Data FAIRification for Data Stewards

FAIR Data is data that is Findable, Accessible, Interoperable and Reusable by computers. This course provides an introduction to the FAIRification of (research) data as part of good practice in Data Stewardship. The course is aimed at information technology and data experts currently employed at research institutions and is instigated by and co-funded with the Health Research Board, Ireland and supported by the GO FAIR Foundation. The FAIR data approach has already impacted both academic and industrial research practices, and has been endorsed by the European Commission, numerous national organizations, as well as the World Economic Forum and the G7 largest economies in the world.

Course date: 25-29 June 2018

What this course delivers
FAIR Data Stewardship, as a new profession, is rapidly gaining momentum. New requirements from national and international funders are driving the need for training of competent, professional data stewards and data managers with knowledge of the FAIR principles and their application. This course introduces the required knowledge and skills in a broader data stewardship context, including topics like semantic data modeling, metadata modeling, the FAIRification process, publishing FAIR Data Points, and other topics related to managing research project’s data requirements. After completion of the course participants will be able to work with domain specialists in making their data FAIR and preserving them for re-use.

Who should take the course
This course aims at librarians or data experts at universities and research institutions who are dealing with the ever growing complexity of data integration. Currently data technicians/ICTers spend between 70 and 80 percent of their time on data wrangling such as dealing with format issues, identifiers, ontologies,. massaging the data so that it is ready for big data analysis. For large organisations choosing to GO FAIR, integration and re-use of data sets becomes a less labor intensive, leaving more time to dive into more complex data analysis answering research questions.

Background and context
FAIR Data aims at improving Findability, Accessibility, Interoperability and Reuse of data. Due to the increase of volume and complexity of data, researchers increasingly rely on automated support in order to be able to integrate and analyse these data in order to answer research questions. A key aspect in this whole process is automated data interoperability, even when data were created in very different format, in different languages, and (as is more often the case) in different research domains. To achieve general, automated, interoperability of data we make explicit the intended meaning for data elements, relations and constraints using semantic approaches fortified with GO FAIR community-based standards.

Go to the course website