PLoS Medicine | www.plosmedicine.org 1315 September 2008 | Volume 5 | Issue 9 | e183
Policy Forum
Towards a Data Sharing Culture:
Recommendations for Leadership
from Academic Health Centers
S
haring biomedical research and
health care data is important but
difficult. Recognizing this, many
initiatives facilitate, fund, request,
or require researchers to share their
data [1–5] . These initiatives address
the technical aspects of data sharing,
but rarely focus on incentives for key
stakeholders [6]. Academic health
centers (AHCs) have a critical role in
enabling, encouraging, and rewarding
data sharing. The leaders of medical
schools and academic-affiliated
hospitals can play a unique role in
supporting this transformation of
the research enterprise. We propose
that AHCs can and should lead
the transition towards a culture of
biomedical data sharing.
Benefits of Data Sharing for
Academic Health Centers
The benefits of data sharing and
reuse have been widely reported.
We summarize them here, from the
perspective of an AHC.
The predominant benefit of data
sharing is accelerated scientific
progress. Advances are clearly valuable
to an AHC when translated into
improved patient outcomes, reduced
research costs, and decreased time in
moving discoveries from the bench to
the bedside.
Of more immediate benefit to AHCs
and their researchers, sharing data
increases the visibility and relevance
of research output. Sharing data
generates opportunities for additional
publications through collaboration,
and may increase the citation rate
of primary publications [7]. Since
publication history and citation impact
are often considered in future funding
decisions, these benefits are likely to
accelerate research programs, and
thus enhance the reputation of the
academic institutions.
Data sharing can also benefit an
AHC in its roles of educator and
employer. Health care professionals
trained in clinical informatics [8]
benefit from exposure to real-world
data. By embracing data sharing goals,
an AHC becomes more appealing
to cutting-edge researchers [9], and
thereby more able to recruit the talent
required for future successes.
Finally, the widespread adoption of a
data sharing culture needs leaders [10],
and thus provides an opportunity for
AHCs to demonstrate excellence.
A Leadership Role
Despite the anticipated benefits,
sharing research data has yet to be
widely adopted in biomedicine [11,12].
Through their interwoven roles in
education, research, and policy, AHCs
can lead the development of best
practices for establishing a data sharing
culture. Practical steps with potentially
powerful impact are discussed below
and summarized in Box 1.
Measure, recognize, and reward
data sharing contributions. The lack
of recognition incentives is regarded
as a crucial and unresolved obstacle
to establishing a data sharing culture
[13,14]. All research institutions,
including AHCs, should develop
and track metrics for data sharing
contributions as part of their academic
research environments. Data sharing
contributions should be explicitly
considered during hiring, tenure,
and promotion decisions [15],
perhaps by providing a bonus to a
publication’s impact factor if the
authors have shared the raw research
data. Promotion committees should
encourage investigators to list their
shared datasets on their CVs, in their
grant applications, and anywhere they
communicate information about their
research accomplishments.
Department chairs should encourage
their faculty to monitor the purposes
for which their data are reused. This
would allow investigators to quantify
the value of their contribution, as well
as personally motivate future sharing
[16]. To this end, we encourage the
development and general adoption of a
data sharing citation index, a concrete
metric for tracking the reuse and
citation of datasets, as envisioned by the
Cancer Biomedical Informatics Grid
Heather A. Piwowar, Michael J. Becich, Howard Bilofsky, Rebecca S. Crowley
*
, on behalf of the caBIG Data Sharing and
Intellectual Capital Workspace
Funding: This work was supported in part by
contracts from the National Cancer Institute Center
for Bioinformatics caBIG Program to the University
of Pittsburgh (#79207CBS10) and University of
Pennsylvania (# 79580CBS10), and in part by National
Library of Medicine Training Grant Number 5T15-
LM007059-19. The funders had no role in the decision
to submit the article or in its preparation.
Citation: Piwowar HA, Becich MJ, Bilofsky H, Crowley
RS, caBIG Data Sharing and Intellectual Capital
Workspace (2008) Towards a data sharing culture:
Recommendations for leadership from academic
health centers. PLoS Med 5(9): e183. doi:10.1371/
journal.pmed.0050183
Copyright: 2008 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.
