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83 results for “national database”
Heritage Site Database Limpopo National Park
<p>Accompanying datafile with list of sites, chronological period, and assessment as explained in the following papers:</p> <p>An assessment system for archaeological sites, the example of Limpopo Valley (in review)</p> <p>Anneli Ekblom, Solange Macamo, Peter Bechtel, Frederico Regala, Susana Carvalho, Mussa Raja, Michel Notelid (2024) A Framework for Cultural Heritage Management in National Parks, Mozambique. Bull. Mus. Anthropol. préhist. Monaco, n° 63. </p> <p> </p>
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Data of Female members of National Federations Sport Governing Boards. Database GESPORT Project.
<p>This database has been built by the authors. The data has been collected from the websites of the national federations of Italy, Portugal, Turkey, Spain and the United Kingdom in 2018.</p> <p>With the support of the European Commission. Erasmus+ Project. "Corporate governance in sport organizations: a gendered approach". Project Reference -EPP-1-2017-1-ES-SPO-SCP</p>
LivWell: a sub-national database on the Living conditions of Women and their Well-being for 52 countries
<p>LivWell is a global longitudinal database which provides a range of key indicators related to women’s socioeconomic status, health and well-being, access to basic services, and demographic outcomes. Data are available at the sub-national level for 52 countries and 447 regions. A total of 134 indicators are based on 199 Demographic and Health Surveys for the period 1990-2019, supplemented by extensive information on socioeconomic and climatic conditions in the respective regions for a total of 190 indicators. The resulting data offer various opportunities for policy-relevant research on gender inequality, inclusive development, and demographic trends at the sub-national level.</p> <p>For a full description, please refer to the article describing the database here: (link to come)</p> <p>The companion repository <em>livwelldata </em>allows to easily use the database in R. The R package can be downloaded following the instructions on the following git repository: https://gitlab.pik-potsdam.de/belmin/livwelldata. The version of the database in the package is the same as in this repository.</p>
A database of soil type and soil depth across Everglades National Park, Florida, USA
This dataset contains record of soil type and soil depth across Everglades National Park. This data was obtained through published literature, unpublished field data, and inter-agency collaboration. Data come from either soil cores to bedrock or refusal (as specified by data source), cores that did not reach bedrock, a soil probe to determine depth, or the depth of installed surface elevation tables. Soil type was classified either along a gradient of organic matter content when data was available: mineral (<40%), intermediate (40-70%), or organic (<70%). When data on organic matter content wasn’t available but qualitative descriptions of soil type were, the type unit of soil was classified as either organic or mineral based on the descriptive lithology. When lithologic descriptions were provided and indicated the soil profile was not entirely one soil type (e.g. peat), depth of peat within core was recorded. When data source had specific coordinates, those coordinates were used. For older studies that only provided a figure showing the location of each estimate at a broad scale, location was estimated by georeferencing the figure in ArcMap, and dropping a point at each location. Records were cross-referenced with a recent vegetation map developed by the National Park Service in order to obtained vegetation community at each location (Ruiz et al. 2017).
