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![Persistent identifiers
Globally unique, resolvable for
data and always for metadata
Structured metadata
Community defined descriptive
metadata using common
terminologies and standards
Linked Data
Vocabularies are FAIR, (meta)data
reference (meta)data, provenance
Automation-
readiness
Access protocols
Open, free and universally
implementable comms protocols
Semantic Web ->
Linked Data ->
Knowledge Graphs.
Machine-processable
metadata.
[Icons: FAIRsharing]](https://image.slidesharecdn.com/nsf-goble-delivered-210528155436/75/FAIRy-stories-the-FAIR-Data-principles-in-theory-and-in-practice-12-2048.jpg)
![Where we are going
Where we are
[Susanna Sansone]
FAIR
first
stage](https://image.slidesharecdn.com/nsf-goble-delivered-210528155436/75/FAIRy-stories-the-FAIR-Data-principles-in-theory-and-in-practice-19-2048.jpg)
![MolecularEntity ChemicalSubstance
Toxicology
Data Aggregator
[with thanks: EgonWillighagen]
MolecularEntity
Gene
Protein
Taxon
Dataset](https://image.slidesharecdn.com/nsf-goble-delivered-210528155436/75/FAIRy-stories-the-FAIR-Data-principles-in-theory-and-in-practice-29-2048.jpg)
![3. Making (legacy) datasets FAIR: FAIRification
[Picture credit: EgonWillighagen]](https://image.slidesharecdn.com/nsf-goble-delivered-210528155436/75/FAIRy-stories-the-FAIR-Data-principles-in-theory-and-in-practice-36-2048.jpg)
Uploaded byCarole Goble
FAIRy stories: the FAIR Data principles in theory and in practice
Carole Goble discusses the importance of FAIR data principles in research, emphasizing the need for enhanced access, sharing, and reusability of data to improve research outcomes, particularly in the context of global challenges like the COVID-19 pandemic. The talk outlines the guiding FAIR principles and the significance of aligning data producers and consumers to foster a culture of data sharing and cooperation across disciplines. Goble also highlights the role of global initiatives and standards in achieving a more interoperable and efficient research data ecosystem.
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Introduction of the FAIR data principles presented by Carole Goble from the University of Manchester.
Emphasizes the need for FAIR data in research, especially highlighted during COVID-19 for accessing historical data.
Introduces guiding principles of FAIR data for better resource reuse, urging a community alignment around data guidelines.
Explains FAIR principles supporting data usability and access protocols, highlighting the differentiation between openness and necessary restrictions.
Describes the role of FAIR principles in an Open Data Ecosystem, discussing adoption complexities and the range of stakeholders involved.
Highlights various global initiatives and projects dedicated to advancing FAIR data practices and policies.
Discusses the awareness level of researchers regarding FAIR data principles based on 2019 survey data.
Explores the integration of Schema.org for enhancing data description and categorization in bioscience.
Details the structure and development of Bioschemas for improved dataset handling and definitions.
Presents practical lessons for implementing FAIR principles in data and research object management.
Discusses approaches and methodologies for making legacy datasets compliant with FAIR principles.
Highlights new variants like FAIR++, discussing ongoing projects and the need for strategic design at data source.
Summarizes the journey of mobilizing FAIR principles into practice and the importance of cooperative effort.
Acknowledges contributors and colleagues involved in the FAIR data initiatives and research.
