Presentation by Eva Méndez from Carlos III University, Madrid, at the RDA National event in Florence Italy, 14-15 November 2016

1. “Cool” metadata for FAIR data-
Dra. Eva Méndez. Univeristy Carlos III of Madrid

2. Eva Méndez Cool metadata for FAIR data
Wait a moment…

3. Eva Méndez Cool metadata for FAIR data
A simple one
 Metadata by definition:
 Data
 Data about data
 Data which provides information about a resource
 Metadata by example:
 Title, author, subject, date, type, coordinates,
(metadata elements - schemas) …
 “physics”, “2004-01-23” (metadata values- schemes)
 Digital format, terms and conditions, location & PID

4. Outline
 Open Science: Here we are
 Metadata at the core for FAIR data
 RDA metadata WG/IG
 What are “cool” metadata (formats and
records)
 Metadata, where the wild things are: EOSC

5. Eva Méndez Cool metadata for FAIR data
Open Science: Here we are!
 A systemic change in the
modus operandi of science
and research
 Affecting the whole
research cycle and its
stakeholders
Commissioner Carlos Moedas
Open Science Presidency Conference
Amsterdam, 4 April 2016

6. 1. Altmetrics on quality and impact
2. Changing publishing models
3. FAIR open data
4. Open Science Cloud (EOSC)
5. Reward System
6. Research integrity
7. Citizen Science
8. Open education and skills
5 policy actions
1. Fostering and creating incentives for open science
2. Removing barriers for open science
3. Mainstreaming and further promoting open access policies
4. Developing an open science cloud
5. Embedding open science in society to make science more
responsive to societal and economic expectations
Here we are… Systemic Change on Science
8 top-level ambitions

7. Eva Méndez Cool metadata for FAIR data
Research data: open by default
 Horizon 2020 grantees are required to:
 deposit underlying research data (and their metadata)
and other research data of their choice in a repository
 Take measures to grant open access to this research data
 Horizon 2020 grantees are encouraged to share datasets
beyond publication
 FAIR data
+ more Rs
Reliable
Reproducible

8. Eva Méndez Cool metadata for FAIR data
FAIR data & Metadata

9. Eva Méndez Cool metadata for FAIR data
(Open) Research Data “mantra”

10. Eva Méndez Cool metadata for FAIR data
 Making data findable, including provisions
for metadata
 What metadata will be created? In case
metadata standards do not exist in your
discipline, please outline what type of
metadata will be created and how.
 Where will the data and associated
metadata, … be deposited?
 Interoperability of your data… What data
and metadata vocabularies, standards
or methodologies will you follow to make
your data interoperable?
 The Research Data Alliance provides a
Metadata Standards Directory that can
be searched for discipline-specific
standards and associated tools.
Metadata at the core of FAIR data

11. Eva Méndez Cool metadata for FAIR data
Metadata IG / Metadata
standards directory/catalog WG-

12. Eva Méndez Cool metadata for FAIR data
Metadata principles
1. The only difference between metadata & data is mode of use
2. Metadata is not just for data, it is also for users, software
services, computing resources
3. Metadata is not just for description and discovery; it is also
for contextualization (relevance, quality, restrictions (rights,
costs) and for coupling users, software and computing
resources to data (to provide a VRE)
4. Metadata must be machine-understandable as well as human
understandable for autonomicity (formalism)
5. Management (meta)data is also relevant (research proposal,
funding, project information, research outputs, outcomes,
impact, etc.)
https://www.rd-alliance.org/system/files/documents/RDA%20Metadata%20Principles%20and%20their%20Use%2020141114.docx

13. Eva Méndez Cool metadata for FAIR data
Metadata standards directory/catalog
http://rd-alliance.github.io/metadata-directory

15. Eva Méndez Cool metadata for FAIR data
Lead users… Scientific communities …long tail
Scaleofscientificactivity(data-drivenscience)
Humanities Citizen science
European
Open Science Cloud
Scientific Landscape for Research Data:
The problem
Lifesciences
Physics
Earthsciences
Economics
Social
sciences
Applied-engineering
……

16. Example: SSH

17. Example: SSH

18. Eva Méndez Cool metadata for FAIR data
What are “cool” metadata? / I
https://dmp.data.jhu.edu/metadata-for-effective-research-data-management

19. Eva Méndez Cool metadata for FAIR data
What are “cool” metadata? / II
 Completeness
 Provenance
 Accuracy
 Conformance (expectations)
 Consistency and Coherence
 Timeliness
 Accessibility
 …
 Openness
http://5stardata.info/en
Quality idicators

20. EOSC
Image from the children’s story by Maurice Sendak
https://en.wikipedia.org/wiki/Where_the_Wild_Things_Are

21. A Cloud on the H2020 Horizon (final draft)
http://www.bluebridge-vres.eu/sites/default/files/HLEG%20EOSC%20first%20Report%20%28draft%29.pdf
Imagine a federated, globally
accessible environment where
researchers, innovators, companies
and citizens can publish, find and re-
use each other's data and tools for
research, innovation and educational
purposes ...
This we believe encapsulates the
concept of the European Open
Science Cloud (EOSC), and indeed
such a federated European
endeavour might be expressed as the
European contribution to a Internet
of FAIR Data and services.
“The European Open Science Cloud is a supporting environment for
Open science and not an ‘open cloud’ for science”

22. Data
layer
Service
layer
Governance
layer
Lead users… Scientific communities …long tail
Scaleofscientificactivity(data-drivenscience)
High performance computing
Data fusion across disciplines
Big data analytics
Privacy and personal data protection
… …
Data discovery and catalogue
Data manipulation and export
Data access and re-use
Trust
Leverage of MS investment
Legacy and sustainability
IPR protection
Federation
Humanities
Data storage
Citizen science
European
Open Science Cloud
Bottom-up governance
EU Open Science Cloud & FAIR(RR) DATA
Vocabularies layer (Interoperability)
Lifesciences
Physics
Earthsciences
Economics
Social
sciences
Applied-engineering
……
Based on Burgelman

23. REFLECTIONS AND TAKE AWAYS

24. • Vocabularies and Interoperable
Metadata for Open Research
Data
• Metadata standards: “toothbrush
effect”
• Metadata quality is not only at
schema level, but also at
scheme and content
• Good research data include good
metadata but sometimes
difficult to translate from LAB to
Open Research eInfra (EOSC)
Challenges!!!
METADATA

25. http://databib.org
Thank you!!
emendez@bib.uc3m.es
@evamen