The global academic research database market is booming, projected to hit $388.2 million in 2025, with a robust CAGR driving growth. This in-depth analysis explores market size, key players (Scopus, Web of Science, PubMed), and future trends shaping this vital sector for researchers and educators.

The databases resulting from articles that created the database and released it are not mentioned here because they will be mentioned later. All Latin America means Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Suriname, Uruguay, Venezuela.

As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of ****. Oracle was the most popular commercial DBMS at that time, with a ranking score of ****.

The Open Database Cloud Platform Solution market is booming, projected to reach $4058 million by 2025, with a CAGR of 15.6% through 2033. Learn about key drivers, trends, and leading companies shaping this rapidly evolving landscape. Discover market insights and forecasts for this lucrative sector.

The size of the Open Source Time Series Database market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

The ODEF brings together data primarily originating from open data portals and webpages controlled by municipal and provincial governments. This database aims to enhance access to a harmonized collection of building addresses across various themes of public interest across Canada. This database is a component of the Linkable Open Data Environment (LODE). The 43 facility types used in the ODEF include:Alternative Learning CentreCampus CollégialCatholicCégepCentre Collégial De Transfert De TechnologieCentre D'EnseignementCharterCollège ConstituantCollège PrivéCollège RégionalConstituanteÉcole GouvernementaleEcs Private OperatorEntité JuridiqueÉtablissement D'EnseignementÉtablissement D'Enseignement Collège PrivéExternal Service FacilityFederal First NationsFederal JailFirst Nations SchoolFrancophoneHospitalIndependent SchoolInstallationInstallation Collège PrivéJunior CollegeMiscellaneousNursing SchoolsOrganisme Décernant Des Grades UniversitairesPrivatePrivate InstitutionPrivate SchoolProtestant SeparateProvincialPublicPublic SchoolRegroupement Administratif UniSeparateSiège SocialStrongstart BcTechnical And VocationalUniversitéUniversityFor visualization purposes, only the top 12 facility types are displayed in the map. To access the additional facilities, go to 'Symbology' and select 'Ungroup'. Data sources and methodology The inputs for the ODEF are primarily datasets provided by municipal, regional or provincial sources available to the general public through open government portals under various types of open data licences, or otherwise published on their webpages and released under an open licence with their permission. The ODEF was created by gathering the microdata on educational facilities from open data portals, provincial or territorial websites (with permission from the data owners), and one federal department. The current version of the database (version 2.0) contains approximately 19,000 records. Collection of data from the above indicated data providers was from August 2019 to March 2021. The individual datasets were collected from their respective sources and processed and harmonized into the ODEF. Within the original datasets, each data provider attached a different set of variables. To see the full list of variables provided by a given data provider, please visit the original sources which are linked in the metadata document that accompanies the ODEF. Each facility in the ODCAF includes the following information:Institution NameInstitution TypeAuthority NameInternational Standard Classification of Education (ISCED) LevelAddressUnitStreet NumberStreet NameMunicipality NameProvincePostal CodeCensus Subdivision NameCensus Subdivision Unique IdentifierLongitudeLatitudeGeocoding SourceSource IDUnique IDFor more information on how the addresses and variables were compiled, see the metadata document that accompanies the ODEF.This is a republishing of the data available from Statistics Canada at https://www.statcan.gc.ca/en/lode/databases/odef. There were a total of 18,944 records and 3444 without cooordinates. All but 41 of the 3444 records were successfully geocoded using the Esri World Geocoder.Current Version: December 13, 2024 — Version 3.0Update Frequency: Once a year

This dataset is a result of the PHFood Brazil Study, a comprehensive initiative that integrates diverse national open databases to provide insights into food production, consumption, environmental impact, and public health in Brazil from 1974 to 2022. The primary hypothesis behind the dataset is that analyzing food systems, nutrition, and agricultural practices over time will reveal patterns that can inform sustainable food policies and health interventions. Research Hypothesis: We hypothesize that by aggregating and harmonizing data from various national sources, it will be possible to identify critical relationships between food production, nutrient availability, and environmental sustainability. This integrated dataset allows us to evaluate how agricultural practices and food consumption patterns have evolved in response to changing environmental, economic, and societal conditions in Brazil. Food Production and Nutrient Data: The dataset includes detailed information on harvested areas, food production (in tons), and nutrient availability (e.g., energy, protein, fiber, vitamins, and minerals) for various food groups across different Brazilian regions and states. Water and Environmental Data: Water usage, deficit, and environmental impact data are linked to agricultural activities, allowing the assessment of water efficiency in food production. Pesticides and Residue Monitoring: Information on pesticide usage, maximum residue levels (MRL), and residue percentage in food items is provided, with additional details on authorized pesticide types, toxicity classifications, and environmental risks. Food Consumption: Data on food acquisition and consumption patterns, broken down by food groups (e.g., beans, vegetables, fruits), are presented, highlighting dietary trends across various population groups. Data Collection and Methodology: The dataset was constructed by linking several government datasets, including agricultural statistics, water use reports, and residue monitoring programs. Each dataset was meticulously harmonized to ensure compatibility and completeness, following Extract, Transform, Load (ETL) processes. The data was collected over several decades, representing longitudinal trends in food systems and health. The harmonization process ensures that all data fields are consistent and comparable across time and regions.

