RDFC2012 Open Access to Research Data

Open access to scientific research data

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 Iceland

Participant in the GEN2PHEN Consortium and the ORCID Technical Working Group

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

mailto:[email protected]

RDFC2012 Conference on Open Access and Digital Rights, Reykjavik, March 29th 2012

Overview

๏ Intro to the world of Big Science & Big Data

•Why is inadequate access to data such a problem?

๏ Incentive-based approaches to tackling the sharing problem

Identification, identification, identification

๏ Key relevant developments internationally

๏ Some food for thought for funders, institutions, other key players

๏ Concluding remarks


Big Science, Big Data

• Scientific research increasingly large-scale and data-driven

• High-profile discipline 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

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


Hypothesis generation guided by available data

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 phenomena

– 4th (1+2+3): Data exploration, e-Science


• From: small-scale datasets that fit into a printed journal article

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 too isincreasingly big and data-driven

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


Biological research too isincreasingly big and data-driven

• To: large-scale collection of biological data in digital form

• 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

RDFC2012 Conference on Open Access and Digital Rights, Reykjavik, March 29th 2012Conference on Unique Identifiers, Vilnius, Feb 14 2012

• UniProt - knowledge base for protein sequence & function

• GenBank - genetic sequence repository, established 1986

Examples: domain repositories for sequence data


• The sequence of the human genome– 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

“Community resource projects” - large-scale data generation for the purpose of making the data available for broad reuse


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

– Favorite quote: “the $1000 genome sequence, followed by the ++$10,000 analysis”

• Integration & analysis - combining 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.

• Key driver = unrestricted sharing of scientifc data deposited in the public domain

Big Data – challenges, opportunities


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

Data = “fuel” of science

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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


• Biology is complex, so data are often very heterogeneous

• Technologies changing rapidly

• Lots of small-scale research projects

• Lots of small/medium datasets

• Data in the long tail usually *not* shared OR not shared in a useful way

The ‘long tail’ of dark bio-data

• Contrast 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)

Biology and data sharing in the “long tail”


[…] Overall, only 47 papers (9%) deposited full primary raw data online. None of the 149 papers not subject to data availability policies made their full primary data publicly available.

Conclusion: A substantial proportion of original research papers published in high-impact journals are either not subject to any data availability policies, or do not adhere to the data availability instructions in their respective journals. This empiric evaluation highlights opportunities for improvement


Lots of published knowledge but hard/impossible to go back and

reproduce work & validate findings

analysed

synthesised

interpreted

INFORMATION

published

KNOWLEDGE

Publication

DATA

Opportunity for maximising the value of data through reuse is wasted

+

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Credit: http://cutcaster.com/photo/800902839-The-hand-drawing-question-WHY/


Koslow. Should the neuroscience community make a paradigm shift to sharing primary data?. Nat Neurosci (2000) vol. 3 (9). http://dx.doi.org/10.1038/78760

Worringly, many authors don't seem to care whether evidence underpinning their published findings is accessible or not

“Don't which digital repository I should upload to”“Too much work, got better things to do!”“My competitors will just take the data and ‘scoop’ me”“It's my data, I collected them and noone else is entitled to use them”“[myriad other reasons]”

Lots and lots of diverse reasons!!Some quotes from researchers:

http://dx.doi.org/10.1038/78760


Gnarly issue #1: “ownership” vs “stewardship”

• Many researchers consider data their property, even if research funded by public money– e.g. want to do further analysis on data in future, publish more papers

• ..which conficts with interests of other stakeholders in the game, e.g. (funders, universities) who want:– to maximize return on investment in the funded research

– to ensure good, solid evidence-based science is done, etc.


Gnarly issue #2 – biomedical data

• Usually sensitive, cannot be shared without restrictions – Detailed, reidentifiable biomedical data that cannot be fully anonymized

– Personal privacy considerations

• Specialized controlled-access archives deal with some of this– NCBI's database of Genotypes and Phenotypes – dbGaP

– European Genome-phenome Archive – EGA

– [specific diseases / disorders, research consortia, others]

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http://djamba.com/how-to-make-a-tackle-in-rugby.html


...which are an imperfect solution

• Arguments that mandates by themselves are not the way

• Mandates likely to ensure only minimum compliance– sharing would be done in minimally useful form (as in, whatever is the least effort)

…. and are meaningless if not enforced (currently the case with many journals)


* Journals increasingly require data to be made available

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

* 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”

Sharing now tends to be driven by mandates...

Using just a stick gets you so only far


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

Strategies focused on encouraging sharing


Treating data as citablepublications in their own right

• Core strategy: enable data to be treated as 1st class citizens of the scholarly record which:

i) are indexed and can be discovered, located and accessed, and

ii) can be properly identified & cited unambiguously like other scholarly works

• Link datasets with the primary journal publication - citation crosslinks

• Give data creators/curators/analysts proper credit for their contribution to the digital resource

• Focus on the benefits to researchers from publishing their data– Data sharing → Data PUBLICATION + CITATION

– Others reuse & cite their stuff → more citations → more impact

– The more useful a dataset, the more likely to be used & cited


“international repository of dataunderlying peer-reviewed articles in the basic and applied biosciences” http://datadryad.org

• Combines– Mandates (journal policy)

and

– Citable data publication

• Citation cross-linking– Paper references dataset

– Dataset references paper

Exemplar – Data Dryad


Key building blocks: the 3 I’s of identification


I1 Identifying scholarly publications (and other research outputs)• Why? So it is possible to..

