SlideShare ist ein Scribd-Unternehmen logo
1 von 23
Downloaden Sie, um offline zu lesen
datareadiness.eng.ox.ac.uk
Allyson Lister, PhD
Content and Community Coordinator
ORCiD: 0000-0002-7702-4495 | Twitter: @allysonlister
@FAIRsharing_org
contact@fairsharing.org
10.25504/FAIRsharing.2abjs5
a FAIR-enabling service
A global resource for all disciplines
An informative and educational resource of three curated and interlinked registries of
standards, repositories and policies; three key elements of the FAIR ecosystem
COMMUNITY STANDARDS
DATA POLICIES
by funders, journals
and other organizations
REPOSITORIES
including databases
and knowledgebases
Identifiers
Terminologies Guidelines
Formats
Promoting repositories, standards, policies
Guides consumers to discover, select and use
these resources with confidence
Helps producers to make their resources more
visible, more widely adopted and cited
Fosters a culture change where the use of
these resources for FAIRer data is
pervasive and seamless
Total of
over 3500
resources
repositories
standards
policies
Users from academic, governmental and industry
sectors
Researchers Developers and
curators
Journal
publishers
Societies and
Alliances
Librarians and
Trainers
Funders
Organisations: collections provide tailored views
Many groups and projects have created
Collections
These are are branded pages that group
selected standards (and/or repositories)
relevant to their community and/or
developed by them
URL: fairsharing.org/collection/IVOA
URL: fairsharing.org/collection/CDISC
Publishing industry: policies registration and
dashboards
Scholarly publishers use FAIRsharing to:
● list standards and repositories they
recommend in their data policy,
● discover new resources and monitor
their evolution to refine or update
their policies
… 232 standards
https://fairsharing.org/collection/ISOCD20691CollectionDRAFT
ISO/CD 20691 specification that details the requirements for
the consistent formatting and documentation of data and
metadata in the life sciences and biotechnology, including
biomedical research and non-human biological research and
development; it covers manual or computational workflows.
This FAIRsharing Collection includes the standards detailed in
the ISO/CD 20691 specification, and serves as a 'live' list to
search and discover these standards, their use by repositories,
as well as their evolution over time.
Biotech industry: knowledge graphs
of standards
● Pistoia Alliance is a not-for-profit organisation of over 100
Life Science R&D companies, including major pharmas and
service providers
○ Working on pre-competitive projects on best practices and
technology challenges, including data FAIRness
● FAIRsharing and the Pistoia Alliance’s FAIR Implementation
WG are working to create Collections of companies’ profiles,
with respect to the standards they use
● The FAIRsharing “Pistoia Alliance Collections” will help with
comparing and discussing standards practices within industry
Life Sciences R&D companies:
semantic and syntactic standards
A growing number of services for:
● data management plans
● data stewardship
● metadata aggregation
● FAIR assessment
access FAIRsharing, via its API, and use it as
look-up and select service for (metadata and
information on) standards and repositories
Services: curated content to power FAIR-enabling
other tools and services
ds-wizard.org
dmponline.dcc.ac.uk
w3id.org/AmIFAIR
openaire.eu
fairshake.cloud
An endorsed output of the
FAIRsharing WG
(since 2015):
Also a WG (since 2015) in:
Users, adopters, communities and working groups
Susanna-Assunta Sansone, PhD
Associate Professor, Information Engineering
Associate Director, Oxford e-Research Centre
ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone
datareadiness.eng.ox.ac.uk
@FAIRplus_eu
fairplus-cookbook@elixir-europe.org
https://w3id.org/faircookbook
What is it?
An online, ‘live’ resource
for the life sciences
A collection of recipes
that cover the operation
steps of FAIR data
management
Who is it for?
Who developed it?
Researchers and data
managers professionals
in the life sciences, from
academia and industry
Including ELIXIR
members
FAIR Cookbook:
turning knowledge into recipes
https://fairplus.github.io/the-fair-cookbook
+50 life sciences professionals, researchers and data managers
FAIR Cookbook: creators and contributors
FARIplus
partners
Industry
+
Academia
ELIXIR
Nodes
represented
Learn how to improve the FAIRness with exemplar datasets
Understand the levels and indicators of FAIRness
Discover open source technologies, tools and services
Find out the required skills
Acknowledge the challenges
FAIR Cookbook: learning objectives
Recipes that cover all aspects of FAIRness
https://doi.org/10.1038/s41597-019-0286-0
Applied examples: these are key!
The capability maturity model
The optimum level of FAIRness
is a trade-off between desired
data reuse level and cost to
achieve that level
Show business value, achieve
the ‘FAIR enough’ on an
enterprise scale
The capability maturity model - the ontology example
No use of
ontologies
Use of internal
ontologies
Use of
community
ontologies
+ Ontology service to
manage several
ontologies, mapping,
versioning etc.
+ Term suggestion,
automatic annotation,
terms conflict
resolution etc.
The optimum level of FAIRness
is a trade-off between desired
data reuse level and cost to
achieve that level
Show business value, achieve
the ‘FAIR enough’ on an
enterprise scale
No use of
ontologies
Use of internal
ontologies
Use of
community
ontologies
+ Ontology service to
manage several
ontologies, mapping,
versioning etc.
+ Term suggestion,
automatic annotation,
terms conflict
resolution etc.
The capability maturity model - the ontology example
A dedicated recipe
will help to move
from Repeatable
to Defined level
Start using the
FAIR Cookbook
Help us to help you,
this is a
user-oriented
resource
Join a network
of FAIR experts
Share and broaden
your expertise,
joining our journey
Contribute
according to
your needs
Create and review,
or signpost gaps and
needs
Benefit from the
power of the
FAIR community
Become a recognised
expert, enlarge your
network
Recommend it
in your guidance
and training
material
For everyone
use it join it
adopt it
leverage it
Included in
the
next
version =>
A live ever-growing resource:
become part of a community of FAIR experts!
1Identify a chapter and a topic
Findability Accessibility Interoperability Reusability
Infrastructure Applied examples Assessment
2 Choose a way of contributing and see our guidelines
Google Docs
HackMD
Git
Markdown cheat sheet
Get recipe template
Tips and tricks
Submit an
outline
3
You can
discuss it
with the
Editorial
Board
Watch the webinar
for more information
elixir-europe.org/events/fairplus-webinar-discovering-fair-cookbook

