Post on 01-Nov-2014
description
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, @UBCPhD in Dept of Biomedical Informatics, U of Pittsburgh
http://www.metmuseum.org/toah/ho/09/euwf/ho_24.45.1.htm
http://www.flickr.com/photos/jsmjr/62443357/
http://www.flickr.com/photos/camilleharrington/3587294608/
http://www.flickr.com/photos/rkuhnau/3318245976/
http://www.flickr.com/photos/conformpdx/1796399674/
http://www.flickr.com/photos/rkuhnau/3317418699/
http://www.flickr.com/photos/zemlinki/261617721/
http://www.flickr.com/photos/tracenmatt/3020786491/
http://www.flickr.com/photos/the-o/2078239333/
http://www.flickr.com/photos/ryanr/142455033/
http://www.flickr.com/photos/75166820@N00/5318468/
FindOrganizeDocumentDeidentifyFormatDecideAskSubmit
Answer questionsWorry about mistakes being foundWorry about data being misinterpretedWorry about being scoopedForgo money and IP and prestige???
not very motivating.
As a result, policy makers have spent lots of time and money ....
http://www.flickr.com/photos/tonivc/2283676770/
http://www.flickr.com/photos/johnnyvulkan/381941233/
building databases, developing standards, articulating best practices
to support public archiving of research datasets
lots of data sharing!
http://www.genome.jp/en/db_growth.html
but how much isn’t shared?
what isn’t shared?
who isn’t sharing it?why not?
what can we do about it?
how much does it matter?
you can not manage what you do not measure
quote: Lord Kelvinhttp://www.flickr.com/photos/archeon/2941655917/
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.
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?
research questions
http://www.flickr.com/photos/paulhami/1020538523//
http://www.flickr.com/photos/paulhami/1020538523//
Which data?
http://www.flickr.com/photos/paulhami/1020538523//
Where?
http://www.flickr.com/photos/paulhami/1020538523//
With whom?
http://www.flickr.com/photos/paulhami/1020538523//
When?
http://www.flickr.com/photos/paulhami/1020538523//
Under what terms?
http://www.flickr.com/photos/paulhami/1020538523//
http://www.flickr.com/photos/paulhami/1020538523//
http://www.flickr.com/photos/paulhami/1020538523//
• gene expression microarray data
• raw intensity data
• upon publication
• publicly on the internet
• (centralized databases)
microarray data
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
http://www.flickr.com/photos/sunrise/35819369/
1. Is there benefit for those who share?
currency of value?
Citations.
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
dataset85 cancer microarray trials published in 1999-2003, as identified by Ntzani and Ioannidis (2003)
citationsISI Web of Science Citation index, citations from 2004-2005
data sharing locationsPublisher and lab websites, microarray databases, WayBack Internet Archive, Oncomine
statisticsMultivariate linear regression
Note:log scale
~70%
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
a) Identify studies that create datasets
http://www.flickr.com/photos/lofaesofa/248546821/
Look for wetlab methods in article full text:
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1522022&tool=pmcentrezhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1590031&tool=pmcentrez
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1482311&tool=pmcentrez#id331936http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2082469&tool=pmcentrez
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=126870&tool=pmcentrez#id442745
Combined, these full-text portals reach 85% of the articles available through U of Pittsburgh library subscriptions.
But how to generate an effective query?
Use open access articles.
•text analysis: automatically catalogued single words and word-pairs from full text
•assessed precision and recall
•combined the high performers:
Derived query:
("gene expression" AND microarray AND cell AND rna)
AND (rneasy OR trizol OR "real-time pcr")
NOT (“tissue microarray*” OR “cpg island*”)
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%)
a) Identify studies that create datasets
b) Determine which of these have in fact been shared
c) Extract attributes about the environment
b) Determine which datasets have in fact been shared
77 %
a) Identify studies that create datasets
b) Determine which of these have in fact been shared
c) Extract attributes about the environment
Is research data shared after publication?