Abbreviations: AHC, academic health center; caBIG,
Cancer Biomedical Informatics Grid; IRB, institutional
review board; NIH, National Institutes of Health
Heather A. Piwowar, Michael J. Becich, and Rebecca
S. Crowley are at the Department of Biomedical
Informatics, University of Pittsburgh Medical School,
Pittsburgh, Pennsylvania, United States of America.
Howard Bilofsky is at The University of Pennsylvania,
School of Engineering and Applied Science,
Philadelphia, Pennsylvania, United States of America.
* To whom correspondence should be addressed.
E-mail: crowleyrs@upmc.edu
Provenance: Not commissioned; externally peer
reviewed
The Policy Forum allows health policy makers around
the world to discuss challenges and opportunities for
improving health care in their societies.

PLoS Medicine | www.plosmedicine.org 1316 September 2008 | Volume 5 | Issue 9 | e183
(caBIG) Data Sharing and Intellectual
Capital Workspace and others [17,18].
Integrate data sharing education into
curricula and practice. Data sharing
must be articulated as a foundational
principle of research conduct.
Standardized and comprehensive
education is likely to be an important
factor in decreasing data withholding
[11]; data sharing should be included
in the curricula of introductory
research courses and throughout
mentored research. Discussing the
ethics of data sharing in clinical and
translational research during medical
training and graduate research studies
can cement a deeper “appreciation
that sharing of raw data may lead
to techniques or findings or further
research that could help alleviate
human distress” [19] . Simultaneously,
education must appropriately place
data sharing within the context of the
federal regulations that guard protected
health information [20,21] and the
ethical obligation to maintain patient
privacy by highlighting the distinction
between openly sharable scientific data
and protected health information.
Addressing these subjects at
institution-wide colloquia, as case
studies in ethics seminars, or as satellite
symposia [22] will provide scientists
an opportunity to hear viewpoints they
might not otherwise consider. Topics
could include the ethical obligation
to patients to both maintain privacy
and achieve the maximum authorized
scientific benefit [19,23,24], the
personal struggles felt by investigators
when trusting peers to be responsible
in data reuse [25], and the impact
of reorienting discussions from data
ownership to data control [26].
AHCs also play a vital role in
educating researchers about the
consumer side of the data sharing
relationship—responsible data reuse.
AHC policies, best-practice guidelines,
and guided mentorship can help
new trainees take advantage of the
enormous opportunities when reusing
data while avoiding misappropriation
and misinterpretation. Furthermore,
understanding the needs and benefits
of data reuse will inspire investigators
to share their own data with the
documentation and annotations that
make it most useful for future reuse.
Recommend best-practice
mechanisms for data sharing. As
biomedical funders begin to require
data sharing plans, they often leave
the mechanism for data sharing
unspecified. Although this choice
provides valuable flexibility, the
myriad of options can be daunting for
investigators. The choice is important:
an appropriate mechanism is crucial for
effective and rewarding data sharing.
An AHC’s office of research can
help its investigators choose best-
practice solutions by recommending
a framework for evaluating data
sharing alternatives. To develop such
a framework, IRB (institutional review
board) directors, chief privacy and
security officers, chief information and
technology officers, technology transfer
officers, and a wide range of patient
advocates and investigators must
articulate the trade-offs inherent in
various models from the perspectives of
privacy, security, intellectual property,
scalability, openness, and equity across
the complete spectrum of stakeholders
[23]. We illustrate three dimensions
of these trade-offs in Table 1, and
recommend several excellent reviews
for further reading [27–29].
Fund and maintain infrastructure
for data sharing. Education, training,
and support are needed again once
a scientist has decided to share data.
Investigators may appreciate detailed
suggestions on what to include in a data
sharing plan, such as those provided by
the National Institutes of Health (NIH)
[30] and caBIG [31]. Mentorship
and training through the institution’s
research office are also crucial when
estimating a data sharing budget, since
“currently, these costs are chronically
underestimated and under-awarded”
[32]. This funding is crucial to pay for
the process of sharing data.