Weitere ähnliche Inhalte

Was ist angesagt?

FAIR data and standards for a coordinated COVID-19 response
FAIR data and standards for a coordinated COVID-19 responseFAIR data and standards for a coordinated COVID-19 response
FAIR data and standards for a coordinated COVID-19 responseSusanna-Assunta Sansone
 
RDA17 FAIRsharing WG sessions: on repositories and policies
RDA17 FAIRsharing WG sessions: on repositories and policiesRDA17 FAIRsharing WG sessions: on repositories and policies
RDA17 FAIRsharing WG sessions: on repositories and policiesSusanna-Assunta Sansone
 
The FAIR movement - Oxford Open Data Week
The FAIR movement - Oxford Open Data WeekThe FAIR movement - Oxford Open Data Week
The FAIR movement - Oxford Open Data WeekSusanna-Assunta Sansone
 
The Software Sustainability Institute Fellowship
The Software Sustainability Institute FellowshipThe Software Sustainability Institute Fellowship
The Software Sustainability Institute FellowshipAlejandra Gonzalez-Beltran
 
FAIRsharing - focus on standards and new features
FAIRsharing - focus on standards and new features FAIRsharing - focus on standards and new features
FAIRsharing - focus on standards and new features Susanna-Assunta Sansone
 
FAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
FAIRification is a Team Sport: FAIRsharing and the FAIR CookbookFAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
FAIRification is a Team Sport: FAIRsharing and the FAIR CookbookSusanna-Assunta Sansone
 
FAIRsharing COVID-19 Collection for The Global Health Network
FAIRsharing COVID-19 Collection for The Global Health NetworkFAIRsharing COVID-19 Collection for The Global Health Network
FAIRsharing COVID-19 Collection for The Global Health NetworkSusanna-Assunta Sansone
 
Metadata challenges research and re-usable data - BioSharing, ISA and STATO
Metadata challenges research and re-usable data - BioSharing, ISA and STATOMetadata challenges research and re-usable data - BioSharing, ISA and STATO
Metadata challenges research and re-usable data - BioSharing, ISA and STATOAlejandra Gonzalez-Beltran
 
