Statistical power, the Belmont report, and the ethics of clinical trials.

Achieving a good clinical trial design increases the likelihood that a trial will take place as planned, including that data will be obtained from a sufficient number of participants, and the total number of participants will be the minimal required to gain the knowledge sought. A good trial design also increases the likelihood that the knowledge sought by the experiment will be forthcoming. Achieving such a design is more than good sense-it is ethically required in experiments when participants are at risk of harm. This paper argues that doing a power analysis effectively contributes to ensuring that a trial design is good. The ethical importance of good trial design has long been recognized for trials in which there is risk of serious harm to participants. However, whether the quality of a trial design, when the risk to participants is only minimal, is an ethical issue is rarely discussed. This paper argues that even in cases when the risk is minimal, the quality of the trial design is an ethical issue, and that this is reflected in the emphasis the Belmont Report places on the importance of the benefit of knowledge gained by society. The paper also argues that good trial design is required for true informed consent.

Generalizing on best practices in image processing: a model for promoting research integrity: Commentary on: Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images.

Modifying images for scientific publication is now quick and easy due to changes in technology. This has created a need for new image processing guidelines and attitudes, such as those offered to the research community by Doug Cromey (Cromey 2010). We suggest that related changes in technology have simplified the task of detecting misconduct for journal editors as well as researchers, and that this simplification has caused a shift in the responsibility for reporting misconduct. We also argue that the concept of best practices in image processing can serve as a general model for education in best practices in research.

Editors' overview: topics in the responsible management of research data.

Responsible data management is a multifaceted topic involving standards within the research community regarding research design and the sharing of data as well as the collection, selection, analysis and interpretation of data. Transparency in the manipulation of images is increasingly important in order to avoid misrepresentation of research findings, and research oversight is also critical in helping to assure the integrity of the research process. Intellectual property issues both unite and divide academe and industry in their approaches to data management. Central to the realization and promulgation of responsible data management is clear and careful communication of standards and expectations within the research community to trainees as well as among colleagues. These topics are examined and explored in depth in a special issue of Science and Engineering Ethics on responsible data management.