Introduction to FAIR Data Management for Geoscientists

September 8, 2020 – September 10, 2020 all-day

Responsible: Joe LaCasce/UiO, Jochen Reuder/UiB

Target group: PhD students working with data

Credit points:  1 ETCS

Dates:  8-10 September, 9:00-15:30

No. of participants: Unlimited

Registration form here.  (application deadline: 1st September)

Submitted applicant list

Course description:

This course will provide an introduction to the FAIR guiding principles for data management, their specific implementations within geoscience and practical exercises. Practical steps towards Findable, Accessible, Interoperable and Reuseable data are discussed and exercised emphasising the data provider and data consumer perspectives. Practical introductions to the various elements of the FAIR guiding principles are related to concepts of discovery metadata, use metadata, persistent identifiers (e.g. Digital Object Identifiers) and how they help traceability of decisions (e.g. through scientific citation of data), containers for data (e.g. NetCDF), semantics for geoscientific data (glossaries, thesaurus, taxonomies and ontologies) in a interdisciplinary context and related to terminology as a mechanism for scientific collaboration, tools for generating FAIR data (e.g. how to work with Rosetta and other tools for converting data, how to use Python), how to work with FAIR data, how to publish data with the help of data centres, how to publish data with the help of (focusing on discoverability by Google), national structures that facilitate data sharing (e.g. Norwegian Marine Data Centre, Norwegian Scientific Data Network, Norwegian Infrastructure for Research Data), how these are connected and how to work with Data Management Plans that are/or will be required by funding agencies and resources providers (e.g. UNINETT Sigma2). Practical work will be based on students bringing their own data, evaluation of their FAIRness and how to improve FAIRness for these using Rosetta and Python to create NetCDF according to the Climate and Forecast (CF) Convention with Attribute Convention for Dataset Discovery (ACDD) embedded.


At the end of the course, students will know the FAIR guiding principles, best practises of FAIR data within geoscience and practical approaches to achieving FAIR data using Rosetta and Python as well as how to work with data management plans for their future career.

Learning modules/structure:

The first day will be a full day (6 hours) of lectures, introducing different concepts, one day will be for self study where students work their own dataset. Lecturers will be available by Zoom (open room outside lecture hours) and a dedicated Slack channel through the full week to support students. A more detailed outline of the lectures will be provided online, students are required to describe and upload the dataset they will work with. At the end of the course (last day), each student presents the status of FAIRness of their data following the exercises undertaken. This session is scheduled for 5 hours (10 minutes presentation by each student and a longer discussion session).