Interpretation and Use: This dataset can be used to explore the connections between food systems, health, and the environment. It supports studies in agrifood systems, sustainability, public health, and climate change. Researchers can investigate the impacts of food production on nutrient availability, water consumption, and pesticide use over time. It can also be used to model future food system sustainability under different climate scenarios and dietary patterns, contributing to the development of food policies that balance human and planetary health.

The Open Database of Buildings (ODB) is a collection of open data on buildings made available under the Open Government License - Canada. The ODB brings together 530 datasets originating from 107 government sources of open data. The database aims to enhance access to a harmonized collection of building features across Canada.

The JRC-PPDB-OPEN database is a first attempt towards a more detailed and coherent, albeit still incomplete, dataset of European power plants.

Open Database of corporate entities. As of 2011-04-09 has information on 7,841,828 companies from around the world. Jurisdictions covered include:

• 41,292 Bermuda
• 3,886,733 United Kingdom
• 96,104 Gibraltar
• 105,640 Isle of Man
• 77,693 Iceland
• 60,827 Jersey
• 92,795 Luxembourg
• 2,188,873 Netherlands
• 97,653 Alaska (US)
• 197,798 District of Columbia (US)
• 996,420 Michigan (US)

There is good API access but currently but no bulk availablitity.

License

See http://opencorporates.com/info/licence. However, should note that most data in OpenCorporates is scraped from elsewhere so this license only covers the 'IP' that OpenCorporates has obtained as a result of their efforts (and license of original databases, e.g. Companies House in the UK, is unclear).

Photometric calibration for 2MASS was performed using observations of calibration fields made at regular intervals during each night of survey operations. Measurements of standard stars in the fields were used to derive the photometric zero point offsets as a function of time during each night. Atmospheric extinction coefficients were derived from 2MASS observations made over long periods.

2MASS calibration fields, or tiles, are 1° long in declination and approximately 8.5' wide in right ascension. There are 35 regular survey calibration fields distributed at approximately two hour intervals in right ascension near declinations of approximately -30°, 0° and +30°. An additional five calibration fields were defined in and around the Large and Small Magellanic Clouds to support the deep observation (6x) campaign towards the end of survey operations.

Over the course of the survey, the regular calibration fields were scanned between 562 and 3692 times in nominally photometric conditions. Equatorial fields were observed from both observatories, so were observed more frequently than those near ±30° which were observed with only one telescope. The special Magellanic Cloud calibration fields were observed 108 to 468 times between November 2000 and February 2001.

Calibration scan Atlas Images, and Point and Extended Source Working Databases (Cal-PSWDB and Cal-XSWDB), analogous to those from the main survey, were produced from the calibration scan pipeline data reduction. The calibration Image Atlas and WDBs are the fundamental processing archives of the data from 73,230 calibration scans taken in photometric conditions during 2MASS survey operations. The Cal-WDBs contain positions, magnitudes and characteristics of 191,464,020 and 403,811 point and extended "source" extractions, respectively. The calibration Image Atlas contains 878,769 FITS images in the three survey bandpasses redundantly covering ~5 deg2 of sky. Unlike the main survey and 6x observations, "Catalogs" have not been derived from the Calibration WDBs.

Open access scientific documents from website eLIBRARY.RU. The dataset consists of a zip-archive with raw text documents and a csv file with documents metadata. It contains information about publication year, topic according to State Rubricator of Scientific and Technical Information (SR STI), language and ID of the document in the www.elibrary.ru database.

Acknowledgments The authors are grateful to the Scientific Electronic Library eLIBRARY.RU and personally Gennady Eremenko and Viktor Glukhov for the data provided.

