

Member-Led Project
There is an enormous amount of unstructured, scientific research data and that is growing exponentially. As a result, researchers waste a lot of time trying to find documents, search data, understand it and interpret how to use it. Because the data is “un-FAIR”, researchers need to repeat experiments due to poor quality results, causing managers to make poor decisions.
Semantic enrichment technology has advanced significantly in recent years and by applying this to text captured as part of an experiment, we will increase the value and use of the data. By using standardized terms with additional parameters to map relationships between terms, the data captured is enriched, more easily searched, and interoperable (FAIR).
This project aims to address a number of common issues:
This will significantly increase researcher productivity by reducing rework (rerun experiments) and analysis time. High-quality results will provide improved insights and conclusions, enabling more effective decision-making.
Delivers a new standard for Drug Safety terms and adds to Bioassay ontology, This will enable Assay/Study type ontology coverage for all of eCTD Module 4 of NDA (New Drug Application) submission.