What will you learn?

The life science community is generally very good at sharing omics data on platforms such as GEO and ArrayExpress.  However, the metadata and clinical data associated with the omics datasets are often very sparse.  Tracking down the metadata and clinical data of omics datasets can be time-consuming for both data owner and researcher.  It is a significant hurdle when trying to compile enriched omics datasets for data analysis and use with machine learning techniques.

In this training, you will learn the benefits of making clinical datasets FAIR (Findable, Accessible, Interoperable, and Reusable), and tools and techniques do so.

Participants are encouraged to familiarise themselves with the Galaxy platform and available training materials

Who is this for?

Data managers, data stewards, researchers publishing datasets.

Course prerequisites

Familiarity with FAIR principles.

Participants are encouraged to check the Galaxy platform and available training materials

Questions and contact

Should you have any questions, please contact us at [email protected]

This course is funded as part of the UKRI Innovation Scholars. Data Science Training in Health and Bioscience call (DaSH). (MR/V038966/1)