Accelerating scientific discovery with FAIR
With its focus on investigating the basis for the sustained existence of living systems, modern biology as always been a fertile, if not challenging, domain to represent knowledge amenable to computational-based discovery. Indeed, the existence of millions of scientific articles, thousands of databases, and hundreds of ontologies, offers an exciting opportunity to reuse our collective knowledge, were we not stymied by incompatible formats, partial and overlapping standards, and heterogeneous data access. In this talk, I will discuss our efforts to develop computational frameworks and methods to wrangle knowledge into simple, but effective representations and to make these FAIR - Findable, Accessible, Interoperable, and Reuseable. Our work sets the stage for a global revolution to take advantage of the data we already have and to increase our confidence and the evidence in reporting, validating, and generating novel scientific discoveries.
Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University. His research focuses on the development of computational methods for scalable integration and reproducible analysis of FAIR (Findable, Accessible, Interoperable and Reusable) data. His group combines semantic web technologies with effective indexing, machine learning and network analysis for drug discovery and personalized medicine. Previously at Stanford University, Dr. Dumontier now leads a new inter-faculty Institute for Data Science at Maastricht University that is thematically aligned to accelerating scientific discovery, improving health and well-being, and strengthening communities. He is a Principal Investigator in the Dutch National Research Agenda, for NIH/NCATS Biomedical Data Translator, and the NIH Data Commons. He is a founder of the FAIR (Findable, Accessible, Interoperable, Re-usable) initiative, and is the scientific director for Bio2RDF, an open source project to generate Linked Data for the Life Sciences. He is the editor-in-chief for the journal Data Science and an associate editor for the journal Semantic Web. He is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies.