Workshop on FAIR principles
Published in 2016, the FAIR principles offer a set of guidelines to maximize the discovery and reuse of digital resources including as datasets, repositories, images, and web services. The FAIR principles have been widely adopted and are actively advocated by research communities, funding agencies, and journals. However, few people know what must be done to meet the expectations set out by the FAIR principles. This workshop aims to familiarize the participant about the FAIR principles and how to definitely apply them to different digital resources.
Programme
Time | Topic |
---|---|
09:00 - 09:15 | Welcome with coffee and tea and introduction organizers |
09:15 - 09:30 | Introduction to FAIR |
09:30 - 10:30 |
Research Data Management I : Metadata . globally unique identifiers . controlled vocabularies . community (meta)data standards . digital metadata repositories |
10:30 - 10:45 | Coffee and tea break |
10:45 - 11:30 |
Hands-on 1:Create your own metadata FAIR data point |
11:30 - 12:30 |
Lab: Assess the FAIRness of metadata . FAIR metrics . FAIR assessment tools |
12:30 - 13:30 | Lunch |
13:30 - 14:30 |
Research Data Management II: Data . semantic knowledge representation . semantic query answering |
14:30 - 16:00 | Lab: Create and query semantic data |
16:00 - 16:30 | Discussion |
16:30 - 16:45 | Wrap-up |
Instructor
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 across scales - from molecules, tissues, organs, individuals, populations to the environment. 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 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 Data Commons Pilot. He is a founding member 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 IOS press journal Data Science and an associate editor for the IOS press journal Semantic Web. He is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies as evidenced by awards, keynote talks at international conferences, and collaborations on international projects.
Information
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