Value of Unique IDs in Academia, Vilnius - Identifying knowledge contributors

G. A. Thorisson, University of Leicester / University of Iceland

Conference on Unique Identifiers, Vilnius, Feb 14 2012

- Identifying knowledge contributors -Applications of scholarly identifiers in research data publication

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-- Overview --

๏ Research data in the life sciences - tackling a problem with sharing

๏ Incentives/rewards, attributing credit

๏ Identifying research outputs, use/reuse and contributors

๏ Emerging solutions & interesting developments

๏ The potentially vital role of ORCID as a central hub and aggregator

This work is published under the Creative Commons Attribution license (CC BY: http://creativecommons.org/licenses/by/3.0/) which means that it can be freely copied, redistributed and adapted, as long as proper attribution is given.

Gudmundur A. Thorisson, PhD <[email protected]> Research associate, University of Leicester

Guest scientist, University of IcelandParticipant in the GEN2PHEN Consortium and the ORCID Technical Working Group

Tuesday, 14 February 12

http://creativecommons.org/licenses/by/3.0/


mailto:[email protected]




Big Science, Big Data

• High-profile examples

– High-energy particle physics - experiments performed in the Large Hadron Collider

– Astronomy - data from ground-based and space telescopes, the Virtual Observatory (VO)

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• Doctorow, C. Big data: Welcome to the petacentre. Nature 455, 16-21 (2008). http://dx.doi.org/10.1038/455016a

• Scientific research increasingly large-scale and data-driven


http://dx.doi.org/10.1038/455016a

http://dx.doi.org/10.1038/455016a



Large-scale data guides hypothesis generation

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Gray, J. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research

• Science paradigms– 1st: Empirical - describing natural phenomena

– 2nd: Theoretical - models, generalizations– 3rd: Computational - simulating complex phenomena4t– 4th (1+2+3): Data exploration, e-Science




Large-scale data guides hypothesis generation

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Kell and Oliver. Bioessays (2004) vol. 26 (1)

Gray, J. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research

• Science paradigms– 1st: Empirical - describing natural phenomena

– 2nd: Theoretical - models, generalizations– 3rd: Computational - simulating complex phenomena4t– 4th (1+2+3): Data exploration, e-Science



Conference on Unique Identifiers, Vilnius, Feb 14 2012 4

Richards, M. et al. Paleolithic and neolithic lineages in the European mitochondrial gene pool. American journal of human genetics 59, 185-203 (1996). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1915109/

Biological research also big and data-driven


http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1915109/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1915109/



• Huge technological advances in last 5-10 years– experimental / observations <-- gathering data with high-throughput equipment

– computer technology <-- storing & analyzing massive data volumes

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• Huge technological advances in last 5-10 years– experimental / observations <-- gathering data with high-throughput equipment

– computer technology <-- storing & analyzing massive data volumes

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• Example: massively-parallel sequencing– Determine human genome sequence in <1 day - the $1000 genome

– Metagenomics: sequence *everything* in environment samples– Large bio-specimen collections

• x100,0000 of individuals in disease/population biobanks





Dealing with ‘Big Data’

• Managing & making sense of large-scale datasets– Data easy/cheap to generate - not so cheap to store & use

– “the $1000 genome sequence, followed by the $10,000 analysis”

• Integration & analysis - combine datasets– more data of the same type - e.g. combine sequences from multiple species– related data of different type - e.g. a person’s genome sequence + his/her phenotype

• Potential for accelerating research, creating new knowledge and (in biomedicine) improving human health.

• Founded on unrestricted sharing of scientific data deposited in the public domain

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[..] If digital technologies are the engine of this revolution, digital data are its fuel. But for many scientific disciplines, this fuel is in short supply.[..]

Smith, V. Data publication: towards a database of everything. BMC Res Notes (2009) vol. 2 (1)

Data = “fuel” of science




Domain repositories for sequence data

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• GenBank - genetic sequence repository, established 1986





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• UniProt - knowledge base for protein sequence & function

• GenBank - genetic sequence repository, established 1986




Domain repositories for structure data

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• Protein Data Bank - structure of proteins, nucleic acids and other bio-molecules




• The human genome sequence– International Human Genome project - mandatory rapid data sharing, the Bermuda

principles

• Pattern of variation in the human genome– International Haplotype Map Project - genotyping population samples– 1000 Genomes Project - sequencing population samples

• Examples of community resource projects - large-scale data generation for the purpose of making the data available for broad reuse

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Large-scale genome projects




Data sharing in the “long tail”• Biology is complex, so data are often very

heterogeneous• Technologies changing rapidly• Lots of small research projects• Lots of small/medium datasets• Data in the long tail usually *not* shared

