Sharing Detailed Research Data Is Associated with
Increased Citation Rate
Heather A. Piwowar*, Roger S. Day, Douglas B. Fridsma
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
Background. Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for
the investigator who makes his or her data available. Principal Findings. We examined the citation history of 85 cancer
microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available
microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with
a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using
linear regression. Significance. This correlation between publicly available data and increased literature impact may further
motivate investigators to share their detailed research data.
Citation: Piwowar HA, Day RS, Fridsma DB (2007) Sharing Detailed Research Data Is Associated with Increased Citation Rate. PLoS ONE 2(3): e308.
doi:10.1371/journal.pone.0000308
INTRODUCTION
Sharing information facilitates science. Publicly sharing detailed
research data–sample attributes, clinical factors, patient outcomes,
DNA sequences, raw mRNA microarray measurements–with
other researchers allows these valuable resources to contribute far
beyond their original analysis[1]. In addition to being used to
confirm original results, raw data can be used to explore related or
new hypotheses, particularly when combined with other publicly
available data sets. Real data is indispensable when investigating
and developing study methods, analysis techniques, and software
implementations. The larger scientific community also benefits:
sharing data encourages multiple perspectives, helps to identify
errors, discourages fraud, is useful for training new researchers,
and increases efficient use of funding and patient population
resources by avoiding duplicate data collection.
Believing that that these benefits outweigh the costs of sharing
research data, many initiatives actively encourage investigators to
make their data available. Some journals, including the PLoS
family, require the submission of detailed biomedical data to
publicly available databases as a condition of publication[2–4].
Since 2003, the NIH has required a data sharing plan for all large
funding grants. The growing open-access publishing movement
will perhaps increase peer pressure to share data.
However, while the general research community benefits from
shared data, much of the burden for sharing the data falls to the study
investigator. Are there benefits for the investigators themselves?
A currency of value to many investigators is the number of times
their publications are cited. Although limited as a proxy for the
scientific contribution of a paper[5], citation counts are often used
in research funding and promotion decisions and have even been
assigned a salary-increase dollar value[6]. Boosting citation rate is
thus is a potentially important motivator for publication authors.
In this study, we explored the relationship between the citation
rate of a publication and whether its data was made publicly
available. Using cancer microarray clinical trials, we addressed the
following questions: Do trials which share their microarray data
receive more citations? Is this true even within lower profile trials?
What other data-sharing variables are associated with an increased
citation rate? While this study is not able to investigate causation,
quantifying associations is a valuable first step in understanding
these relationships. Clinical microarray data provides a useful
environment for the investigation: despite being valuable for reuse
and extremely costly to collect, is not yet universally shared.
RESULTS
We studied the citations of 85 cancer microarray clinical trials
published between January 1999 and April 2003, as identified in
a systematic review by Ntzani and Ioannidis[7] and listed in
Supplementary Text S1. We found 41 of the 85 clinical trials
(48%) made their microarray data publicly available on the
internet. Most data sets were located on lab websites (28), with
a few found on publisher websites (4), or within public databases (6
in the Stanford Microarray Database (SMD)[8], 6 in Gene
Expression Omnibus (GEO)[9], 2 in ArrayExpress[10], 2 in the
NCI GeneExpression Data Portal (GEDP)(gedp.nci.nih.gov); some
datasets in more than one location). The internet locations of the
datasets are listed in Supplementary Text S2. The majority of
datasets were made available concurrently with the trial
publication, as illustrated within the WayBackMachine internet
archives (www.archive.org/web/web.php) for 25 of the datasets
and mention of supplementary data within the trial publication
itself for 10 of the remaining 16 datasets. As seen in Table 1, trials
published in high impact journals, prior to 2001, or with US
authors were more likely to share their data.
The cohort of 85 trials was cited an aggregate of 6239 times in
2004–2005 by 3133 distinct articles (median of 1.0 cohort citation
per article, range 1–23). The 48% of trials which shared their data
received a total of 5334 citations (85% of aggregate), distributed as
shown in Figure 1.
