![“Does anyone want your data?
That’s hard to predict […]
After all, no one ever knocked on your door asking to
buy those figurines collecting dust in your cabinet
before you listed them on eBay.
Your data, too, may simply be awaiting an effective
matchmaker.”
Got data? Nature Neuroscience (2007)](https://image.slidesharecdn.com/bliss2010presentation-101004183916-phpapp01/85/Public-data-archiving-Who-does-Who-doesn-t-What-can-we-do-about-it-113-320.jpg)
Public data archiving: Who does? Who doesn't? What can we do about it?
- 1. Public data archiving: Who shares? Who doesn’t? What can we do about it? Heather Piwowar Presented at UBC BLISS, Sept 2010 DataONE postdoc with Dryad and NESCent, @UBC PhD in Dept of Biomedical Informatics, U of Pittsburgh
- 13. Find Organize Document Deidentify Format Decide Ask Submit Answer questions Worry about mistakes being found Worry about data being misinterpreted Worry about being scooped Forgo money and IP and prestige???
- 15. As a result, policy makers have spent lots of time and money .... http://www.flickr.com/photos/johnnyvulkan/381941233/ http://www.flickr.com/photos/tonivc/2283676770/
- 17. lots of data sharing! http://www.genome.jp/en/db_growth.html
- 18. but how much isn’t shared? what isn’t shared? who isn’t sharing it? why not? how much does it matter? what can we do about it?
- 19. you can not manage what you do not measure quote: Lord Kelvin http://www.flickr.com/photos/archeon/2941655917/
- 20. As we seek to embrace and encourage data sharing, understanding patterns of adoption will allow us to make informed decisions about tools, policies, and best practices. Measuring adoption over time will allow us to note progress and identify best practices and opportunities for improvement.
- 21. research questions 1. Is there benefit for those who share? 2. How can we study data sharing behaviour in a scalable, systematic way? 3. What factors are correlated with sharing and withholding data?
- 23. Which data? http://www.flickr.com/photos/paulhami/1020538523//
- 24. Where? http://www.flickr.com/photos/paulhami/1020538523//
- 25. With whom? http://www.flickr.com/photos/paulhami/1020538523//
- 26. When? http://www.flickr.com/photos/paulhami/1020538523//
- 27. Under what terms? http://www.flickr.com/photos/paulhami/1020538523//
- 30. • gene expression microarray data • raw intensity data • upon publication • publicly on the internet • (centralized databases) http://www.flickr.com/photos/paulhami/1020538523//
- 31. http://en.wikipedia.org/wiki/DNA_microarray http://en.wikipedia.org/wiki/Image:Heatmap.png http://commons.wikimedia.org/wiki/ File:DNA_double_helix_vertikal.PNG microarray data
- 32. microarray data
- 33. 1. Is there benefit for those who share? http://www.flickr.com/photos/sunrise/35819369/
- 34. currency of value? Citations.
- 35. currency of value? Citations. $50! Diamond,Arthur M. What is a Citation Worth?. The Journal of Human Resources (1986) vol. 21 (2) pp. 200-215
- 36. dataset 85 cancer microarray trials published in 1999-2003, as identified by Ntzani and Ioannidis (2003) citations ISI Web of Science Citation index, citations from 2004-2005 data sharing locations Publisher and lab websites, microarray databases, WayBack Internet Archive, Oncomine statistics Multivariate linear regression
- 37. Note: log scale
- 39. ~70%
- 40. 2. Need automated methods to: a) Identify studies that create datasets b) Determine which of these have in fact been shared c) Extract attributes about the environment
- 41. a) Identify studies that create datasets http://www.flickr.com/photos/lofaesofa/248546821/
- 42. Look for wetlab methods in article full text: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1522022&tool=pmcentrez http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1590031&tool=pmcentrez http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1482311&tool=pmcentrez#id331936 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2082469&tool=pmcentrez http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=126870&tool=pmcentrez#id442745
- 43. Combined, these full-text portals reach 85% of the articles available through U of Pittsburgh library subscriptions.
- 44. But how to generate an effective query? Use open access articles.
- 45. • text analysis: automatically catalogued single words and word-pairs from full text • assessed precision and recall • combined the high performers:
- 46. Derived query: ("gene expression" AND microarray AND cell AND rna) AND (rneasy OR trizol OR "real-time pcr") NOT (“tissue microarray*” OR “cpg island*”)
- 47. Evaluation: Ochsner et al. Nature Methods (2008) 400 studies across 20 journals Precision: 90% (conf int: 86% to 93%) Recall: 56% (conf int: 52% to 61%)
- 48. a) Identify studies that create datasets b) Determine which of these have in fact been shared c) Extract attributes about the environment
- 49. b) Determine which datasets have in fact been shared
- 51. 77 %
- 53. a) Identify studies that create datasets b) Determine which of these have in fact been shared c) Extract attributes about the environment
- 54. Funder Journal Investigator Institution Study Is research data shared after publication?
