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Practical
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#Python3
#zooma-annotator-script.py
file
def
get_annotations(propertyTy
pe, propertyValues, filters =
""): "””
Get Zooma annotations for
the values of a given
property of a given type.
""”
import requests
annotations = []
no_annotations = []
Where is the value?](https://image.slidesharecdn.com/sas-fairificationisateamsport-march2022-220309184505/75/FAIRification-is-a-Team-Sport-FAIRsharing-and-the-FAIR-Cookbook-35-2048.jpg)
Uploaded bySusanna-Assunta Sansone
FAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
The document discusses the FAIR principles essential for improving data management and enhancing the reproducibility of research. It emphasizes the importance of findable, accessible, interoperable, and reusable data, alongside the role of community-defined standards and identifiers. Additionally, it presents the FAIR Cookbook as a resource for researchers to implement best practices in data management across various life science domains.
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FAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
- 1. FAIRification is aTeam Sport Susanna-Assunta Sansone, PhD AR-BIC 2022 8th Annual Conference, 10-11 March 2022 Slides: https://www.slideshare.net/SusannaSansone Group: datareadiness.eng.ox.ac.uk ORCiD: 0000-0001-5306-5690 Twitter: @SusannaASansone Professor of Data Readiness Associate Director, Oxford e-Research Centre ELIXIR Interoperability Platform ExCo elixir-europe.org Founding Academic Editor nature.com/sdata
- 2. Discoveries are madeusing shared data and this requires data that are: • Retrievable and structured in standard format(s) • Self-described so that third parties can make sense of it The problem Forbes article on 2016 Data Scientist Report https://www.forbes.com/sites/gilpress/2016/03/23/data- preparation-most-time-consuming-least-enjoyable-data-science- task-survey-says/#276a35e6f637 Data preparation accounts for about 80% of the work of data scientists
- 3. A set ofprinciples to enhance the value of all digital resources and its reuse by humans and machines Discoverability and reuse of data at scale
- 4. Globally unique, resolvable, and persistentidentifiers To retrieve and connect data Community-defined descriptive metadata To enhance discoverability and interpretability Community-defined terminologies To use the same term and mean the same thing Detailed provenance and workflows To contextualize the data and facilitate use in applications Terms of access “as open as possible, as closed a necessary” To understand how data can be accessed Terms of use and clear licenses To enable innovation and reuse, ensuring credit as needed Findable Accessible Interoperable Reusable
- 5. doi.org/10.2777/986252 www.gov.uk/government/publications/open- research-data-task-force-final-report www.turing.ac.uk/research/impact- stories/changing-culture-data-science www.fair-access.net.au doi.org/10.1787/25186167 ark:/48223/pf0000374837 FAIR has alignedthe broad community around common guidelines doi.org/10.7486/DRI.tq582c863
- 6. FAIR-driven digital transformationby pharmas • Biopharma R&D productivity can be improved by implementing the FAIR Principles • FAIR enables powerful new AI analytics to access data for machine learning and prediction • Requirements ▪ financial, technical, training • Challenges ▪ change the culture, show business value, achieve the ‘FAIR enough’ on an enterprise scale
- 7. Findable Accessible Interoperable Reusable Providing for acontinuum of features, attributes and behaviours FAIR Principles: aspirational guidance
- 8. Making FAIR areality in the research ecosystem doi.org/10.2777/1524
- 9. An intergovernmental organisationthat brings together life science resources from across Europe, to coordinate them so that they form a single infrastructure
- 10. ELIXIR - asustainable infrastructure for biological data
- 11. ELIXIR Nodes: connecting nationaldata infrastructures ELIXIR Nodes are the permanent structure, funded mainly by national roadmap funding, competitive grants and industry collaborations, and: • Act as national coordinating entities • Bring together national experts • Provide services, databases, tools and resources
- 12. What services doELIXIR offer and in which domains? Databases and Data Resources Interoperability Resources Bioinformatics Tools Compute Capabilities Bioinformatics Training Opportunities Food & Nutrition + Toxicology Domain experts, who are also service providers and/or users, drive the developments in the Platforms
- 13. Focus on theinteroperability platform Databases and Data Resources Interoperability Resources Bioinformatics Tools Compute Capabilities Bioinformatics Training Opportunities
- 14. FAIR service framework Ontologies, formats,reporting guidelines, Identifier Authorities Metadata Annotation & Validation Citation Harvesting, Indexing, Search Ontology Mapping Identifier Mapping Ontology Lookup Identifier resolution Ontology Management Identifier minting Standards & databases Ontologies Tools Workflows Identifiers Registries Type specific Mapping & resolution Extract- Transform-Load APIs Standards Services Type specific KBs, integration & aggregation Knowledge
- 15. IMI2 project guidelinesfor open access to publications and research data Recommended by European funders FAIR service framework: focus on two resources
- 17. An informative andeducational resource, and a service FAIRsharing provides curated descriptions and relationship graphs of standards, databases and policies in all disciplines COMMUNITY STANDARDS POLICIES by funders, journals and other organizations DATABASES including repositories and knowledgebases Identifiers Terminologies Guidelines Formats
- 18. Researchers Developers and curators Journal publishers Societiesand Alliances Librarians and Trainers Funders FAIRsharing: working with and for all stakeholders
- 19. Guides consumers todiscover, select and use these resources with confidence Helps producers to make their resources more visible, more widely adopted and cited Total of over 3595 resources (March 2022) repositories standards policies Promoting repositories, standards, policies