OR not shared in a useful way

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The ‘long tail’ of dark bio-data

• Contrasts with other data-intensive disciplines with– a long history of sharing research data - a “culture of sharing”

– big, expensive, shared facilities = the only way to do this kind of research– relatively homogeneous datasets, easier to scale up to big volumes (e.g. telescope images)




The “data sharing problem’ in life sciences

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Lots of published knowledge but impossible to to go back reproduce

work and validate findings

analysedsynthesisedinterpreted

Information

published

Knowledge

Publication

Data

Opportunity for maximising the value of data through reuse is wasted




- Too much work, no incentive for me -

Koslow. Should the neuroscience community make a paradigm shift to sharing primary data?. Nat Neurosci (2000) vol. 3 (9)

‘Genuine’ reasons for not sharing freely, e.g.:-ethical: patient confidentiality / consent-IP: intellectual property

- The ‘My Data’ syndrome -‘ownership’ vs ‘stewardship’

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LOTS of different reasons for not sharing

- My competitors can take the data and ‘scoop’ me -

- No suitable repository -




- Too much work, no incentive for me -

Koslow. Should the neuroscience community make a paradigm shift to sharing primary data?. Nat Neurosci (2000) vol. 3 (9)

‘Genuine’ reasons for not sharing freely, e.g.:-ethical: patient confidentiality / consent-IP: intellectual property

Most reasons can be dismissed by referring to basic premise of scientific discourse

- The ‘My Data’ syndrome -‘ownership’ vs ‘stewardship’

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LOTS of different reasons for not sharing

- My competitors can take the data and ‘scoop’ me -

- No suitable repository -




Journals increasingly require data to be available

“Provide supporting data in a repository OR we won’t publish your paper”

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Sharing currently driven by mandates






Funders increasingly require data sharing plan & budget baked into grant proposals.

“Publish data we are funding you to generate OR we will not fund your research again”

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Using just a stick gets you so far





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Using just a stick gets you so far





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Mandates likely to ensure onlyminimum compliance - AND - often are not enforced




Complimentary strategies to encourage sharing

- Make it easy -- Make it useful - - Make it citable -

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Prof Anthony J Brookes GEN2PHEN coordinatorChair, Bioinformatics and GenomicsDepartment of GeneticsUniversity of Leicester, UK




Researcher IDs in GEN2PHEN

• The aim is to– create the mechanisms for researchers to get credit for their contributions to scientific

knowledge, even if contributions don’t fit into* the human-readable ‘aliquots’ of a conventional journal article* some vague notion of attributing credit by way of position in authorlist

– ..and thereby provide incentives for the kind of thankless work that people now do without any kind of reward.

• Main use cases being explored– Research datasets in disease genetics– Database management and curation

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Key building blocks: the 3 I’s

Identifying the scholarly work or research output– e.g. Bell et al. 2009. Science 323(5919) doi:10.1371/journal.pone.0024357

Identifying use/reuse - measuring impact– e.g number of full-text downloads for article X first 6 months after publication

Identifying contributors - attributing credit– e.g. who contributed to highly-cited resource X?

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Identifying resources - persistent, unique IDs

• The problem: unstable URL references to online digital materials– http://www.le.ac.uk/~gt50/files/important_data/env2012.dat

– URL decay, tend to have short half-life

• URLs are locators for online resources - need persistent identifiers

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http://www.le.ac.uk/~gt50/files/important_data/env2012.dat

http://www.le.ac.uk/~gt50/files/important_data/env2012.dat




• Articles published in scientific journals– Digital Object Identifiers (DOIs) issued to papers via CrossRef

– Identifier resolves to metadata record, link to current location of content• Example: http://dx.doi.org/10.1038/nbt1109-984b => ‘landing page’ on journal website

– Content moves => DOI metadata is updated => stable identifier points to new location


http://dx.doi.org/10.1038/nbt1109-984b

http://dx.doi.org/10.1038/nbt1109-984b




• Biological datasets published in digital repositories– Institutional vs. domain-specific, often offer Handles or other

persistent ID scheme

– DataCite Consortium created in 2009– to promote the use of ‘data DOIs’– Data Dryad, FigShare, others





• Biological databases, added-value from expert curation– No identification scheme - acknowledgement based conventional papers, URLs, names

– e.g. http://www.uniprot.org, GenBank, Database of COL3A1 gene mutations – The new BioDBCore initiative is building the foundations for a registry of bio-databases

