The Responsible Collection, Retention, Sharing, and Interpretation of Data
Author(s):
Caroline Whitbeck
Background
and Module Contents
"Research data include detailed experimental protocols, primary
data from laboratory instruments, and the procedures applied to
reduce and analyze primary data." Responsible Science,
Volume I: Ensuring the Integrity of the Research Process, Panel on
Scientific Responsibility and the Conduct of Research, National
Academy of Sciences, National Academy of Engineering, Institute of
Medicine, 1992.P. 138
The integrity of research results is the sine qua non
of scientific research. To ensure the integrity of research
results, data must be treated with a scrupulousness that exceeds
the care with which we treat most information in daily life.
Fabrication or falsification of data are of course unacceptable,
but there are many other matters of responsible data collection,
retention, sharing and interpretation that bear on the integrity of
data or on other matters of research ethics, such as the fair
treatment of collaborators including fair apportionment of credit,
preservation of confidentiality of research subjects or of the
proprietary knowledge of sponsors and collaborators. There are also
prudential reasons for many of the same norms for the collection,
retention, sharing and interpretation of data, such as preservation
of one's own claims to priority in discovery or invention, although
it will be the ethical matters that will be the focus of this
module.
Some general norms for the responsible management of data can be
simply stated and are found in various forms in recent policy
statements of many research institutions and funding agencies.
Among them are:
- The primary data, the methods used to obtain them, and the
procedures applied to the primary data to create compilations of
them or derivations from them must be accurately reported.
- If the primary data are based on human observation, those
observations should be recorded promptly and accurately and in
sufficient detail to preserve the record of factors that might turn
out to be significant, and in a way that minimizes doubt about the
time of the occurrence or the time at which it was recorded.
- The data record should be kept reasonably free from risk of
damage. Where practicable, copies may be made of the data either to
facilitate sharing among collaborators or to further safeguard
it.
- Necessary research materials should be made available to others
who attempt to replicate your work.
- Institutions that are the recipients of research grants own the
data from those research projects. The Principal Investigator (PI)
for a project has custody of that data and primary responsibility
for maintenance of the data record and such matters as preserving
the confidentiality of sensitive information about human subjects,
if any, in the data record. Collaborators on the research project
for which the data was collected, including trainee collaborators,
have the right of access to the data.
- The research data should preserved for a reasonable number of
years after the appearance of final reports or publications
resulting from the research. The amount of time varies with the
nature of the research.
Good data management practices depend the character of the data.
Data may be in the form of a written record, photographs, gels, or,
as in high energy physics, in the form computer record of masses of
data, which, though primary, is filtered as it is collected, to
exclude what is judged to be "noise". Students and other trainees
need to have field-specificcriteria for data management made
explicit in order to understand the specific actions required of
them.
This module informs participants about the emerging literature
on responsible data management. Through the use of scenarios that
bring out many areas of potential confusion or conflict, the module
helps groups to develop agreement on more specific field-specific
and institution-specific norms for supervisors and trainees in
their relationship with one another. Topics include data
collection, compilation of primary data, interpretation of data,
ownership, custody and access to the data, and issues of the
dissemination research results.
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Method and
Scenarios
Distribution of scenarios and related discussion questions to
the students and faculty.
Below are scenarios from other modules that also raise issues of
responsible data management
- Panel discussion based on those scenarios and questions and any
others that students or faculty wish to add.
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Readings
(recommended preparation for the discussion of scenarios)
If you do biomedical research, it is useful to
read the following brief sections of the International Committee of
Medical Journal Editors' "Uniform Requirements for Manuscripts
Submitted to Biomedical Journals." This statement was published in
1997 in the New England Journal of Medicine 335:
309-315, and was updated May 2000.
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If you are in the physical sciences or engineering, the detailed
advice given by the ICMJE above or the less detailed statement, but
for engineers and chemists, more familiar source, the Ethical Guidelines to Publication of Chemical
Research by the American Chemical Society(ACS). These
guidelines were first created in 1985 and have served as a model
for many other societies, including the Optical Society of America
and the American Geological Society. This is a link to a pdf file
with their latest (January 2000) version. Notice that although both
sources agree on most points that they both address, the ACS
Guidelines, in the final section of the ACS Guideline, Ethical
Obligations of Scientists Publishing outside the Scientific
literature, they take a more cautious view of the implications for
later scientific publication of first publishing one's findings in
another way.