- 55. Funder Journal Investigator Institution Study funded by impact years since sector humans? NIH? factor first paper size mice? size of strength of # pubs grant policy impact plants? # citations rank sharing open cancer? plan req’d? access? previously country shared? clinical funded by number of trial? non-NIH? microarray previously reused? number of studies authors published gender year
- 56. journal rank
- 57. journal data sharing policy “An inherent principle of publication is that others should be able to replicate and build upon the authors' published claims. Therefore, a condition of publication in a Nature journal is that authors are required to make materials, data and associated protocols available in a publicly accessible database …” http://www.nature.com/authors/editorial_policies/availability.html http://www.nature.com/nature/journal/v453/n7197/index.html
- 58. institution rank Yu et al. BMC medical informatics and decision making (2007) vol. 7 pp. 17
- 59. study type
- 60. author “experience” Author publication history: Author name Author-ity web service Torvik & Smalheiser. (2009). Author Name disambiguation: Disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data, 3(3):11. Citation counts:
- 61. author gender
- 62. funding level PubMed grant lists + NIH grant details
- 63. funder mandates Requires a data sharing plan for studies funded after October 2003 that receive more than $500 000 in direct funding per year
- 64. funder mandates Proxy for NIH data sharing policy applicability: If in any year since 2004, • funded by an NIH grant number with a “1” or “2” type code • received more than $750 000 in total funding from the grant
- 65. and so on... 124 variables
- 66. Now equipped with automated methods to: a) Identify studies that create datasets b) Determine which of these have in fact been shared c) Extract attributes about the environment
- 67. 3. What factors are correlated with sharing and withholding data? http://www.flickr.com/photos/cogdog/123072/
- 68. 11,603 datapoints 25% had links from datasets in databases
- 70. Proportion of articles with shared datasets, by year 0.35 Proportion of articles with datasets found in GEO or ArrayExpress 0.30 0.25 0.20 0.15 Across time 0.10 0.05 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year article published
- 71. Proportion of datasets shared 0.0 0.2 0.4 0.6 0.8 1.0 Physiol Genomics PLoS Genet Genome Biol Microbiology PLoS One BMC Genomics Plant Cell Genome Res Eukaryot Cell Appl Environ Microbiol BMC Med Genomics Hum Mol Genet Proc Natl Acad Sci U S A Infect Immun Am J Respir Cell Mol Biol Dev Biol J Bacteriol Mol Endocrinol BMC Cancer Plant Physiol Biol Reprod Blood J Immunol FASEB J Toxicol Sci J Exp Bot Nucleic Acids Res Diabetes Mol Cell Biol Mol Cancer Ther BMC Bioinformatics Stem Cells FEBS Lett J Neurosci Am J Pathol J Biol Chem J Virol OTHER Cancer Res J Clin Endocrinol Metab Plant Mol Biol Clin Cancer Res Genomics Journals Invest Ophthalmol Vis Sci Mol Hum Reprod Carcinogenesis Gene Endocrinology Oncogene Cancer Lett Biochem Biophys Res Commun (Physiological Genomics)
- 72. Proportion of datasets shared 0.0 0.2 0.4 0.6 0.8 1.0 Stanford University University of Pennsylvania University of Illinois University of California, Los Angeles University of Wisconsin, Madison University of Washington University of California, Davis The University of British Columbia University of California, San Francisco University of Florida University of California, San Diego University of Minnesota, Twin Cities Baylor College of Medicine OTHER Max Planck Gesellschaft Harvard University Duke University Medical Center Yale University Johns Hopkins University University of Pittsburgh (Stanford) Washington University in Saint Louis University of Toronto University of California, Berkeley University of Michigan, Ann Arbor Michigan State University Institutions National Cancer Institute Tokyo Daigaku
- 73. Proportion of datasets shared 0.0 0.2 0.4 0.6 0.8 1.0 1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 rank 1401 1501 1601 1701 1801 1901 Institution
- 76. factor analysis
- 88. multivariate logistic regression over the first-order factors
