Market research dataset covering growth of the global open-source software market, including benefits, adoption, and enterprise usage in 2025.

In a recent survey, more than ** percent of the respondents stated that their organizations had either increased or maintained their use of open-source software in the past year. Participants from Africa, with ** percent, noted a significant increase in usage.

The graph shows the number of articles published in the discipline of ^.

Open-Source Software Security Market Report is Segmented by Component (Solutions and Services), Deployment Mode (On-Premises and Cloud/SaaS), Organization Size (Large Enterprises and Small and Medium-Sized Enterprises), Security Function (Software Composition Analysis, Secrets Detection and Leakage Prevention, and More), End-User Industry (BFSI and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

At the end of 2022, there were approximately *** million JavaScript open source projects in the Maven Central Repository and around ** million JavaScript project versions worldwide. While JavaScript is the largest ecosystem in the Maven Central Repository, Java, Python, and .NET also have thousands of available open source projects.

Electronic commerce and technology, use of open source software by North American Industry Classification System (NAICS), for Canada from 2005 to 2007. (Terminated)

This dataset lists out all software in use by NASA.

In 2023, Sonatype reported over *** thousand malicious attacks on the open-source software (OSS) supply chain, aimed at exploiting any weaknesses in upstream open-source ecosystems, such as JavaScript, Java, .NET, and Python. This figure represents a nearly *** percent growth from the previous year and is over the double of the sum of the attacks from all the reported previous years (from 2019 to 2022).

The presentation explains in the simplest possible way what you need to know about open source licenses when starting from scratch. It also sums up the course "Open Source Licensing Basics for Software Developers (LFC191)" (Linux Foundation)

The global open source software market is projected to reach a valuation of approximately USD 60 billion by 2033, growing at a compound annual growth rate (CAGR) of 18% from 2025 to 2033.

Among the programming languages presented here, JavaScript most frequently used open source software within their active public repositories worldwide in 2020, with ** percent. Depending on open source software may lead to vulnerabilities in code, emphasizing the need for open source security.

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.

The open-source software (OSS) market is experiencing robust growth, driven by factors such as cost-effectiveness, flexibility, community support, and increasing security concerns. The market's expansion is fueled by the rising adoption of OSS across various sectors, including enterprise and personal use. The diverse range of OSS applications, from simple shareware to sophisticated Advanced Driver Assistance Systems (ADAS) in automobiles, contributes to its widespread appeal. While the specific market size for 2025 isn't provided, considering a plausible CAGR of 15% based on industry averages and the substantial growth drivers, a reasonable estimate for the 2025 market size could be $150 billion. This substantial valuation highlights the significance of the OSS market in the global tech landscape. The segment breakdown is significant, with enterprise adoption likely dominating due to the potential for cost savings and customization, followed by personal use and specialized applications like ADAS. Regional growth will likely be strongest in North America and Asia-Pacific, reflecting the concentration of technology hubs and strong digital infrastructure in these areas. However, challenges such as concerns around security, support, and integration complexities continue to act as restraints. Overcoming these challenges and fostering further community development will be key to continued market expansion. The future of the OSS market looks promising, with several key trends shaping its trajectory. The increasing sophistication of OSS solutions, particularly in areas like cloud computing and AI, is driving adoption. The rise of collaborative development models and stronger community engagement are further solidifying the stability and security of OSS offerings. As businesses increasingly prioritize agility and cost-efficiency, the attractiveness of OSS will continue to grow. While companies like Intel, IBM, and Oracle contribute significantly to the OSS ecosystem through development and support, a considerable portion of the market is driven by smaller, specialized contributors and individual developers. The continued growth is anticipated to be fueled by increasing demand for customized software solutions, particularly in sectors like automotive (ADAS) and industrial automation. The forecast period of 2025-2033 presents ample opportunities for growth and innovation within the OSS market. A continued focus on addressing the existing limitations while nurturing innovation and community engagement will propel the market towards even greater heights.

The size of the Open-Source Software Security 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.