Data publication: Discover, Explore, Visualise
Data publication: Discover, Explore, VisualiseData publication: Discover, Explore, Visualise
Data publication: Discover, Explore, VisualiseAlejandra Gonzalez-Beltran
 
FAIRsharing, FAIR principles and metrics - Working with/for the Agro domain
FAIRsharing, FAIR principles and metrics - Working with/for the Agro domainFAIRsharing, FAIR principles and metrics - Working with/for the Agro domain
FAIRsharing, FAIR principles and metrics - Working with/for the Agro domainSusanna-Assunta Sansone
 
Managing Big Data - Berlin, July 9-10, 201.
Managing Big Data - Berlin, July 9-10, 201.Managing Big Data - Berlin, July 9-10, 201.
Managing Big Data - Berlin, July 9-10, 201.Susanna-Assunta Sansone
 
FAIRsharing: how we assist with FAIRness
FAIRsharing: how we assist with FAIRnessFAIRsharing: how we assist with FAIRness
FAIRsharing: how we assist with FAIRnessSusanna-Assunta Sansone
 

Was ist angesagt? (20)

FAIR data and standards for a coordinated COVID-19 response
FAIR data and standards for a coordinated COVID-19 responseFAIR data and standards for a coordinated COVID-19 response
FAIR data and standards for a coordinated COVID-19 response
 
Metadata for Interoperable Bioscience
Metadata for Interoperable BioscienceMetadata for Interoperable Bioscience
Metadata for Interoperable Bioscience
 
FAIRsharing for RDA Funders Forum
FAIRsharing for RDA Funders ForumFAIRsharing for RDA Funders Forum
FAIRsharing for RDA Funders Forum
 
The FAIR Cookbook in a nutshell
The FAIR Cookbook in a nutshellThe FAIR Cookbook in a nutshell
The FAIR Cookbook in a nutshell
 
FAIR, FAIRplus and the FAIR Cookbook
FAIR, FAIRplus and the FAIR Cookbook FAIR, FAIRplus and the FAIR Cookbook
FAIR, FAIRplus and the FAIR Cookbook
 
RDA17 FAIRsharing WG sessions: on repositories and policies
RDA17 FAIRsharing WG sessions: on repositories and policiesRDA17 FAIRsharing WG sessions: on repositories and policies
RDA17 FAIRsharing WG sessions: on repositories and policies
 
The FAIR movement - Oxford Open Data Week
The FAIR movement - Oxford Open Data WeekThe FAIR movement - Oxford Open Data Week
The FAIR movement - Oxford Open Data Week
 
The Software Sustainability Institute Fellowship
The Software Sustainability Institute FellowshipThe Software Sustainability Institute Fellowship
The Software Sustainability Institute Fellowship
 
FAIRsharing - focus on standards and new features
FAIRsharing - focus on standards and new features FAIRsharing - focus on standards and new features
FAIRsharing - focus on standards and new features
 
FAIRsharing poster
FAIRsharing posterFAIRsharing poster
FAIRsharing poster
 
FAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
FAIRification is a Team Sport: FAIRsharing and the FAIR CookbookFAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
FAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
 
The FAIR Cookbook poster
The FAIR Cookbook posterThe FAIR Cookbook poster
The FAIR Cookbook poster
 
FAIRsharing COVID-19 Collection for The Global Health Network
FAIRsharing COVID-19 Collection for The Global Health NetworkFAIRsharing COVID-19 Collection for The Global Health Network
FAIRsharing COVID-19 Collection for The Global Health Network
 
Metadata challenges research and re-usable data - BioSharing, ISA and STATO
Metadata challenges research and re-usable data - BioSharing, ISA and STATOMetadata challenges research and re-usable data - BioSharing, ISA and STATO
Metadata challenges research and re-usable data - BioSharing, ISA and STATO
 
Data publication: Discover, Explore, Visualise
Data publication: Discover, Explore, VisualiseData publication: Discover, Explore, Visualise
Data publication: Discover, Explore, Visualise
 
FAIRsharing, FAIR principles and metrics - Working with/for the Agro domain
FAIRsharing, FAIR principles and metrics - Working with/for the Agro domainFAIRsharing, FAIR principles and metrics - Working with/for the Agro domain
FAIRsharing, FAIR principles and metrics - Working with/for the Agro domain
 
Managing Big Data - Berlin, July 9-10, 201.
Managing Big Data - Berlin, July 9-10, 201.Managing Big Data - Berlin, July 9-10, 201.
Managing Big Data - Berlin, July 9-10, 201.
 