India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research
<p>This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate.</p> <p>v4.0: Development of District Flood Severity Index (DFSI)</p> <p>v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district. </p> <p>v1.0: India Flood Inventory (IFI) 1967-2016.</p> <p>v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area.</p> <p><strong>REFERENCES</strong></p> <p>Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a></p> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>
Decarbonization scenario database for the Fifth National Climate Assessment (NCA5)
<p>The decarbonization scenario database for the U.S. Fifth National Climate Assessment (NCA5) forms part of the evidence base for the Mitigation chapter of the assessment. The database contains scenarios that were submitted in response to an open call for contributions for U.S. scenarios that reach net-zero emissions across the economy by midcentury. These scenarios inform the figures and text in the Mitigation chapter about decarbonization pathways for the U.S.</p><p>Visit the interactive NCA5 Scenario Explorer at <a href="https://data.ece.iiasa.ac.at/nca5">https://data.ece.iiasa.ac.at/nca5</a>.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for hydro 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>hydroelectric</span> <span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Hydroelectric power is the largest source of renewable energy, supplying 15% of global electricity.</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>1011</span></span><span><span> datapoints from </span></span><span><span>11</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 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><span> </span> Technoeconomic data on utility-scale hydroelectric power was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for battery storage 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>battery energy storag</span><span>e </span><span>systems</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Battery energy storage is the fastest growing form of power system </span><span>flexibility, and</span><span> will be critical to integrating large shares of variable renewable energy.</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>671</span></span><span><span> datapoints from </span></span><span><span>18</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 </span><span>literature</span> <span>to </span><span>validate</span><span> model inputs and outputs</span><span>.</span> <span>It is 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><span> </span>Technoeconomic data on utility-scale batteries was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for wind 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>wind</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Wind energy supplies 7% of global electricity, and production has grown three-fold in the decade to 2022.</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>1506</span></span><span><span> datapoints from </span></span><span><span>28</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 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><span> </span>Technoeconomic data on utility-scale wind energy was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for gas 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>gas</span><span>-fired power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Natural gas supplies 23% of global </span><span>electricity, but</span><span> must be rapidly phased down to meet global decarbonisation </span><span>objectives</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>620</span></span><span><span> datapoints from </span></span><span><span>14</span></span><span><span> sources</span><span>.</span></span></p> <p> </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 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 new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for coal 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>coal-fired power </span><span>generation</span> <span>from</span><span> the open literature. </span><span>Coal supplies 35% of global </span><span>electricity, but</span><span> must be rapidly phased down to meet global decarbonisation </span><span>objectives</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>345</span><span> datapoints from </span><span>12</span><span> sources</span><span>.</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 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 new-build coal-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.</p>
The Global and National Energy Systems Techno-Economic (GNESTE) Database: Economic and performance data for solar 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>solar</span><span> power </span><span>generation</span></span><span> <span>from</span><span> the open literature. </span></span><span><span>Solar energy supplies 5% of global electricity, and production has grown ten-fold in the decade to 2022</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>753</span></span><span><span> datapoints from </span></span><span><span>31</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><span> </span>Technoeconomic data on utility-scale solar PV was collected from websites, reports, academic articles and databases of national and international organisations.</p>
Fig. 9 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 9. Web page of management of common names in Database of Korean National Species List. This web page displays the list of common names registered in the database.
Fig. 8 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 8. Web pages of management function of special list of species defined by law. (A) Presents the web interface of searching taxon with five functions. (B) shows the list of registered species defined by law with Korean name.
Fig. 10 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 10. Web pages of management of references in Database of Korean National Species List. (A) Shows the list of references with function to assign references to taxa. (B) Web interface of function assigning reference to taxa.
Fig. 7 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 7. Web page of list of special list of species defined by law. This web page provides management function of special list of species defined by law.
Fig. 5 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 5. Introduction page of Korean National Species List. Introduction page of National Species List of Korea in the platform for biodiversity in Korea (http://www.kbr.go.kr/content/view.do?menuKey = 446&contentKey = 14).
Fig. 4 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 4. Web interface of list of taxa with search options. (A) Displays complex interface for searching taxon. (B) is the list view of taxa searched. (C) shows the example of brief information of taxon which will be appeared by clicking KTSN number in the list view.
Fig. 2 in Database of National Species List of Korea: the taxonomical systematics platform for managing scientific names of Korean native species
Fig. 2. Web interface of taxon in Database of Korean National Species List. (A) Provides hierarchical structure of taxon with the tree interface. (B) shows basic information of taxon including KTSN, species properties, representative common names, and status of KTSN. (C) displays ranks, names, common names, identifiers, origins and etc. (D) displays information higher taxa based on hierarchical systems. (E) shows the list of synonyms. (F) is the list of references related to this taxon. (G) is the list of all common names except representative name.
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.