- 20. Search by subject Poweredby our Subject Ontology of 436 terms https://fairsharing.org/browse/subject https://github.com/FAIRsharing/subject-ontology https://www.ebi.ac.uk/ols/ontologies/srao
- 21. Identifiers Terminologies Guidelines Formats Conceptual model,conceptual schema, exchange formats to represent, contain and move information Controlled vocabularies, thesauri, ontologies to disambiguate terms and enable semantic relationships Minimum information reporting requirements, or checklists to report the same core, essential information Unambiguous, persistent and context-independent schema to identify data and metadata elements Interoperability standards are the pillars of FAIR Source:
- 22. Identifiers Terminologies Guidelines Formats Natural, engineering,humanities & social sciences 816 486 192 19 More than 1500 data and metadata standards Source:
- 23. Standard organizations, e.g.:Grass-roots groups, e.g.: Life and biomedical sciences Identifiers Terminologies Guidelines Formats 486 275 151 8 More than 900 data and metadata standards Source:
- 24. Standard organizations, e.g.:Grass-roots groups, e.g.: • Industry-level standards • Mostly regulators-driven • Participation is often regulated • Standards are sold or licenced • Formal development process, often less flexible, could be lengthy • Charges apply to advanced training or programmatic access • Mostly research-level standards • Open to any interested party • Volunteering efforts • Standards are free for use • Development process varies, more flexible and adaptable to changes • Minimal or little funds for carry out the work, let alone provide training Understanding their life cycle and landscape Source: Identifiers Terminologies Guidelines Formats Formulation Development Maintenance
- 25.
- 26. Translational Medicine Clinical Developments URL:beta.fairsharing.org/PistoiaAllianceFIPs (work in progress!) A collaboration with their FAIR Implementation WG Disclaimer: These profiles speak for a limited community and do not represent any company standards Building and comparing “FAIR profiles”
- 27. Clinical Developments Disclaimer: Theseprofiles speak for a limited community and do not represent any company standards Snapshot of the semantic and syntactic standards used
- 28. Findability Sitemap.xml, JSON Markup withSchema.org for search indexes DOI unique persistent identifiers for each record ORCID for author credit and authentication Accessibility read/write REST API read OAI-PMH Interoperability JSON markup Standardized semantics Cross-links to or import from records in other registries ROR for organizations (ongoing) FundRef for funders (ongoing) Reusability CC BY 4.0 license JSON export The FAIRness of the FAIRsharing
- 30. Beyond the hype Largebody of generic FAIR guidance Motivations Non-specific guidance for the life sciences Ambitions Target specific situations to deliver a guide with applied examples Join academia and industry forces to make the case for FAIR data management Build capacity for high quality data management in the private and public sectors
- 31. FAIR Cookbook: turning knowledgeinto recipes 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 faircookbook.elixir-europe.org
- 32. FAIR Cookbook: learningobjectives 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 faircookbook.elixir-europe.org
- 33. Recipes that coverall aspects of FAIRness Recipe summary card, examples:
- 34.
- 35. Step by stepprocess Guidelines, process, description References What should I read next? Ingredients An idea of tools/skills needed Examples Practical elements, code snippets #Python3 #zooma-annotator-script.py file def get_annotations(propertyTy pe, propertyValues, filters = ""): "”” Get Zooma annotations for the values of a given property of a given type. ""” import requests annotations = [] no_annotations = [] Where is the value?
- 36. ● How tomeasures the FAIRness level of data? ○ For use in the FAIRification processes to define initial/final level of data FAIRness ● How to measures capability and performance of an organization for FAIR data generation and management? ○ For use at the strategy level to identify investment areas, monitor processes ○ E.g. ability to provide ETL capability, an ontology look-up service, or mapping services FAIR indicators and capability maturity model The FAIRification process
- 37. The capability maturitymodel Which capabilities are needed to improve data reusability? The optimum level of FAIRness is a trade-off between desired data reuse level and cost to achieve that level
- 38. The capability maturitymodel - the ontology example Which capabilities are needed to improve data reusability? The optimum level of FAIRness is a trade-off between desired data reuse level and cost to achieve that level 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.
- 39. No use of ontologies Useof 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
- 40. +50 life sciencesprofessionals, researchers and data managers FARIplus partners Industry + Academia FAIR Cookbook: creators and contributors ELIXIR Nodes represented
- 41. A live ever-growingresource: 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
- 43. Findability Sitemap.xml, JSON-LD Markup withSchema.org, Bioschemas w3id.org unique persistent identifiers for each recipe ORCID for authors Accessibility HTTPS protocol Interoperability JSON-LD markup Cross-links to objects in other registries incl. Biotools (tools) FAIRsharing (repositories, standards) CreDiT attribution ontology Reusability CC BY 4.0 license for all content The FAIRness of the FAIR Cookbook
- 44. Watch the webinarfor more information, or watch out for the new one! Scheduled: 1 June 2022 datascience.nih.gov/nih-data-sharing-and-reuse- seminar-series elixir-europe.org/events/fairplus-webinar- discovering-fair-cookbook May 2021 faircookbook.elixir-europe.org
- 45. FAIRification is ateam sport, it takes a village, but it is no longer optional. Because better data means better science!