FAIRcookbook: working with biopharmas
FAIRcookbook: working with biopharmasFAIRcookbook: working with biopharmas
FAIRcookbook: working with biopharmas
 
FAIRsharing: how we assist with FAIRness
FAIRsharing: how we assist with FAIRnessFAIRsharing: how we assist with FAIRness
FAIRsharing: how we assist with FAIRness
 
FAIR overview - MAQC Society, Feb 2018
FAIR overview - MAQC Society, Feb 2018FAIR overview - MAQC Society, Feb 2018
FAIR overview - MAQC Society, Feb 2018
 

Ähnlich wie Open Science FAIR 2021: FAIRsharing and the FAIR Cookbook

FAIR, standards and FAIRsharing - MAQC Society 2019
FAIR, standards and FAIRsharing - MAQC Society 2019FAIR, standards and FAIRsharing - MAQC Society 2019
FAIR, standards and FAIRsharing - MAQC Society 2019Susanna-Assunta Sansone
 
FAIRsharing presentation at the Japan Science and Technology Agency
FAIRsharing presentation at the Japan Science and Technology AgencyFAIRsharing presentation at the Japan Science and Technology Agency
FAIRsharing presentation at the Japan Science and Technology AgencyPeter McQuilton
 
RDA BioSharing WG/ELIXIR Session Montreal 2017
RDA BioSharing WG/ELIXIR Session Montreal 2017RDA BioSharing WG/ELIXIR Session Montreal 2017
RDA BioSharing WG/ELIXIR Session Montreal 2017Peter McQuilton
 
BioSharing, an ELIXIR Interoperability Platform resource
BioSharing, an ELIXIR Interoperability Platform resourceBioSharing, an ELIXIR Interoperability Platform resource
BioSharing, an ELIXIR Interoperability Platform resourcePeter McQuilton
 
FAIRsharing and FAIRmetrics - RDA, March 2018
FAIRsharing and FAIRmetrics - RDA, March 2018FAIRsharing and FAIRmetrics - RDA, March 2018
FAIRsharing and FAIRmetrics - RDA, March 2018Susanna-Assunta Sansone
 
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...Peter McQuilton
 
FAIRsharing - ENVRI-FAIR Webinar
FAIRsharing - ENVRI-FAIR WebinarFAIRsharing - ENVRI-FAIR Webinar
FAIRsharing - ENVRI-FAIR WebinarPeter McQuilton
 
How are we Faring with FAIR? (and what FAIR is not)
How are we Faring with FAIR? (and what FAIR is not)How are we Faring with FAIR? (and what FAIR is not)
How are we Faring with FAIR? (and what FAIR is not)Carole Goble
 
A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...
A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...
A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...Allyson Lister
 
ELIXIR Webinar: BioSharing
ELIXIR Webinar: BioSharingELIXIR Webinar: BioSharing
ELIXIR Webinar: BioSharingPeter McQuilton
 
FAIRsharing and DataCite: Data Repository Selection- Criteria That Matter
FAIRsharing and DataCite: Data Repository Selection- Criteria That MatterFAIRsharing and DataCite: Data Repository Selection- Criteria That Matter
FAIRsharing and DataCite: Data Repository Selection- Criteria That MatterSusanna-Assunta Sansone
 
Turning FAIR into Reality - Role for Libraries
Turning FAIR into Reality - Role for Libraries Turning FAIR into Reality - Role for Libraries
Turning FAIR into Reality - Role for Libraries dri_ireland
 
FAIRsharing: what we do for policies
FAIRsharing: what we do for policiesFAIRsharing: what we do for policies
FAIRsharing: what we do for policiesAllyson Lister
 
FAIR landscape in ELIXIR: FAIR metrics and other initiatives
FAIR landscape in ELIXIR: FAIR metrics and other initiativesFAIR landscape in ELIXIR: FAIR metrics and other initiatives
FAIR landscape in ELIXIR: FAIR metrics and other initiativesPeter McQuilton
 
2010-05 CIARD Detailed Presentation - English
2010-05 CIARD Detailed Presentation - English2010-05 CIARD Detailed Presentation - English
2010-05 CIARD Detailed Presentation - EnglishCIARD
 

