Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.

Check Market Research Intellect's Open Source Database Market Report, pegged at USD 15 billion in 2024 and projected to reach USD 30 billion by 2033, advancing with a CAGR of 8.8% (2026-2033).Explore factors such as rising applications, technological shifts, and industry leaders.

The size of the Open-Source Database Software 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 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.

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 size of the Open Source Database Solution 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.

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

Digital data from the political sphere is abundant, omnipresent, and more and more directly accessible through the Internet. Project Vote Smart (PVS) is a prominent example of this big public data and covers various aspects of U.S. politics in astonishing detail. Despite the vast potential of PVS’ data for political science, economics, and sociology, it is hardly used in empirical research. The systematic compilation of semi-structured data can be complicated and time consuming as the data format is not designed for conventional scientific research. This paper presents a new tool that makes the data easily accessible to a broad scientific community. We provide the software called pvsR as an add-on to the R programming environment for statistical computing. This open source interface (OSI) serves as a direct link between a statistical analysis and the large PVS database. The free and open code is expected to substantially reduce the cost of research with PVS’ new big public data in a vast variety of possible applications. We discuss its advantages vis-à-vis traditional methods of data generation as well as already existing interfaces. The validity of the library is documented based on an illustration involving female representation in local politics. In addition, pvsR facilitates the replication of research with PVS data at low costs, including the pre-processing of data. Similar OSIs are recommended for other big public databases.

This description is part of the blog post "Systematic Literature Review of teaching Open Science" https://sozmethode.hypotheses.org/839

According to my opinion, we do not pay enough attention to teaching Open Science in higher education. Therefore, I designed a seminar to teach students the practices of Open Science by doing qualitative research.About this seminar, I wrote the article ”Teaching Open Science and qualitative methods“. For the article ”Teaching Open Science and qualitative methods“, I started to review the literature on ”Teaching Open Science“. The result of my literature review is that certain aspects of Open Science are used for teaching. However, Open Science with all its aspects (Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools) is not an issue in publications about teaching.

Based on this insight, I have started a systematic literature review. I realized quickly that I need help to analyse and interpret the articles and to evaluate my preliminary findings. Especially different disciplinary cultures of teaching different aspects of Open Science are challenging, as I myself, as a social scientist, do not have enough insight to be able to interpret the results correctly. Therefore, I would like to invite you to participate in this research project!

I am now looking for people who would like to join a collaborative process to further explore and write the systematic literature review on “Teaching Open Science“. Because I want to turn this project into a Massive Open Online Paper (MOOP). According to the 10 rules of Tennant et al (2019) on MOOPs, it is crucial to find a core group that is enthusiastic about the topic. Therefore, I am looking for people who are interested in creating the structure of the paper and writing the paper together with me. I am also looking for people who want to search for and review literature or evaluate the literature I have already found. Together with the interested persons I would then define, the rules for the project (cf. Tennant et al. 2019). So if you are interested to contribute to the further search for articles and / or to enhance the interpretation and writing of results, please get in touch. For everyone interested to contribute, the list of articles collected so far is freely accessible at Zotero: https://www.zotero.org/groups/2359061/teaching_open_science. The figure shown below provides a first overview of my ongoing work. I created the figure with the free software yEd and uploaded the file to zenodo, so everyone can download and work with it:

To make transparent what I have done so far, I will first introduce what a systematic literature review is. Secondly, I describe the decisions I made to start with the systematic literature review. Third, I present the preliminary results.

Systematic literature review – an Introduction

Systematic literature reviews “are a method of mapping out areas of uncertainty, and identifying where little or no relevant research has been done.” (Petticrew/Roberts 2008: 2). Fink defines the systematic literature review as a “systemic, explicit, and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.” (Fink 2019: 6). The aim of a systematic literature reviews is to surpass the subjectivity of a researchers’ search for literature. However, there can never be an objective selection of articles. This is because the researcher has for example already made a preselection by deciding about search strings, for example “Teaching Open Science”. In this respect, transparency is the core criteria for a high-quality review.

In order to achieve high quality and transparency, Fink (2019: 6-7) proposes the following seven steps:

Selecting a research question.

Selecting the bibliographic database.

Choosing the search terms.

Applying practical screening criteria.

Applying methodological screening criteria.

Doing the review.

Synthesizing the results.

I have adapted these steps for the “Teaching Open Science” systematic literature review. In the following, I will present the decisions I have made.

