Data Management

Author: Michael Kalichman, 2001
Contributors: P.D. Magnus, Dena Plemmons
Updates: Michael Kalichman, 2016

What are data?

  • Data can be defined as measurements, observations, or any other primary products of research activity.
  • Data are the empirical basis for scientific findings. The integrity of research depends on integrity in all aspects of data management, including the collection, use, storage, and sharing of data.
  • Data are not just numbers in a lab notebook. Depending on the research, data might include: images, audio or video recordings, genetically modified organisms, specialized software, ancient artifacts, or geological samples.
Nominal “best practices”
  • All researchers have an interest in, and responsibility to, protect the integrity of the research record.

      Questions to be asked to foster data integrity:

      1. How will the data be collected?
      2. How should records be kept and stored?
      3. How, if at all, will data be backed up?
      4. How long should data be kept?
      5. Who owns the data?
      6. When and with whom should data be shared?

  • Research groups should at least be clear about how the above questions are answered for their particular circumstance.
  • Research records should be sufficient to reconstruct what was done:
    Both for the purpose of future research and to verify that the work had been done as described in subsequent publications.


Because of concern about many cases of research misconduct, the Department of Health and Human Services (1990) convened a workshop on data management. The workshop highlighted the many ways in which research depends fundamentally on responsible data management. Several good resources provide comprehensive reviews of good data management practices (e.g., Macrina, 2014; Mays and Macrina, 2014) and recordkeeping in particular (e.g., Kanare, 1985; NIH Office of the Director, 2008).

Responsible research:

  • begins with experimental design and protocol approval;
  • is supported by recordkeeping that ensures accuracy and avoids bias;
  • is defined by criteria for including and excluding data from statistical analyses; and
  • entails responsibility for collection, use, and sharing of data.

  • Everyone with a role in research has a responsibility to ensure the integrity of the data.
  • The ultimate responsibility belongs to the principal investigator.
  • However, the central importance of data to all research means that this responsibility extends to anyone who helps in planning the study, collecting the data, analyzing or interpreting the research findings, publishing the results of the study, or maintaining the research records.


Data management in research is rarely regulated, except:

  • most prominently for research subject to FDA requirements (FDA, 2016) and
  • some instances regarding record retention and data sharing as noted below.
Otherwise, data management is subject to professional (e.g., Steward and Balice-Gordon, 2014; American Psychological Association, 2015; American Statistical Association, 2016), institutional (e.g., University System of New Hampshire, 2015; Michigan State University, 2016; UC San Francisco, 2016), organizational (Blum, 2012; Howard Hughes Medical Institute, 2006), or even research group-specific guidelines.

The summaries below present guidelines that might commonly be considered by most researchers.

Data Collection

Because data collection can be repetitious, time-consuming, and tedious, there is a temptation to underestimate its importance.

However, adequate planning and preparation can:

  • decrease the risk of wasted resources
  • increase the likelihood of useful results
  • assure that those responsible for collecting data are sufficiently trained and motivated
  • generate research designs to limit or eliminate the effects of bias


The best model for recordkeeping will not be the same for all areas of research.

However, nearly all types of research include records that could reasonably and usefully be kept in bound lab notebooks.

Nominal records would include:

  • date
  • investigators
  • what was done, and
  • where corresponding research products can be found.
Lab notebooks should be supplemented as needed by specialized methods of recordkeeping such as computer files, videotapes, and gels.

Ownership of Data

Research data belong to the institution

Research ownership typically passes from the funder of the research (e.g., a federal agency or a private funder) to the University or institution, not to the research investigators.

Although the products of research involve creative contributions to new knowledge, the resulting data are in effect no different from the routine products of employees in any other private or public institution.

Equipment, materials and reagents, and the resulting data all belong to the institution in which they are purchased or produced, despite the language and practice of science.

The issue of institutional ownership becomes especially salient if:

  • a marketable product is produced
  • someone moves from one institution to another
If a principal investigator moves, then she or he can normally expect to take the data, but exceptions do occur and equipment transfer is nearly always a matter for negotiation.

Ownership by Principal Investigator

In practice, even though the University or institution has legal standing to make decisions about what can or will be done with research data, it does not typically do so.

Absent an explicit agreement or ruling to the contrary, the principal investigator (PI) has primary responsibility for decisions about the collection, use, and sharing of data.

Retention of Data

The quality of data supporting published work is moot if the data are lost or discarded.

Retaining records of research is necessary not only for the purpose of research, but to:

  • validate priority for claims of intellectual property
  • demonstrate ownership or patent rights
  • respond to requests under the Freedom of Information Act
  • document the validity of allegations of misconduct
These concerns raise questions about what should be retained, who keeps the records, how they should be stored, and for how long.

What should be retained?

This depends in part on the nature of the products of research.

Some materials, such as thin sections for electron microscopy, cannot be kept indefinitely because of degradation.

It is also impractical to store extraordinarily large volumes of primary data.

At minimum, enough data should be retained to reconstruct what was done.

Who keeps the records?

Original data are the responsibility of the principal investigator (PI) and should be kept in her or his lab or office.

Although most researchers have the expectation that graduating students may take copies of their research records, student or postdoctoral researchers should assume unless told otherwise that their original data will stay with the PI.

If regulations or other considerations preclude researchers taking copies, then the PI has a responsibility to make this clear to the research group before work begins.

How should records be stored?

Any stored data will be rendered useless if there are insufficient records to locate and identify the material in question.

Ease of access must be balanced against security, for instance if the study involved human subjects with a reasonable expectation of confidentiality.