Market Overview The global open source software composition analysis (OSSCA) market is projected to reach a value of USD 3.5 billion by 2033, exhibiting a CAGR of 15.8% during the forecast period. Rising concerns over software supply chain attacks and the increasing adoption of open source software across industries are key growth drivers. The market is segmented by deployment type (on-premises and cloud-based) and application (manufacturing, healthcare, finance, etc.). North America is the largest regional market, followed by Europe and Asia Pacific. Key Trends and Challenges The market is witnessing a surge in demand for cloud-based OSSCA solutions due to their flexibility and cost-effectiveness. Trends such as DevOps adoption, increased regulatory compliance requirements, and the emergence of threat intelligence platforms are promoting market growth. However, challenges related to data privacy, interoperability, and the availability of skilled professionals may restrain market expansion. Key players include Synopsys, Veracode, Palo Alto Networks, and Snyk. Strategies such as partnerships, acquisitions, and new product launches are expected to drive market consolidation and technological advancements. Introduction Open source software composition analysis (OSSCA) is a crucial practice in securing the software supply chain. By identifying and analyzing open source components within software applications, businesses can mitigate the risks associated with open source vulnerabilities. This report delves into the Open Source Software Composition Analysis market, providing insights into its dynamics, trends, and key players.

Thanks to a variety of software services, it has never been easier to produce, manage and publish Linked Open Data. But until now, there has been a lack of an accessible overview to help researchers make the right choice for their use case. This dataset release will be regularly updated to reflect the latest data published in a comparison table developed in Google Sheets [1]. The comparison table includes the most commonly used LOD management software tools from NFDI4Culture to illustrate what functionalities and features a service should offer for the long-term management of FAIR research data, including:

• ConedaKOR
• LinkedDataHub
• Metaphacts
• Omeka S
• ResearchSpace
• Vitro
• Wikibase
• WissKI

The table presents two views based on a comparison system of categories developed iteratively during workshops with expert users and developers from the respective tool communities. First, a short overview with field values coming from controlled vocabularies and multiple-choice options; and a second sheet allowing for more descriptive free text additions. The table and corresponding dataset releases for each view mode are designed to provide a well-founded basis for evaluation when deciding on a LOD management service. The Google Sheet table will remain open to collaboration and community contribution, as well as updates with new data and potentially new tools, whereas the datasets released here are meant to provide stable reference points with version control.

The research for the comparison table was first presented as a paper at DHd2023, Open Humanities – Open Culture, 13-17.03.2023, Trier and Luxembourg [2].

[1] Non-editing access is available here: docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link To get editing access contact the authors.

[2] Full paper will be made available open access in the conference proceedings.

As of 2024, for around **** percent of organizations worldwide, open-source external technology solutions were very important, while for *** it was of critical significance. Only around **** percent of firms found open-source not important.

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

The Open Source Service Market Report is Segmented by Service Type (Consulting and Implementation, Support, Maintenance, and Management, and More), Deployment Mode (On-Premise and Cloud), Application (Infrastructure Management, Application Development and Integration, and More), End-User Industry (Banking, Financial Services and Insurance (BFSI), and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white- and blacklisting, and use them through three heuristics to identify 17,252 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.

The main dataset is provided as a 17,252 record tab-separated file named enterprise_projects.txt with the following 27 fields.

• url: the project's GitHub URL
• project_id: the project's GHTorrent identifier
• sdtc: true if selected using the same domain top committers heuristic (9,006 records)
• mcpc: true if selected using the multiple committers from a valid enterprise heuristic (8,289 records)
• mcve: true if selected using the multiple committers from a probable company heuristic (7,990 records),
• star_number: number of GitHub watchers
• commit_count: number of commits
• files: number of files in current main branch
• lines: corresponding number of lines in text files
• pull_requests: number of pull requests
• most_recent_commit: date of the most recent commit
• committer_count: number of different committers
• author_count: number of different authors
• dominant_domain: the projects dominant email domain
• dominant_domain_committer_commits: number of commits made by committers whose email matches the project's dominant domain
• dominant_domain_author_commits: corresponding number for commit authors
• dominant_domain_committers: number of committers whose email matches the project's dominant domain
• dominant_domain_authors: corresponding number of commit authors
• cik: SEC's EDGAR "central index key"
• fg500: true if this is a Fortune Global 500 company (2,232 records)
• sec10k: true if the company files SEC 10-K forms (4,178 records)
• sec20f: true if the company files SEC 20-F forms (429 records)
• project_name: GitHub project name
• owner_login: GitHub project's owner login
• company_name: company name as derived from the SEC and Fortune 500 data
• owner_company: GitHub project's owner company name
• license: SPDX license identifier

The file cohost_project_details.txt provides the full set of 309,531 cohort projects that are not part of the enterprise data set, but have comparable quality attributes.

• url: the project's GitHub URL
• project_id: the project's GHTorrent identifier
• stars: number of GitHub watchers
• commit_count: number of commits