If you do not read the ICMJE section, also read:
Objectives
- To have an open and candid discussion of data management,
including difficult issues, including those on which investigators
may differ.
- Improved understanding of the data management practices that
are appropriate to one's field.
- Establishment of some agreed upon methods of resolving
conflicts and misunderstandings when they arise
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Selected Bibliography (for further
reading)
Bailer, John C. 1997, "Science, Statistics and Deception",
Research Ethics: A Reader, Deni Elliot and Judy E.
Stern,eds., Hanover, University Press of New England
Bird, Stephanie J., and Housman, David E. 1995. "Trust and the
collection, selection, analysis and interpretation of data: A
scientist's view." Science and Engineering Ethics 1
(October): 371-82.
Carlson, Adam. 2001 "Data Mining: Finding Nuggets of Knowledge
in Mountains of Data", Northwest Science &
Technology
Grinnell, Frederick. 1992. The scientific attitude.
2nd ed. New York: The Guilford Press.
IOM (1989). The responsible conduct of research in the health
sciences. Washington DC: National Academy Press.
Jones, Anne Hudson and Faith Mclellan (Editors). (2000)
Ethical Issues in Biomedical Publication. Baltimore:
Johns Hopkins Press.
Macrina, Francis L. 1995. Scientific integrity: An
introductory text with cases. Washington, DC: ASM Press.
Marshall, Eliot. 1991. "Fight over Data Disrupts Michigan State
Project", Science 251, 23-24.
Marshall, Eliot. 1993. "MSU Officials Criticized for Mishandling
Data Dispute", Science 259, 592-594.
Marshall, Eliot. 1993. Court orders 'sharing' of data.
Science, 261,(16 July), 284.
Mishkin, Barbara. (1995). "Urgently needed: Policies on access
to data by erstwhile collaborators." Science, 270.
927-928.
Rennie, Drummond; V. Yank and linda Emanuel. (1997) "When
authorship fails: A proposal to make contributors accountable."
J Amer. Med. Assoc. 278: 579-585. A proposal for a
policy change to make investigators less likely to seek or accept
credit through the mechanism of undeserved authorship.
Resnick, David. 2000 "Statistics, Ethics, and Research: An
Agenda for Education and Reform", Accountability in
Research, Vol. 8
See also Carl Djerassi's novel Cantor's Dilemma.
(New York: Doubleday, 1989) describes the agony of an investigator
who finds that others are not able to replicate an important
finding in a paper he co-authored.
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Relevant Web
Resources
- The
Endocrine Society Ethics Advisory Committee, Ethical Aspects of
Conflicts of Interest
- Will download a PDF. This review includes information on
conflicts of interest both for the organization at large as well as
for individual clinician and researcher members.
- Responsible Use of Statistical Methods
- Part of an online Ethics Series from North Carolina State
University. Although this article begins with an emphasis on the
question of when a failure of responsible treatment rises to the
level of misconduct, it goes on to discuss many subtle issues about
data and statistical treatment of them.
- Jack
Fry's Interview
- This case raises two primary issues: data sharing and
recognition of the contributions of others. The first issue
concerns when it is appropriate to share the work of one's
colleagues. If the standards for sharing the work of a colleague
are not explicitly stated, the door is open for abuse.
- Avoiding
Self-Deception in Science by Terry Ann Krulwich
- We are always influenced by our working hypothesis or
preconceived notions. How do we avoid letting those notions
influence how rigorously we test them and how we interpret
data?
- "Publication Ethics: Rights and Wrongs"
- Stephen K. Ritter, Chemical & Engineering
News, Washington. Science & Technology, November 12,
2001, Volume 79, Number 46, CENEAR 79 46 pp. 24-31, ISSN 0009-2347.
Balancing obligations and interests surrounding dissemination of
research is an arduous task. This article explores how objectivity
relies on integrity and trust, the hallmarks of the scientific
discovery and publication process.
- NIH Data Sharing Information
- This is an extension of NIH policy on sharing research
resources, and reaffirms NIH support for the concept of data
sharing.
- Responsible Conduct in Data Management
- The purpose of this online module is to give a brief
introduction to integrity issues related to data management and
increase researchers’ awareness of such issues. The module is
intended for self-paced learning especially by those in the early
stages of their research careers and become aware of data
management issues that can be encountered when dealing with
research data.