Skip to content

Commit

Permalink
Update schemas.md
Browse files Browse the repository at this point in the history
  • Loading branch information
ddooley authored Nov 8, 2024
1 parent d697867 commit c303a01
Showing 1 changed file with 2 additions and 2 deletions.
4 changes: 2 additions & 2 deletions docs/Data_Documentation/schemas.md
Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@ Better data schemas aid researchers in sharing data with the research community.
{: .highlight }
Documenting your data with a schema makes it more FAIR.

Formalized, machine-readable schemas are very useful and can be expressed in a number of languages including LinkML, Overlays Capture Architecture (OCA), JSON Schema, XML Schema Definition, and JSON-LD. Different schema languages have different benefits but the biggest advantage of using any schema language to document a schema is that the schema is documented and in a machine-readable format.
Formalized, machine-readable schemas are very useful and can be expressed in a number of languages including LinkML, Overlays Capture Architecture (OCA), JSON Schema, XML Schema Definition, and JSON-LD. Different schema languages have different benefits but the biggest advantage of using any schema language to document a schema is that the schema is documented and in a machine-readable format. Schemas also facilitiate application interface (API) development and exposes the structural information that enables users to query datasets directly.

With a machine-readable schema you can use it for many other tasks including data verification, data entry and data harmonization. For example, the [Semantic Engine](https://www.semanticengine.org) helps researchers write their own data schemas using the OCA schema language. The [Data Harmonizer](https://github.com/cidgoh/DataHarmonizer) uses custom LinkML schemas to let researchers edit and validate tabular data according to the LinkML schema.
With a machine-readable schema you can use it for many other tasks including data verification, data entry and data harmonization. For example, the [Semantic Engine](https://www.semanticengine.org) helps researchers write their own data schemas using the OCA schema language. The [Data Harmonizer](https://github.com/cidgoh/DataHarmonizer) uses custom LinkML schemas to let researchers edit and validate tabular data according to the LinkML schema.

- written by Carly Huitema

0 comments on commit c303a01

Please sign in to comment.