It is often difficult for investigators
to decide where to share types of data
that do not have a public, centralized,
and well-recognized database. We
recommend that research leadership in
AHCs support solutions that optimize
data persistence, visibility, ease of
interpretation and integration, privacy,
accountability, and openness. Such
solutions could involve participating
in data sharing collaborative projects,
choosing information technology
solutions that facilitate data sharing
and provide required access logs,
hosting data sources that do not have
a more appropriate home, adopting
syntactic and semantic standards [33],
providing consultation to investigators
who need help sharing their research
effectively, encouraging participation
in professional societies such as the
HealthGrid (http://www.healthgrid.
org/), or lobbying for national
networked infrastructure [34].
Revise policies and guidelines
to reflect data sharing goals. We
encourage AHCs to recognize the
importance of data sharing across the
organization, and then take steps to
harmonize all relevant policies and
guidelines with their data sharing
goals. Many of the issues are clear, such
as ensuring that data sharing goals
are consistent with material transfer
agreements, industrial partnerships,
intellectual property policies,
technology-transfer guidelines, IRB
review criteria, and de-identification
Box 1: Recommendations for
Academic Health Centers to
Encourage Data Sharing
1. Commit to sharing research data
as openly as possible, given privacy
constraints. Streamline IRB, technology
transfer, and information technology
policies and procedures accordingly.
2. Recognize data sharing contributions
in hiring and promotion decisions,
perhaps as a bonus to a publication’s
impact factor. Use concrete metrics when
available.
3. Educate trainees and current
investigators on responsible data
sharing and reuse practices through
class work, mentorship, and professional
development. Promote a framework for
deciding upon appropriate data sharing
mechanisms.
4. Encourage data sharing practices as
part of publication policies. Lobby
for explicit and enforceable policies in
journal and conference instructions, to
both authors and peer reviewers.
5. Encourage data sharing plans as part of
funding policies. Lobby for appropriate
data sharing requirements by funders,
and recommend that they assess a
proposal’s data sharing plan as part of its
scientific contribution.
6. Fund the costs of data sharing, support
for repositories, adoption of sharing
infrastructure and metrics, and research
into best practices through federal grants
and AHC funds.
7. Publish experiences in data sharing to
facilitate the exchange of best practices.

PLoS Medicine | www.plosmedicine.org 1317 September 2008 | Volume 5 | Issue 9 | e183
tools and policies. Other issues are
often overlooked. For example, AHCs
need to ensure that data sharing
agreements contain appropriate
remedies and are enforced whenever
investigators are unwilling or unable to
fulfill their commitments [35].
Today’s spirit of translational
research does not stop at the
boundaries of the AHC. Departments
of physics and computer science have
a successful history of data sharing
and may be able to provide guidance.
Other departments within science,
engineering, business, librarianship,
and law are addressing the same issues;
it may be possible to forge alliances
that advance data sharing. Involving
key officials at the University level, such
as Vice Presidents of Research and
university legal counsel, could yield
more consistent policies across campus.
Engage national leadership in data
sharing decisions. AHCs are actively
involved with many members of
the biomedical community. Firmly
establishing a data sharing culture will
Table 1. Selected Attributes of Example Data Sharing Frameworks and Systems
Questions Attribute Description Example Impact for Data
Producers
Impact for Data
Consumers
Impact for Other
Stakeholders
Where are the data
stored? How are the
data integrated with
other data sets?
Centralized Multiple datasets
hosted at a single
location in a common
format
The Cancer Genome
Atlas (TCGA) Data
Portal (http://
cancergenome.nih.
gov/dataportal/)
Sharing often
facilitated by well-
developed interfaces.
High visibility,
easy retrieval, easy
aggregation within
repository.
Requires funding of
centralized repository
development and
maintenance, often
limited to common
data types.
Federated Physically separate
datasets that
use information
technology to provide
a virtual common
dataset
Cancer Biomedical
Informatics Grid
(caBIG) (https://cabig.
nci.nih.gov/)
Limited to federation
participants. Often
requires strict data
standards.
