreach program Bee Hunt (www.discoverlife.org/bee) will educate the public, including students in underserved communities, about bee diversity and the importance of pollination services. Using digital photography and rigorous research protocols, Bee Hunt will empower people at biological field stations, nature centers, parks, schools, and other sites to collect high-quality data to augment information from specimen records.</p>
Afterslip Model Database RC2022 (Afterslip Moment Scaling and Variability from a Global Compilation of Estimates)
<p>Churchill2022AfterslipDatabase.xlsx is a detailed database of aseismic afterslip models and corresponding mainshock information compiled by Robert Churchill (under the supervision of Maximilian Werner, Juliet Biggs and Åke Fagereng). The database contains afterslip models of mainshocks since 1979, with a publication cut-off at the end of 2018. The database is near complete, but not exhaustive. Descriptions of each column can be found as comments in the header field, as well as in the accompanying paper. Not all fields are not complete, some are also approximate or inferred values.</p> <p>This accompanies the paper:</p> <p>Churchill, R.M., Werner, M.J., Biggs, J. and Fagereng, Å., 2022. Afterslip Moment Scaling and Variability from a Global Compilation of Estimates. <em>Journal of Geophysical Research: Solid Earth</em>, p.e2021JB023897. <a href="https://doi.org/10.1029/2021JB023897">https://doi.org/10.1029/2021JB023897</a>.</p> <p>We hope the database serves as a useful resource to the afterslip community. Please reference our associated paper when using this database, as well as the database itself. References for individual afterslip papers can be found on the second sheet of the database, and references for additional data used in our study can be found in the third sheet. In the future, the database may be updated to include additional (missed) studies, however, this first version accompanies our study.</p> <p>*Headers for columns U and V are mislabelled Afterslip Upper Depth Limit (km) and Afterslip Lower Depth Limit (km), when these should be Coseismic Slip Upper Depth Limit (km) and Coseismic Slip Lower Depth Limit (km). The data in these columns is otherwise correct.</p> <p> </p>
GLOWCAD: A global database of woody tissue carbon concentrations/fractions
<p><span>Woody tissue carbon (C) concentration is a key wood trait necessary for accurately estimating forest C stocks and fluxes, which also varies widely across species and biomes. However, coarse approximations of woody tissue C (e.g., 50%) remain commonplace in forest C estimation and reporting protocols, despite leading to substantial errors in forest C estimates. Here, we describe the Global Woody Tissue Carbon Concentration Database (GLOWCAD): a database containing 3,676 individual records of woody tissue C concentrations from 864 tree species. Woody tissue C concentration data—i.e., the mass of C per unit dry mass—were obtained from live and dead woody tissues from 130 peer-reviewed sources published between 1980-2020. Auxiliary data for each observation include tissue type, as well as decay class and size characteristics for dead wood. In GLOWCAD, 1,242 data points are associated with geographic coordinates, and are therefore presented alongside 46 standardized bioclimatic variables extracted from climate databases. GLOWCAD represents the largest available woody tissue C concentration database, and informs studies on forest C estimation, as well as analyses evaluating the extent, causes, and consequences of inter- and intraspecific variation in wood chemical traits.</span></p>
Resolved EXiobase (REX II) with regionalized biodiversity loss impact assessment of global mining – second version of a highly-resolved MRIO database for the year 2014
<p>This repository provides a new version of the highly-resolved global multi-regional input-output database called REX II (Resolved EXiobase) for the year 2014 with improved data quality for all mining and metals processing sectors, including a regionalized biodiversity impact assessment for all mining sectors. This regionalized impact assessment is based on the global mining area data set of Maus et al (2020). The database REX II is described in the study <em>"Hotspots of mining-related biodiversity loss in global supply chains and the potential for reduction by renewable electricity".</em></p> <p>Study: <a href="https://doi.org/10.1021/acs.est.2c04003">https://doi.org/10.1021/acs.est.2c04003</a></p> <p>Open-access preprint: <a href="https://doi.org/10.31223/X5T064">https://doi.org/10.31223/X5T064</a></p> <p> </p> <p>An earlier version of this database (REX I) with time series from 1995–2015 is provided under: <a href="http://doi.org/10.5281/zenodo.3993659">http://doi.org/10.5281/zenodo.3993659</a> and described here: <a href="https://doi.org/10.1016/j.scitotenv.2020.142587">https://doi.org/10.1016/j.scitotenv.2020.142587</a></p> <p> </p> <p>The repository REXIA_2014 contains the following files (<em>*.mat-files</em>) referring to the year 2014:<br> T_REXIA: transaction matrix<br> Y_REXIA: final demand matrix<br> Ext_REXIA and Ext_hh_REXIA: the satellite matrices of the economy and the final demand<br> The labels of all matrices are described in the excel file Labels_REXIA.xlsx</p>
GSHP: Global database of soil hydraulic properties
