David Shotton

Image BioInformatics Research GroupDepartment of Zoology

University of Oxford, UK

http:/ibrg.zoo.ox.ac.uk

Doing more with less: data sharing and integration in an age of data glut and economic contraction

© David Shotton, 2010 Published under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Licence

e-mail: david.shotton@zoo.ox.ac.uk

Dryad-UK Discussion MeetingHEFCE Offices, Centre Point, London

27-28 April 2010

We live in an age of bioinformatics data glut . . .

Attwood TK et al. (2009) Calling International Rescue: knowledge lost in literature and data landslide! Biochemical Journal 424:317–333.

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Cochrane GR, Galperin MY (2010) Nucleic Acids Research 38:D1-D4

There are now over 1200 bioinformatics databases, between which data integration is difficult

Data integration for many researchers amounts to nothing more sophisticated than cutting and pasting into a Word document !!

Research data – Universals and Particulars

Gene sequences and protein structures represent ‘universal truths’

The data need only be discovered once

The data are intinsically simple and form bounded data sets

Data are cheap per bit, and re-acquisition is becoming cheaper

Public databases exist for these data (GenBank, PDB, etc.)

The whole of bioinformatics is build on their free availability

Life science research data can also be ‘particulars’, for example individual assay results, disease reports, observations, electron micrographs, videos

These data are heterogeneous and form unbounded data sets – typical of ‘long tail’ science rather than ‘big science’

Data collection is costly in human resources, and re-acquisitionmay be impossible, e.g. for observational data

Datasets thus often have a high intrinsic value per bit

The majority of such research datasets are never published,rotting on the abandoned hard drives of departed postdocs

In this open access age, that is little short of scandalous!

The problems of obtaining infectious disease data

Quote from Professor Angela McLean, after taking three months to amass appropriate disease incidence data for her 2007 J. Virology paper on HIV escape mutations:

“When I was a graduate student, I spent long hours in libraries copying numbers from dusty journals.

“Things have not improved much since ! ”

I have a particular concern about infectious disease data, since I believe that timely availability of reliable data in this domain may have an important impact on global health.

The benefits and risks of published data

The benefits of open data publication:

review and validation by others,

re-use in another contexts, and

integration with other data to create a new greater whole

Governments, funding agencies, publishers and researchers agree that the results of publicly funded research should be made publicly available

The problems of sharing data (From RIN – BL Report, November 2009 Patterns of Information Use and Exchange: Case Studies in the Life Sciences)

Ethical constraints and IPR issues

Concerns about misuse and data ownership

“As researchers, we see data as a critical part of our ‘intellectual capital’, generated by investment of time, effort and skill.”

Lack of personal attribution and credit for data publication

Difficulties in creating appropriate metadata

Appropriate repositories to archive and publish research datasets

Semantic publishing of structured research datasets

Semantic publishing is the use of simple Semantic Web technologies:

to enhance the meaning of on-line published research articles

to provide access to the articles’ published data in actionable form

to facilitate the integration of semantically related data

so that data, information and knowledge can more easily be found, extracted, combined and reused

For research datasets to be maximally useful, they have to be:

saved in machine-processable form, in conformity with appropriate Web standards (e.g. XML, RDF, OWL)

published and made freely accessible on the Web

referenced by globally unique and resolvable identifiers (e.g. DOIs)

accompanied by useful metadata based upon minimal information standards and ontologies, including provenance information

Features of the original PLoS NTD article, relating to data

Good

The article contained a rich variety of data types (geospatial, disease incidence, serological assay, and questionnaire) presented in formats amenable to semantic enrichments (maps, bar charts, tablesand graphs)

Poor

While figures and table can be downloaded, they can only be so as images !

The numerical data are not directly available in actionable form

http://dx.doi.org/10.1371/journal.pntd.0000228.x001

http://dx.doi.org/10.1371/journal.pcbi.1000361

Drosophila gene expression data exists in many databases

FlyAtlas

Data from four sources combined in an OpenFlyData window

Query for schuy over cached RDF data from FlyTED, BDGP, FlyAtlas and FlyBase

http://openflydata.org/

In conclusion:

data publishing and global warming

Waiting for some international committee in Copenhagen to create the perfect solution to the data publication problem is not the way forward

Just as we can each act locally to reduce our carbon footprint,

so we can each do something personally to increase our data footprint

Each of us, whether researcher, publisher or government agency, can take responsibility for the open publication of our own research data

The important thing is to make a start !

end

Advantages of repository over supplementary data files

Dryad Suppl

Searchable: published metadata allows Google search for data files Confirmable: author can confirm descriptive metadata terms used Citable: unique identifiers (DOIs) permit citation of data files ?

Increased exposure of source journal articles through data citation ?

Permanent: data files securely archived in perpetuity ? ?Linked: datasets linked to article based on them Metadata will be available as RDF: part of the “web of linked data” ?Curated: quality verified, stable formats used, content virus-checked ?Ease of deposit: authors can upload multiple or zipped files ?Updatable: new versions of data files can be added, with provenance Embargo: can delay release of data up to one year after publication Open access: no restrictions for users, no subscription required ?Scalable: many journals and societies can leverage economies of scale

Convergence between journals and databases

PLoS Comp. Biol. 2005 1(3) e34

In this paper, Philip Bourne, Editor-in Chief of PLoS Computational Biology and Co-Director of the Protein Data Bank, contends that the distinction between an on-line journal and an on-line database is diminishing

He calls for “seamless integration” between papers reporting results and the data used to compute those results

My critique of Philip Bourne’s ideas

We need to maintain a clear distinction between journal publications:

peer reviewed

immutable dated ‘versions of record’ – part of the history of science –

that provide the citable authorities for research datasets

and research databases:

that should present user with access to complete, impartial, up-to-date datasets, both for further exploration and automated data mining

with curators responsible for correction of errors after submission

Thus “seamless integration” is not desirable

Articles are rhetorical

Datasets are analytical

Researchers require the “seams” to be kept clearly visible, so they know which presuppositional spectacles to wear when reading

Nevertheless, both frictionless interoperability and reciprocal citation between papers and datasets are highly desirable