This document discusses open data, FAIR data principles, and research data management. It provides the following key points: 1) Open/FAIR data aims to make research data available for reuse by shifting away from traditional models where data is undervalued. Data can have varying degrees of sharing from open to restricted to closed. 2) FAIR data principles describe attributes that enable data to be findable, accessible, interoperable, and reusable by humans and machines. Data can adhere to FAIR principles to varying degrees. 3) FAIR data does not necessarily mean data needs to be openly shared - data can be both FAIR and restricted. Good research data management is needed to plan, collect,

1. Open Data, FAIR Data, Research
Data Management?
Some clarifications
Myriam Mertens | Ghent University Library
Image CC0 by Patrick Hochstenbach

2. Open/FAIR are about making data available
for reuse
2
Shift from traditional model of scholarly
communication, where research data
are undervalued & neglected
Image CC-BY by Auke Herrema

3. Degrees of data sharing
3
OPEN RESTRICTED CLOSED
“Can be freely used,
modified & shared by
anyone for any
purpose”
http://opendefinition.org
Limits on who can
access & use data, how,
or for what purpose
- only certain (types
of) users
- only certain types of
use
- …
Under embargo
Unable to share
“As open as possible, as closed as necessary”
Adapted from ‘Managing and sharing research data’ by S. Jones, CC-BY

4. FAIR data principles
• Describe attributes that enable & enhance data re-use by humans
and machines
• Originated in the life sciences, but gaining much traction beyond
• Spectrum: data can be FAIR to a greater or lesser degree
4
https://www.nature.com/articles/sdata201618
Adapted from ‘The FAIR data concept’, by S. Jones, CC-BY 4.0. Image CC-BY-SA by SangyaPundir

5. 5
It should be possible for others to
discover your data. Rich metadata
should be available online in a
searchable resource, and the data
should be assigned a persistent
identifier (e.g. DOI, Handle…).
It should be possible for humans and machines to gain
access to your data, under specific conditions or
restrictions where appropriate (i.e. data retrievable by
their PID & by using a standard protocol such as http;
authentication and authorization steps if necessary).
There should be metadata, even if the data aren’t
accessible.
Data and metadata should be conform to recognized
formats and standards to allow them to be combined &
exchanged (file formats, metadata schemas, controlled
vocabularies, keywords, ontologies, qualified references
& links to other related data).
Lots of documentation is needed
to support data interpretation
and reuse. It is clear how, why &
by whom data were created &
processed (provenance). The data
should conform to community
norms and be clearly licensed so
others know what kinds of reuse
are permitted.
Adapted from ‘How FAIR are your data?’ checklist, CC-BY by Sarah Jones & Marjan Grootveld, EUDAT. Image CC-BY-SA by SangyaPundir

6. FAIR vs. Open?
Not synonyms - FAIR does not mean that data need to be open!
6
OPEN
DATA
FAIR
DATA
Data can both, one, or
neither
Also check out the ARDC FAIR self-
assessment tool!
Adapted from ‘FAIR data: what it means, how we achieve it, and the role of RDA’ by S. Jones, CC-BY

7. Why share data in the first place?
7Image CC-BY by Brian Hole

8. FAIR/open data require good RDM!
The active management of research data throughout the lifecycle
8
Planning for
data
management
Collecting or
creating data
Processing &
analyzing data
Preserving data
Giving access
to data
Discovering &
re-using data
Adapted from ‘Managing and sharing research data’ by S. Jones, CC-BY. Image CC-BY-NC-SA by IT Services, University of Oxford

9. Thank you for listening!
9Image CC-BY by digitalbevaring.dk