Good data management for good research ethics

It is important for researchers to understand how data management relates to the responsible conduct of research. Researchers are generally aware that falsification of data is misconduct, but poor documentation and carelessness can also result in misconduct.  

The Office of Research Integrity (ORI) of the US Department of Health and Human Services identifies eight key data management concepts that PI's must address.

• Data ownership - Who has the legal rights to the data and who retains the data after the project is complete, including the PI's right to transfer data between institutions.
• Data collection - Ensure that the research team is collecting data in a consistent, systematic manner with appropriate processes in place for reviewing and evaluating both data quality and research processes.
• Data storage - Create guidelines so that all appropriate data is stored and the project results can be reconstructed.
• Data protection - Provide adequate security so that both hardcopy and electronic data is protected from damage, tampering or theft.
• Data retention - Plan for data to be kept for an adequate length of time based on funder or sponsor guidelines and for secure destruction of data as appropriate.
• Data analysis - Guide selection, evaluation and interpretation that supports significant and meaningful conclusions.
• Data sharing - Plan for the timing and method of dissemination of both project results and published and unpublished data for use by other researchers and the public.
• Data reporting - Publish conclusive findings, both positive and negative, after the conclusion of the project.

From:  Coulehan, M. B., & Well, J. F. (2006). Guidelines for responsible data management in scientific research. Clinical Tools, Incorporated.

The Oklahoma State University Library provides frequent workshops for training in data management principles and a variety of software tools to facilitate project and data management.  The schedule of current workshops are available at Managing Research Data: Workshops.

Slides and recordings of workshops:

• Data Bytes - Better Data Management - slides from a 2022 workshop and link to a 2021 recorded workshopl.
• Data Bytes - Drilling Down on DMPs - workshop materials for researchers on creating data management plans and data management planning 
• Data & Donuts - Using Open Science Framework for Project Management - recording and materials for a workshop on using the Open Science Framework (OSF) platform for organizing and sharing research project elements.  Projects can be created so that they are open or private at the component level.

Some educational resources from DataONE about documenting data changes:

• https://dataoneorg.github.io/Education/bestpractices/provide-version-information  DataOne is an excellent resource for a range of data management best practices.

• https://dataoneorg.github.io/Education/bestpractices/ensure-datasets-used  General information about ensuring that data is valid and reproducible.

• https://dataoneorg.github.io/Education/bestpractices/develop-a-quality.html  Planning ahead for data quality can prevent or catch errors