The global academic research databases market is projected to grow from $439 million in 2025 to $739 million by 2033, at a CAGR of 6.1%. The market is driven by the increasing demand for access to academic content, the growing number of students and researchers, and the adoption of digital technologies in education and research. The major players in the market include Scopus, Web of Science, PubMed, ERIC, and ProQuest. The growth of the academic research databases market is also fueled by the increasing availability of open access content and the rising use of artificial intelligence (AI) in research. AI can be used to automate tasks such as literature search and data analysis, which can save researchers time and effort. Additionally, the development of new technologies such as virtual reality (VR) and augmented reality (AR) is creating new opportunities for researchers to access and interact with research content. These trends are expected to continue to drive the growth of the academic research databases market in the coming years.

List of Academic Databases Freely Open by the Academia Sinica

Abstract

A UK Primary Care Database

Documentation

IMRD, incorporating THIN, a Cegedim Database in electronic form, and otherwise, is a longitudinal patient database. Primary care practices in the UK are recruited by Cegedim to participate in the data collection scheme. The data collection software removes practice, practitioner and patient identifiers at source, retaining information on patient’s, (1) the physical health or condition of that patient, (2) the mental health or condition of that patient, (3) the diagnosis of the condition of that patient, (4) the care or treatment given to that patient, and (5) other information which is to an extent derived, directly or indirectly, from such information.

Data provided by: IQVIA

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Global Academic Research Databases market size 2025 was XX Million. Academic Research Databases Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

ERS supports a broad spectrum of food and nutrition assistance research and has compiled an electronic database of over 900 peer-reviewed reports and articles based on ERS-supported research. The database is searchable by title, lead author, topic, year of publication, and data set analyzed.

This is an RO-Crate of a database from the Online Heritage Resource Manager, a databasing system from the eScholarhip Research Centre of the University of Melbourne. The crate has been built from a dump of the OHRM database. While we have made the best effort possible to create a valid RO-Crate, it is possible for various reasons that the crate contains broken links or improperly described files. If you are the owner of the original database, please contact the maintainer of this RO-Crate in Figshare.

To view the contents of the crate, download the deposited file, unzip it, and open the ro-crate-preview.html file in the top directory.

The eICU Collaborative Research Database is a large multi-center critical care database made available by Philips Healthcare in partnership with the MIT Laboratory for Computational Physiology.

Frequency of keywords appearing in football research papers.

Comprehensive dataset of 656 Academic departments in North Carolina, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 560 Academic departments in Michigan, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.

The purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.

Comprehensive dataset of 139 Academic departments in Mississippi, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Abstract (en): The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units. The American College Catalog Study Database (CCS) is intended to allow researchers to examine changes in the structure of institutionalized knowledge in four-year colleges and universities within the United States. For information on the study design, including detailed coding conventions, please see the Original P.I. Documentation section of the ICPSR Codebook. The data are not weighted. Dataset 1, Characteristics Variables, contains three weight variables (IDAWT, CCSWT, and CASEWEIGHT) which users may wish to apply during analysis. For additional information on weights, please see the Original P.I. Documentation section of the ICPSR Codebook. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Approximately 75 percent of IDA institutions are included in CCS. For additional information on response rates, please see the Original P.I. Documentation section of the ICPSR Codebook. Four-year not-for-profit colleges and universities in the United States. Smallest Geographic Unit: state CCS includes 286 institutions drawn from the IDA sample of 384 United States four-year colleges and universities. CCS contains every IDA institution for which a full set of catalogs could be located at the initiation of the project in 2000. CCS contains seven datasets that can be linked through an institutional identification number variable (PROJ_ID). Since the data are organized in a panel format, it is also necessary to use a second variable (YEAR) to link datasets. For a brief description of each CCS dataset, please see Appendix B within the Original P.I. Documentation section of the ICPSR Codebook.There are date discrepancies between the data and the Original P.I. Documentation. Study Time Periods and Collection Dates reflect dates that are present in the data. No additional information was provided.Please note that the related data collection featuring the Institutional Data Archive on American Higher Education, 1970-2011, will be available as ICPSR 34874. Additional information on the American College Catalog Study Database (CCS) and the Institutional Data Archive (IDA) database can be found on the Colleges and Universities 2000 Web site.