Relatively easy
retrieval and
aggregation
for federation
participants.
Requires funding of
relatively complex
infrastructure and
participant adoption.
Distributed Physically and virtually
separate datasets
Data posted on Web
site, as supplementary
information, or
emailed on request
Control retained over
location, format, and
data elements.
Low visibility, often
difficult retrieval,
interpretation,
aggregation,
consistency, and
sustainability.
Requires no
centralized funding.
Allows only ad-hoc
access control. Rarely
maintained long term.
What control is placed
on access to the data?
Open All data can be viewed
and reused by anyone
Single Nucleotide
Polymorphism
database (dbSNP)
(http://www.ncbi.nlm.
nih.gov/projects/SNP)
Open sharing of all
data, no opportunities
for decreasing security
risks.
Easy and open
participation for all
investigators and
project types.
Maximizes potential
benefits of reuse.
Appropriate for non-
sensitive datasets.
Hybrid A subset of the data
is provided openly,
while other data are
available only to
permitted individuals
through access or
reuse limitations
Database of Genotype
and Phenotype
(dbGaP) (http://www.
ncbi.nlm.nih.gov/
sites/entrez?db=gap)
Allows efficient and
appropriate reuse
of all data, provides
opportunity to limit
risks for sensitive
subsets.
Easy and open
participation for
low-risk data;
additional steps and
qualifications required
for complete data
access.
Maximizes reuse while
providing mechanism
to protect sensitive
data subsets. Requires
ongoing access-
granting role.
Controlled Only permitted
individuals can access
the data
National Institute of
Mental Health (NIMH)
Human Genetics
Initiative (http://
nimhgenetics.org/)
Allows appropriate
sharing of very
sensitive data; risks
are minimized.
Data available for
appropriate reuse;
access permission
is relatively time-
consuming and
complex.
Necessary wherever
privacy and security of
the data are a major
consideration (e.g.,
de-identification can
not be guaranteed).
When access
is controlled,
who determines
permissions?
a
Local Access decisions for
external investigators
made by local data
stewards on a study-
by-study basis
The Cancer Text
Information Extraction
System (caTIES)
(http://caties.cabig.
upmc.edu/)
Local data producers
are comfortable
because they retain
control, requires
ongoing access-
decision role.
Equity depends on
local adherence to
formal guidelines,
otherwise decision-
making may appear
ad-hoc and opaque.
Facilitates gradual
transition from
sharing within a
community to sharing
more openly, as
organizations gain
comfort with risks and
benefits.
Central Access decisions
made by a usage
committee or central
source of authority
Shared Pathology
Informatics Network
(SPIN) (http://spin.nci.
nih.gov/)
Data providers and
custodians surrender
control decisions,
must trust central
authority.
Equity depends on
central adherence to
formal guidelines.
Enables binding
decisions across a
diverse community.
a
Common access and use limitations include: use limited to academic projects, use limited to “qualified investigators,” use limited to approved project plans, use limited to
collaboration with original investigators, use requires attribution of data source and provider, and/or use prohibits attempts at patient re-identification.
doi:10.1371/journal.pmed.0050183.t001

PLoS Medicine | www.plosmedicine.org 1318 September 2008 | Volume 5 | Issue 9 | e183
require joint efforts between AHCs,
funders, publishers, academic societies,
industry, legislators, patient advocates,
clinicians, and researchers. We
recommend that AHC faculty and staff
leverage their roles in the community
to promote philosophies and policies
that facilitate data sharing. This could
involve promoting new funding
mechanisms to support data sharing
and data archiving [32], working
with journal editors to raise the level
of data sharing deemed appropriate
and necessary for publication [5],
supporting legislation to encourage
privacy-protected data sharing [36],
developing standards for appropriate
reuse of health care data [26,37],
establishing grant review guidelines
for evaluating data sharing plans as
part of the scientific contribution of
a proposal, expanding NIH guidance
and support for data sharing across
all data types [38], encouraging the
study of incentives for team science
[39], developing methods to quantify
the extent and impact of data sharing
and reuse, and finally, encouraging
programs and funding that enable
investigators to share data with
accuracy, accountability, responsibility,
and recognition [40]. We further
recommend that AHCs publish their
experiences in data sharing to facilitate
the development of best practices.
