What is fair open access?
Fair Open Access embodies transparency, inclusivity, and collaboration. It ensures that data science resources are accessible to all, fostering innovation and progress in research. By promoting up-to-date, actively maintained, and freely available tools, we empower scientists and researchers to drive breakthroughs and advance knowledge without barriers. Join us in championing a fair and open scientific community.
- Transparency: Ensures that research processes, data, and findings are open and accessible to all. This openness builds trust within the scientific community and with the public, fostering an environment where information is shared freely and widely.
- Inclusivity: Strives to provide equal access to data science resources, regardless of geographical location, institutional affiliation, or financial capability. By removing barriers, it enables a diverse range of researchers and institutions to participate in and contribute to scientific advancements.
- Collaboration: Encourages cooperative efforts across disciplines and borders. Open access platforms facilitate the sharing of ideas, data, and tools, leading to more robust and innovative solutions to complex problems. It promotes a community-driven approach to science where collective efforts drive progress.
- Sustainability: Advocates for the development and maintenance of up-to-date and actively supported resources. Unlike traditional models, which often suffer from neglect, fair open access emphasizes the continuous improvement and sustainability of data science tools and datasets.
- Innovation: By providing free and open access to the latest tools and data, fair open access accelerates the pace of innovation. Researchers can build on existing work without restrictions, leading to faster and more impactful scientific discoveries.
- Equity: Ensures that all researchers, regardless of their economic status or institutional support, have the opportunity to access high-quality data and tools. This equitable approach helps to level the playing field and allows for a more diverse set of voices and ideas in the scientific community.
Why it matters?
The accessibility and use of data science resources remains a major hurdle, with 91% of GitHub codebases being outdated by over four years or inactive for more than two years. Open-source creators generally miss out on the benefits of their creations, leading to decreased engagement and innovation.
Fair Open Access is critical in today’s scientific landscape where data is the backbone of research and development. By adhering to these values, we can overcome the challenges posed by traditional access models, such as outdated and inaccessible resources, and pave the way for a more open, innovative, and equitable scientific community.
Gencovery's Fair Open Access initiative aims to provide easy-to-access, up-to-date, and actively supported data science resources. With Constellab, creators get open access to start, create open-source projects, earn credits from your work
We aims to build a robust community of open data science practitioners.
A virtuous cycle
Benefits for all
The core benefits of fair open access are
- data access for all
- community engagement (to ensure sustainable data science)
- high-quality codes and data