<p>A total of 15,259 SWCCs from 2,702 sites were assembled from published literature and other sources, standardized, and quality-checked to obtain global database of soil hydraulic properties (GSHP). The GSHP database covers most regions across the globe, with the highest number of curves from North America followed by Africa, Europe, Asia, South America, Australia/Oceania. In addition to SWCCs, other soil variables such as soil texture (12,233 measurements), bulk density (15,125 measurements), and soil organic carbon (2,255 measurements) are also listed in the database.</p> <p>The R code used for this study is available here: https://github.com/ETHZ-repositories/GSHP-database</p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., and Or, D., (2022): Global Soil Hydraulic Properties dataset based on legacy site observations and robust parameterization”. Manuscript accepted to <strong>Scientific Data.</strong></li> </ul> <p>Examples of using the GSHP database to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p><strong>Description of the files</strong>:</p> <p>The datasets in this repository include: </p> <p><strong>WRC_dataset_surya_et_al_2021_final </strong>provides a global compilation of soil hydraulic properties and the information described in the<strong> Readme_GSHP file</strong>. <strong>Dataset_notebook </strong>shows the graphical representation of the GSHP database. </p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). </p>
Field data synthesis accompanying "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>Synthesis of fuel load and fuel consumption field measurements accompanying the publication:</p><p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p><p>Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5</p><p>1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br>2Department of Earth System Science, University of California, Irvine, CA 92697, USA<br>3Woodwell Climate Research Center, Falmouth, MA 02540, USA<br>4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br>5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p><p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p><p> </p><p>Units are g C / m2</p>
A global review of subaqueous spreading and its morphological and sedimentological characteristics: A database for highlighting the current state of the art
<p>Subaqueous spreading, a type of extensional mass transport that is characterized by a ridge and trough<br> morphology, has been documented globally but is poorly understood. Subaqueous spreading is observed on<br> gently inclined surfaces (typically <3◦) when sediment bodies experience a sudden reduction of shear strength<br> along their basal plane during clay softening or liquefaction of sands or silty sand sediment. Historically,<br> spreading has been associated with very large landslides, but many unknown aspects of these mass movements<br> have yet to be clarified. Does spreading influences the large catastrophic failure? What are the sedimentological<br> and morphological aspects that contribute in initiating this process? These are some of the research questions<br> that spurred the present work. Here, we introduce a database that incorporates information from thirty-two case<br> studies, and use this to provide key insights into the sedimentary and morphological aspects of subaqueous<br> spreading that will assist in the identification of spreading elsewhere. We find that subaqueous spreading is most<br> common along passive glacial margins, but is also observed along active margins. The occurrence of contourites<br> interlayered with glaciogenic deposits is, in most cases, associated with landslides (or landslide complexes) with<br> spreading morphology. The database shows that seismic loading is commonly suggested to be the dominant<br> trigger mechanism, although more geotechnical observations and modelling analysis would be needed to support<br> this conclusion. We compare subaqueous spreading with terrestrial spreading, in particular to earthquake-related<br> lateral spreading and clay landslides. We find that subaqueous spreading shares the same driving processes and<br> potentially also some of the trigger mechanisms that are associated with the terrestrial spreading cases. Future<br> work will be required to address the association between spreading and its occurrence on some of the largest<br> landslides on Earth, its development mechanism, and its potential hazard implications.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Cost and performance data for electricity generation and storage technologies
<p>Here, we present a database which collates historical, current, and future cost and performance data and assumptions for the six most prominent electricity generation technologies; coal, gas, hydroelectric, nuclear, solar photovoltaic (PV) and wind power, which together accounted for over 92% of installed generation capacity in 2022. In addition, we provide the same data for utility-scale battery energy storage systems (BESS), regarded as critical to the integration of variable renewables such as wind and solar PV.</p> <p>The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 5510 datapoints from 56 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 in future.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.