An Open Access publisher’s perspective on data publishing Matthew Cockerill Managing Director, BioMed Central Dryad-UK meeting HEFCE, London, 28 April 2010

About BioMed Central  Largest publisher of peer-reviewed open access research journals  Launched first open access journals in 2000  Part of Springer since October 2008  Now publishes 207 OA titles  ~70,000 peer-reviewed OA articles published  All research articles Creative Commons licensed  Costs covered by 'article processing charge’ (APC)

Data is a first class citizen in BioMed Central publications  Electronic version of article is authoritative  “Additional files” not “Supplementary material”  Additional files can be central to the reported findings of the paper  Where possible, file is presented in a convenient embedded form (movies, chemical structures, KML etc) while also making downloadable  “Mini-websites” provide a generic (too generic?) approach for presentation of complex data

Efficient online publication processes can facilitate dataset publication  Only a fraction of experimental data sets make it into the literature  Many more datasets have the potential to be useful, but do not warrant a traditional publication  For certain standard types of data, appropriate databases exist (e.g. nucleotide sequences)  But if such databases do not exist, or if further description of the experimental context is required?

Plans to extend reusability of data  BioMed Central aims to provide more explicit guidelines to facilitate data reuse both generic, and specific to particular disciplines and formats  Making authors original vector-based figure files available expands their reuse capability.  Similar possibility with data: Make any table of data from within articles conveniently downloadable in spreadsheet form

Scientific cloud computing  Bioinformaticists have been rapid adopters of cloud computing (as they were of the web)  Cloud computing can reduce the barriers to reproducibility  Publications can include or refer to necessary datasets and the computational tools that can be fired up to carry out/reproduce the analysis  Large datasets can live in cloud – take analysis to the data, rather than vice versa

Preservation  Publishers not best placed to run repositories for long term preservation of large datasets  Mirrors of publisher content not able to accept arbitrary amounts of additional data  Long term preservation presents a challenge with respect to continuity  Redundant international mirrors with independent governance and funding could help to reduce risk

Huge culture variation between disciplines  Value is maximized if everyone shares data  But cultural norms vary heavily by discipline  Prisoner’s dilemma – if no one else is sharing their data, you have little to gain, and much to lose by sharing your own data  Funders are theoretically well placed to enforce norms for sharing data  But effectiveness of funder data sharing policies is questionable

Data sharing in medicine  Clinical trial data is one example of data which presents challenges re: privacy and consent  Perfect anonymization often impossible - certainly not without losing key aspects of data  Increasing collection of genomic data in trials accentuates this issue  Trial consent should include info re: limits of anonymizability  Full access to underlying data set could be made available for approved research purposes