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
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
