Scientific and research data

Science generates vast data, increasingly open and reusable. This guide covers scientific and research data and how to source it for R&D and evidence.

Why research data matters

Research datasets and publications underpin R&D, evidence-based decisions and innovation. Open-science movements have made much of it accessible and reusable.

The data landscape

• Research datasets: experimental and observational.
• Publications and citations: the literature.
• Repositories: domain and general archives.
• Metadata: FAIR descriptors.

FAIR data

The FAIR principles (Findable, Accessible, Interoperable, Reusable) guide research-data sharing, improving discoverability and reuse, though adoption varies.

Common use cases

R&D and evidence, literature and citation analysis, benchmarking and validation, and innovation scouting.

Sourcing considerations

Much research data is open but under varied licences (attribution, share-alike), and quality and documentation vary. Combining and harmonising across repositories is the work.

In a managed model

A managed partner can source and harmonise research data and publications with documented licensing.

Open but varied

Research datasets and publications underpin R&D and evidence, and the FAIR principles guide discoverability and reuse, though adoption varies. Much is open but under varied licences (attribution, share-alike), and quality and documentation vary.

Harmonise across repositories

Combining and harmonising across repositories is the work, with documented licensing for credible R&D and innovation use.

Sources & further reading

• European Open Science Cloud (EOSC) and repositories.
• FAIR data principles.
• OpenAIRE and publication databases.
• Domain research archives.