Research misconduct has been thoroughly discussed in the literature, but mainly in terms of definitions and prescriptions for proper conduct. Even when case studies are cited, they are generally used as a repository of “lessons learned.” What has been lacking from this conversation is how the lessons of responsible conduct of research are imparted in the first place to graduate students, especially those in technical fields such as engineering. Nor has there been much conversation about who is responsible for what (...) in training students in Responsible Conduct of Research or in allocating blame in cases of misconduct. This paper explores three seemingly disparate cases of misconduct—the 2004 plagiarism scandal at Ohio University; the famous Robert Millikan article of 1913, in which his reported data selection did not match his notebooks; and the 1990 fabrication scandal in Dr. Leroy Hood’s research lab. Comparing these cases provides a way to look at the relationship between the graduate student (or trainee) and his/her advisor (a relationship that has been shown to be the most influential one for the student) as well as at possibly differential treatment for established researchers and researchers-in-training, in cases of misconduct. This paper reflects on the rights and responsibilities of research advisers and their students and offers suggestions for clarifying both those responsibilities and the particularly murky areas of research-conduct guidelines. (shrink)

There has been relatively little empirical research into the causes of research misconduct. To begin to address this void, the authors collected data from closed case files of the Office of Research Integrity (ORI). These data were in the form of statements extracted from ORI file documents including transcripts, investigative reports, witness statements, and correspondence. Researchers assigned these statements to 44 different concepts. These concepts were then analyzed using multidimensional scaling and cluster analysis. The authors chose a solution consisting of (...) seven clusters: (1) personal and professional stressors, (2) organizational climate, (3) job insecurities, (4) rationalizations A, (5) personal inhibitions, (6) rationalizations B and, (7) personality factors. The authors discuss the implications of their findings for policy and for future research. (shrink)

: This essay provides an analysis of conflicts of interest in science. It gives an overview of some current conflict of interest policies and distinguishes between real, apparent, and potential conflicts of interest. The essay argues that scientists should disclose real, apparent, and potential conflicts of interest and that they should avoid conflicts that threaten scientific objectivity or trustworthiness. The essay also uses several hypothetical scenarios to illustrate some of the key points made in the analysis and suggests some strategies (...) for buffering the impact of conflicts of interest in science. (shrink)

Modern scientific knowledge is increasingly collaborative. Much analysis in social epistemology models scientists as self-interested agents motivated by external inducements and sanctions. However, less research exists on the epistemic import of scientists’ moral concern for their colleagues. I argue that scientists’ trust in their colleagues’ moral motivations is a key component of the rationality of collaboration. On the prevailing account, trust is a matter of mere reliance on the self-interest of one’s colleagues. That is, scientists merely rely on external compulsion (...) to motivate self-interested colleagues to be trustworthy collaborators. I show that this self-interest account has significant limitations. First, it cannot fully account for trust by relatively powerless scientists. Second, reliance on self-interest can be self-defeating. For each limitation, I show that moral trust can bridge the gap—when members of the scientific community cannot rely on the self-interest of their colleagues, they rationally place trust in the moral motivations of their colleagues. Case studies of mid-twentieth-century industrial laboratories and exploitation of junior scientists show that such moral trust justifies collaboration when mere reliance on the self-interest of colleagues would be irrational. Thus, this paper provides a more complete and realistic account of the rationality of scientific collaboration. (shrink)

Social scientists have begun elucidating the variables that influence public trust in science, yet little is known about hype in biotechnology and its effects on public trust. Many scholars claim that hyping biotechnology results in a loss of public trust, and possibly public enthusiasm or support for science, because public expectations of the biotechnological promises will be unmet. We argue for the need for empirical research that examines the relationships between hype, public trust, and public enthusiasm/support. We discuss the complexities (...) in designing empirical studies that provide evidence for a causal link between hype, public trust, and public enthusiasm/support, but also illustrate how this may be remedied. Further empirical research on hype and public trust is needed in order to improve public communication of science and to design evidence-based education on the responsible conduct of research for scientists. We conclude that conceptual arguments made on hype and public trust must be nuanced to reflect our current understanding of this relationship. (shrink)

This essay analyzes the concept of public trust in science and offers some guidance for ethicists, scientists, and policymakers who use this idea defend ethical rules or policies pertaining to the conduct of research. While the notion that public trusts science makes sense in the abstract, it may not be sufficiently focused to support the various rules and policies that authors have tried to derive from it, because the public is not a uniform body with a common set of interests. (...) Well-focused arguments that use public trust to support rules or policies for the conduct of research should specify (a) which public is being referred to (e.g. the general public or a specific public, such as a particular community or group); (b) what this public expects from scientists; (c) how the rule or policy will ensure that these expectations are met; and (d) why is it important to meet these expectations. (shrink)

This essay discusses some of the problems with current authorship practices and puts forward a proposal for a new system of credit allocation: in published works, scientists should more clearly define the responsibilities and contributions of members of research teams and should distinguish between different roles, such as author, statistican, technician, grant writer, data collector, and so forth.

Developments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General Data Protection Regulation (GDPR) within (...) a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research—among researchers and with data subjects—is grounded in trust, which should be more explicitly recognised in the governance of health data research. (shrink)