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FAIRy stories: the FAIR Data principles in theory and in practice
- 1. FAIRy stories: theFAIR Data principles in theory and in practice Carole Goble The University of Manchester, UK [email protected] The views expressed in this talk are my own NSF Convergence Accelerator Series Tracks A&B webinar, 19th May 2021
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- 3. Why do weneed FAIR data in Research? “there must be loads of legacy data. We’re desperately trying to go back and look at what we knew from SARS 10 years ago” https://www.covid19dataportal.org/ https://www.rd-alliance.org/group/rda-covid19-rda-covid19-omics-rda-covid19-epidemiology-rda-covid19- clinical-rda-covid19-1 https://doi.org/10.15497/rda00052
- 4. Why do weneed FAIR data in Research? COVID Data sharing boost – mobilising people, infrastructure & initiatives Spotlighted technical, territorial & practices Provider: collection, upload and governance bottlenecks User: find and access to datasets, licenses, data and metadata quality Access to data for processing at scale, common standards Behaviour inertia and relapse Long term sustainability “global pandemic is not sufficient to radically modify scientific practices”* * Larregue et al https://blogs.lse.ac.uk/impactofsocialsciences/2020/11/30/covid-19-where-is-the-data/
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- 6. Why do weneed FAIR data in Research? information flows, secondary use Figure: KnowledgeTurning, Information Flow Josh Sommer, Chordoma Foundation, 2011 Community domain enclaves Resource fragmentation Flow across platforms/ sovereignties Pan-discipline drivers Knowledge churn, loss and cost
- 7. 2016 A set ofGUIDING PRINCIPLES to enhance the value of all digital resources and their reuse by PEOPLE and by MACHINES ALIGNING a COMMUNITY around common data guidelines FAIR Research Data
- 8. branding a trend (re)-stimulatinga movement
- 9. What ARE theFAIR principles? Aspirational guardrails Not a standard, nor metrics A contract between data provider and user In the original paper https://www.go-fair.org/fair-principles/ Relaunch a dialogue - research and policy communities. Reboot a journey - wider accessibility and reusability of data.
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- 11. “enhancing the abilityof machines to automatically find and use data or any digital object, and support its reuse by individuals” INCF Statement
- 12. Persistent identifiers Globally unique,resolvable for data and always for metadata Structured metadata Community defined descriptive metadata using common terminologies and standards Linked Data Vocabularies are FAIR, (meta)data reference (meta)data, provenance Automation- readiness Access protocols Open, free and universally implementable comms protocols Semantic Web -> Linked Data -> Knowledge Graphs. Machine-processable metadata. [Icons: FAIRsharing]
- 13. Open as possible,Closed as necessary Clear licences for innovation and reuse Sensitive data, GDPR, IPR, jumpy Deans. Crossing sovereignty boundaries • Data sharing becomes data visiting & federated analysis An industry in controlled secure access…. • Data Usage Ontology, Beacon Passports, Trusted Research Environments etc…. Terms of access and use: FAIR ≠ OPEN FAIR OPEN SAFE Privacy preservation Regulatory rigour
- 14. FAIR Implicit Assumptions& Implications Data are first class objects Primarily aimed at data creators and providers for benefit of consumers. Operating in an (Open) Data Ecosystem. Adoption at scale in legacy settings. Data sharing
- 15. The Life Sciences& pan-European scale data infrastructure
- 16. The Life SciencesInfrastructure Zoo Flows around a Federated & Diverse System 1466 data repositories (100+ in EOSC-Life) 916 data format and metadata standards* from compounds to clinical trials https://fairsharing.org/ accessed May 2021 Common standards & agreements mappings of PIDs and metadata moving metadata around accountability and responsibility
- 17. FAIR players simplified Researchersand company scientists who generate and use the data Service providers who manage data and infrastructure Local -> Global level Public -> Commercial Authorities who drive policy, practice & resources Funders, Policy makers, Publishers, Professional societies, Standards organisations, Institutions
- 18. Global and nationalinitiatives Dedicated projects Community Orgs Funders Policy Publishers FAIR first stage Dedicated Services
- 19. Where we aregoing Where we are [Susanna Sansone] FAIR first stage
- 20. FAIR first stage: Policymakers, Data service providers How to define, measure compliance and certify FAIR data? What is a dataset? General repos vs Curated authoritative archives? Principles for Data Repositories https://www.rd-alliance.org/trust-principles-rda-community-effort https://fairassist.org/
- 21. https://www.natureindex.com/news-blog/what-scientists-need-to-know-about-fair-data Open Data Survey,2019 81% of researcher respondents unfamiliar with FAIR
- 23. 1. A commonmechanism for metadata Respect and work with the huge legacy resources: repositories, registries, tools … community standards Find, register, index, search resources Move metadata between services withoutAPIs Repositories ->Tools, Aggregators (e.g. licenses) -> Registries (upload, auto-curation) Registries -> Registries (across disciplines) Contribute to Knowledge Graphs a little bit of semantics at scale semantic underware invisible to users visible to developers & services