- 89. Multivariate nonlinear regressions with interactions Odds Ratio 0.25 0.50 1.00 2.00 4.00 8.00 Has journal policy Multivariate nonlinear regressions with interactions Count of R01 & other NIH grants Odds Ratio 0.95 0.25 0.50 1.00 2.00 4.00 8.00 Authors prev GEOAE sharing & OA & microarray creation Has journal policy NO K funding other P funding Count of R01 & or NIH grants 0.95 Authors prev GEOAE sharing & OA & microarray creation NO K Journalfunding funding or P impact Institution high citations & collaboration Journal policy consequences & Journal impact long halflife Journal policy consequences & long halflife Institution high citations NOTcollaboration & animals or mice Instititution is government & NOT higher ed NOT animals or mice Last author num prev pubs & first year pub Large NIH grant Instititution is government & NOT higher ed Humans & cancer NO geo reuse + YES high institution output Last author num prev pubs & first year pub First author num prev pubs & first year pub Large NIH grant Humans & cancer NO geo reuse + YES high institution output First author num prev pubs & first year pub
- 90. Multivariate nonlinear regressions with interactions Odds Ratio 0.25 0.50 1.00 2.00 4.00 8.00 Has journal policy Multivariate nonlinear regressions with interactions Count of R01 & other NIH grants Odds Ratio 0.95 0.25 0.50 1.00 2.00 4.00 8.00 Authors prev GEOAE sharing & OA & microarray creation Has journal policy NO K funding other P funding Count of R01 & or NIH grants 0.95 Authors prev GEOAE sharing & OA & microarray creation NO K Journalfunding funding or P impact Institution high citations & collaboration Journal policy consequences & Journal impact long halflife Journal policy consequences & long halflife Institution high citations NOTcollaboration & animals or mice Instititution is government & NOT higher ed NOT animals or mice Last author num prev pubs & first year pub Large NIH grant Instititution is government & NOT higher ed Humans & cancer NO geo reuse + YES high institution output Last author num prev pubs & first year pub First author num prev pubs & first year pub Large NIH grant Humans & cancer NO geo reuse + YES high institution output First author num prev pubs & first year pub
- 91. logistic regression using second-order factors
- 92. Multivariate nonlinear regression with interactions Odds Ratio 0.25 0.50 1.00 2.00 4.00 OA journal & previous GEO-AE sharing Amount of NIH funding 0.95 Journal impact factor and policy Higher Ed in USA Cancer & humans
- 93. Multivariate nonlinear regression with interactions Odds Ratio 0.25 0.50 1.00 2.00 4.00 OA journal & previous GEO-AE sharing Amount of NIH funding 0.95 Journal impact factor and policy Higher Ed in USA Cancer & humans
- 94. Conclusions: • data sharing rates are increasing, but overall levels are low Preliminary evidence: • levels are particularly low in cancer • levels are highest for those who • publish in a journal with a policy • publish in an open access journal • have shared data before
- 95. • data and filters were imperfect • many assumptions • didn’t capture all types of sharing • don’t know how generalizable across datatypes • should be considered hypothesis-generating http://www.flickr.com/photos/vlastula/300102949/
- 97. NSF-funded distributed framework and cyberinfrastructure for environmental science. Dryad is a repository of data underlying scientific publications, with an initial focus on evolution, ecology, and related fields. The National Evolutionary Synthesis Center, NSF-funded: • Duke University, • UNC at Chapel Hill • North Carolina State University
- 98. 1. new domain
- 101. • evolution and ecology datasets • raw data that support results • upon publication or short embargo • publicly on the internet http://www.flickr.com/photos/paulhami/1020538523//
- 102. challenges! 1. No PubMed 2. Diverse data types, norms, repositories 3. Data almost always collected for a specific hypothesis 4. Less public sharing so far
- 103. 2. new initiatives
- 105. JDAP • The American Naturalist • Evolution • Journal of Evolutionary Biology • Molecular Ecology • Evolutionary Applications • Genetics • Heredity • Molecular Biology and Evolution • Systematic Biology • Paleobiology • BMC Evolutionary Biology
- 106. Blumenthal et al. Acad Med. 2006 Campbell et al. JAMA. 2002. Kyzas et al. J Natl Cancer Inst. 2005. Vogeli et al. Acad Med. 2006. Reidpath et al. Bioethics 2001. http://www.flickr.com/photos/jima/606588905/
- 107. 3. Reuse. http://www.flickr.com/photos/boitabulle/3668162701/
- 108. who reuses data? why? when? who doesn’t? which datasets are most likely to be reused? how many datasets could be reused but aren’t? why aren’t they? does it matter? what can we do about it?
