FAIR data—What does making data available mean?

FAIR data—What does making data available mean?
A deep dive into research data management 

In this webinar series on research data management, the attendees will get detailed information on specific topics. This is aimed at helping researchers make better use of their time and effort for fulfilling the requirements of major research funders and research-conducting organizations. While University Library, Nord University provides real-time guidance on these issues through courses, seminars, webinars and one-to-one meetings, being better informed on these topics will make it easier to understand and comply with the requirement of managing data in accordance with the FAIR principles. In addition, anyone interested in these topics are welcome to register.

Program of the day

9:15-9:50 A.M. FAIR data—What does making data available mean? — Sagnik Sengupta (Senior Research Librarian, Nord University) 

Break (10 minutes) 

10:00-11:00 A.M. FAIR-ing and sharing qualitative data — Agata Bochynska (Senior Academic Librarian, University of Oslo) 

The webinar be in english.

Registration deadline 31 March.

A teams link will be sent out to the participants in advance. 

If you have any questions, please contact research-data@nord.no

More about the topics 

FAIR data—What does making data available mean? 

FAIR is the umbrella principle in research data management. The strategy of a researcher for complying with FAIR principles is assessed in research grant applications. In addition, the necessity of building up a record of compliance with FAIR principles is foreseeable, especially for young researchers. 

NFR and Horizon Europe require all research data to be FAIR in order to maximize the benefits that academia and other sectors can obtain from research data. As a researcher, it is an obligation to make the data FAIR — findable, accessible, interoperable and reusable. This webinar will touch upon the basics of FAIR principles:

FAIR-ing and sharing qualitative data

Handling qualitative data comes with a typical set of challenges. Anonymising and sharing involves a lot of planning and work. Then why is it a requirement to make qualitative data FAIR? How should a researcher plan a project with qualitative data? We will listen to insights and experiences from our guest speaker. 

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