Prof. Barend Mons, Biosemantics Group at Leiden University Medical Center and Head of Node of ELIXIR-NL - Keynote "Bringing Data to Broadway"
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RDA Fourth Plenary Keynote - Prof. Barend Mons, Biosemantics Group at Leiden University Medical Center and Head of Node of ELIXIR-NL - Keynote "Bringing Data to Broadway" - Monday 22nd Sept 2014, Amsterdam, the Netherlands https://rd-alliance.org/plenary-meetings/fourth-plenary/plenary4-programme.html
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Prof. Barend Mons, Biosemantics Group at Leiden University Medical Center and Head of Node of ELIXIR-NL - Keynote "Bringing Data to Broadway"
- 1. by Barend Mons Brought to you for in V parts A Plea For Professional Datapublishing Bringing Data to Broadway
- 2. FAIR play For Research Data and other Research Objects Findable Accessible Interoperable Reusable The Cast
- 3. Part I Moaning and Lamenting
- 5. A-The Curse of Multidisciplinarity
- 6. I can not keep my data experts !!!
- 7. f 2005: Text Mining ? Why Bury it first and then mine it again !
- 10. Part II The Explicitome and the Elusive Part (our own fault) The Explicitome: everything we already asserted
- 11. The Elusive Explicitome Phenomenon example from: Yepes & Verspoor, 2013 narrative Tables/figures abstract # of assertions Supplementary data 5 500* 1000 50K-1M+ # of SNP-Phen: 2% 4% 50%* The Elusive Explicitome: what escapes us (95%) Hurdle 1: Paywalls Hurdle 2: ‘TIF’walls Hurdle 3: The Wall of Broken Links
- 12. Data loss is real and significant, while data growth is staggering Nature news, 19 December 2013 • Computer speed and storage capacity is doubling every 18 months and this rate is steady • DNA sequence data is doubling every 6-8 months over the last 3 years and looks ‘Oops, that link was the laptop of my PhD student’ to continue for this decade
- 13. The trends in e-Science Computer Analytics (takes charge) Enormity of datasets (beyond narrative) Collaborative Intelligence (calls for million minds) Irreversable movement (towards OA) FAIR ? Data Publishing & Stewardship
- 18. Professionalise Data Stewardship A F R I Educate, Reward and Keep Data Experts
- 19. Part 3 Unavoidable: some science of ‘our own’ Part III INTERMEZZO Some Research…. but…..as ….Sorry examples, for the LS examples….. sorry
- 20. Simplified eScience RO’s The Explicitome + WorkFlows Ridiculogram New dataset User New Insights
- 21. Thanks to Peter WittenBurg
- 22. FAIR for computers FAIR for people AERIAL SURVEY pattern recognition in Ridiculograms HUMAN EXCAVATION rationalisation and ‘confirmational reading’ X ‘Why would I believe this association’???
- 23. For KD we need each association only once 23 Cardinal Assertion (<1011) n identical assertions ‘n’ different provenances
- 24. We publish about less than a million LS Concepts ! 24 106 concept clusters (Knowlets)
- 25. www.biosemantics.org LUMC - LIACS BioSemantics Knowledge Discovery Pipeline ⊲ data sources ‘coordinated’ data ! nanopub cache cardinal assertion store semantic data indexing modelling reasoning algorithms trends phase transitions ‘new’ data alerts differentials { funding priorities • gene • disease semantic query {
- 26. © Phortos Consultants 44,000 hypotheses (PPI) What about the other 43,999 ?
- 27. Part 3 Unavoidable: some science of ‘our own’ Part IV Towards Solutions Bigger is not Better Zipping the Explicitome but…..as examples, sorry
- 28. Electronic Health Databases The Rescued Explicitome Value Added Databases narrative Tables/figures Supplementary data abstract PROVENANCE Total Explicitome an estimated 1014 asserted associations in 2,500 data sources ETL to FAIR FAIR to read
- 29. Assertions Concepts 1014 1011 106 Semantic MedLine U+C+CT+EG+GO = 36 M 80% 20% Cardinal Zipping the Explicitome
- 30. Part 3 Unavoidable: some science of ‘our own’ Part V (FAIR) data should take CENTER STAGE but…..as examples, sorry
- 31. DOI PID ARK Handles UUID TURI’s ?
- 32. PID Metadata (intrinsic) 'provenance' (user defined) Data (elements) A simplified diagram of a Digital (data) Object irrespective of technological choices and naming
- 33. Digital Object Architecture PID Metadata (intrinsic) 'provenance' (user defined) Data (elements) s are Digital Objects Some Research Objects Nanopublications are Research Objects are
- 34. Data as increasingly FAIR Digital Objects Totally UNFAIR PID Metadata (intrinsic) 'provenance' (user defined) Data (elements) Usable for Humans PID Findable Metadata (intrinsic) 'provenance' (user defined) Data (elements) PID FAIR metadata Metadata (intrinsic) 'provenance' (user defined) Data (elements) PID FAIR data-restricted access Metadata (intrinsic) 'provenance' (user defined) Data (elements) FAIR data- Open Access PID Metadata (intrinsic) 'provenance' (user defined) Data (elements) Open Access/Functionally Linked PID FAIR data- Metadata (intrinsic) 'provenance' (user defined) Data (elements)
- 35. The Data Stewardship Cycle 35 5%
- 37. Data Owners (supp) data Data bases Repositories FAIRport proof of concept ELIXIR FAIR Data Search Index End-users FAIR L2 ELIXIR Data FAIR Port ELIXIR federated data ELIXIR semantic data repository FAIR L1 Search for datasets Download data (sub) sets in many formats (xml, rdf, json etc) FAIR L3 FAIR L4 ASPs, Inhouse IT, Bioinformatics Etc.. Tools & Applications Elixir Fin. Elixir Esp. Elixir Nor. Elixir Elixir UK Elixir SWE NL.. Elixir Fin. Elixir Esp. Elixir Nor. Elixir Elixir UK Elixir SWE NL.. www.nanopubmed.org
- 38. Parties needed Typical Candidates NL-example Tusted Party Usually Public Sector With 'data stewardship' mandate 1 Executive Party/ Coordinator Usually Public or Private Sector With Expert Knowledge on Project ans relation management 2 Technology Providers 3 4 PID/ARTA stewards DTL/ELIXIR-nl others 5 DOA architecture/IMS CNRI + EURETOS 6 Publishing pipeline EURETOS 7 Repository Software 8 eInfrastructure
- 39. Malpractices……. Journal Impact Factor Ignore Altmetrics No data stewardship plan Obstruct Tenure Data Experts ‘supplementary data’ Knowledge Sharing Impaired
- 40. NITRD FORCE11 ORCID VIVO 4/10/14 EUDAT 40 DATAVERSE BD2K DANS ELIXIR NIHCom mons H2020 DRYAD RDA FigShare Nanopub Biosharing Elsevier Science Nature SageBio HVP DataCite EGA Reseach Objects Nebulus Embassy SADI EURETOS YARCdata IMI interoperability ISA Open PHACTS Data Fabric
- 41. Good practices (apart from collaborating) ‘professional data publishing’ RO Impact Factor Award Altmetrics 5% for data stewardship plan Train & Tenure Data Experts FAIR play
- 42. THE END Thank you!
- 43. Endorsed by 82 organisations and [y] individuals 1. FAIR guiding principles with public discussion forum: https://www.force11.org/group/fairgroup/fairprinciples 2. Notes and Annexes: https://www.force11.org/node/6062/ 3. Group home page https://www.force11.org/group/fairgroup COMMENT: (till October 1st) ENDORSE: (after October 1st)