Conclusion
We recognize that there are real and
perceived impediments to sharing
biomedical research data. Some
individual donors may have personal
interests in privacy and confidentiality
that exceed their desire to contribute to
new methods of detecting and treating
disease. Investigators may restrict access
to data to maximize their professional
and economic benefit. Academic health
centers may view data sharing as a
threat to intellectual property, possibly
impeding entrepreneurial spin-offs and
technology transfers that bring revenue
and act as incubators for future research.
AHCs may also worry that the data
could be used to critique their health
care practices rather than advance the
research frontier. Industrial sponsorship
can hinder plans for sharing data,
and the regulatory environment may
necessitate stringent oversight to ensure
compliance and minimize risk.
These issues can and must be
addressed as we work to embrace a data
sharing culture. The hurdles may not
be as high as we think: 99% of senior
technology transfer officers at highly
funded NIH universities agree that
academic scientists should freely share
data with other academic scientists
after publication [41]. The systems
and architectures in Table 1 provide
a future vision of research in which
data are more universally available
and interoperable. Recent initiatives
for making research publications
freely available [42–45] demonstrate
a political and academic commitment
“to help advance science and improve
human health” [46] by widely sharing
research results.
Academic health centers will benefit
by leading the transition towards a
culture of biomedical data sharing.
More widespread awareness of these
benefits can motivate key stakeholders
to take concrete steps to enable,
inspire, and reward data sharing within
and beyond their institutions.

Acknowledgments
We thank the members of the caBIG Data
Sharing and Intellectual Capital Workspace
for their insightful comments.
Competing Interests: The authors have
declared that no competing interests exist.
References
1. National Institutes of Health (2003) Final NIH
statement on sharing research data [NOT-
OD-03-032]. Available: http://grants.nih.gov/
grants/guide/notice-files/not-od-03-032.html.
Accessed 30 July 2008.
2. National Institutes of Health (2007) Policy for
sharing of data obtained in NIH supported or
conducted genome-wide association studies
[NOT-OD-07-088]. Available: http://grants.
nih.gov/grants/guide/notice-files/NOT-
OD-07-088.html. Accessed 30 July 2008.
3. caBIG Strategic Planning Workspace (2007)
The Cancer Biomedical Informatics Grid
(caBIG): Infrastructure and applications for a
worldwide research community. Stud Health
Technol Inform 12: 330-334.
4. Grethe JS, Baru C, Gupta A, James M,
Ludaescher B, et al. (2005) Biomedical
informatics research network: Building a
national collaboratory to hasten the derivation
of new understanding and treatment of
disease. Stud Health Technol Inform 112:
100-109.
5. Piwowar HA, Chapman WW (2008) A review
of journal policies for sharing research data.
In: Proceedings of the 12th International
Conference on Electronic Publishing; 25-27
June 2008; Toronto, Canada. Available: http://
elpub.scix.net/cgi-bin/works/Show?001_
elpub2008. Accessed 30 July 2008.
6. [No authors listed] (2005) Let data speak to
data. Nature 438: 531-531.
7. Piwowar HA, Day RS, Fridsma DB (2007)
Sharing detailed research data is associated
with increased citation rate. PLoS ONE 2: e308.
doi:10.1371/journal.pone.0000308
8. American Medical Informatics Association
(2007) AMIA 10x10 Program. Available:
http://www.amia.org/10x10. Accessed 30 July
2008.
9. Butler D (2007) Data sharing: The next
generation. Nature 446: 10-11.
10. [No authors listed] (2007) Time for leadership.
Nat Biotechnol 25: 821.
11. Blumenthal D, Campbell EG, Gokhale M, Yucel
R, Clarridge B, et al. (2006) Data withholding
in genetics and the other life sciences:
Prevalences and predictors. Acad Med 81:
137-145.
12. Teeters J, Harris K, Millman K, Olshausen
B, Sommer F (2008) Data sharing for
computational neuroscience. Neuroinformatics
6: 47-55.
