100+ datasets found
  1. S

    Science Data Bank - An Open and General-Purpose Data Repository

    • scidb.cn
    Updated Nov 11, 2023
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    Zeyu Zhang (2023). Science Data Bank - An Open and General-Purpose Data Repository [Dataset]. http://doi.org/10.57760/sciencedb.13239
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 11, 2023
    Dataset provided by
    Science Data Bank
    Authors
    Zeyu Zhang
    License

    Attribution-NoDerivs 4.0 (CC BY-ND 4.0)https://creativecommons.org/licenses/by-nd/4.0/
    License information was derived automatically

    Description

    Science Data Bank - An Open and General-Purpose Data Repository

  2. S

    Science Data Bank:the building and service of an international data...

    • scidb.cn
    Updated Sep 29, 2021
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    Yuanchun Zhou; Lulu Jiang; Chengzan Li (2021). Science Data Bank:the building and service of an international data repository [Dataset]. http://doi.org/10.11922/sciencedb.01187
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 29, 2021
    Dataset provided by
    Science Data Bank
    Authors
    Yuanchun Zhou; Lulu Jiang; Chengzan Li
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This talk was delivered during The 4th Forum For World STM Journals on 29th July 2021 in Beijing.

  3. S

    ScienceDB data policy

    • scidb.cn
    Updated Nov 7, 2020
    + more versions
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    ScienceDB Data Curation Team (2020). ScienceDB data policy [Dataset]. http://doi.org/10.11922/sciencedb.datapolicy
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 7, 2020
    Dataset provided by
    Science Data Bank
    Authors
    ScienceDB Data Curation Team
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ScienceDB data policy is maintained by ScienceDB data curation team. The policy includes Data Type, Codes of Conduct for Depositors, Data Review Criteria, Data License Agreements, How to cite data published on ScienceDB, Data Retraction and Others. The first version of the policy is published on November 7,2020. And the last update is on March 1, 2022.

  4. S

    Science Data Bank Data Desensitization Commitment Statement

    • scidb.cn
    Updated Mar 15, 2022
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    yuan zhi yuan (2022). Science Data Bank Data Desensitization Commitment Statement [Dataset]. http://doi.org/10.11922/sciencedb.01587
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 15, 2022
    Dataset provided by
    Science Data Bank
    Authors
    yuan zhi yuan
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Science Data Bank Data Desensitization Commitment Statement

  5. u

    Data from: Inventory of online public databases and repositories holding...

    • agdatacommons.nal.usda.gov
    • catalog.data.gov
    • +1more
    txt
    Updated Feb 8, 2024
    + more versions
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    Erin Antognoli; Jonathan Sears; Cynthia Parr (2024). Inventory of online public databases and repositories holding agricultural data in 2017 [Dataset]. http://doi.org/10.15482/USDA.ADC/1389839
    Explore at:
    txtAvailable download formats
    Dataset updated
    Feb 8, 2024
    Dataset provided by
    Ag Data Commons
    Authors
    Erin Antognoli; Jonathan Sears; Cynthia Parr
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to

    establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data

    Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered.
    Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review:

    Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection.
    Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation.

    See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

  6. d

    USGS Dam Removal Science Database v4.0

    • catalog.data.gov
    • data.usgs.gov
    Updated Sep 24, 2025
    + more versions
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    U.S. Geological Survey (2025). USGS Dam Removal Science Database v4.0 [Dataset]. https://catalog.data.gov/dataset/usgs-dam-removal-science-database-v4-0
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    Dataset updated
    Sep 24, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Description

    This database is the result of an extensive literature search aimed at identifying documents relevant to the emerging field of dam removal science. In total the database contains 296 citations that contain empirical monitoring information associated with 207 different dam removals across the United States and abroad. Data includes publications through 2020 and supplemented with the U.S. Army Corps of Engineers National Inventory of Dams database, U.S. Geological Survey National Water Information System and aerial photos to estimate locations when coordinates were not provided. Publications were located using the Web of Science, Google Scholar, and Clearinghouse for Dam Removal Information.

  7. S

    Paper related data

    • scidb.cn
    Updated Jan 8, 2024
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    Lv Zimin (2024). Paper related data [Dataset]. http://doi.org/10.57760/sciencedb.15017
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 8, 2024
    Dataset provided by
    Science Data Bank
    Authors
    Lv Zimin
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Here are some pictures and tables related to the process of the paper

  8. S

    Construction idea and practice of international scientific data publishing...

    • scidb.cn
    • datapid.cn
    Updated Nov 12, 2021
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    Yuanchun Zhou (2021). Construction idea and practice of international scientific data publishing platform [Dataset]. http://doi.org/10.11922/sciencedb.01330
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 12, 2021
    Dataset provided by
    Science Data Bank
    Authors
    Yuanchun Zhou
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This report was released by Prof. Zhou Yuanchun at the 25th annual conference of Society of China University Journals. The report mainly analyzes the significance of scientific data sharing,and how can academic journals practice data sharing. Finally, the report elaborates what services data publishing platform can provide for journal data sharing.