Although the institution is the legal owner of the data, it is usually the responsibility of the principal investigator to ensure that records are stored in a secure, accessible fashion.

How long should records be kept?

Under current National Institutes of Health (NIH, 2015) and National Science Foundation (NSF, 2005) requirements, research records must be maintained for at least three years after the last expenditure report.

Federal regulations or institutional guidelines may require that data be retained for longer periods. However, these formal requirements are minimal constraints. Decisions about retention of records should take into account:

  • extent to which a line of research is still being pursued
  • likelihood of ongoing interest in the research
  • continued assurances of confidentiality for any human subjects
  • space and expense necessary for storage.

Sharing of Data

Federal agencies, particularly the NIH (2003) and NSF (2010), have made funding contingent on plans to share research data and products, particularly after publication.

An open data policy reflects positively on those who share and benefits science by increasing the likelihood for new insights, collaboration, and reciprocal sharing.

Although sharing of data is generally in the best interests of science and the individual, it is clear that such sharing can place an individual scientist at risk:

  • Sharing data before publication could result in loss of credit or opportunity
  • Exposure of data to the prejudiced scrutiny of competitors or detractors
  • Risk of compromising confidentiality of human subjects
  • Expense of time and resources to meet requests for sharing of data
However, reasonable strategies to minimize potential problems should make it possible to choose sharing over secrecy. Before publication, it is best to maintain an open data policy with appropriate caution. After publication, be prepared to grant reasonable access to the raw data; that is, honor requests that are in the interest of scientific inquiry and can be accomplished without inordinate expense or delay.

In 2003, the National Institutes of Health put out a Final NIH Statement on Sharing Research Data. This document addresses some of the concerns listed above, and makes clear that data sharing is a crucial and necessary part of responsible conduct in research.

Discussion Questions

  1. What products of your research might reasonably be classified as data and/or necessary to verify the integrity of your work?
  2. In your field of research, what are some of the steps an investigator can take during the planning stages to help ensure the integrity of a research project?
  3. How are research records maintained in your research group? Does this approach meet the proposed goal of documenting what was done, when the work was done, who did the work, and the location of the corresponding research products?
  4. Under what circumstances is it acceptable in your field of research to exclude an anomalous data point from analysis? If data were excluded from an analysis, then how should the published manuscript reflect that not all data are reported?
  5. Is it unethical to choose a statistical test only after seeing which of several tests provide a statistically significant result? Why or why not?
  6. When someone leaves your research group, what restrictions, if any, are imposed on what research records he/she takes with them?
  7. If two people work together on a research project that is not yet published, and then decide to stop working together, who has the right to use the data in a future publication (both, the more senior of the two investigators, or neither)? In cases where this is not clear, what could be done in your institution to resolve the dispute?
  8. In your area of research, what advantages might be gained by sharing your data and findings with other research groups?
  9. In your area of research, what disadvantages might result from sharing your data and findings with other research groups?
  10. What rules or guidelines does your institution have for data sharing?
  11. How long after the final expenditure report for a Public Health Service- or National Science Foundation-funded project must research records be retained? What rules or guidelines does your institution have for data retention?
Case Studies

  1. American Psychological Association (2015): Data Sharing: Principles and Considerations for Policy Development.
  2. American Statistical Association (2016): Ethical Guidelines for Statistical Practice.
  3. Blum C (2012): Access to and Retention of Research Data: Rights and Responsibilities. Council on Governmental Relations, Washington, DC.
  4. Department of Health and Human Services (1990): Data Management in Biomedical Research, Report of a Workshop, April 1990 Chevy Chase, Maryland.
  5. FDA (2016): Regulations. U.S. Food and Drug Administration. U.S. Department of Health and Human Services.
  6. Ferguson AR,Nielson JL,Cragin MH,Bandrowski AE, Martone ME (2014): Big data from small data: data-sharing in the 'long tail' of neuroscience. Nature Neuroscience 17: 1442–1447.
  7. Howard Hughes Medical Institute (2006): Chapter 8. Data management and Laboratory Notebooks. A Practical Guide to Scientific Management for Postdocs and New Faculty. Pp. 143-152.
  8. Kanare HM (1985): Writing the Laboratory Notebook, American Chemical Society, Washington, DC.
  9. Macrina FL (2014): Chapter 10. Scientific Recordkeeping. In (Macrina FL, au): Scientific Integrity, 4th ed., ASM Press, Washington, DC, pp. 329-359.
  10. Mays TD, Macrina FL (2014): Chapter 9. Research Data and Intellectual Property. In (Macrina FL, au): Scientific Integrity, 4th ed., ASM Press, Washington, DC, pp. 287-357.
  11. Michigan State University (2016): Life Cycle Data Management Planning.
  12. NIH (2003): Final NIH Statement on Sharing Research Data.
  13. NIH (2015): 8.4.2 Record Retention and Access. Monitoring, Administrative Requirements, NIH Grants Policy Statement.
  14. NIH Office of the Director (2008): Guidelines for Scientific Record Keeping in the Intramural Research Program at the NIH.
  15. NSF (2005): Records Retention and Audit. Chapter III - Grant Administration. NSF Grant Policy Manual.
  16. NSF (2010): Data Management and Sharing Frequently Asked Questions (FAQs).
  17. Steward O, Balice-Gordon R (2014): Rigor or Mortis: Best Practices for Preclinical Research in Neuroscience. Neuron 84(3):572–581.
  18. UC San Francisco (2016): Data Sharing & Data Management.
  19. University System of New Hampshire (2015): UNH Policy on Ownership, Management, and Sharing of Research Data.