13. [No authors listed] (2007) Got data? Nat
Neurosci 10: 931-931.
14. [No authors listed] (2007) Compete,
collaborate, compel. Nat Genet 39: 931.
15. Davies HD, Langley JM, Speert DP (1996)
Rating authors’ contributions to collaborative
research: The PICNIC survey of university
departments of pediatrics. Pediatric
Investigators’ Collaborative Network on
Infections in Canada. Can Med Assoc J 155:
877-882.
16. Rashid A, Ling K, Tassone R, Resnick P,
Kraut R, et al. (2006) Motivating participation
by displaying the value of contribution. In:
Proceedings of ACM CHI 2006 Conference
on Human Factors in Computing Systems;
22-28 April 2006; Montreal, Canada.
Available: http://www.communitylab.
org/?q=publication/chi06rashidAl.pdf.
Accessed 30 July 2008.
17. Altman M, King G (2007) A proposed standard
for the scholarly citation of quantitative data.
D-Lib Magazine 13: 3/4. Available: http://
gking.harvard.edu/files/cite.pdf. Accessed 5
August 2008.
18. Piwowar HA, Chapman WW (2008)
Envisioning a data reuse registry [poster].
AMIA 2008 Annual Symposium. Available:
http://sharescienceideas.wikispaces.com/
Data+Reuse+Registry. Accessed 5 August 2008.
19. Vickers A (2006) Whose data set is it anyway?
Sharing raw data from randomized trials. Trials
7: 15.
20. US Department of Health and Human
Services (2003) Standards for privacy of
individually identifiable health information
and security standards for the protection
of electronic protected health information
(HIPAA privacy and security rules). 45 CFR
Parts 160 and 164. Available: http://www.
hipaadvisory.com/REGS/finalprivacy/.
Accessed 30 July 2008.
21. US Department of Health and Human Services:
Office for Human Research Protections
(2005) Basic HHS policy for protection of
human research subjects. 45 CFR Part 46
Subpart A. Available: http://www.hhs.gov/
ohrp/humansubjects/guidance/45cfr46.htm.
Accessed 30 July 2008.
22. Liu Y, Ascoli GA (2007) Value added by
data sharing: Long-term potentiation of
neuroscience research: A commentary on the
2007 SfN Satellite Symposium on Data Sharing.
Neuroinformatics 5: 143-145.
23. Foster MW, Sharp RR (2007) Share and share
alike: Deciding how to distribute the scientific
and social benefits of genomic data. Nat Rev
Genet 8: 633-639.
24. Fienberg SE (1994) Sharing statistical data
in the biomedical and health sciences:
Ethical, institutional, legal, and professional
dimensions. Annu Rev Public Health 15: 1-18.
25. [No authors listed] (2001) ‘Send me all of your
reagents and ideas. We want to work on the
same experiments.’ By Caveman. J Cell Sci 114:
1037-1038.
26. Safran C, Bloomrosen M, Hammond WE,
Labkoff S, Markel-Fox S, et al. (2007) Toward
a national framework for the secondary use of
health data: An American Medical Informatics
Association White Paper. J Am Med Inform
Assoc 14: 1-9.

PLoS Medicine | www.plosmedicine.org 1319 September 2008 | Volume 5 | Issue 9 | e183
27. Committee on Responsibilities of Authorship
in the Biological Sciences, National Research
Council (2003) Sharing publication-related
data and materials: Responsibilities of
authorship in the life sciences. The National
Academy of Sciences. Available: http://www.
nap.edu/catalog.php?record_id=10613.
Accessed 30 July 2008.
28. Sinnott RO, Macdonald A, Lord PW, Ecklund
D, Jones A (2005) Large-scale data sharing in
the life sciences: Data standards, incentives,
barriers and funding models (The Joint Data
Standards Study). The Biotechnology and
Biological Sciences Research Council, The
Department of Trade and Industry, The Joint
Information Systems Committee for Support
for Research, The Medical Research Council,
The Natural Environment Research Council
and The Wellcome Trust. Available: http://
www.mrc.ac.uk/Utilities/Documentrecord/
index.htm?d=MRC002552. Accessed 30 July
2008.