– Could become a kind of ‘journal’ for database resources– Example persistent resource URI: http://identifiers.org/biodbcore/COL


http://identifiers.org/biodbcore/COL

http://identifiers.org/biodbcore/COL



Identifying reuse - beyond the Impact Factor

• Reliance on citations and citation-based metrics– ISI Impact Factor really a way to measure influence of scholarly journals– Not going away: citation remains the gold standard

• Lots of other use/reuse indicators for impact of scholarly articles– Focus on the impact of the *publication* itself, not the journal in which it appears– Indicators: no. full-text downloads, tweets (i.e. mentions on Twitter), social bookmarking– AltMetrics - a growing grassroots movement “ to better measure and reward all the

different ways that people contribute to the messy and complex process of scientific progress [..] born out of a simple recognition: Many of the traditional measurements are too slow or simplistic to keep pace with today’s Internet-age science”

– Lots new tools emerging to explore possibilities in this space

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Identifying reuse - beyond the Impact Factor

• Reliance on citations and citation-based metrics– ISI Impact Factor really a way to measure influence of scholarly journals– Not going away: citation remains the gold standard

• Lots of other use/reuse indicators for impact of scholarly articles– Focus on the impact of the *publication* itself, not the journal in which it appears– Indicators: no. full-text downloads, tweets (i.e. mentions on Twitter), social bookmarking– AltMetrics - a growing grassroots movement “ to better measure and reward all the

different ways that people contribute to the messy and complex process of scientific progress [..] born out of a simple recognition: Many of the traditional measurements are too slow or simplistic to keep pace with today’s Internet-age science”

– Lots new tools emerging to explore possibilities in this space

• Key obstacle: access to citation links, downloads, ‘clickstream’ data28




[Or: what’s ORCID go to do with all this??]

• Vast majority of repositories and database currently rely on names for identifying creators/curators/persons in general => leads to disambiguation problems

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Identifying contributors - the common thread




– For content, many different identifier schemes will be needed, because there’s a range of different types of scholarly content

– For contributors, a single, unified identifier scheme is needed, because a person will tend to contribute to various types of content over his/her career

• What kind of contribution? – Conventional authorship – Analytical, data managing, technical vs. intellectual / scientific, support roles etc.– Editing / peer-review, incl. future post-publication review systems

• Complications relating to attributing credit– like High-ener paper, >1000 authors. they’re not really all *authors* are they?

• Intellectual input vs. pure support?

• designed experiment, ran key analysis, technical support staff etc.

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• But, wait! there’s many more ways of contributing to science than publishing papers/data and curating databases!

21st century ‘Digital scholars’ should also be credited for contributions to...

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... Wikipedia articles

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... open-source software projects

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... distributed analytical workflows + other digital research objects (published e.g. on myExperiment.org)

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• .. and many more!– future post-publication open peer-review systems

– stuff we haven’t even thought of

• ORCID can potentially have a pivotal role here– as source of scholarly identities– as central aggregator & index of data on links between author & content




Lessons from social networking domain

• We NOW HAVE:– Lots of 3rd party applications built around social IDs that users already have on Twitter,

Facebook and other sites

• We WILL HAVE in the near future:– online scholarly communication tools built around ORCID IDs that scholars already

have– Rich ecosystem of ‘ORCID apps’ – Lower the barrier to participation - tackle the “multiple profiles syndrome”

• The 1st-generation ORCID central service will support this

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Technologically, this is not rocket science

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Single sign-on with social IDs

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Some points for discussion

• Is ORCID the right way forward?• International organization with many stakeholders

– Big mass, moves slowly, complex politics, BUT this also gives it ‘oomph’

• The upcoming contributor ID service will be centralized– Single place to point to when promoting in community – Developer-friendly: integration app with a single service

• The organization & registry is open (enough)– Enable reuse & collaboration with others, BUT slices of data will be for members only

• Engaging with end users - scholars to manage their profile• Journal publishers are involved and committed

– Will be key to adoption via journal article submission, BUT what about anti-OA and other questionable practices?

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An online identity as a digital scholar, anchored in a ORCID profile?




ORCID ID: B-1242-2010G. Thorisson, Univ. LeicesterG. A. Thorisson, Univ. LeicesterG. A. Thorisson, Cold Spring Harbor Lab.

An online identity as a digital scholar, anchored in a ORCID profile?




Acknowledgements GEN2PHEN Consortium

http://www.gen2phen.org/about-gen2phen/partners

Prof Anthony J. Brookes Bioinformatics Group, Leicester

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This work has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)under grant agreement number 200754 - the GEN2PHEN project.

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Value of Unique IDs in Academia, Vilnius - Identifying knowledge contributors

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