Ähnlich wie Open Science FAIR 2021: FAIRsharing and the FAIR Cookbook (20)

FAIRcookbook: GSRS22-Singapore
FAIRcookbook: GSRS22-SingaporeFAIRcookbook: GSRS22-Singapore
FAIRcookbook: GSRS22-Singapore
 
Metadata Standards
Metadata StandardsMetadata Standards
Metadata Standards
 
FAIR, standards and FAIRsharing - MAQC Society 2019
FAIR, standards and FAIRsharing - MAQC Society 2019FAIR, standards and FAIRsharing - MAQC Society 2019
FAIR, standards and FAIRsharing - MAQC Society 2019
 
FAIRsharing presentation at the Japan Science and Technology Agency
FAIRsharing presentation at the Japan Science and Technology AgencyFAIRsharing presentation at the Japan Science and Technology Agency
FAIRsharing presentation at the Japan Science and Technology Agency
 
RDA BioSharing WG/ELIXIR Session Montreal 2017
RDA BioSharing WG/ELIXIR Session Montreal 2017RDA BioSharing WG/ELIXIR Session Montreal 2017
RDA BioSharing WG/ELIXIR Session Montreal 2017
 
BioSharing, an ELIXIR Interoperability Platform resource
BioSharing, an ELIXIR Interoperability Platform resourceBioSharing, an ELIXIR Interoperability Platform resource
BioSharing, an ELIXIR Interoperability Platform resource
 
FAIRsharing & FAIRcookbook at RDA 2023
FAIRsharing & FAIRcookbook at RDA 2023FAIRsharing & FAIRcookbook at RDA 2023
FAIRsharing & FAIRcookbook at RDA 2023
 
FAIRsharing and FAIRmetrics - RDA, March 2018
FAIRsharing and FAIRmetrics - RDA, March 2018FAIRsharing and FAIRmetrics - RDA, March 2018
FAIRsharing and FAIRmetrics - RDA, March 2018
 
FAIR-4-GSC-Sansone-Aug23.pdf
FAIR-4-GSC-Sansone-Aug23.pdfFAIR-4-GSC-Sansone-Aug23.pdf
FAIR-4-GSC-Sansone-Aug23.pdf
 
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...
FAIRsharing - Mapping the Landscape of Databases, Repositories, Standards and...
 
FAIRsharing - ENVRI-FAIR Webinar
FAIRsharing - ENVRI-FAIR WebinarFAIRsharing - ENVRI-FAIR Webinar
FAIRsharing - ENVRI-FAIR Webinar
 
How are we Faring with FAIR? (and what FAIR is not)
How are we Faring with FAIR? (and what FAIR is not)How are we Faring with FAIR? (and what FAIR is not)
How are we Faring with FAIR? (and what FAIR is not)
 
A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...
A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...
A Whirlwind tour of the FAIR Principles, ELIXIR, and FAIRsharing in the conte...
 
ELIXIR Webinar: BioSharing
ELIXIR Webinar: BioSharingELIXIR Webinar: BioSharing
ELIXIR Webinar: BioSharing
 
FAIRsharing and DataCite: Data Repository Selection- Criteria That Matter
FAIRsharing and DataCite: Data Repository Selection- Criteria That MatterFAIRsharing and DataCite: Data Repository Selection- Criteria That Matter
FAIRsharing and DataCite: Data Repository Selection- Criteria That Matter
 
FAIRsharing: what we do for policies
FAIRsharing: what we do for policiesFAIRsharing: what we do for policies
FAIRsharing: what we do for policies
 
Turning FAIR into Reality - Role for Libraries
Turning FAIR into Reality - Role for Libraries Turning FAIR into Reality - Role for Libraries
Turning FAIR into Reality - Role for Libraries
 
FAIRsharing: what we do for policies
FAIRsharing: what we do for policiesFAIRsharing: what we do for policies
FAIRsharing: what we do for policies
 
FAIR landscape in ELIXIR: FAIR metrics and other initiatives
FAIR landscape in ELIXIR: FAIR metrics and other initiativesFAIR landscape in ELIXIR: FAIR metrics and other initiatives
FAIR landscape in ELIXIR: FAIR metrics and other initiatives
 
2010-05 CIARD Detailed Presentation - English
2010-05 CIARD Detailed Presentation - English2010-05 CIARD Detailed Presentation - English
2010-05 CIARD Detailed Presentation - English
 