29. Lowrance W (2006) Access to collections
of data and materials for heath research: A
report to the Medical Research Council and
the Wellcome Trust. Available: http://www.
wellcome.ac.uk/About-us/Publications/
Books/Biomedical-ethics/WTX030843.htm.
Accessed 30 July 2008.
30. National Institutes of Health (2007) Guidance
for developing data-sharing plans for GWAS.
Available: http://grants.nih.gov/grants/gwas/
gwas_data_sharing_plan.pdf. Accessed 30 July
2008.
31. caBIG (2008) Data sharing plan content
guideline draft. Available: https://cabig.
nci.nih.gov/working_groups/DSIC_SLWG/
Documents/caBIG_Data_Sharing_Plan_
Guideline_20080109.pdf. Accessed 30 July
2008.
32. Ball CA, Sherlock G, Brazma A (2004) Funding
high-throughput data sharing. Nat Biotechnol
22: 1179-1183.
33. Ruttenberg A, Clark T, Bug W, Samwald
M, Bodenreider O, et al. (2007) Advancing
translational research with the Semantic Web.
BMC Bioinformatics 8: S2.
34. Detmer DE (2003) Building the national
health information infrastructure for personal
health, health care services, public health, and
research. BMC Med Inform Decis Mak 3: 1.
35. Theologis A, Davis RW (2004) To give or not
to give? That is the question. Plant Physiol 135:
4-9.
36. Altman RB, Benowitz N, Gurwitz D,
Lunshof J, Relling M, et al. (2007) Genetic
nondiscrimination legislation: A critical
prerequisite for pharmacogenomics data
sharing. Pharmacogenomics 8: 519.
37. National Committee on Vital Health
and Health Statistics (2007) Enhanced
protections for uses of health data: A
stewardship framework for “secondary uses” of
electronically collected and transmitted health
data. Available: http://www.centerforhit.org/
x2125.xml. Accessed 30 July 2008.
38. National Institutes of Health (2007) Points
to consider for IRBs and institutions in their
review of data submission plans for institutional
certifications under NIH’s policy for sharing of
data obtained in NIH Supported or conducted
genome-wide association studies (GWAS).
Available: http://grants.nih.gov/grants/gwas/
gwas_ptc.pdf. Accessed 30 July 2008.
39. Haga S (2007) Exploring attitudes about
data disclosure and data-sharing in
genomics research. NIH grant number
1R03HG004312-01.
40. Gardner D, Toga AW, Ascoli GA, Beatty JT,
Brinkley JF, et al. (2003) Towards effective and
rewarding data sharing. Neuroinformatics 1:
289-295.
41. Campbell EG, Bendavid E (2003) Data-sharing
and data-withholding in genetics and the
life sciences: Results of a national survey of
technology transfer officers. J Health Care Law
Policy 6: 241-255.
42. Harvard Faculty of Arts and Sciences (2008)
Harvard to collect, disseminate scholarly
articles for faculty. Available: http://www.
fas.harvard.edu/home/news_and_events/
releases/scholarly_02122008.html. Accessed 30
July 2008.
43. SPARC (2008) Berkeley steps forward with
bold initiative to pay authors’ open-access
charges. Available: http://www.arl.org/sparc/
publications/articles/memberprofile-berkeley.
shtml. Accessed 30 July 2008.
44. University of Wisconsin–Madison (2005)
Seed money for open access publishing.
Available: http://www.library.wisc.edu/scp/
openaccess/response.html#fund. Accessed 30
July 2008.
45. University of North Carolina–Chapel Hill
(2005) Open access authors’ fund. Available:
http://www.hsl.unc.edu/Collections/
ScholCom/OAFundAnnounce.cfm. Accessed
30 July 2008.
46. National Institutes of Health (2008) Revised
policy on enhancing public access to archived
publications resulting from NIH-funded
research [NOT-OD-08-033]. Available: http://
grants.nih.gov/grants/guide/notice-files/not-
od-08-033.html. Accessed 30 July 2008.