Data can be collected manually or electronically, and can be quantitative or qualitative. Data can be represented as numerical figures, text, images, audio/video, etc. Data sources can be human or animal subjects, field notes, journals, laboratory specimens, observations, etc. Different disciplines can have different notions of what constitutes data in their disciplines and how it can be managed. The underlying issue however is how to manage research data responsibly.

Data management issues encompass all stages of research from conceptualization of a project to the archiving and disposal of research materials. Those involved in research can face integrity issues in each stage, and therefore, should be prepared deal with and address the issues that may arise from these issues. For the purpose of this module, data-related integrity issues have been organized under the following topics:

• Data selection
• Data collection
• Data analysis
• Data handling
• Data reporting and publishing
• Data ownership

Along with the mentioned topics, research conceptualization and training of research staff can have a significant impact on the integrity of research data. Research staff includes not only those who are directly involved in conducting the research activities but also those who provide support for such activities, and as a result, can have an impact on the integrity of the research effort. Proper conceptualization of a research project and the use of appropriate research methods along with adequate training of all those staff directly and indirectly involved in the research project will ensure data integrity. Those who are new to a research area or particular research methods can unintentionally commit mistakes or misuse research methods that can impact the integrity of research data along with every other aspect of a project. But issues related to conceptualization of research and research methods are difficult to cover in the core modules of RCR instruction due to the project-specific nature of such issues.

Research staff should be trained not only on the research methods but also on the relevant standards and regulations. For example, data standards can play an important role in data management in some disciplines such as geography (example, federal spatial data standards, http://www.fgdc.gov/), while federal and state guidelines on data collection on animal or human subjects can have a significant impact on data integrity in other disciplines such as biology or health sciences. Research staff should also be aware of the open-ended nature of some basic research which can at times conflict with the regulations imposed by state, federal, and other bodies for the purpose of protecting research subjects, and be prepared to deal with the potential conflicts associated with such exploration for the purpose of advancing science. Therefore, training and supervising research staff adequately on the necessary research methods, data standards, institutional policies and regulations, and sponsors’ requirements relevant to the research project is essential to prepare them to make better decisions that ensure research integrity.

Another aspect of data integrity that is becoming increasingly important is related to the use technology in research projects for data collection, storage, analysis, archival, etc. These technologies include electronic instruments or hand-held devices for collecting data, computer systems for storing and sharing data, and software for analyzing data. But the use of technology can create additional integrity concerns that researchers must be prepared to deal with and act responsibly. Adequate training of research staff in the application and implications of technology used in a project can help to prevent technology-related integrity violations.

In summary, researchers should be familiar with the contextual nature of data and the six areas of data management mentioned earlier to have a better understanding of data integrity issues. Adequate project planning, training and supervision of research staff, understanding of standards and regulations, and knowledge of the implications of technology used can prevent or reduce data-related integrity violations in research. Finally, researchers should recognize the overlapping nature of data management issues with all the other core areas of RCR instruction and be prepared to deal with data integrity issues in a professionally responsible manner at all stages of research projects.

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