Mehr von Susanna-Assunta Sansone

Mehr von Susanna-Assunta Sansone (9)

FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
FAIR, FAIRsharing, FAIR Cookbook and ELIXIR - Sansone SA - Boston 2024
 
FAIRsharing-Standards-4-GSC-Aug23.pdf
FAIRsharing-Standards-4-GSC-Aug23.pdfFAIRsharing-Standards-4-GSC-Aug23.pdf
FAIRsharing-Standards-4-GSC-Aug23.pdf
 
NFDI Physical Sciences Colloquium - FAIR
NFDI Physical Sciences Colloquium - FAIRNFDI Physical Sciences Colloquium - FAIR
NFDI Physical Sciences Colloquium - FAIR
 
FAIR Cookbook
FAIR Cookbook FAIR Cookbook
FAIR Cookbook
 
FAIR, community standards and data FAIRification: components and recipes
FAIR, community standards and data FAIRification: components and recipesFAIR, community standards and data FAIRification: components and recipes
FAIR, community standards and data FAIRification: components and recipes
 
FAIRsharing and the FAIR Cookbook
FAIRsharing and the FAIR Cookbook FAIRsharing and the FAIR Cookbook
FAIRsharing and the FAIR Cookbook
 
FAIRsharing for EOSC
FAIRsharing for EOSC FAIRsharing for EOSC
FAIRsharing for EOSC
 
FAIR: standards and services
FAIR: standards and servicesFAIR: standards and services
FAIR: standards and services
 
ELIXIR FAIR Activities - Examplars
ELIXIR FAIR Activities - ExamplarsELIXIR FAIR Activities - Examplars
ELIXIR FAIR Activities - Examplars
 

Kürzlich hochgeladen

Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBoston Institute of Analytics
 
IBEF report on the Insurance market in India
IBEF report on the Insurance market in IndiaIBEF report on the Insurance market in India
IBEF report on the Insurance market in IndiaManalVerma4
 
Non Text Magic Studio Magic Design for Presentations L&P.pdf
Non Text Magic Studio Magic Design for Presentations L&P.pdfNon Text Magic Studio Magic Design for Presentations L&P.pdf
Non Text Magic Studio Magic Design for Presentations L&P.pdfPratikPatil591646
 
Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...
Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...
Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...boychatmate1
 
Role of Consumer Insights in business transformation
Role of Consumer Insights in business transformationRole of Consumer Insights in business transformation
Role of Consumer Insights in business transformationAnnie Melnic
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxSimranPal17
 
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Boston Institute of Analytics
 
Principles and Practices of Data Visualization
Principles and Practices of Data VisualizationPrinciples and Practices of Data Visualization
Principles and Practices of Data VisualizationKianJazayeri1
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfblazblazml
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Boston Institute of Analytics
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...Dr Arash Najmaei ( Phd., MBA, BSc)
 
knowledge representation in artificial intelligence
knowledge representation in artificial intelligenceknowledge representation in artificial intelligence
knowledge representation in artificial intelligencePriyadharshiniG41
 
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdfWorld Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdfsimulationsindia
 
Rithik Kumar Singh codealpha pythohn.pdf
Rithik Kumar Singh codealpha pythohn.pdfRithik Kumar Singh codealpha pythohn.pdf
Rithik Kumar Singh codealpha pythohn.pdfrahulyadav957181
 
Statistics For Management by Richard I. Levin 8ed.pdf
Statistics For Management by Richard I. Levin 8ed.pdfStatistics For Management by Richard I. Levin 8ed.pdf
Statistics For Management by Richard I. Levin 8ed.pdfnikeshsingh56
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Boston Institute of Analytics
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectBoston Institute of Analytics
 

Kürzlich hochgeladen (20)

Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
 
IBEF report on the Insurance market in India
IBEF report on the Insurance market in IndiaIBEF report on the Insurance market in India
IBEF report on the Insurance market in India
 
Non Text Magic Studio Magic Design for Presentations L&P.pdf
Non Text Magic Studio Magic Design for Presentations L&P.pdfNon Text Magic Studio Magic Design for Presentations L&P.pdf
Non Text Magic Studio Magic Design for Presentations L&P.pdf
 
Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...
Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...
Introduction to Mongo DB-open-­‐source, high-­‐performance, document-­‐orient...
 
