“Cool” metadata for FAIR data-
Dra. Eva Méndez. Univeristy Carlos III of Madrid
Eva Méndez Cool metadata for FAIR data
Wait a moment…
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
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
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
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
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
Eva Méndez Cool metadata for FAIR data
FAIR data & Metadata
Eva Méndez Cool metadata for FAIR data
(Open) Research Data “mantra”
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
Eva Méndez Cool metadata for FAIR data
Metadata IG / Metadata
standards directory/catalog WG-
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
Eva Méndez Cool metadata for FAIR data
Metadata standards directory/catalog
http://rd-alliance.github.io/metadata-directory
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
……
Example: SSH
Example: SSH
Eva Méndez Cool metadata for FAIR data
What are “cool” metadata? / I
https://dmp.data.jhu.edu/metadata-for-effective-research-data-management
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
EOSC
Image from the children’s story by Maurice Sendak
https://en.wikipedia.org/wiki/Where_the_Wild_Things_Are
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”
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
REFLECTIONS AND TAKE AWAYS
• 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
http://databib.org
Thank you!!
emendez@bib.uc3m.es
@evamen

"Cool" metadata for FAIR data

  • 1.
    “Cool” metadata forFAIR data- Dra. Eva Méndez. Univeristy Carlos III of Madrid
  • 2.
    Eva Méndez Coolmetadata for FAIR data Wait a moment…
  • 3.
    Eva Méndez Coolmetadata 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 Coolmetadata 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 onquality 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 Coolmetadata 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 Coolmetadata for FAIR data FAIR data & Metadata
  • 9.
    Eva Méndez Coolmetadata for FAIR data (Open) Research Data “mantra”
  • 10.
    Eva Méndez Coolmetadata 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 Coolmetadata for FAIR data Metadata IG / Metadata standards directory/catalog WG-
  • 12.
    Eva Méndez Coolmetadata 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 Coolmetadata for FAIR data Metadata standards directory/catalog http://rd-alliance.github.io/metadata-directory
  • 15.
    Eva Méndez Coolmetadata 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.
  • 17.
  • 18.
    Eva Méndez Coolmetadata for FAIR data What are “cool” metadata? / I https://dmp.data.jhu.edu/metadata-for-effective-research-data-management
  • 19.
    Eva Méndez Coolmetadata 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 thechildren’s story by Maurice Sendak https://en.wikipedia.org/wiki/Where_the_Wild_Things_Are
  • 21.
    A Cloud onthe 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… Scientificcommunities …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.
  • 24.
    • Vocabularies andInteroperable 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.