SEED will enable a FAIR aligned comprehensive semantic capture and translation of data across eLN providers at the point of entry. 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