Role of Consumer Insights in business transformation
Role of Consumer Insights in business transformationRole of Consumer Insights in business transformation
Role of Consumer Insights in business transformation
 
What To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptxWhat To Do For World Nature Conservation Day by Slidesgo.pptx
What To Do For World Nature Conservation Day by Slidesgo.pptx
 
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
Data Analysis Project : Targeting the Right Customers, Presentation on Bank M...
 
2023 Survey Shows Dip in High School E-Cigarette Use
2023 Survey Shows Dip in High School E-Cigarette Use2023 Survey Shows Dip in High School E-Cigarette Use
2023 Survey Shows Dip in High School E-Cigarette Use
 
Principles and Practices of Data Visualization
Principles and Practices of Data VisualizationPrinciples and Practices of Data Visualization
Principles and Practices of Data Visualization
 
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdfEnglish-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
English-8-Q4-W3-Synthesizing-Essential-Information-From-Various-Sources-1.pdf
 
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
Data Analysis Project Presentation: Unveiling Your Ideal Customer, Bank Custo...
 
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
6 Tips for Interpretable Topic Models _ by Nicha Ruchirawat _ Towards Data Sc...
 
knowledge representation in artificial intelligence
knowledge representation in artificial intelligenceknowledge representation in artificial intelligence
knowledge representation in artificial intelligence
 
Data Analysis Project: Stroke Prediction
Data Analysis Project: Stroke PredictionData Analysis Project: Stroke Prediction
Data Analysis Project: Stroke Prediction
 
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdfWorld Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
World Economic Forum Metaverse Ecosystem By Utpal Chakraborty.pdf
 
Rithik Kumar Singh codealpha pythohn.pdf
Rithik Kumar Singh codealpha pythohn.pdfRithik Kumar Singh codealpha pythohn.pdf
Rithik Kumar Singh codealpha pythohn.pdf
 
Statistics For Management by Richard I. Levin 8ed.pdf
Statistics For Management by Richard I. Levin 8ed.pdfStatistics For Management by Richard I. Levin 8ed.pdf
Statistics For Management by Richard I. Levin 8ed.pdf
 
Insurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis ProjectInsurance Churn Prediction Data Analysis Project
Insurance Churn Prediction Data Analysis Project
 
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
Decoding the Heart: Student Presentation on Heart Attack Prediction with Data...
 
Decoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis ProjectDecoding Patterns: Customer Churn Prediction Data Analysis Project
Decoding Patterns: Customer Churn Prediction Data Analysis Project
 