CSCD defines the entire school campus of all public schools to allow spatial analysis, including the full extent of lands used for public education in California. CSCD is suitable for a wide range of planning, assessment, analysis, and display purposes.The lands in CSCD are defined by the parcels owned, rented, leased, or used by a public California school district for the primary purpose of educating youth. CSCD provides vetted polygons representing each public school in the state.Data layers include: K-12 schools, university lands, community college campusesFull documentation is available in the User ManualSuggested improvements welcome via MapCollaborator (TM)

The dataset for the development and validation of Scale for assessing university digital educational environment (AUDEE Scale by M. Sorokova, M. Odintsova, and N. Radchikova) is presented (N = 406). AUDEE Scale has six subscales: “DEE Learning Process Satisfaction”, “DEE Communication Satisfaction and Learning Motivation”, “DEE Stress Tension”, “Need for Support in the DEE Learning Activities”, “DEE Dishonest Strategies Prevalence”, and “DEE Accessibility” as well as the total score indicating the degree of positive attitude towards the digital educational environment. Full text of AUDEE Scale questionnaire in Russian and in English is available along with the dataset

We conducted two literature searches to help guide the development of a conceptual model of a barrier island and shoreline system in response to cumulative effects of restoration projects. The first search targeted examples of cumulative effects assessments and/or existing conceptual models from which a system-specific conceptual model can be built. The second search targeted the identification of barrier island and shoreline environmental system components, drivers and stressors. There are two data sheets in this dataset; one set of records from each literature search. Each spreadsheet includes record information pulled directly from the Web of Science searches, such as title, authors, abstract, and publication source. We also screened the records for relevance to our needs and additional information contained in the titles and abstracts, including environmental system components (e.g. structure or function), specific drivers and stressors, and more.

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

This database results from retrieving bibliometric information from the Web of Science database; therefore, we used a query line to extract medical-related documents published by Mexican Authors. We cleaned the database and enriched it with different techniques (NLP, Cluster Analysis, temporal analysis, and manual curation). The specific query is as follows: CU=Mexico AND (SU=ANATOMY OR SU=MEDICAL ETHICS OR SU=BIOLOGY OR SU=GENETICS OR SU=MICROBIOLOGY OR SU=BIOPHYSICS OR SU=NEUROLOGY OR SU=DENTISTRY OR SU=ONCOLOGY OR SU=DERMATOVENEROLOGY OR SU=OPHTHALMOLOGY OR SU=EPIDEMIOLOGY OR SU=OTOLARYNGOLOGY OR SU=FORENSIC MEDICINE OR SU=PATHOLOGY OR SU=GYNECOLOGY OR SU=OBSTETRICS OR SU=PEDIATRICS OR SU=HISTOLOGY OR SU=EMBRYOLOGY OR SU=PHARMACOLOGY OR SU=HYGIENE OR SU=PHYSIOLOGY OR SU=PATOPHYSIOLOGY OR SU=BIOCHEMISTRY OR SU=PSYCHOLOGY OR SU=PSYCHIATRICS OR SU=IMMUNOLOGY OR SU=RADIOLOGY OR SU=INFECTIOUS DISEASES OR SU=SOCIAL MEDICINE OR SU=TELEMEDICINE OR SU=SPORTS MEDICINE OR SU=INTERNAL MEDICINE OR SU=SURGERY)Data is stored in .csv; codified in UTF-8; UNIX (LF) linebreak; String delimiter "

The global full-text database market is projected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX% during the forecast period. The growth is attributed to increasing demand for information retrieval, advancements in technology, and rising need for efficient research and development. Key drivers of the market include growing adoption of digital libraries, rising demand for personalized content, and increasing focus on research and development. Key trends in the full-text database market include the emergence of artificial intelligence (AI) and machine learning (ML) technologies, the growth of open access publishing, and the increasing adoption of cloud-based solutions. The market is segmented by application (academic research, corporate research, legal research, and others) and by type (bibliographic, full-text, and abstract). Major players in the market include John Wiely & Sons, ICPSR, IEEE, EBSCO, UMI, Blackwell, Springer Link, Elsevier Science, Apache Solr, Elastic N.V., CNKI, China Science and Technology Journal Database, Wanfang Data Knowledge Service Platform, China Science Citation Database, and Chinese, Western, Japanese and Russian Journals Joint Directory Database. The market is expected to witness significant growth in emerging economies, such as China and India, due to rising literacy rates and increasing demand for information access.

These files include the data from the "The societal impact of Open Science - a scoping review", part of a series of studies conducetd within the PathOS Horizon Europe project on the academic, economic, and societal impacts of Open Science. This study was conducted in two phases. In phase 1 an academic database search was conducted. For phase 2 an automatic snowball search was performed based on results from phase 1 ( and grey literature was searched manually.

The upload contains five files:

1. Main file with extracted information for the 485 studies included in the review ("academic_impact_included_all_data.csv").
2. Excel file documenting the grey literature search ("Grey_Literature_Search.xlsx").
3. R project for the snowball search ("academic_impact_snowball.zip").
4. R project for cleaning data and producing summaries and figures ("academic_impact_stats.zip").
5. Excel file mapping the frascati codes used in file (1) to their textual representations ("Frascati definitions.xlsx")

For more details on the methods see the protocol and its addendum. For the background, results and discussion see the deliverable (reporting on phase 1) and the pre-print.