Principles and Practice in (Encouraging) the Sharing of Public Research Data

1. Principles and Practice in (Encouraging) the Sharing of Public Research Data Chris Taylor, The MIBBI Projectchris.taylor@ebi.ac.uk Project website: http://mibbi.org/

2. Mechanisms of scientific advance

3. Well-oiled cogs meshing perfectly (would be nice) “Publicly-funded research data are a public good, produced in the public interest” “Publicly-funded research data should be openly available to the maximum extent possible.” How well are things working? • Cue the Tower of Babel analogy… • Situation is improving with respect to standards • But few tools, fewer carrots (though some sticks) Why do we care about that..? • Data exchange • Comprehensibility (/quality) of work • Scope for reuse (parallel or orthogonal)

4. Science in press (out Oct 9th)

5. So what (/why) is a standards body again..? Consider the three main ‘omics standards bodies’ • PSI (proteomics), GSC (genomics), MGED (transcriptomics) • What defines such (candidate) standards-generating bodies? • “A beer and an airline” (Zappa) • Requirements, formats, vocabulary • Regular full-blown open attendance meetings, lists, etc. Hugely dependent on their respective communities • Requirements (What are we doing and why are we doing it?) • Development (By the people, for the people. Mostly.) • Testing (No it isn’t finished, but yes I’d like you to use it…) • Uptake, by all of the various kinds of stakeholder: • Publishers, funders, vendors, tool/database developers • The user community (capture, store, search, analyse)

6. MIAPE (http://psidev.info/miape) Minimum Information About a Proteomics Experiment Published in Nature Biotechnology (August 2007) Recommendation for journals, repositories, funders and others Technology-specific modules associated with a parent document Users assemble modules into a bespoke reporting requirement Molecular interactions (MIMIx) published in NBT in 2007 Mass spec, MS informatics, gels published in NBT in 2008 Gel informatics, columns, CE published in NBT this year Example: Minimum reporting requirements

7. Spanish multi-site collaboration: provision of proteomics services MIAPE customer satisfaction survey (compiled November 2008) http://www.proteored.org/MIAPE_Survey_Results_Nov08.html Responses from 31 proteomics experts representing 17 labs ProteoRED’s MIAPE satisfaction survey Yes: 95% No: 5%

8. Domain specialists&IT types(initial drafts, evolution) Journals The real issue for any MI project is getting enough people to comment on what you have (distinguishes a toy project from something to be taken seriously — community buy-in) Having journals help in garnering reviews is great (editorials, web site links, mail shots even). Their motive of course being that fuller reporting = better content = higher citation index. Funders MI projects can claim to be slightly outside of 'normal' science; may form funding policy components (arguments about maximum value) Funders therefore have a motive (similar to journals) to ensure that MI guidelines, which they may endorse down the line, are representative and mature They can help by allocating slots at (appropriate) meetings of their award holders for you to show your stuff. Things like that. Ingredients for MI pie

9. Vendors The cost of MIs in person-hours will be the major objection Vendors can implement parameter export to an appropriate file format, ideally using some helpful CV (somebody else's problems) Vendors also have engineers (and some sales staff) who really know their kit and make for great contributors/reviewers For some standards bodies (like PSI, MGED) their sponsorship has been very helpful too (believe it or not, it would seem possible to monetise a standards body) Food / pharma Already used to better, if rarely perfect data capture and management; for example, 21 CFR Part 11 (MI = exec summary…) Trainers There is a small army of individuals training scientists, especially in relation to IT (EBI does a lot of this but I mean commercial training providers)  ‘Resource packs’ Ingredients for MI pie

10. Modelling the biosciences Biologically-delineated views of the worldA: plant biology B: epidemiology C: microbiology …and… Generic features (‘common core’) — Description of source biomaterial — Experimental design components Technologically-delineated views of the worldA: transcriptomics B: proteomics C: metabolomics …and… MS MS Gels NMR Arrays Columns FTIR Scanning Arrays &Scanning Columns

11. Modelling the biosciences (slightly differently)

12. Investigation / Study / Assay (ISA) Infrastructure http://isatab.sourceforge.net/ Ontology of Biomedical Investigations (OBI) http://obi.sourceforge.net/ Functional Genomics Experiment (FuGE) http://fuge.sourceforge.net/ Rise of the Metaprojects

13. Reporting guidelines — a case in point • MIAME, MIAPE, MIAPA, MIACA, MIARE, MIFACE, MISFISHIE, MIGS, MIMIx, MIQAS, MIRIAM, (MIAFGE, MIAO), My Goodness… • ‘MI’ checklists usually developed independently, by groups working within particular biological or technological domains • Difficult to obtain an overview of the full range of checklists • Tracking the evolution of single checklists is non-trivial • Checklists are inevitably partially redundant one against another • Where they overlap arbitrary decisions on wording and sub structuring make integration difficult • Significant difficulties for those who routinely combine information from multiple biological domains and technology platforms • Example: An investigation looking at the impact of toxins on a sentinel species using proteomics (‘eco-toxico-proteomics’) • What reporting standard(s) should they be using?