Systematic literature review – decisions I made

Research question: I am interested in the following research questions: How is Open Science taught in higher education? Is Open Science taught in its full range with all aspects like Open Access, Open Data, Open Methodology, Open Science Evaluation and Open Science Tools? Which aspects are taught? Are there disciplinary differences as to which aspects are taught and, if so, why are there such differences?

Databases: I started my search at the Directory of Open Science (DOAJ). “DOAJ is a community-curated online directory that indexes and provides access to high quality, open access, peer-reviewed journals.” (https://doaj.org/) Secondly, I used the Bielefeld Academic Search Engine (base). Base is operated by Bielefeld University Library and “one of the world’s most voluminous search engines especially for academic web resources” (base-search.net). Both platforms are non-commercial and focus on Open Access publications and thus differ from the commercial publication databases, such as Web of Science and Scopus. For this project, I deliberately decided against commercial providers and the restriction of search in indexed journals. Thus, because my explicit aim was to find articles that are open in the context of Open Science.

Search terms: To identify articles about teaching Open Science I used the following search strings: “teaching open science” OR teaching “open science” OR teach „open science“. The topic search looked for the search strings in title, abstract and keywords of articles. Since these are very narrow search terms, I decided to broaden the method. I searched in the reference lists of all articles that appear from this search for further relevant literature. Using Google Scholar I checked which other authors cited the articles in the sample. If the so checked articles met my methodological criteria, I included them in the sample and looked through the reference lists and citations at Google Scholar. This process has not yet been completed.

Practical screening criteria: I have included English and German articles in the sample, as I speak these languages (articles in other languages are very welcome, if there are people who can interpret them!). In the sample only journal articles, articles in edited volumes, working papers and conference papers from proceedings were included. I checked whether the journals were predatory journals – such articles were not included. I did not include blogposts, books or articles from newspapers. I only included articles that fulltexts are accessible via my institution (University of Kassel). As a result, recently published articles at Elsevier could not be included because of the special situation in Germany regarding the Project DEAL (https://www.projekt-deal.de/about-deal/). For articles that are not freely accessible, I have checked whether there is an accessible version in a repository or whether preprint is available. If this was not the case, the article was not included. I started the analysis in May 2019.

Methodological criteria: The method described above to check the reference lists has the problem of subjectivity. Therefore, I hope that other people will be interested in this project and evaluate my decisions. I have used the following criteria as the basis for my decisions: First, the articles must focus on teaching. For example, this means that articles must describe how a course was designed and carried out. Second, at least one aspect of Open Science has to be addressed. The aspects can be very diverse (FOSS, repositories, wiki, data management, etc.) but have to comply with the principles of openness. This means, for example, I included an article when it deals with the use of FOSS in class and addresses the aspects of openness of FOSS. I did not include articles when the authors describe the use of a particular free and open source software for teaching but did not address the principles of openness or re-use.

Doing the review: Due to the methodical approach of going through the reference lists, it is possible to create a map of how the articles relate to each other. This results in thematic clusters and connections between clusters. The starting point for the map were four articles (Cook et al. 2018; Marsden, Thompson, and Plonsky 2017; Petras et al. 2015; Toelch and Ostwald 2018) that I found using the databases and criteria described above. I used yEd to generate the network. „yEd is a powerful desktop application that can be used to quickly and effectively generate high-quality diagrams.” (https://www.yworks.com/products/yed) In the network, arrows show, which articles are cited in an article and which articles are cited by others as well. In addition, I made an initial rough classification of the content using colours. This classification is based on the contents mentioned in the articles’ title and abstract. This rough content classification requires a more exact, i.e., content-based subdivision and evaluation by others, who are experts in the respective fields/disciplines.

Market Research Intellect presents the Open-Source Database Software Market Report-estimated at USD 5.1 billion in 2024 and predicted to grow to USD 12.4 billion by 2033, with a CAGR of 10.5% over the forecast period. Gain clarity on regional performance, future innovations, and major players worldwide.

The dataset contains bibliographic information about scientific articles published by researchers from Norwegian research organizations and is an enhanced subset of data from the Cristin database. Cristin (current research information system in Norway) is a database with bibliographic records of all research articles with an Norwegian affiliation with a publicly funded research institution in Norway. The subset is limited to metadata about journal articles reported in the period 2013-2021 (186,621 records), and further limited to information of relevance for the study (see below). Article metadata are enhanced with open access status by several sources, particularly unpaywall, DOAJ and hybrid-information in case an article is part of a publish-and-read-deal.

