SLSepiaLog
Deliver · SepiaLog feature

Research metadata and FAIR readiness built during the project

Document scope, files, variables, access and reuse conditions gradually, with local suggestions and a practical FAIR-readiness checklist.

What makes research metadata useful?

Useful research metadata explains what a dataset represents, how it was produced, what its variables and codes mean, who may access it and how it should be cited. It allows a careful researcher to understand the data without depending on the original creator’s memory.

The research problem

Metadata written only at deposit time becomes a reconstruction exercise. Units, missing-value conventions, coverage changes and access decisions are easiest to preserve when they are still current.

SepiaLog Metadata screen showing FAIR readiness and local metadata suggestions
Genuine SepiaLog application view.

How the workflow works

  1. Complete the guided metadata sections gradually.
  2. Optionally scan supported tabular files locally for suggested structure and coverage information.
  3. Review FAIR readiness and export human-readable or repository-oriented metadata.

What SepiaLog provides

  • Guided project and variable documentation
  • Local smart metadata suggestions
  • Findable, Accessible, Interoperable and Reusable checklist
  • Word, PDF, DataCite and Dublin Core exports

Common questions

Does FAIR mean open?

No. Sensitive data can be FAIR when access conditions and the route to legitimate access are clearly documented.

Can progress be saved?

Yes. Metadata can be completed over time rather than in one session.

Does SepiaLog create a codebook?

It supports structured variable documentation and exports that can be used as a codebook.