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Data Management

Data Management

Web Resources

Data Acquisition and Management

Data Management

Web Resources

Data Acquisition and Management

This online tutorial developed by Columbia University includes an introduction to data acquisition and management, a case study, questions about the case study, an annotated version of the case including expert commentary, and a section of further resources on the subject.

This online tutorial (available via Flash or HTML) gives an introduction of how to manage data during scientific research It includes issues of data selection, collection handling, analysis, publishing and reporting, and issues of data ownership and privacy. The interactive module includes games, case studies, and quizzes to help users grasp key concepts.

Online Learning Tool for Research Integrity and Image Processing

This online tutorial, developed by the Center for Ethics and Values in the Sciences at the University of Alabama, Birmingham, introduces students and faculty members to some key concepts and guidelines on handling images included in reports, research papers, and articles to be submitted for publications. It includes video case studies that shows how the adherence to best practices in the handling of images can benefit all parties involved in the research community, an interview with a journal editor about the relationship between best practices and compliance, and a list of guidelines for best practices on handling image processing, accompanied by videos showing want is allowed and not allowed when using image manipulation software like Adobe Photoshop.

Developed by North Carolina State University at Raleigh, this online tutorial focuses on good statistical practices. The introduction distinguishes between two types of activities; one, those involving the study design and protocol (a priori) and two, those actions taken with the results (post hoc.) The authors note that right practice is right ethics, discusses the distinction between a mistake and misconduct and emphasizes the importance of how the central hypothesis is stated.

Books & Guidelines

National Academy of Sciences, Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age.Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age. Washington, D.C.: National Academy of Engineering, 2009.

The digital era provides researchers with greatly enhanced ability to analyze and share data, but a new report warns that technology also makes it easier for data to be distorted. The report, from the National Academy of Sciences, National Academy of Engineering, and Institute of Medicine, recommends that research institutions ensure that every investigator receives appropriate training on managing data responsibly. Further, the report urges these institutions, along with professional societies, journals and research sponsors, to develop standards for ensuring the integrity of research data and specific data-management guidelines to account for new technologies.

Nature Publishing Group. 2009. Guidelines for Digital Images.

Nature Publishing Group has posted a series of guidelines for authors including digital images with the manuscripts they submit for review. These include that images must be minimally processed, that authors should retain their unprocessed data and metadata files, and that the use of touch-up tools, or any feature that deliberately obscures manipulations, is to be avoided.

Steneck, Nicholas. 2007. Part III: Conducting Research” Introduction to the Responsible Conduct of Research. Washington, D.C.: United States Office of Research Integrity.

Provides a concise presentation on most of the issues in data management and offers lists that highlight the main points to consider in data management.

Journal Articles

Bailer, J. 1997. Science Statistics and Deception. In Research Ethics: A Reader, Deni Elliott and J.E. Stern eds. Hanover, N.H.: University Press of New England. 113-117.

This short article discusses the difference between proper and deceptive uses of statistics.

Couzin, J. 2006. Don’t pretty up that picture just yet. Science 314(5807): 1866-1868.

Scientists are used to enhancing images, but journal editors worry that the results can be misleading and are cracking down on the practice--some more forcefully than others.

Jennings, R. C. 2004. Data selection and responsible conduct: Was Millikan a fraud? Science and Engineering Ethics 10(4): 639-643.

This paper addresses a problem in reporting scientific research, especially in how to distinguish between justifiable and unjustifiable data selection. The author focuses on the case study of Robert Millikan, while historians and defenders see his data selection as understandable and legitimate, current statements about the Responsible Conduct of Research imply his selection was illegitimate. This paper discusses two main issues that arise in assessing his conduct, whether he was intentionally misleading and whether he actually did mislead the scientific community.

Pascal, C.B. 2006. Managing data for integrity: Policies and procedures for ensuring the accuracy and quality of data in the laboratory. Science and Engineering Ethics. 12(1): 23-39.

Management of the research data is an extremely important responsibility of the principal investigator (PI) and other members of the research team. Without accurate data, no worthwhile conclusions can be drawn from the research study. Integrity in data management is critical to the success of the research group and to public trust in the research outcomes. One of the primary responsibilities of the PI is to provide proper training to the junior members of the lab. This effort can be buttressed by institutional data policies that are implemented at the group level. Extensive and frequent guidance in good research practices by the PI and other senior research staff is critical to the proper training of new scientists.

Parrish, Debra and Brigit Noonan. 2009. Image manipulation as research misconduct. Science and Engineering Ethics. 15(2): 161-167.

The article looks at cases handled by the Office of Research Integrity involving image manipulations and discusses detection methods, and the final outcomes of the cases. It discusses the sanctions imposed on researchers found guilty, and contributing factors to those where were found not guilty, though the images in question were clearly flawed.

Last updated 8 July 2010 by Kelly Laas