research practices that professional standards either have not yet been established or are in flux.27 The research enterprise needs to redouble efforts to set clear expectations for appropriate behavior and effectively communicate those expectations.

Recommendation 3: The research enterprise and its stakeholders—research institutions, research sponsors, professional societies, journals, and individual researchers—should develop and disseminate professional standards for ensuring the integrity of research data and for ensuring adherence to these standards. In areas where standards differ between fields, it is important that differences be clearly defined and explained. Specific guidelines for data management may require reexamination and updating as technologies and research practices evolve.

To date, research communities have responded to the new challenges of the digital age in a largely decentralized fashion, adapting traditional ethical standards to new circumstances. This decentralized approach is appropriate in that data management practices are so varied across research fields that a “one size fits all” approach would not address important issues, and the imposition of detailed standards from outside a field is unlikely to be effective. In some cases, fields of research within and across disciplines may be able to cooperate in developing standards for ensuring the integrity of research data.

The application of professional standards can be complicated in the case of interdisciplinary research, where investigators in different fields bring different practices to joint projects. In this case, familiarity with the standards and expectations of all the fields represented by that research is preferable to the blanket imposition of overly broad standards. Better education and training in data management for investigators, combined with expanded access to research data across disciplines (which is the subject of the next chapter), will best serve the advance of knowledge and other public interests.


Although all researchers should understand digital technologies well enough to be confident in the integrity of the data they generate, they cannot always be expected to be able to take full advantage of new capabilities. Instead, they may have to rely on collaborations with colleagues who have specialized training in applying digital technologies in research. Through their in-depth knowledge of digital technologies and how those technologies can advance


The quality standards applied to microarray data in proteomics provide a good example of ongoing efforts to improve the data generated by a rapidly evolving technology. See S. Rogers and A. Cambrosio. 2007. Making a new technology work: The standardization and regulation of microarrays. Yale Journal of Biology and Medicine 80:165–178.

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