2.4 Overall strategies for ethical data management

Ethical handling of research data requires not only acting according to ethical principles but also learning about the policies, resources, procedures, and practices that contribute to the collecting, analysis, sharing, and preservation of data. The following units in this tutorial will introduce such resources and practices as they relate to particular stages of the lifecycle of research data. Here we share some overall strategies for ethical data management: careful planning, effective communication, proper training, and clearly defining and distributing responsibility in the research group.

 

Figure 5 Developing and Communicating Guidelines for Data Handling within a Research Group.
(Penn State News / CC BY-NC-ND 2.0)

 

Planning

As we mention in “the research process and relevant actors,” the stage of research design provides an important opportunity to establish guidelines for data management followed by the entire research group. Nowadays many research grants (e.g., NSF) require the applicants to submit a “data management plan.” Researchers might use the “data management plan” as a starting point to develop more extensive guidelines for their groups. An effective guideline will outline what types of data are needed, how will data be generated and collected, what methods will be used for data analysis, and how the data will be used to answer research questions. The guideline should also clearly specify different researchers’ responsibility in ensuring the quality of data. For example, what mechanisms should be utilized to monitor the data quality? What channels of communication should be followed had any questions about data management arisen? A guideline is more effective when it is created in an inclusive and deliberative process, which allows discussions of important questions, such as the ownership of data, and who should be responsible for data preservation after the project is complete or during an incident where key personnel leave the research group.

 

Communicating

Because research often involves numerous uncertainties, shifting objectives, and other unexpected challenges, maintaining timely, effective, and regular communication is a key factor for ensuring high quality research data. A number of factors could pose challenges to communication within a research group: researchers’ busy schedules, the size of the group, geographically separated facilities, the power difference between students and senior personnel, etc. Sometimes members of a research group may be confused about when to communicate, what to communicate, and to whom should one communicate her concerns. Therefore, establishing explicit policy for communication is an important means to ensure the quality of data. For example, a research group might use a “communication map” to specify which members should be in conversation about what types of issues on a regular basis, and who should be informed if anomaly happens.

 

Training

A research group usually contains a mixture of junior and senior researchers, and some groups have frequent change of  membership as older students finish their degrees and new students join the group. Therefore, it is crucial to provide proper training and refreshment so that everyone in the group has a shared understanding of the expectations, standard procedures, and best practices of handling research data. Training on ethical data management can take a number of forms. For example, best practices of collecting and documenting data can be included in training sessions on lab techniques and safety; proper data analysis can be introduced in group research meetings; and training on data sharing and ownership can be included in events of professional development, such as seminars on intellectual property.

 

Defining and distributing responsibility

Finally, ethical and effective management of research data requires every member in the research group to clearly understand and meet his or her responsibility. It is very important that different researchers’ responsibility with regard to data quality should be clearly defined and communicated. According to Guidelines for Responsible Data Management in Scientific Research, members of a research group usually assume one of five roles: Principal Investigator (PI), Research Director, Research Associate, Research Assistant, and Statistician (Coulehan and Wells, 2006). With regard to data management, PIs and research directors are usually responsible for elaborating different members’ responsibility and rights, supervising the development and implementation of data protocols, determining plans for data storage, protection, analysis, and dissemination, as well as addressing research misconduct. Research associates and research assistants take primary responsibility in collecting and protecting valid data. Statisticians are responsible for solid and comprehensive data analysis. Usually PIs are ultimately responsible for the integrity of data, thus they should assign proper members to review and monitor data collection and analysis. In general, all members of the research group should understand the data analysis so as to explain the results and implications of their research (Coulehan and Wells, 2006).

 

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