The Carbon Storage Open Database is a collection of spatial data obtained from publicly available sources published by several NATCARB Partnerships and other organizations. The carbon storage open database was collected from open-source data on ArcREST servers and websites in 2018, 2019, 2021, and 2022. The original database was published on the former GeoCube, which is now EDX Spatial, in July 2020, and has since been updated with additional data resources from the Energy Data eXchange (EDX) and external public data resources. The shapefile geodatabase is available in total, and has also been split up into multiple databases based on the maps produced for EDX spatial. These are topical map categories that describe the type of data, and sometimes the region for which the data relates. The data is separated in case there is only a specific area or data type that is of interest for download. In addition to the geodatabases, this submission contains: 1. A ReadMe file describing the processing steps completed to collect and curate the data. 2. A data catalog of all feature layers within the database. Additional published resources are available that describe the work done to produce the geodatabase: Morkner, P., Bauer, J., Creason,more » C., Sabbatino, M., Wingo, P., Greenburg, R., Walker, S., Yeates, D., Rose, K. 2022. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104945 Morkner, P., Bauer, J., Shay, J., Sabbatino, M., and Rose, K. An Updated Carbon Storage Open Database - Geospatial Data Aggregation to Support Scaling -Up Carbon Capture and Storage. United States: N. p., 2022. Web. https://www.osti.gov/biblio/1890730 Morkner, P., Rose, K., Bauer, J., Rowan, C., Barkhurst, A., Baker, D.V., Sabbatino, M., Bean, A., Creason, C.G., Wingo, P., and Greenburg, R. Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights. United States: N. p., 2020. Web. https://www.osti.gov/biblio/1777195 Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.« less

Planimetric basemap layer containing open spaces (excluding parks). Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-planimetrics/blob/master/Capture_Rules.md.

Explore the booming Open Source Database Solution market, projected to reach $16 billion by 2025 with a 13% CAGR. Discover key drivers, restraints, trends, and regional insights for cloud databases and enterprise data management.

Shared database of videos featuring juvenile and adult male rats and adult male mice exploring stationary objects.This dataset includes wide variety of experimental conditions including genetic, pharmacological, and sensory deprivation interventions to explore how active sensing behavior is modulated by different factors. Database of high speed videos of whisking behavior in freely moving rodents.Open source high speed infrared videography database to study principles of active sensing in freely navigating rodents.

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.

Geo Open is an IP address geolocation per country in MMDB format. The database can be used as a replacement for software using the MMDB format. Information about MMDB format: https://maxmind.github.io/MaxMind-DB/ Open source server using Geo Open: https://github.com/adulau/mmdb-server Open source library to read MMDB file: https://github.com/maxmind/MaxMind-DB-Reader-python Historical dataset: https://cra.circl.lu/opendata/geo-open/ The database is automatically generated from public BGP AS announces matching the country code. The precision is at country level.

The data in this set was culled from the Directory of Open Access Journals (DOAJ), the Proquest database Library and Information Science Abstracts (LISA), and a sample of peer reviewed scholarly journals in the field of Library Science. The data include journals that are open access, which was first defined by the Budapest Open Access Initiative: By ‘open access’ to [scholarly] literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. Starting with a batch of 377 journals, we focused our dataset to include journals that met the following criteria: 1) peer-reviewed 2) written in English or abstracted in English, 3) actively published at the time of..., Data Collection In the spring of 2023, researchers gathered 377 scholarly journals whose content covered the work of librarians, archivists, and affiliated information professionals. This data encompassed 221 journals from the Proquest database Library and Information Science Abstracts (LISA), widely regarded as an authoritative database in the field of librarianship. From the Directory of Open Access Journals, we included 144 LIS journals. We also included 12 other journals not indexed in DOAJ or LISA, based on the researchers’ knowledge of existing OA library journals. The data is separated into several different sets representing the different indices and journals we searched. The first set includes journals from the database LISA. The following fields are in this dataset:

Journal: title of the journal

Publisher: title of the publishing company

Open Data Policy: lists whether an open data exists and what the policy is

Country of publication: country where the journal is publ..., , # Open access practices of selected library science journals

The data in this set was culled from the Directory of Open Access Journals (DOAJ), the Proquest database Library and Information Science Abstracts (LISA), and a sample of peer reviewed scholarly journals in the field of Library Science.

The data include journals that are open access, which was first defined by the Budapest Open Access Initiative:Â

By ‘open access’ to [scholarly] literature, we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself.

Starting with a batch of 377 journals, we focused our dataset to include journals that met the following criteria: 1) peer-reviewed 2) written in Engli...

Database of crystal structures of organic, inorganic, metal-organic compounds and minerals, excluding biopolymers. It currently contains ~291204 entries (July 2014) in crystallographic information file format, with nearly full coverage of the International Union of Crystallography publications, and is growing in size and quality. Deposit your data: An interface allows you to upload, validate and edit CIF files before submitting them for deposition.