Aims & Scope

For years, researchers struggled to access others’ raw data—despite investing time, funding, and effort in rigorous studies. Published outputs often compress millions of observations into a few tables or charts, leaving underlying datasets inaccessible and hard to reuse.

The result is repeated work, adapted methods with limited comparability, and slower discovery. Raw Data Library exists to unlock those underlying datasets and make reproducible, data-driven research the default.

Raw Data Library (RDL) is a platform for sharing, storing, discovering, and trading raw research data. Researchers upload datasets from completed projects to a global library so others can cite, reuse, and extend the work.

By increasing data reuse, RDL boosts the efficiency of research while offering financial incentives to data providers—including individuals, labs, and institutions.

• Academics & Researchers: Monetize experimental or theoretical datasets and increase impact.
• Universities & Research Institutions: Balance budgets by offering high-value datasets from funded projects.
• Industry & R&D Teams: Source ready-to-use datasets to accelerate product and research pipelines.

RDL supports structured uploads and stable hosting with rich metadata, making discovery and reuse simple.

Explore datasets across physics, biology, engineering, social sciences and more. Discipline and facet filters help you pinpoint exactly what your study requires.

• Open Access: Publicly downloadable with clear citation guidance.
• Licensed Access: Usage permitted under specified terms and fees.
• Special Access: Restricted datasets available only to approved users.

Data owners can list raw datasets for a fee. Researchers and institutions create new funding streams while enabling others to discover and reuse high-quality data instantly.

• Encryption: Data is protected in transit and at rest.
• Backups: Regular backups safeguard long-term access and integrity.

Move beyond summarized tables—share underlying raw data so others can replicate results, run secondary analyses, and accelerate discovery across fields.