Academic Editor: John Ioannidis, University of Ioannina School of Medicine,
Greece
Received December 13, 2006; Accepted February 26, 2007; Published March 21,
2007
Copyright:
2007 Piwowar et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
Funding: HAP was supported by NLM Training Grant Number 5T15-LM007059-19.
The NIH had no role in study design, data collection or analysis, writing the paper,
or the decision to submit it for publication. The publication contents are solely
the responsibility of the authors and do not necessarily represent the official
views of the NIH.
Competing Interests: The authors have declared that no competing interests
exist.
* To whom correspondence should be addressed. E-mail: hpiwowar@cbmi.pitt.
edu
PLoS ONE | www.plosone.org 1 March 2007 | Issue 3 | e308

Whether a trial’s dataset was made publicly available was
significantly associated with the log of its 2004–2005 citation rate
(69% increase in citation count; 95% confidence interval: 18 to
143%, p = 0.006), independent of journal impact factor, date of
publication, and US authorship. Detailed results of this multivar-
iate linear regression are given in Table 2. A similar result was
found when we regressed on the number of citations each trial
received during the 24 months after its publication (45% increase
in citation count; 95% confidence interval: 1 to 109%, p = 0.050).
To confirm that these findings were not dependent on a few
extremely high-profile papers, we repeated our analysis on a subset
of the cohort. We define papers published after the year 2000 in
journals with an impact factor less than 25 as lower-profile
publications. Of the 70 trials in this subset, only 27 (39%) made
their data available, although they received 1875 of 2761 (68%)
aggregate citations. The distribution of the citations by data
availability in this subset is shown in Figure 2. The association
between data sharing and citation rate remained significant in this
lower-profile subset, independent of other covariates within
a multivariate linear regression (71% increase in citation count;
95% confidence interval: 19 to 146%, p = 0.005).
Lastly, we performed exploratory analysis on citation rate within
the subset of trials which shared their microarray data; results are
given in Table 3 and raw covariate data in Supplementary Data S1.
The number of patients in a trial and a clinical endpoint correlated
with increased citation rate. Assuming shared data is actually re-
analyzed, one might expect an increase in citations for those trials
which generated data on a standard platform (Affymetrix), or
released it in a central location or format (SMD, GEO, GEDP)[11].
However, the choice of platform was insignificant and only those
trials located in SMD showed a weak trend of increased citations. In
fact, the 6 trials with data in GEO (in addition to other locations for 4
of the 6) actually showed an inverse relationship to citation rate,
though we hesitate to read much into this due to the small number of
trials in this set. The few trials in this cohort which, in addition to
gene expression fold-change or other preprocessed information,
shared their raw probe data or actual microarray images did not
receive additional citations. Finally, although finding diverse
microarray datasets online is non-trivial, an additional increase in
citations was not noted for trials which mentioned their Supple-
mentary Material within their paper, nor for those trials with datasets
identified by a centralized, established data mining website. In
summary, only trial design features such as size and clinical endpoint
showed a significant association with citation rate; covariates relating
to the data collection and how the data was made available only
showed very weak trends. Perhaps with a larger and more balanced
sample of trials with shared data these trends would be more clear.
Table 1. Characteristics of Eligible Trials by Data Sharing.
..................................................................................................................................................
Number of Articles
Odds Ratio (95% confidence interval)
Total Data Shared Data Not Shared
TOTAL 85 41 (48%) 44 (52%)
High Impact (. = 25) 12 12 (100%) 0 (0%) ‘ (3.8 to ‘)
Low Impact Journal 73 29 (40%) 44 (60%)
Published 1999–2000 6 5 (83%) 1 (17%) 6.0 (0.6 to 288.5)
Published 2001–2003 79 36 (46%) 43 (54%)
Include a US Author 56 35 (63%) 21 (38%) 6.4 (2.0 to 21.9)
No US Authors 29 6 (21%) 23 (79%)
doi:10.1371/journal.pone.0000308.t001.
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Figure 1. Distribution of 2004–2005 citation counts of 85 trials by
data availability. The 41 clinical trial publications which publicly shared
their microarray data received more citations, in general, than the 44
publications which did not share their microarray data. In this plot of
the distribution of citation counts received by each publication, the
extent of the box encompasses the interquartile range of the citation
counts, whiskers extend to 1.5 times the interquartile range, and lines
within the boxes represent medians.
doi:10.1371/journal.pone.0000308.g001
Table 2. Multivariate regression on citation count for 85
publications
......................................................................
