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

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

- Identifying knowledge contributors -
Applications of scholarly identiﬁers in research data publication
Gudmundur A. Thorisson, PhD <gt50@leicester.ac.uk>
Research associate, University of Leicester
Guest scientist, University of Iceland
Participant in the GEN2PHEN Consortium and the ORCID Technical Working Group

-- 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.

Conference on Unique Identiﬁers, Vilnius, Feb 14 2012 1
Tuesday, 14 February 12


Big Science, Big Data
• Scientiﬁc research increasingly large-scale and data-driven

• 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)

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


Large-scale data guides hypothesis generation

• Science paradigms
– 1st: Empirical - describing natural phenomena
– 2nd: Theoretical - models, generalizations
– 3rd: Computational - simulating complex phenomena4t
– 4th (1+2+3): Data exploration, e-Science

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


Large-scale data guides hypothesis generation

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

• Science paradigms
– 1st: Empirical - describing natural phenomena
– 2nd: Theoretical - models, generalizations
– 3rd: Computational - simulating complex phenomena4t
– 4th (1+2+3): Data exploration, e-Science

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


Biological research also big and data-driven

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/


• Huge technological advances in last 5-10 years
– experimental / observations <-- gathering data with high-throughput equipment
– computer technology <-- storing & analyzing massive data volumes



• Huge technological advances in last 5-10 years
– experimental / observations <-- gathering data with high-throughput equipment
– computer technology <-- storing & analyzing massive data volumes

• 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



Data = “fuel” of science

[..] 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)



Domain repositories for sequence data




• GenBank - genetic sequence
repository, established 1986




• GenBank - genetic sequence
repository, established 1986

• UniProt - knowledge base for
protein sequence & function


Domain repositories for structure data

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



Large-scale genome projects
• 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



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 The ‘long tail’ of dark bio-data
• Data in the long tail usually *not* shared
OR not shared in a useful way

• 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



Data
analysed
synthesised
interpreted

Information

published

Knowledge

Publication

Lots of published knowledge but
impossible to to go back reproduce
work and validate ﬁndings

Opportunity for maximising the value
of data through reuse is wasted



LOTS of different reasons for not sharing
- No suitable repository -

- Too much work, no incentive for me -

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

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

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

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



LOTS of different reasons for not sharing
- No suitable repository -

- Too much work, no incentive for me -

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

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

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

Most reasons can be dismissed by referring to
Koslow. Should the neuroscience community make
basic premise of scientific discourse
a paradigm shift to sharing primary data?. Nat
Neurosci (2000) vol. 3 (9)



Sharing currently driven by mandates
Journals increasingly require data to be available

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

15




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”

15






Using just a stick
gets you so far

15






Mandates likely to ensure only
Using just a stick
minimum compliance
gets you so far - AND -
often are not enforced
15


Complimentary strategies to encourage sharing

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



Prof Anthony J Brookes
GEN2PHEN coordinator
Chair, Bioinformatics and Genomics
Department of Genetics
University of Leicester, UK

4


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



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?



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




• 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




• 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



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



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’ data


Identifying contributors - the common thread

[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



– 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.



• 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...



... Wikipedia articles



... open-source software projects



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



• .. 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



Technologically, this is not rocket science



Single sign-on with social IDs



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?



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

42


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

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

42


Acknowledgements
GEN2PHEN Consortium
This work has received funding from the
http://www.gen2phen.org/about-gen2phen/partners European Community's Seventh
Framework Programme (FP7/2007-2013)
under grant agreement number 200754 -
Prof Anthony J. Brookes Bioinformatics Group, Leicester
the GEN2PHEN project.

Contact me!

<gt50@le.ac.uk> |<gthorisson@gmail.com>
http://www.linkedin.com/in/mummi
http://www.twitter.com/gthorisson
Published under the CC BY license (http://
http://www.gthorisson.name creativecommons.org/licenses/by/3.0/)


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