SEED will enable a FAIR-aligned comprehensive semantic capture and translation of data across eLN providers at the point of entry. The output will be computer-readable standard data, increasing capacity for provenance and attribute connection for insight and analysis

Project Deliverables

Phase 1 successfully completed!

In October, the project delivered:

  • A new standard assay Ontology (ADME PD) now available to the community
  • A working exemplar of ADME and PD workflow to semantically tag unstructured text
  • An active community that supported Phase 1 and are sharing ideas for Phase II

seed project phase 1

Why is this important?

  • Volumes of unstructured eLN content are exponentially growing
  • Most eLN content is free text, unstructured information and thus challenging to identify common concepts, to effectively search and extract deeper Insights
  • Retrospective data analysis and searching of generally unstructured content is a huge challenge
  • Data Workflows to/from eLNs can be relatively restricted in approach. Connectivity to Study Registration systems to enable Scientists to reuse such metadata through API to eLN are a must
  • Availability of persistent identifiers challenges the capability of making data to be digitally discoverable and hinders aggregation of data inter-applications
  • Valuable insights are hampered by unstructured content which prevents deeper data analysis thereby losing competitive advantage