Funder Journal Investigator Institution Study
funded by NIH?
size of grant
sharing plan req’d?
funded by non-NIH?
impact factor
strength of policy
open access?
number of microarray studies published
years since first paper
# pubs
# citations
previously shared?
previously reused?
gender
sector
size
impact rank
country
humans?
mice?
plants?
cancer?
clinical trial?
number of authors
year
Funder Journal Investigator Institution Study
journal rank
“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
journal data sharing policy
institution rank
Yu et al. BMC medical informatics and decision making (2007) vol. 7 pp. 17
study type
Author publication history:
Citation counts:
Author-ity web serviceTorvik & Smalheiser. (2009). Author Name Disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data, 3(3):11.
Author name disambiguation:
author “experience”
author gender
funding level
PubMed grant lists + NIH grant details
funder mandates
Requires a data sharing planfor studies funded after October 2003
that receive more than $500 000 in direct funding per year
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
funder mandates
and so on...
124 variables
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
http://www.flickr.com/photos/cogdog/123072/
3. What factors are correlated with sharing and withholding data?
11,603 datapoints
25% had links from datasets in databases
univariate analysis
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Year article published
Pro
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Proportion of articles with shared datasets, by year
Across time
Ph
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PL
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Bio
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Microbiology
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Gene
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Oncogene
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Bio
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om
mu
n
Pro
port
ion o
f data
sets
share
d
0.0
0.2
0.4
0.6
0.8
1.0 Journals(Physiological Genomics)
Sta
nfo
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niv
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ity
Un
ive
rsity o
f P
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nsylv
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Un
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Un
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0.0
0.2
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1.0
Institutions(Stanford)
1
101
201
301
401
501
601
701
801
901
1001
1101
1201
1301
1401
1501
1601
1701
1801
1901
Pro
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0.0
0.2
0.4
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0.8
1.0
Institutionrank
multivariate analysis
factor analysis
multivariate logistic regression over the first-order factors
Odds Ratio
0.25 0.50 1.00 2.00 4.00 8.00
Has journal policy0.95Count of R01 & other NIH grants
Authors prev GEOAE sharing & OA & microarray creation
NO K funding or P funding
Institution high citations & collaboration
Journal impact
Journal policy consequences & long halflife
NOT animals or mice
Instititution is government & NOT higher ed
Last 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
Multivariate nonlinear regressions with interactions
Odds Ratio
0.25 0.50 1.00 2.00 4.00 8.00
Has journal policy0.95Count of R01 & other NIH grants
Authors prev GEOAE sharing & OA & microarray creation
NO K funding or P funding
Journal impact
Journal policy consequences & long halflife
Institution high citations & collaboration
NOT animals or mice
Instititution is government & NOT higher ed
Last 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
Multivariate nonlinear regressions with interactions
Odds Ratio
0.25 0.50 1.00 2.00 4.00 8.00
Has journal policy0.95Count of R01 & other NIH grants
Authors prev GEOAE sharing & OA & microarray creation
NO K funding or P funding
Institution high citations & collaboration
Journal impact
Journal policy consequences & long halflife
NOT animals or mice
Instititution is government & NOT higher ed
Last 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
Multivariate nonlinear regressions with interactions
Odds Ratio
0.25 0.50 1.00 2.00 4.00 8.00
Has journal policy0.95Count of R01 & other NIH grants
Authors prev GEOAE sharing & OA & microarray creation
NO K funding or P funding
Journal impact
Journal policy consequences & long halflife
Institution high citations & collaboration
NOT animals or mice
Instititution is government & NOT higher ed
Last 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
Multivariate nonlinear regressions with interactions
logistic regressionusing second-order factors
Odds Ratio
0.25 0.50 1.00 2.00 4.00
OA journal & previous GEO-AE sharing
0.95Amount of NIH funding
Journal impact factor and policy
Higher Ed in USA
Cancer & humans
Multivariate nonlinear regression with interactions
Odds Ratio
0.25 0.50 1.00 2.00 4.00
OA journal & previous GEO-AE sharing
0.95Amount of NIH funding
Journal impact factor and policy
Higher Ed in USA
Cancer & humans
Multivariate nonlinear regression with interactions
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
• 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/
http://www.flickr.com/photos/gatewaystreets/3838452287/
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
1. new domain
http://www.flickr.com/photos/paulhami/1020538523//
http://www.flickr.com/photos/paulhami/1020538523//
http://www.flickr.com/photos/paulhami/1020538523//
• evolution and ecology datasets
• raw data that support results
• upon publication or short embargo
• publicly on the internet
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
2. new initiatives
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
http://www.flickr.com/photos/jima/606588905/
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.