Journal of Chemistry Acceptance Rate - ResearchHelpDesk - Journal of Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles on all aspects of fundamental and applied chemistry. Journal of Chemistry is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. Journal of Chemistry is included in many leading abstracting and indexing databases. For a complete list, click here. The most recent Impact Factor for Journal of Chemistry is 1.727 according to the 2018 Journal Citation Reports released by Clarivate Analytics in 2019. The journal’s most recent CiteScore is 1.32 according to the CiteScore 2018 metrics released by Scopus. Abstracting and Indexing Academic Search Alumni Edition Academic Search Complete AgBiotech Net AgBiotech News and Information AGRICOLA Agricultural Economics Database Agricultural Engineering Abstracts Agroforestry Abstracts Animal Breeding Abstracts Animal Science Database Biofuels Abstracts Botanical Pesticides CAB Abstracts Chemical Abstracts Service (CAS) CNKI Scholar Crop Physiology Abstracts Crop Science Database Directory of Open Access Journals (DOAJ) EBSCOhost Connection EBSCOhost Research Databases Elsevier BIOBASE - Current Awareness in Biological Sciences (CABS) EMBIOlogy Energy and Power Source Global Health Google Scholar J-Gate Portal Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index Reaxys Science Citation Index Expanded Scopus Textile Technology Index The Summon Service WorldCat Discovery Services

About DOAJ

The aim of the Directory of Open Access Journals is to increase the visibility and ease of use of open access scientific and scholarly journals thereby promoting their increased usage and impact. The Directory aims to be comprehensive and cover all open access scientific and scholarly journals that use a quality control system to guarantee the content. In short a one stop shop for users to Open Access Journals.

Definitions

Open Access Journal: We define open access journals as journals that use a funding model that does not charge readers or their institutions for access. From the BOAI definition [1] of "open access" we take the right of users to "read, download, copy, distribute, print, search, or link to the full texts of these articles" as mandatory for a journal to be included in the directory. [1] http://www.earlham.edu/~peters/fos/boaifaq.htm#openaccess

'''Quality Control''': The journal must exercise peer-review or editorial quality control to be included.

'''Research Journal''': Journals that report primary results of research or overviews of research results to a scholarly community.

'''Periodical''': A serial appearing or intended to appear indefinitely at regular intervals, generally more frequently than annually, each issue of which is numbered or dated consecutively and normally contains separate articles, stories, or other writings.

Selection Criteria

'''Coverage''': '''Subject''': all scientific and scholarly subjects are covered Types of resource: scientific and scholarly periodicals that publish research or review papers in full text. Acceptable sources: academic, government, commercial, non-profit private sources are all acceptable. '''Level''': the target group for included journals should be primarily researchers. '''Content''': a substantive part of the journal should consist of research papers. All content should be available in full text. All languages

'''Access''': All content freely available. Registration: Free user registration online is acceptable. Open Access without delay (e.g. no embargo period).

'''Quality''': For a journal to be included it should exercise quality control on submitted papers through an editor, editorial board and/or a peer-review system.

'''Periodical''': The journal should have an ISSN (International Standard Serial Number, for information see http://www.issn.org).

Metadata information

Resources will be catalogued on journal title level. To make article level content searchable in the system, journal owners are encouraged to supply us with article metadata when a journal has been added into the directory. If you are a journal owner and have not received this information, please contact us.

Project Background

The proliferation of freely accessible online journals, the development of subject specific pre- and e-print archives and collections of learning objects provides a very valuable supplement of scientific knowledge to the existing types of published scientific information (books, journals, databases etc.). However these valuable collections are difficult to overview and integrate in the library and information services provided by libraries for their user constituency.

At the First Nordic Conference on Scholarly Communication in Lund/Copenhagen the idea of creating a comprehensive directory of Open Access Journals was discussed. The conclusion was that it would be a valuable service for the global research and education community. Open Society Institute (OSI) supported the initial project work.

Available technologies make it possible to collect and organize these resources in a way that allow libraries worldwide to integrate these resources in existing services thus offering added value both for the service providers of these resources and for the global research and education community.

[Deprecated version, used in the support article, please download the last version]

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community.
It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available.

Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).
A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.
The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.

1- DISCLAIMER

The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses.

2- DESCRIPTION

Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.

Please refer to the support article for more details (see above).

The dataset contains the following columns:

• "First column" : index
• airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.
• acft_icao : ICAO code of the aircraft type
• acft_class : Aircraft class identifier, own classification.
  • WB: Wide Body
  • NB: Narrow Body
  • RJ: Regional Jet
  • PJ: Private Jet
  • TP: Turbo Propeller
  • PP: Piston Propeller
  • HE: Helicopter
  • OTHER
• seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.
• source: Original data source for the record, before compilation and enrichment.
  • ANAC: Brasilian Civil Aviation Authorities
  • AUS Stats: Australian Civil Aviation Authorities
  • BTS: US Bureau of Transportation Statistics T100
  • Estimation: Own model, estimation on Wikipedia-parsed route database
  • Eurocontrol: Aggregation and enrichment of R&D database
  • OpenSky
  • World Bank
• seats: Number of seats available for the data entry, AFTER airport residual scaling
• n_flights: Number of flights of the data entry, when available
• iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.
• departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous
• departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import
• departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import
• seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling
• distance_km: Flight distance (km)
• ask: Available Seat Kilometres
• rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)
• fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available
• fuel_burn: Total fuel burn of the data entry (kg)
• co2: Total CO2 emissions of the data entry (kg)
• domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation

Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

Discover the booming open-source database market! This in-depth analysis reveals key trends, growth drivers, and leading players shaping the future of database solutions, including cloud adoption, market segmentation, and regional analysis (2019-2033). Explore the potential of open-source databases for your business.

Amazon Web Services (AWS) was the most popular cloud provider for the hosting of open source database management systems (DBMSs) as of 2020. Around 45 percent of DBMSs ran on public cloud at that time, and another ** percent ran on hybrid cloud.

CottonGen (https://www.cottongen.org) is a curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data to enable basic, translational and applied research in cotton. Built using the open-source Tripal database infrastructure, CottonGen supersedes CottonDB and the Cotton Marker Database, which includes sequences, genetic and physical maps, genotypic and phenotypic markers and polymorphisms, quantitative trait loci (QTLs), pathogens, germplasm collections and trait evaluations, pedigrees, and relevant bibliographic citations, with enhanced tools for easier data sharing, mining, visualization, and data retrieval of cotton research data. CottonGen contains annotated whole genome sequences, unigenes from expressed sequence tags (ESTs), markers, trait loci, genetic maps, genes, taxonomy, germplasm, publications and communication resources for the cotton community. Annotated whole genome sequences of Gossypium raimondii are available with aligned genetic markers and transcripts. These whole genome data can be accessed through genome pages, search tools and GBrowse, a popular genome browser. Most of the published cotton genetic maps can be viewed and compared using CMap, a comparative map viewer, and are searchable via map search tools. Search tools also exist for markers, quantitative trait loci (QTLs), germplasm, publications and trait evaluation data. CottonGen also provides online analysis tools such as NCBI BLAST and Batch BLAST. This project is funded/supported by Cotton Incorporated, the USDA-ARS Crop Germplasm Research Unit at College Station, TX, the Southern Association of Agricultural Experiment Station Directors, Bayer CropScience, Corteva/Agriscience, Dow/Phytogen, Monsanto, Washington State University, and NRSP10. Resources in this dataset:Resource Title: Website Pointer for CottonGen. File Name: Web Page, url: https://www.cottongen.org/ Genomic, Genetic and Breeding Resources for Cotton Research Discovery and Crop Improvement organized by :

Species (Gossypium arboreum, barbadense, herbaceum, hirsutum, raimondii, others), Data (Contributors, Download, Submission, Community Projects, Archives, Cotton Trait Ontology, Nomenclatures, and links to Variety Testing Data and NCBISRA Datasets), Search options (Colleague, Genes and Transcripts, Genotype, Germplasm, Map, Markers, Publications, QTLs, Sequences, Trait Evaluation, MegaSearch), Tools (BIMS, BLAST+, CottonCyc, JBrowse, Map Viewer, Primer3, Sequence Retrieval, Synteny Viewer), International Cotton Genome Initiative (ICGI), and Help sources (User manual, FAQs).

Also provides Quick Start links for Major Species and Tools.

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.