  9. U

    St. Petersburg Coastal and Marine Science Center's Geologic Core and Sample...

    • data.usgs.gov
    • s.cnmilf.com
    • +1more
    Updated Aug 9, 2023
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    Breanna Williams; Heather Schreppel; Christopher Reich; Kathryn Smith; Ginger Tiling-Range; Chelsea Stalk; Steven Douglas; Shawn Dadisman; James Flocks; Lauren Toth; Anastasios Stathakopoulos (2023). St. Petersburg Coastal and Marine Science Center's Geologic Core and Sample Database Metadata [Dataset]. http://doi.org/10.5066/F7319TR3
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    Dataset updated
    Aug 9, 2023
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Breanna Williams; Heather Schreppel; Christopher Reich; Kathryn Smith; Ginger Tiling-Range; Chelsea Stalk; Steven Douglas; Shawn Dadisman; James Flocks; Lauren Toth; Anastasios Stathakopoulos
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Time period covered
    Sep 7, 1974 - Dec 15, 2021
    Description

    This database contains a comprehensive inventory of geologic (coral, coral reef, limestone, and sediment) cores and samples collected, analyzed, published, and/or archived by, or in collaboration with, the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). The SPCMSC Geologic Core and Sample Database includes geologic cores and samples collected beginning in the 1970s to present day, from study sites across the world. This database captures metadata about samples throughout the USGS Science Data Lifecycle: including field collection, laboratory analysis, publication of research, and archival or deaccession. For more information about the USGS Science Data Lifecycle, see USGS Open-File Report 2013-1265 (https://doi.org/10.3133/ofr20131265). The SPCMSC Geologic Core and Sample Database also includes storage locations for physical samples and cores archived in a repository (USGS SPCMSC or elsewhere, if known). The majority of the samples and cores ...

  10. S

    Basic data

    • scidb.cn
    Updated Sep 26, 2022
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    Zheng Nuo; Liu Hailong (2022). Basic data [Dataset]. http://doi.org/10.57760/sciencedb.j00140.00002
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 26, 2022
    Dataset provided by
    Science Data Bank
    Authors
    Zheng Nuo; Liu Hailong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Droplet spread diameter and other basic data

  11. S

    Data from: Relieving the photosensitivity of organic field-effect...

    • scidb.cn
    Updated Oct 13, 2022
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    Lang Jiang (2022). Relieving the photosensitivity of organic field-effect transistors [Dataset]. http://doi.org/10.57760/sciencedb.03505
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 13, 2022
    Dataset provided by
    Science Data Bank
    Authors
    Lang Jiang
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    database of "Relieving the photosensitivity of organic field-effect transistors"

  12. S

    DATA

    • scidb.cn
    Updated Feb 21, 2024
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    Huimin Tong (2024). DATA [Dataset]. http://doi.org/10.57760/sciencedb.15670
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 21, 2024
    Dataset provided by
    Science Data Bank
    Authors
    Huimin Tong
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    数千个数据,18 列和 12776 行

  13. S

    Data from: Data used in the research

    • scidb.cn
    Updated Apr 7, 2025
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    MA Junjie; Gu Jing; Yang Xiaoguang (2025). Data used in the research [Dataset]. http://doi.org/10.57760/sciencedb.21519
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 7, 2025
    Dataset provided by
    Science Data Bank
    Authors
    MA Junjie; Gu Jing; Yang Xiaoguang
    Description

    Research data on public attention and the degree of financialization of physical enterprises from 2010 to 2022

  14. S

    database of irAEs

    • scidb.cn
    Updated May 17, 2022
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    Zhening Zhang; Fangli Jiang (2022). database of irAEs [Dataset]. http://doi.org/10.57760/sciencedb.01770
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 17, 2022
    Dataset provided by
    Science Data Bank
    Authors
    Zhening Zhang; Fangli Jiang
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    part of the data in our study

  15. S

    Data for graphs

    • scidb.cn
    Updated Feb 20, 2023
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    Xiuran Li (2023). Data for graphs [Dataset]. http://doi.org/10.57760/sciencedb.07320
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 20, 2023
    Dataset provided by
    Science Data Bank
    Authors
    Xiuran Li
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    The 66 general graphs are from SuiteSparse Matrix Collection and Gunrock benchmark datasets.

  16. S

    A multinational dataset of game players’ behaviors in a virtual world and...