3. Reuse.
http://www.flickr.com/photos/boitabulle/3668162701/
who reuses data?when?
why aren’t they?
which datasets are most likely to be reused?
what can we do about it?
how many datasets could be reused but aren’t?
why?
who doesn’t?
does it matter?
http://upload.wikimedia.org/wikipedia/commons/thumb/e/e6/Gamma_distribution_pdf.svg/500px-Gamma_distribution_pdf.svg.png
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/
“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)
thank you
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
http://www.flickr.com/photos/jep42/3017149415/in/set-72157608797298056/
variables
Journal mandates
• readers
• reusers
• authors
• editors
• reviewers
• funders
• database designers, maintainers, curators
• patients, subjects, or populations
perspectives,
and also driving towards actionable results for these groups
http://www.flickr.com/photos/sunrise/35819369/http://www.flickr.com/photos/fboyd/2156630044/
Blumenthal et al. Acad Med. 2006
industry involvement
perceived competitiveness of field
male
sharing discouraged in training
human participants
academic productivity
0 1 2 3
Correlates with self‐reported data withholding
Campbell et al. JAMA 2002.
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 results0% 20% 40% 60% 80%
Self‐reported reasons for data withholding
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
Table 3: Second-order factor loadings, by original variables
Amount of NIH funding 0.87 nih.cumulative.years.tr 0.85 num.grants.via.nih.tr 0.84 max.grant.duration.tr 0.82 num.grant.numbers.tr 0.80 pubmed.is.funded.nih 0.79 nih.max.max.dollars.tr 0.70 nih.sum.avg.dollars.tr 0.70 nih.sum.sum.dollars.tr 0.59 has.R.funding 0.59 num.post2003.morethan500k.tr 0.58 country.usa 0.58 has.U.funding 0.57 has.R01.funding 0.55 num.post2003.morethan750k.tr 0.53 has.T.funding 0.53 num.post2003.morethan1000k.tr 0.49 num.post2004.morethan500k.tr 0.45 num.post2004.morethan750k.tr 0.44 has.P.funding 0.43 num.post2004.morethan1000k.tr 0.43 num.nih.is.nci.tr 0.35 num.post2005.morethan500k.tr 0.32 num.nih.is.nigms.tr 0.31 num.post2005.morethan750k.tr
Cancer & humans 0.60 pubmed.is.cancer 0.59 pubmed.is.humans 0.52 pubmed.is.cultured.cells 0.43 pubmed.is.core.clinical.journal 0.39 institution.is.medical -0.58 pubmed.is.plants -0.50 pubmed.is.fungi -0.37 pubmed.is.shared.other -0.30 pubmed.is.bacteria
OA journal & previous GEO-AE sharing 0.40 first.author.num.prev.geoae.sharing.tr 0.37 pubmed.is.open.access 0.37 first.author.num.prev.oa.tr 0.35 last.author.num.prev.geoae.sharing.tr 0.32 pubmed.is.effectiveness 0.32 last.author.num.prev.oa.tr 0.31 pubmed.is.geo.reuse -0.38 country.japan
Journal impact factor and policy 0.48 journal.impact.factor.log 0.47 jour.policy.requires.microarray.accession 0.46 jour.policy.mentions.exceptions 0.46 pubmed.num.cites.from.pmc.tr 0.45 journal.5yr.impact.factor.log 0.45 jour.policy.contains.word.miame.mged 0.42 last.author.num.prev.pmc.cites.tr 0.41 jour.policy.requests.accession 0.40 journal.immediacy.index.log 0.40 journal.num.articles.2008.tr 0.39 years.ago.tr 0.36 jour.policy.says.must.deposit 0.35 pubmed.num.cites.from.pmc.per.year 0.33 institution.mean.norm.citation.score 0.32 last.author.year.first.pub.ago.tr 0.31 country.usa 0.31 last.author.num.prev.pubs.tr 0.31 jour.policy.contains.word.microarray -0.31 pubmed.is.open.access
Higher Ed in USA 0.36 institution.stanford 0.36 institution.is.higher.ed 0.35 country.usa 0.35 has.R.funding 0.33 has.R01.funding 0.30 institution.harvard -0.37 institution.is.govnt