14. The MIBBI Project (mibbi.org) • International collaboration between communities developing ‘Minimum Information’ (MI) checklists • Two distinct goals (Portal and Foundry) • Raise awareness of various minimum reporting specifications • Promote gradual integration of checklists • Lots of enthusiasm (drafters, users, funders, journals) • 32 projects committed (to the portal) to date, including: • MIGS, MINSEQE & MINIMESS (genomics, sequencing) • MIAME (μarrays), MIAPE (proteomics), CIMR (metabolomics) • MIGen & MIQAS (genotyping), MIARE (RNAi), MISFISHIE (in situ)

15. Nature Biotechnol 26(8), 889–896 (2008) http://dx.doi.org/10.1038/nbt.1411

16. The MIBBI Project (mibbi.org)

17. The MIBBI Project (mibbi.org)

18. The MIBBI Project (mibbi.org) Interaction graph for projects (line thickness & colour saturation show similarity)

19. The MIBBI Project (mibbi.org)

20. ‘Pedro’ tool → XML→ (via XSLT) Wiki code (etc.)

21. MICheckout: Supporting Users

22. MIBBI and other standardisation efforts • Ontology support: • MIBBI module schema allows for specified ontology references • Any number of terms (leaf or node) can be ‘attached’ to an element • We expect software to offer the specified choices to users • Format support: • MIBBI has no specific implementation for data exchange formats • BUT: we can achieve the same end by supporting tools • Currently implementing ISAcreator configuration file generation • Will allow capture of MIBBI Foundry-specified content in ISA-Tab • Also an example of software implementing our ontology links

23. OBO

24. Example of guiding the experimentalist to search and select a term from the EnvO ontology, to describe the habitat of a sample Ontologies, accessed in real time via the Ontology Lookup Service and BioPortal 24 The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project

25. BUT…

26. Why should I dedicate resources to providing data to others? Pro bono arguments have no impact (altruism is a myth) ‘Sticks’ from funders and publishers get the bare minimum No traceability in most contexts (intellectual property = ?) This is just a ‘make work’ scheme for bioinformaticians Bioinformaticians get a buzz out of having big databases Parasites benefitting from others’ work ( mutualism..?) I don’t trust anyone else’s data — I’d rather repeat work Problems of quality, which are justified to an extent But what of people lacking resources or specific expertise? How on earth am I supposed to do this anyway..? Perception that there is no money to pay for this No mature free tools — Excel sheets are no good for HT Worries about vendor support, legacy systems (business models) The objections to fuller reporting

27. Methods remain properly associated with the results generated Data sets generated by specific techniques or using particular materials can be reliably identified and retrieved from repositories (or excluded from results sets) No need to repeatedly construct sets of contextualizing information Facilitates the sharing of data with collaborators Avoids the risk of loss of information through staff turnover Enables time-efficient handover of projects For industry specifically (in the light of 21 CFR Part 11) The relevance of data can be assessed through summaries without wasting time wading through full data setsin diverse proprietary formats (‘business intelligence’) Public data can be leveraged as ‘commercial intelligence’ 1. Increased efficiency

28. Enables fully-informed assessment of results (methods used etc.) Supports the assessment of results that may have been generated months or even years ago (e.g. for referees or regulators) Facilitates better-informed comparisons of data sets Increased likelihood of discovering the factors (controlled and uncontrolled) that might differentiate those data sets Supports the discovery of sources of systematic or random error by correlating errors with metadata features such as the date or the operator concerned Requires sufficient information to support the design of appropriate parallel or orthogonal studies to confirm or refute a given result 2. Enhanced confidence in data

29. Re-using existing data sets for a purpose significantly different to that for which the data were generated Building aggregate data sets containing (similar) data from different sources (including standards-compliant public repositories) Integrating data from different domains For example, correlating changes in mRNA abundances, protein turnover and metabolic fluxes in response to a stimulus Design requirements become both explicit and stable MIAPE modules as driving use cases (tools, formats, CV, DBs) Promotes the development of sophisticated analysis algorithms Presentation of information can be ‘tuned’ appropriately Makes for a more uniform experience 3. Added value, tool development

30. Data sharing is more or less a given now, and tools are emerging Lots of sticks, but they only get the bare minimum How to get the best out of data generators? Need standards- and user-friendly tools, and meaningful credit Central registries of data sets that can record reuse Well-presented, detailed papers get cited more frequently The same principle should apply to data sets ISNIs for people, DOIs for data: http://www.datacite.org/ Side-benefits, challenges Would also clear up problems around paper authorship Would enable other kinds of credit (training, curation, etc.) Community policing — researchers ‘own’ their credit portfolio (enforcement body useful, more likely through review) Problem of ‘micro data sets’ and legacy data Credit where credit’s due