- 24. Picture: Carole Goble,Turing Lecture 2018 Schema.org: Semantic Mark up for the Web Cartel of commercial search engines Wide web use, web infrastructure Web pages and sitemaps Types (830+) IceCreamShop Properties (1300+) hasMenu Not targeted at science - too much / too little Dataset type – 120 properties (Google Data Profile requires 2 properties) No type for Protein, Gene, Taxon
- 25. Harnessing Schema.org forBioscience Profile Data model Marginality information Controlled vocabularies Cardinality Documentation Examples New (properties | types) definition & consensus deployment and use tools & support Opinionated conventions Profiles & Link to domain ontologies }Add Bioscience properties & types if necessary Examples &Usage Guidelines } Community
- 26. Harnessing Schema.org forBioscience ChemicalSubstance definition & consensus deployment and use tools & support Opinionated conventions Profiles & Link to domain ontologies Add Bioscience properties & types if necessary Examples &Usage Guidelines Community
- 27. Bioschemas metadata stratification broad& shallow / deepish & narrowish Generic Subject specific MolecularEntity, Protein, Sample,Taxon, ChemicalSubstance… DataCatalog Dataset dataset 5 minimum, 8 recommended properties license & provenance https://bioschemas.org/profiles/ Crosswalks to metadata schemas * • DCAT, DataCite,CrossRef, OpenAIRE, DDI • DCT:issued <-> Schema:dataPublished What is a dataset? Include community ontologies • Type: ChemicalSubstance • Property: biologicalRole • ExpectedType: ChEBI ontology * https://zenodo.org/record/4420116#.YKFOpaHTX18
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- 29. MolecularEntity ChemicalSubstance Toxicology Data Aggregator [withthanks: EgonWillighagen] MolecularEntity Gene Protein Taxon Dataset
- 30. Lessons: Putting FAIRinto Practice A little bit of semantics at scale -> build critical mass Profiles • Schema.org culture – Catch 22 • Consensus building, retention & Ontology-itis Provider mark-up • Developer friendly in house tools & wacky web implementations • Adoption incentives & costs of adapting database processes Consumer services • Adoption incentives – Catch 22 & tipping points • DataCatalog & Dataset popular -> Google Dataset search Consumer-provider readiness • Tools and training community take-up….
- 31. 2. Packaging ResearchObjects Gather together into a “crate” files, unbounded references, & other crates. FAIR content: metadata, identifiers, provenance, citation about the content FAIR crates: metadata, PIDs, provenance, citation about the crate. more FAIR middleware -> towards FAIR Digital Objects* *FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units: https://doi.org/10.3390/publications8020021
- 32. Why “crate up”objects? FAIR+R Flows: Researchers work with multiple and different objects using multiple infrastructures over periods of time exchange between platforms and people Parts: Research has associated objects linked together by context metadata files with files datasets, scripts, SOPs, articles … 0 held in different places made at different times by different people & processes publish, report, reuse, cite, reproduce register, deposit, archive, port point to big, sensitive & active content
- 33. Aggregate files and/orany URI-addressable content with structured metadata Web and Linked Data Native machine and human readable PIDs + JSON-LD + Schema.org, search engine & developer friendly Flex for open ended content, respect legacy typed by a profile + add more schema.org and domain ontologies http://www.researchobject.org/ro-crate/ Archive file format FAIR Object Middleware
- 34. FAIR Middleware metadata carryinginterchange format Knowledge Graph of Research Objects
- 35. It’s FAIR metadatamiddleware, stupid • smart use of wheels already invented • get tools, services on board • developer friendly, firm best practice Known and Unknown unknowns One size does not fit all • contextual interpretation • descriptive openedness , multi-interpretation Analogous to FAIR Software • RDA/ReSA FAIR4Research SoftwareWG Lessons: Putting FAIR into Practice
- 36. 3. Making (legacy)datasets FAIR: FAIRification [Picture credit: EgonWillighagen]
- 37. Credit to: IanHarrow, FAIR & OM projects FAIR as enabler for the digital transformation ● Biopharma R&D productivity can be improved by implementing the FAIR Data Principles. ● FAIR enables powerful new AI analytics access to data for machine learning and prediction ● Fairly AI Ready ● Challenges ○ change the culture, show business value, achieve the ‘FAIR enough’ ○ Sustain FAIR solutions and activities Slide credit: Susanna Sansone
- 38. Making (legacy) datasetsFAIR: FAIRification > 100 Public-Private partnerships of European Commission, universities SMEs and Big Pharma translational projects Pharma’s own datasets
- 39. *https://www.go-fair.org/how-to-go-fair/fair-data-point/ Data visiting througha FAIR Data Point* Linked Data / RDF tech Dataset transformation Methodology Linkset services RDFWarehouse (Knowledge Graph) - API not SPARQL - Sustainability & maintenance - Linksets PID mapping services
- 40. FAIRification of legacydatasets Practical advice Assessment processes FAIR levels of projects / data Selection of datasets Cost/Benefit analysis Methodology Steps for 1 or more datasets Cultural change Legal templates Squads & BYODs Maturity models
- 41. Interlinking data fromdifferent sources The lessons of good global and persistent identifiers. Mapping identifiers and services for mapping ids to ids and concepts to concepts. https://fairplus.github.io/the-fair-cookbook/content/recipes/interoperability/identifier-mapping.html
- 42. FAIR by Design Atthe start of a collection, built in throughout the life cycle change management, capacity building FAIRifying Retrospectively Legacy datasets, build a cohort, cost benefit and FAIR readiness over a collection of datasets
- 43.
