FAIR Data Stewardship course
NeIC and DM-Forum in Denmark are jointly hosting the second Nordic course “FAIR Data Stewardship” on 18-22 November 2019, this time in Copenhagen.
This is a fully fledged 5-day training event which will provide the much needed foundational skills for competent data stewards and data managers in the Nordic countries with knowledge of the FAIR principles and their application. This course is aimed at librarians or data experts whose work it is to facilitate sharing and re-use of research data. The course will be held by trainers from GO-FAIR and provides a broad introduction to data stewardship. Registration will be limited to max 35 persons and the course is subsidised by NeIC and DM-Forum.
To register follow this link: https://bit.ly/FAIRds-Nordic
Overview
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 attend
This course is aimed at librarians or data experts at universities, research institutions and research support centres 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 data selection & retrieval, 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 less labor intensive, leaving more time to dive into more complex data analysis answering research questions.