FAIR Data in Trustworthy Data Repositories

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

Everybody wants to play FAIR, but how do we put the principles into practice?

There is a growing demand for quality criteria for research datasets. The presenters argue that the DSA (Data Seal of Approval for data repositories) and FAIR principles get as close as possible to giving quality criteria for research data. They do not do this by trying to make value judgments about the content of datasets, but rather by qualifying the fitness for data reuse in an impartial and measurable way. By bringing the ideas of the DSA and FAIR together, we will be able to offer an operationalization that can be implemented in any certified Trustworthy Digital Repository. In 2014 the FAIR Guiding Principles (Findable, Accessible, Interoperable and Reusable) were formulated. The well-chosen FAIR acronym is highly attractive: it is one of these ideas that almost automatically get stuck in your mind once you have heard it. In a relatively short term, the FAIR data principles have been adopted by many stakeholder groups, including research funders. The FAIR principles are remarkably similar to the underlying principles of DSA (2005): the data can be found on the Internet, are accessible (clear rights and licenses), in a usable format, reliable and are identified in a unique and persistent way so that they can be referred to. Essentially, the DSA presents quality criteria for digital repositories, whereas the FAIR principles target individual datasets. In this
webinar the two sets of principles will be discussed and compared and a tangible operationalization will be presented.

Authoring Person(s) Name: 
Peter Doorn
Ingrid Dillo
License - link to legal statement specifying the copyright status of the learning resource: 
Standard YouTube License
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
Access rights
Accessible data - FAIR Data Principle
Data access
Data Seal of Approval (DSA)
Findable data - FAIR Data Principle
Licenses - Core Trustworthy Data Repositories Requirements
Open data
Re-usable data - FAIR Data Principle
Published / Broadcast: 
Monday, December 12, 2016
Publisher - organization credited with publishing or broadcasting the learning resource: 
EUDAT
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Event - time-based happening that is portrayed or covered by the learning resource, e.g., a webinar.
Contributor Organization(s): 
Name: 
EUDAT
Type: 
Organizer
Name: 
OpenAIRE
Type: 
Organizer
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Research scientist
Mid-career research scientist
Early-career research scientist
Research faculty
Data manager
Repository manager
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)
Framework - A community-based organization plan or set of steps for education or training: 
ICSU - World Data System Training Resources Guide