..cite the work unambiguously (‘..we used the method described in Thorisson et al (2009)’) ..locate the work (retrieve Nature article as PDF from journal website) ..give credit to persons/entities who contributed to the work (G. Thorisson authored paper X)

• Need for globally unique, persistent identifiers to combat unstable Web URLs, broken hyperlinks

• e.g. Digital Object Identifiers (DOIs) for pubs, datasets and more:

– Bell et al. 2009. Science 323(5919) doi:10.1371/journal.pone.0024357

– Goodwillie C et al (2005) Data from: The evolutionary enigma of mixed mating systems in plants: occurrence, theoretical explanations, and empirical evidence. Dryad Digital Repository. doi:10.5061/dryad.292q34fp


I2 Identifying use/reuse - measuring impact

– Historical reliance on formal citations and citation-based metrics

– ISI Impact Factor widely used, but really metric for infuence of a scholarly journal

– Citation analysis not going away - remains the gold standard

– Many other use/reuse indicators for impact of individual research outputs

• 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” http://altmetrics.org

– Lots new tools and projects emerging to explore possibilities in this space

• e.g. http://total-impact.org


I3 Identifying contributors – attributing credit

– Why? So we can.. ..link content creators with their works - attribute credit accurately ..figure out: who contributed to publication X? which publications has person/organization Y contributed to?

– What kind of contributions? Characterizing ‘contributorship’ author, creator, analyst, reviewer, ‘conceived of study & designed experiment’ etc


Tackling the author name ambiguity problem(or ‘Who’s Who?’)

Are these authors all the same person?G. Thorisson, University of LeicesterG. A. Thorisson, University of LeicesterG. A. Thorisson, Cold Spring Harbor Laboratory

How about these?

J. SmithJ. SmithJ. SmithJ. SmithJ. Smith [etc.]

Or these?

∼2/3 of the ∼6 million authors in MEDLINE share a last name and first initial with at least one other author, and an ambiguous name refers to ∼8 persons on average.Torvik and Smalheiser. Author name disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data (2009) vol. 3 (3)


Launched end of 2009, ORCID will work to support the creation of a permanent, clear and unambiguous record of scholarly communication by enabling reliable attribution of authors and contributors through unique identifiers

The Open Researcher & Contributor ID initiative


ORCID will add value for scholars and the organizations that they are interacting with, including universities, scholarly societies, funding organizations and publishers

•Joins faculty or student body•Joins scholarly society•Applies for grant•Submits manuscript

The Open Researcher & Contributor ID initiative

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ORCID transcends discipline, geographic, national and institutional boundaries - now >300 participants

http://www.orcid.org

http://www.orcid.org/


• Status of research data in Iceland is unclear → need research– Build on & extend 2007 Rannís report “Gagnagrunnar á Íslandi um náttúru, umhverfi og orku”

• Funders to take lead– Mandates (aka sticks) - require data management plan + budget in grant proposals

• Many best practices & tools available to draw upon, e.g. by the UK Digital Curation Centre

– Call for & fund research proposals to build infrastructural foundations & explore technologies/initiatives

– Raise awareness in the local research community

Some food for thought / recommendation kind of stuff to conclude

Rannís, we´re looking at you!


• Universities & other research institutions need to– Take research data seriously

– Build infrastructure for data storage & preservation, support personnel (e.g. data officers / coordinators)

– Include datasets and other non-conventional outputs in professional evalutations

• Identify & engage with key international initiatives in this space– ORCID, DataCite, Dryad, Open Knowledge Foundation, others

– OpenAIRPlus ← Solveig's talk coming up!

Even more food for thought / recommendation kind of stuff


Final bite of food-for-thought

Let's make research data an integral part of theOA mission in Iceland, NOT an afterthought

ORCID - http://www.orcid.org

Acknowledgements

GEN2PHEN Consortium http://www.gen2phen.org/about-gen2phen/partners

Prof Anthony J. Brookes Bioinformatics Group, Leicester

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

Contact me!

<[email protected]>http://www.linkedin.com/in/mummihttp://www.twitter.com/gthorisson

http://www.gthorisson.name

Contact me!

<[email protected]>http://www.linkedin.com/in/mummihttp://www.twitter.com/gthorisson

http://www.gthorisson.name Published under the Creative Commons BY license (http://creativecommons.org/licenses/by/3.0/)

http://www.orcid.org/




http://www.linkedin.com/in/mummi




http://www.linkedin.com/in/mummi

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RDFC2012 Open Access to Research Data