Open Science FAIR 2021: FAIRsharing and the FAIR Cookbook

  • 1. datareadiness.eng.ox.ac.uk Allyson Lister, PhD Content and Community Coordinator ORCiD: 0000-0002-7702-4495 | Twitter: @allysonlister @FAIRsharing_org contact@fairsharing.org 10.25504/FAIRsharing.2abjs5 a FAIR-enabling service
  • 2. A global resource for all disciplines An informative and educational resource of three curated and interlinked registries of standards, repositories and policies; three key elements of the FAIR ecosystem COMMUNITY STANDARDS DATA POLICIES by funders, journals and other organizations REPOSITORIES including databases and knowledgebases Identifiers Terminologies Guidelines Formats
  • 3. Promoting repositories, standards, policies Guides consumers to discover, select and use these resources with confidence Helps producers to make their resources more visible, more widely adopted and cited Fosters a culture change where the use of these resources for FAIRer data is pervasive and seamless Total of over 3500 resources repositories standards policies
  • 4. Users from academic, governmental and industry sectors Researchers Developers and curators Journal publishers Societies and Alliances Librarians and Trainers Funders
  • 5. Organisations: collections provide tailored views Many groups and projects have created Collections These are are branded pages that group selected standards (and/or repositories) relevant to their community and/or developed by them URL: fairsharing.org/collection/IVOA URL: fairsharing.org/collection/CDISC
  • 6. Publishing industry: policies registration and dashboards Scholarly publishers use FAIRsharing to: ● list standards and repositories they recommend in their data policy, ● discover new resources and monitor their evolution to refine or update their policies
  • 7. … 232 standards https://fairsharing.org/collection/ISOCD20691CollectionDRAFT ISO/CD 20691 specification that details the requirements for the consistent formatting and documentation of data and metadata in the life sciences and biotechnology, including biomedical research and non-human biological research and development; it covers manual or computational workflows. This FAIRsharing Collection includes the standards detailed in the ISO/CD 20691 specification, and serves as a 'live' list to search and discover these standards, their use by repositories, as well as their evolution over time. Biotech industry: knowledge graphs of standards
  • 8. ● Pistoia Alliance is a not-for-profit organisation of over 100 Life Science R&D companies, including major pharmas and service providers ○ Working on pre-competitive projects on best practices and technology challenges, including data FAIRness ● FAIRsharing and the Pistoia Alliance’s FAIR Implementation WG are working to create Collections of companies’ profiles, with respect to the standards they use ● The FAIRsharing “Pistoia Alliance Collections” will help with comparing and discussing standards practices within industry Life Sciences R&D companies: semantic and syntactic standards
  • 9. A growing number of services for: ● data management plans ● data stewardship ● metadata aggregation ● FAIR assessment access FAIRsharing, via its API, and use it as look-up and select service for (metadata and information on) standards and repositories Services: curated content to power FAIR-enabling other tools and services ds-wizard.org dmponline.dcc.ac.uk w3id.org/AmIFAIR openaire.eu fairshake.cloud
  • 10. An endorsed output of the FAIRsharing WG (since 2015): Also a WG (since 2015) in: Users, adopters, communities and working groups
  • 11. Susanna-Assunta Sansone, PhD Associate Professor, Information Engineering Associate Director, Oxford e-Research Centre ORCiD: 0000-0001-5306-5690 | Twitter: @SusannaASansone datareadiness.eng.ox.ac.uk @FAIRplus_eu fairplus-cookbook@elixir-europe.org https://w3id.org/faircookbook
  • 12. What is it? An online, ‘live’ resource for the life sciences A collection of recipes that cover the operation steps of FAIR data management Who is it for? Who developed it? Researchers and data managers professionals in the life sciences, from academia and industry Including ELIXIR members FAIR Cookbook: turning knowledge into recipes https://fairplus.github.io/the-fair-cookbook
  • 13.
  • 14. +50 life sciences professionals, researchers and data managers FAIR Cookbook: creators and contributors FARIplus partners Industry + Academia ELIXIR Nodes represented
  • 15. Learn how to improve the FAIRness with exemplar datasets Understand the levels and indicators of FAIRness Discover open source technologies, tools and services Find out the required skills Acknowledge the challenges FAIR Cookbook: learning objectives
  • 16. Recipes that cover all aspects of FAIRness
  • 18. The capability maturity model The optimum level of FAIRness is a trade-off between desired data reuse level and cost to achieve that level Show business value, achieve the ‘FAIR enough’ on an enterprise scale
  • 19. The capability maturity model - the ontology example No use of ontologies Use of internal ontologies Use of community ontologies + Ontology service to manage several ontologies, mapping, versioning etc. + Term suggestion, automatic annotation, terms conflict resolution etc. The optimum level of FAIRness is a trade-off between desired data reuse level and cost to achieve that level Show business value, achieve the ‘FAIR enough’ on an enterprise scale
  • 20. No use of ontologies Use of internal ontologies Use of community ontologies + Ontology service to manage several ontologies, mapping, versioning etc. + Term suggestion, automatic annotation, terms conflict resolution etc. The capability maturity model - the ontology example A dedicated recipe will help to move from Repeatable to Defined level
  • 21. Start using the FAIR Cookbook Help us to help you, this is a user-oriented resource Join a network of FAIR experts Share and broaden your expertise, joining our journey Contribute according to your needs Create and review, or signpost gaps and needs Benefit from the power of the FAIR community Become a recognised expert, enlarge your network Recommend it in your guidance and training material For everyone use it join it adopt it leverage it Included in the next version =>
  • 22. A live ever-growing resource: become part of a community of FAIR experts! 1Identify a chapter and a topic Findability Accessibility Interoperability Reusability Infrastructure Applied examples Assessment 2 Choose a way of contributing and see our guidelines Google Docs HackMD Git Markdown cheat sheet Get recipe template Tips and tricks Submit an outline 3 You can discuss it with the Editorial Board
  • 23. Watch the webinar for more information elixir-europe.org/events/fairplus-webinar-discovering-fair-cookbook