- 44. FA(I)R New FAIRVariants FAIR++ Legal >Organisational > Semantic >Technical* Business and change analysis. Cost Benefit Analysis. Scientific / BusinessValue Sustainability “…make a decision that these data are valuable enough to invest in the work required for FAIRification.” interoperability *EOSC Interoperability Framework
- 45. What does FAIRifyinga dataset mean? A database?A pdf? Depositing to a public archive? Identifier and ontology selecting, assigning, mapping between and to existing vocabs, and knowing about ontology services. High-fidelity ETL loss-less moving (meta)data from one system to another Lessons: Putting FAIR into Practice
- 46. Lessons: Putting FAIRinto Practice FAIR enough. Repository manager Admin monitoring Bioscientist Scientific analysis “Fairness does mean everyone gets the same. Fairness means everyone gets what they need” (Rick Riordan). Maturity and importance spectrum Its not all worth it. FAIR gardens + FAIR scrub How to assess FAIR maturity levels, not to be certified but to make decisions.
- 47. FAIR ≠ FREE- an expensive, expert team sport Mostly manual, mostly specific
- 48. “It is atruth universally acknowledged that a Knowledge Graph must be in want of FAIR data. And FAIR data is in want of Knowledge Graphs.” harvesting added value DataCite PID Graph Bottlenecks: identifiers and ontologies curating and ingest pipelines of data providers
- 49. 4. FAIR Databy Design at Source Data management platform for Project Hubs organising, cataloguing, sharing and publishing multiple kinds of research objects in multiple repositories for multi-partner projects. Community developed Knowledge Hub for guides, examples, tools, and pointers. Assembled and written by Life Science researchers and data stewards for their peers. https://rdmkit.elixir-europe.org https://fair-dom.org
- 50. Lessons: Putting FAIRinto Practice Data creators • Retention not sharing, act local not global • Advantage*: intimate knowledge, data flirting, credits & incentives Process change and values • Access to infrastructure with seamless information flows,Values • Time & resources to embed into practice FAIR Stewardship skills • Professionalisation & know-how *Pasquetto, I. V., Borgman, C. L., & Wofford, M. F. (2019). Uses and Reuses of Scientific Data: The Data Creators’ Advantage. Harvard Data Science Review, 1(2). https://doi.org/10.1162/99608f92.fc14bf2d
- 51. Summary: FAIRy stories Theory-> mobilised some Practice -> marathon that takes a village Move the story from data providers to enabling creators & consumers prepare to share FAIR -> Research on Research Authorities Change Mgt Stewardship Service Providers Sustained infrastructure
- 52. Acknowledgements Special thanks to •Stian Soiland-Reyes (Uni of Manchester/Uni of Amsterdam) • Nick Juty & Ebtisam Alharbi (University of Manchester) • Susanna Sansone (University of Oxford) • Tony Burdett (EMBL-EBI) • Ibrahim Emam (ImperialCollege) • EgonWillighagen (Maastricht University) • Alasdair Gray (Heriot-Watt University) Manchester, Research Object, RDMkit, FAIRDOM, FAIRplus, Bioschemas colleagues (about 130 people) Icons from the noun project (https://thenounproject.com/)