    • scidb.cn
    Updated Oct 9, 2021
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    Quan-Hoang Vuong; Manh-Toan Ho; Viet-Phuong La; Tam-Tri Le; Thanh Huyen T. Nguyen; Minh-Hoang Nguyen (2021). A multinational dataset of game players’ behaviors in a virtual world and environmental perceptions [Dataset]. http://doi.org/10.11922/sciencedb.j00104.00098
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 9, 2021
    Dataset provided by
    Science Data Bank
    Authors
    Quan-Hoang Vuong; Manh-Toan Ho; Viet-Phuong La; Tam-Tri Le; Thanh Huyen T. Nguyen; Minh-Hoang Nguyen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    World
    Description

    The current dataset offers valuable resources regarding environmental worldviews and behaviors in the virtual world of 640 Animal Crossing: New Horizons (ACNH) gameplayers from 29 countries around the globe. The dataset consists of six major categories: 1) socio-demographic profile, 2) COVID-19 concern, 3) environmental perception, 4) game-playing habit, 5) in-game behavior, and 6) game-playing feeling. By making this dataset open, we aim to provide policymakers, game producers, and researchers with valuable resources for understanding the interactions between behaviors in the virtual world and environmental perceptions, which could help produce video games in compliance with the United Nations (UN) Sustainable Development Goals.

  17. S

    Papers about environmental education research from 2013 to 2022 from WoS

    • scidb.cn
    Updated Oct 26, 2023
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    Yongli Tian (2023). Papers about environmental education research from 2013 to 2022 from WoS [Dataset]. http://doi.org/10.57760/sciencedb.12696
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 26, 2023
    Dataset provided by
    Science Data Bank
    Authors
    Yongli Tian
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    1851 papers about environmental education research from 2013 to 2022 from the Web of Science core collection database were included in this dataset.

  18. S

    Figure 6 Complete Data

    • scidb.cn
    Updated Apr 8, 2024
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    Yujing Zhu (2024). Figure 6 Complete Data [Dataset]. http://doi.org/10.57760/sciencedb.11348
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 8, 2024
    Dataset provided by
    Science Data Bank
    Authors
    Yujing Zhu
    Description

    The complete data of Figure 6 in the paper: details of the two-layer network cluster of CCL and patents

  19. S

    National and provincial population and economy projection databases under...

    • scidb.cn
    Updated Apr 18, 2022
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    Tong Jiang; Buda Su; Cheng Jing; Yanjun Wang; Jinlong Huang; Huanhuan Guo; Yuming Yang; Guojie Wang; Yong Luo (2022). National and provincial population and economy projection databases under Shared Socioeconomic Pathways(SSP1-5)_v2 [Dataset]. http://doi.org/10.57760/sciencedb.01683
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 18, 2022
    Dataset provided by
    Science Data Bank
    Authors
    Tong Jiang; Buda Su; Cheng Jing; Yanjun Wang; Jinlong Huang; Huanhuan Guo; Yuming Yang; Guojie Wang; Yong Luo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    V1 dataset:Under the global framework of Shared Socioeconomic Pathways (SSPs), based on localized population and economic parameters, a Population Development Environment (PDE) model is adopted to construct population grid data for SSPs from 2020 to 2100; Using the Cobb Douglas model, construct economic data for SSPs from 2020 to 2100.The v1 dataset includes:Population grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5°GDP grid data of the world, The Belt and Road region, and China, with a spatial resolution of 0.5 °Grid data on the output value of three industries in the Chinese region, with a spatial resolution of 0.1 °V2 dataset:Based on the data from the 7th National Population Census of China, starting from 2020, the parameters such as fertility rate, mortality rate, migration rate, and education level in the Population Development Environment (PDE) model were updated. Under the Shared Socioeconomic Pathways (SSP1-5), a new version (v2) of the total population and age and gender specific population projection dataset for China and its provinces from 2020 to 2100 was created. Based on the data from the 7th National Population Census and the 4th Economic Census of China, with 2020 as the starting year, the parameters of total factor productivity, capital stock, labor input, and capital elasticity coefficient in the Cobb Douglas model were updated. Under the shared SSP1-5, a new version (v2) of China and its provincial GDP projectiondataset from 2020 to 2100 was created.The v2 (2024 version) dataset includes:Total Population Data of China and Provinces (2020-2100)Population data by age and gender in China (2020-2100)China and Provincial GDP Data (2020-2100)

  20. S

    experimental data

    • scidb.cn
    Updated Jun 26, 2024
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    Chloe (2024). experimental data [Dataset]. http://doi.org/10.57760/sciencedb.09467
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 26, 2024
    Dataset provided by
    Science Data Bank
    Authors
    Chloe
    Description

    erp filesexported data in Excelpre-test of material in ExcelBehaviour data

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Zeyu Zhang (2023). Science Data Bank - An Open and General-Purpose Data Repository [Dataset]. http://doi.org/10.57760/sciencedb.13239

Science Data Bank - An Open and General-Purpose Data Repository

Explore at:
287 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Nov 11, 2023
Dataset provided by
Science Data Bank
Authors
Zeyu Zhang
License

Attribution-NoDerivs 4.0 (CC BY-ND 4.0)https://creativecommons.org/licenses/by-nd/4.0/
License information was derived automatically

Description

Science Data Bank - An Open and General-Purpose Data Repository

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