Responsible Conduct of Research (RCR)
Data Acquisition, Management, Sharing and
Ownership
Good data management begins with creating a record of research that thoroughly,
accurately, and clearly documents the work and evidence that went into
creating a scholarly product, such as a paper, book, patent, computer
program, etc. Beyond data collection, good data management includes recognizing
who owns data, when and how data should be shared, and when data can be
destroyed.
What is Data?
Most definitions of data are very broad. For example, the National Institutes
of Health (NIH) uses the following definition in its grants manual in
connection with rules on the availability of research results.
For this purpose, "data" means recorded information, regardless of the form or media on which it may be recorded, and includes writings, films, sound recordings, pictorial reproductions, drawings, designs or other graphic representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing or computer programs (software), statistical records, and other research data (NIH Grants Policy Statement (03/01)).
Further, NIH requires that researchers who receive its funds make available not only data, but also "unique resources," to other scholars. This includes a wide range of information and biologicals, such as "synthetic compounds, organisms, cell lines, viruses, cell products, and cloned DNA" (NIH Grants Policy Statement (03/01)).
Data Collection Guidelines
The most rigorous standards for data collection come from industry and
human subjects research. Since Congress passed the Bayh-Dole Act in 1980,
which gives universities control over intellectual property created by
researchers with federal grants, patenting has significantly increased
on university campuses and, with this trend, universities have moved towards
industrial standards for data collection. These standards focus on what
should be recorded in a laboratory notebook and how a notebook should
be kept. Guidelines often recommend that notebooks include:
• Descriptions of reasons for experiments
• Experimental protocols
• Observations, measurements, and other experimental results
• Printouts, photographs, and other machine generated data
• Mathematical calculations performed on raw data
• Brief interpretations of the results
The following style conventions are widely recognized for laboratory
notebooks:
• Permanent binding
• Consecutively numbered pages
• Tables of contents
• Explanations of abbreviations
• Dated entries
• If the date of the experiment is different from the date of recording,
recording both dates
• Dating and initialing all changes
• Keeping legible and clear records in permanent ink
• Periodic review and signing of notebooks by someone not directly
involved in the research
Data Sharing
A traditional value of academic communities has been the sharing of research
results. The federal government requires that data and unique resources
created with its funding be shared and encourages timely dissemination
of results through publication and presentation in academic venues. In
the last two decades, emphases on industrial collaboration and patenting
in medicine and the life sciences have challenged older values. For more
on this topic, see the section on collaborative
research.
Data Retention
Guidelines for data retention range from three years (NIH regulations)
to twenty-three years (Patent Office). When no other concerns supersede,
UW-Madison often uses seven years past publication or completion of research
as a rule-of-thumb for how long data should be retained. The Wisconsin
state statute of limitations that applies to actions for fraud underlies
this recommendation.
What Rules Apply?
For investigators with funding from National Institutes of Health, data
ownership, data sharing, and data retention are addressed in the NIH
Grants Policy Statement (12/03). Other funding agencies may have their
own policies concerning data management.