Percent increase in
citation count (95%
confidence interval) p-value
Publish in a journal with twice the impact
factor
84% (59 to 109%) ,0.001
Increase the publication date by a month 23% (25to22%) ,0.001
Include a US author 38% (1 to 89%) 0.049
Make data publicly available 69% (18 to 143%) 0.006
We calculated a multivariate linear regression over the citation counts,
including covariates for journal impact factor, date of publication, US
authorship, and data availability. The coefficients and p-values for each of the
covariates are shown here, representing the contribution of each covariate to
the citation count, independent of other covariates.
doi:10.1371/journal.pone.0000308.t002.
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Sharing Data Citation Rate
PLoS ONE | www.plosone.org 2 March 2007 | Issue 3 | e308

DISCUSSION
We found that cancer clinical trials which share their microarray
data were cited about 70% more frequently than clinical trials
which do not. This result held even for lower-profile publications
and thus is relevant to authors of all trials.
A parallel can be drawn between making study data publicly
available and publishing a paper itself in an open-access journal.
The association with an increased citation rate is similar[12].
While altruism no doubt plays a part in the motivation of authors
in both cases, studies have found that an additional reason authors
choose to publish in open-access journals is that they believe their
articles will be cited more frequently[13,14], endorsing the
relevance of our result as a potential motivator.
We note an important limitation of this study: the demonstrated
association does not imply causation. Receiving many citations
and sharing data may stem from a common cause rather than
being directly causally related. For example, a large, high-quality,
clinically important trial would naturally receive many citations
due to its medical relevance; meanwhile, its investigators may be
more inclined to share its data than they would be for a smaller
trial-perhaps due greater resources or confidence in the results.
Nonetheless, if we speculate for a moment that some or all of the
association is indeed causal, we can hypothesize several mechan-
isms by which making data available may increase citations. The
simplest mechanism is due to increased exposure: listing the
dataset in databases and on websites will increase the number of
people who encounter the publication. These people may then
subsequently cite it for any of the usual reasons one cites a paper,
such as paying homage, providing background reading, or noting
corroborating or disputing claims ([15] provides a summary of
research into citation behavior). More interestingly, evidence
suggests that shared microarray data is indeed often reana-
lyzed[16], so at least some of the additional citations are certainly
in this context. Finally, these re-analyses may spur enthusiasm and
synergy around a specific research question, indirectly focusing
publications and increasing the citation rate of all participants.
These hypotheses are not tested in this study: additional research is
needed to study the context of these citations and the degree,
variety, and impact of any data re-use. Further, it would be
interesting to assess the impact of reuse on the community,
quantifying whether it does in fact lead to collaboration,
a reduction in resource use, and scientific advances.
Since it is generally agreed that sharing data is of value to the
scientific community[16–21], it is disappointing that less than half
of the trials we looked at made their data publicly available. It is
possible that attitudes may have changed in the years since these
trials were published, however even recent evidence (in a field
tangential to microarray trials) demonstrates a lack of willingness
and ability to share data: an analysis in 2005 by Kyzas et al.[22]
found that primary investigators for 17 of 63 studies on TP53
status in head and neck squamous cell carcinoma did not respond
to a request for additional information, while 5 investigators
replied they were unable to retrieve raw data.
Indeed, there are many personal difficulties for those who
undertake to share their data[1]. A major cost is time: the data
have to be formatted, documented, and released. Unfortunately
this investment is often larger than one might guess: in the realm of
microarray and particularly clinical information, it is nontrivial to
Figure 2. Distribution of 2004–2005 citation counts of the 70 lower-
profile trials by data availability. For trials which were published after
2000 and in journals with an impact factor less than 25, the 27 clinical
trial publications which publicly shared their microarray data received
more citations, in general, than the 43 publications which did not share
their microarray data. In this plot of the distribution of citation counts
received by each publication, the extent of the box encompasses the
interquartile range of the citation counts, whiskers extend to 1.5 times
the interquartile range, and lines within the boxes represent medians.
doi:10.1371/journal.pone.0000308.g002
Table 3. Exploratory regressions on citation count for the 41 publications with shared data
..................................................................................................................................................
