projects; they can be especially acute for the small-scale projects that continue to constitute the bulk of the research enterprise.
This report examines the consequences of the changes affecting research data with respect to three issues: integrity, accessibility, and stewardship. Because of the enormous range in the detailed procedures and styles of research from field to field, it is impossible to formulate specific recommendations for every field. Instead, for each of the three issues examined in this report, the authoring committee has developed a fundamental principle that applies in all fields of research regardless of the pace or nature of technological change. The report then explores the implications of these three central principles for the various components of the research enterprise.1
Developing the policies, standards, and infrastructure needed to ensure the integrity, accessibility, and stewardship of research data is a critically important task. It will require sustained effort on the part of all stakeholders in the research enterprise. The committee believes that the broad principles stated in this report provide the appropriate framework for this undertaking.
The fields of science, engineering, and medicine span the totality of physical, biological, and social phenomena. Research in all these fields is based on certain fundamental procedures and convictions. However, each research field has its own characteristic methods and scientific style. Consequently, research is too broad an enterprise to permit many generalizations about its conduct.
One theme, however, threads through its many fields: the primacy of scrupulously recorded data. Because the techniques that researchers employ to ensure the integrity—the truth and accuracy—of their data are as varied as the fields themselves, there are no universal procedures for achieving technical accuracy. The term “integrity of data” also has a structural meaning, related to the data’s preservation and presentation. This is the subject of Chapter 4. There are, however, broadly accepted practices for generating and analyzing research. In most fields, for instance, experimental observations must be shown to be reproducible in order to be credible. Even this fundamental principle can have exceptions. For instance, observations with an historical element, such as the explosion of a supernova or the growth of an epidemic, cannot be reproduced. Other general practices include checking and rechecking data to confirm their accuracy and validity and submitting data and research results to peer review to ensure that the interpretation is valid. In addition, some practices may be employed only within specific fields, such as the use of double-blind clinical trials.
Many of the traditional methods for ensuring the integrity of data—whether universal or discipline specific—are being modified as digital technologies alter