Principles and Practice in (Encouraging) the Sharing of

Public Research Data

Chris Taylor, The MIBBI Project chris.taylor@ebi.ac.uk

Project website: http://mibbi.org/

Mechanisms of scientific advance

Well-oiled cogs meshing perfectly (would be nice)

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)

“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.”

Sciencein press

(out Oct 9th)

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)

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

ProteoRED’s MIAPE satisfaction survey

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

Yes: 95%No: 5%

Ingredients for MI pie

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.

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

Technologically-delineated views of the world A: transcriptomics B: proteomics C: metabolomics …and…

Biologically-delineated views of the world A: plant biology B: epidemiology C: microbiology …and…

Generic features (‘common core’) — Description of source biomaterial — Experimental design components

Arrays

Scanning Arrays &Scanning

Columns

GelsMS MS

FTIR

NMR

Columns

Modelling the biosciences

Modelling the biosciences (slightly differently)

Assay: Omics and miscellaneous techniques

Investigation:

Medical syndrome, environmental effect, etc.Study: Toxicology, environmental science, etc.

Investigation / Study / Assay (ISA) Infrastructurehttp://isatab.sourceforge.net/

Ontology of Biomedical Investigations (OBI)http://obi.sourceforge.net/

Functional Genomics Experiment (FuGE)http://fuge.sourceforge.net/

Rise of the Metaprojects

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?

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)

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

http://dx.doi.org/10.1038/nbt.1411

The MIBBI Project (mibbi.org)

[†] Denotes that a specification is provided as a suite of related documents

CONCEPT SPECIALISATION ● C

IMR [†]

● M

IACA

● M

IAM

E

● M

IAM

E/E

nv

● M

IAM

E/N

utr

● M

IAM

E/P

lant

● M

IAM

E/T

ox

● M

IAPA

● M

IAPE [†]

● M

IARE

● M

IFlo

wCyt

● M

IGen

● M

IGS/M

IMS

● M

IMIx

● M

IMPP

● M

INI

study inputs study design ●generic organism ●

cells / microbes

plant

animal

mouse

human

population

environmental sample

environment / habitat

in silico model

study procedures organism maintenance

animal husbandry

cell / microbe culture

plant cultivation

acclimation

preconditioning / pretreatment ●organism manipulation

assay inputs generic study input

organism part ●organism state

organism trait

biomolecule

synthetic analyte ●silencing RNA reagent

Version 0.7 (2008-04-10)

Comparison of MIBBI-registered projects [21] ● Release

Granularity Coarse Medium Fine

Maturity ● Planned ● Drafting

The MIBBI Project (mibbi.org)

The MIBBI Project (mibbi.org)

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

The MIBBI Project (mibbi.org)

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

MICheckout: Supporting Users

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

The International Conference on Systems Biology (ICSB), 22-28 August, 2008 Susanna-Assunta Sansone www.ebi.ac.uk/net-project

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

BUT…

The objections to fuller reporting

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)

1. Increased efficiency

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’

2. Enhanced confidence in data

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

3. Added value, tool development

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

Credit where credit’s due

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