- 109. http://upload.wikimedia.org/wikipedia/commons/thumb/e/e6/ Gamma_distribution_pdf.svg/500px-Gamma_distribution_pdf.svg.png
- 112. I post my data, code, and statistical scripts on GitHub (links from http://researchremix.org) Share yours too! http://www.flickr.com/photos/myklroventine/892446624/
- 113. “Does anyone want your data? That’s hard to predict […] After all, no one ever knocked on your door asking to buy those figurines collecting dust in your cabinet before you listed them on eBay. Your data, too, may simply be awaiting an effective matchmaker.” Got data? Nature Neuroscience (2007)
- 114. Dept of Biomedical Informatics at U of Pittsburgh Wendy Chapman for support and feedback Todd Vision, Mike Whitlock for ongoing discussions NIH NLM. NSF through DataONE, NESCent, Dryad. Open science online community and those who release their articles, datasets and photos openly thank you
- 117. Journal mandates variables
- 118. • readers • reusers perspectives, • authors and also driving towards • editors actionable results for these groups • reviewers • funders • database designers, maintainers, curators • patients, subjects, or populations
- 122. Correlates with self‐reported data withholding industry involvement perceived competitiveness of field male sharing discouraged in training human participants academic productivity 0 1 2 3 Blumenthal et al. Acad Med. 2006
- 123. Self‐reported reasons for data withholding sharing is too much effort want student or jr faculty to publish more they themselves want to publish more cost industrial sponsor confidentiality commercial value of results 0% 20% 40% 60% 80% Campbell et al. JAMA 2002.
- 124. Table 2: Second-order factor loadings, by first-order factors Amount of NIH funding 0.88 Count of R01 & other NIH grants 0.49 Large NIH grant -0.55 NO K funding or P funding Cancer & humans 0.83 Humans & cancer OA journal & previous GEO-AE sharing 0.59 Authors prev GEOAE sharing & OA & microarray creation 0.43 Institution high citations & collaboration 0.31 First author num prev pubs & first year pub -0.36 Last author num prev pubs & first year pub Journal impact factor and policy 0.57 Journal impact 0.51 Last author num prev pubs & first year pub Higher Ed in USA 0.40 NO geo reuse + YES high institution output -0.44 Institution is government & NOT higher ed
- 125. Table 3: Second-order factor loadings, by OA journal & previous GEO-AE sharing original variables 0.40 first.author.num.prev.geoae.sharing.tr Amount of NIH funding 0.37 pubmed.is.open.access 0.87 nih.cumulative.years.tr 0.37 first.author.num.prev.oa.tr 0.85 num.grants.via.nih.tr 0.35 last.author.num.prev.geoae.sharing.tr 0.84 max.grant.duration.tr 0.32 pubmed.is.effectiveness 0.82 num.grant.numbers.tr 0.32 last.author.num.prev.oa.tr 0.80 pubmed.is.funded.nih 0.31 pubmed.is.geo.reuse 0.79 nih.max.max.dollars.tr -0.38 country.japan 0.70 nih.sum.avg.dollars.tr 0.70 nih.sum.sum.dollars.tr Journal impact factor and policy 0.59 has.R.funding 0.48 journal.impact.factor.log 0.59 num.post2003.morethan500k.tr 0.47 jour.policy.requires.microarray.accession 0.58 country.usa 0.46 jour.policy.mentions.exceptions 0.58 has.U.funding 0.46 pubmed.num.cites.from.pmc.tr 0.57 has.R01.funding 0.45 journal.5yr.impact.factor.log 0.55 num.post2003.morethan750k.tr 0.45 jour.policy.contains.word.miame.mged 0.53 has.T.funding 0.42 last.author.num.prev.pmc.cites.tr 0.53 num.post2003.morethan1000k.tr 0.41 jour.policy.requests.accession 0.49 num.post2004.morethan500k.tr 0.40 journal.immediacy.index.log 0.45 num.post2004.morethan750k.tr 0.40 journal.num.articles.2008.tr 0.44 has.P.funding 0.39 years.ago.tr 0.43 num.post2004.morethan1000k.tr 0.36 jour.policy.says.must.deposit 0.43 num.nih.is.nci.tr 0.35 pubmed.num.cites.from.pmc.per.year 0.35 num.post2005.morethan500k.tr 0.33 institution.mean.norm.citation.score 0.32 num.nih.is.nigms.tr 0.32 last.author.year.first.pub.ago.tr 0.31 num.post2005.morethan750k.tr 0.31 country.usa 0.31 last.author.num.prev.pubs.tr Cancer & humans 0.31 jour.policy.contains.word.microarray 0.60 pubmed.is.cancer -0.31 pubmed.is.open.access 0.59 pubmed.is.humans 0.52 pubmed.is.cultured.cells Higher Ed in USA 0.43 pubmed.is.core.clinical.journal 0.36 institution.stanford 0.39 institution.is.medical 0.36 institution.is.higher.ed -0.58 pubmed.is.plants 0.35 country.usa -0.50 pubmed.is.fungi 0.35 has.R.funding -0.37 pubmed.is.shared.other 0.33 has.R01.funding -0.30 pubmed.is.bacteria 0.30 institution.harvard -0.37 institution.is.govnt