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Principles and Practice in (Encouraging) the Sharing of Public Research Data Chris Taylor, The MIBBI Project [email protected] Project website: Mechanisms of scientific advance. Well-oiled cogs meshing perfectly (would be nice).

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

Chris Taylor, The MIBBI [email protected]

Project website:

Well oiled cogs meshing perfectly would be nice
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)


in press

(out Oct 9th)

So what why is a standards body again
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)

Example minimum reporting requirements


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

Spanish multi-site collaboration: provision of proteomics services

MIAPE customer satisfaction survey (compiled November 2008)

Responses from 31 proteomics experts representing 17 labs

ProteoRED’s MIAPE satisfaction survey

Yes: 95%

No: 5%

Domain servicesspecialists&IT types(initial drafts, evolution)


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.


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

Vendors services

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…)


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

Modelling the biosciences
Modelling the biosciences services

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…









Arrays &Scanning


Rise of the metaprojects

Investigation / Study / Assay (ISA) Infrastructure services

Ontology of Biomedical Investigations (OBI)

Functional Genomics Experiment (FuGE)

Rise of the Metaprojects

Reporting guidelines a case in point
Reporting guidelines — a case in point services


  • ‘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?

The mibbi project mibbi org
The MIBBI Project services (

  • 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)

Nature Biotechnol 26(8), 889–896 (2008) services

The mibbi project mibbi org1
The MIBBI Project services (

The mibbi project mibbi org2
The MIBBI Project services (

The mibbi project mibbi org3
The MIBBI Project services (

Interaction graph for projects (line thickness & colour saturation show similarity)

The mibbi project mibbi org4
The MIBBI Project services (

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

Mibbi and other standardisation efforts
MIBBI and other standardisation efforts services

  • 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

OBO services

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


The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone

BUT… a term from the

Why should I dedicate resources to providing data to others? a term from the

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

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

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

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

Data sharing different to that for which the data were generatedis 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:

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