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Keynote (confirmed only)
Picture of Frederik Coppens
Picture of Michel Dumontier
Picture of Christine Durinx
Picture of Patricia Palagi

Michel Dumontier,
University of Maastricht, The Netherlands.

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, he now leads a new inter-faculty Institute for Data Science at Maastricht University with a focus on accelerating scientific discovery, improving health and well-being, and strengthening communities. He is a Principal Investigator for the NCATS Biomedical Data Translator and a co-Investigator for the NIH BD2K Center for Expanded Data Annotation and Retrieval (CEDAR). He is a technical lead for the FAIR (Findable, Accessible, Interoperable, Re-usable) data 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 IOS press journal Data Science and an associate editor for the IOS press journal Semantic Web.

Are we FAIR yet? And will it be worth it?
The FAIR Principles propose essential characteristics that all digital resources (e.g. datasets, repositories, web services) should possess to be Findable, Accessible, Interoperable, and Reusable by both humans and machines. The Principles act as a guide that researchers and data stewards should expect from contemporary digital resources, and in turn, the requirements on them when publishing their own scholarly products. As interest in, and support for the Principles has spread, the diversity of interpretations has also broadened, with some resources claiming to already “be FAIR”.
This talk will elaborate on what FAIR is, what it entails, and how we should evaluate FAIRness. I will describe new social and technological infrastructure to support the creation and evaluation of FAIR resources, and how FAIR fits into institutional, national and international efforts. Finally, I will discuss the merits of the FAIR principles (and what we ask of people) in the context of strengthening data-driven scientific inquiry.

Christine Durinx,
Swiss Institute of Bioinformatics, Switzerland.

Christine Durinx is the Associate Director of SIB Swiss Institute of Bioinformatics since 2014. She is co-lead of the ELIXIR Data platform. The ELIXIR Data platform has developed a process to identify European data resources that are of fundamental importance to research in the life sciences and are committed to the long term preservation of data. These resources are called ELIXIR Core Data Resources. Christine was also involved in a study on funding models to improve the long term sustainability of the data science infrastructure.
At SIB, Christine is responsible for the Communications and Training departments, the Director’s Office, and the Technology Group. Christine has a Pharmacy degree and a PhD in Pharmaceutical Sciences from the University of Antwerp, Belgium. Before joining SIB, she worked in the pharmaceutical industry for over 10 years.

ELIXIR Core Data Resources: FAIR, community and impact
The core mission of ELIXIR is to build a stable and sustainable infrastructure for biological information across Europe. Central to defining this infrastructure is the identification of Core Data Resources that are of fundamental importance to the broad life sciences community. In this presentation we will describe the Core Data Resource indicators, compare them with the FAIR principles, and explain their application within ELIXIR’s sustainability strategy and science policy actions, in capacity building, life cycle management and technical actions.