Number of articles (% of total) Number of citations (% of total) Percent increase in citation count p-value
TOTAL 41 5334
Trial size.25 patients 26 (63%) 3704 (69%) 122% ,0.001
Clinical endpoint 18 (44%) 3404 (64%) 79% 0.01
Affymetrix platform 22 (54%) 2735 (51%) 18% 0.43
In GEO database 6 (15%) 939 (18%) 252% 0.02
In SMD database 6 (15%) 1114 (21%) 24% 0.48
Raw data available 20 (49%) 2437 (46%) 22% 0.91
Pub mentions Suppl. Data 35 (85%) 4854 (91%) 11% 0.73
Has Oncomine profile 35 (85%) 4884 (92%) 19% 0.54
The coefficient and p-value for each covariate in the table were calculated from separate multivariate linear regressions over the citation count, including covariates for
journal impact factor, date of publication, and US authorship.
doi:10.1371/journal.pone.0000308.t003.
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Sharing Data Citation Rate
PLoS ONE | www.plosone.org 3 March 2007 | Issue 3 | e308

decide what data to release, how to de-identify it, how to format it,
and how to document it. Further, it is sometimes complicated to
decide where to best publish data, since supplementary in-
formation and laboratory sites are transient[23,24] Beyond a time
investment, releasing data can induce fear. There is a possibility
that the original conclusions may be challenged by a re-analysis,
whether due to possible errors in the original study[25],
a misunderstanding or misinterpretation of the data[26], or simply
more refined analysis methods. Future data miners might discover
additional relationships in the data, some of which could disrupt
the planned research agenda of the original investigators.
Investigators may fear they will be deluged with requests for
assistance, or need to spend time reviewing and possibly rebutting
future re-analyses. They might feel that sharing data decreases
their own competitive advantage, whether future publishing
opportunities, information trade-in-kind offers with other labs, or
potentially profit-making intellectual property. Finally, it can be
complicated to release data. If not well-managed, data can become
disorganized and lost. Some informed consent agreements may
not obviously cover subsequent uses of data. De-identification can
be complex. Study sponsors, particularly from industry, may not
agree to release raw detailed information. Data sources may be
copyrighted such that the data subsets can not be freely shared,
though it is always worth asking.
Although several of these difficulties are challenging to
overcome, many are being addressed by a variety of initiatives,
thereby decreasing the barriers to data sharing. For example,
within the area of microarray clinical trials, several public
microarray databases (SMD[27], GEO[9], ArrayExpress[10],
CIBEX[28], GEDP(gedp.nci.nih.gov)) offer an obvious, central-
ized, free, and permanent data storage solution. Standards have
been developed to specify minimal required data elements
(MIAME[29] for microarray data, REMARK[30] for prognostic
study details), consistent data encoding (MAGE-ML[31] for
microarray data), and semantic models (BRIDG (www.bridgpro-
ject.org) for study protocol details). Software exists to help de-
identify some types of patient records (De-ID[32]). The NIH and
other agencies allow funds for data archiving and sharing. Finally,
large initiatives (NCI’s caBIG[33]) are underway to build tools and
communities to enable and advance sharing data.
Research consumes considerable resources from the public
trust. As data sharing gets easier and benefits are demonstrated for
the individual investigator, hopefully authors will become more
apt to share their study data and thus maximize its usefulness to
society.
In the spirit of this analysis, we have made publicly available the
bibliometric detailed research data compiled for this study (see
Supplementary Information and http://www.pitt.edu/,hap7).
MATERIALS AND METHODS
Identification and Eligibility of Relevant Studies
We compared the citation impact of clinical trials which made
their cancer microarray data publicly available to the citation
impact of trials which did not. A systematic review by Ntzani and
Ioannidis[7] identified clinical trials published between January
1999 and April 2003 which investigated correlations between
microarray gene expression and human cancer outcomes and
correlates. We adopted this set of 85 trials as the cohort of interest.
Data Extraction
We assessed whether each of these trials made its microarray data
publicly available by examining a variety of publication and
internet resources. Specifically, we looked for mention of
Supplementary Information within the trial publication, searched
the Stanford Microarray Database (SMD)[8], Gene Expression
Omnibus (GEO)[9], ArrayExpress[10], CIBEX[28], and the NCI
GeneExpression Data Portal (GEDP)(gedp.nci.nih.gov), investi-
gated whether a data link was provided within Oncomine[34], and
consulted the bibliography of data re-analyses. Microarray data
release was not required by any journals within the timeframe of
these trial publications. Some studies may make their data
available upon individual request, but this adds a burden to the
data user and so was not considered ‘‘publicly available’’ for the
purposes of this study.
We attempted to determine the date data was made available
through notations in the published paper itself and records within
the WayBackMachine internet archive (www.archive.org/web/
web.php). Inclusion in the WayBackMachine archive for a given
date proves a resource was available, however, because archiving
is not comprehensive, absence from the archive does not itself
demonstrate a resource did not exist on that date.
The citation history for each trial was collected through the
Thomson Scientific Institute for Scientific Information (ISI)
Science Citation Index at the Web of Science Database (www.
isinet.com). Only citations with a document type of ‘Article’ were
considered, thus excluding citations by reviews, editorials, and
other non-primary research papers.
For each trial, we also extracted the impact factor of the
publishing journal (ISI Journal Citation Reports 2004), the date of
publication, and the address of the authors from the ISI Web of
Science. Trial size, clinical endpoint, and microarray platform
were extracted from the Ntzani and Ioannidis review[7].
Analysis
The main analyses addressed the number of citations each trial
received between January 2004 and December 2005. Because the
pattern of citations rates is complex–changing not only with
duration since publication but also with maturation of the general
microarray field–a confirmatory analysis was performed using the
number of citations each publication received within the first
24 months of its publication.
Although citation patterns covering a broad scope of literature
types are left-skewed[35], we verified that citation rates within our
relatively homogeneous cohort were roughly log-normal and thus
used parametric statistics.
Multivariate linear regression was used to evaluate the
association between the public availability of a trial’s microarray
data and number of citations (after log transformation) it received.
The impact factor of the journal which published each trial, the
date of publication, and the country of authors are known to
correlate to citation rate[36], so these factors were included as
covariates. Impact factor was log-transformed, date of publication
was measured as months since January 1999, and author country
was coded as 1 if any investigator has a US address and
0 otherwise.
Since seminal papers–often those published early in the history
a field or in very high-impact journals–receive an unusually high
number of citations, we performed a subset analysis to determine
whether our results held when considering only those trials which
were published after 2000 and in lower-impact (,25) journals.
Finally, as exploratory analysis within the subset of all trials with
publicly available microarray data, we looked at the linear
regression relationships between additional covariates and citation
count. Covariates included trial size, clinical endpoint, microarray
platform, inclusion in various public databases, release of raw data,
mention of supplementary information, and reference within the
Oncomine[34] repository.
Sharing Data Citation Rate
PLoS ONE | www.plosone.org 4 March 2007 | Issue 3 | e308

Statistical analysis was performed using the stats package in R
version 2.1[37]; the code is included as Supplementary Text S3. P-
values are two-tailed.
SUPPORTING INFORMATION
Text S1 Cohort Publication Bibliography
Found at: doi:10.1371/journal.pone.0000308.s001 (0.05 MB
DOC)
Text S2 Locations of Publicly Available Data for the Cohort
Found at: doi:10.1371/journal.pone.0000308.s002 (0.05 MB
DOC)
Text S3 Statistical Analysis R-code
Found at: doi:10.1371/journal.pone.0000308.s003 (0.01 MB
TXT)
Data S1 Raw Citation Counts and Covariates
Found at: doi:10.1371/journal.pone.0000308.s004 (0.04 MB
XLS)
ACKNOWLEDGMENTS
Author Contributions
Conceived and designed the experiments: HP. Performed the experiments:
HP. Analyzed the data: HP. Wrote the paper: HP. Other: Reviewed the
data analysis and interpretation, reviewed the paper: RD Discussed the
study motivation and scope, reviewed the paper: DF.
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