Conversation Guide: Research Ethics and Data Management

This conversation guide is intended for supervisors and PhD candidates to discuss ethical issues in doctoral work.

We recommend that the conversation take place before the mandatory course in research ethics (UV9010). Which questions are relevant will vary between projects, fields, and methods.

Key areas for ethical assessment

Authorship and roles

• Have the supervisor and candidate discussed what qualifies for co-authorship?

• Is there a shared understanding of how contributions will be acknowledged and how disagreements about authorship will be managed?

Research integrity, dissemination, and societal context

• Are there conflicts of interest or imbalances in power and resources between the parties in the project, for example between funder, partner institution, and researcher, that may affect the independence of the research?

• Is there a risk that the results may be used selectively or taken out of context?

• Is there a risk that groups are presented in ways that reinforce stereotypical or unfair images, for example related to gender, ethnicity, disability, or social background?

• How is responsible dissemination ensured, particularly in projects dealing with sensitive or controversial topics?

• Is user involvement relevant for the project, and how is any such involvement academically and ethically justified?

• How is openness and transparency (FAIR) balanced with the protection of personal data or other considerations in publication?

Considerations for individuals, groups, and research participants

• How is free, informed consent to participate in the project ensured?

• Is there a risk that participants may feel pressured to take part, or that the project may cause them harm, discomfort, or unreasonable burden?

• Does the research address sensitive topics, such as psychosocial conditions, ethnicity, or health?

• Is there a plan for how unforeseen ethical dilemmas will be handled along the way?

Remember: Consent and information about the project is the main rule regardless of legal basis for processing.

Data basis, privacy, and data management

• Is there a plan for how data will be processed, stored, and shared throughout the project?

• Is there a risk of indirect identification of individuals in the data?

• Can datasets or analyses reproduce or reinforce existing biases between groups?

• Has it been assessed whether the use of existing data is in line with the original consent?

Remember that UV’s checklist for research data must be reviewed before the project starts. Here you can read more about storage and sharing of data.

Use of artificial intelligence

• Is generative AI used in any part of the research process, and is there a plan for quality assurance and documentation of its use?

• Are AI or predictive models used in the analyses, and has the risk of errors, biases, or discriminatory effects been assessed?

• Is there a plan for how the use of AI is documented and what impact it has had on the results?

• Has the energy consumption associated with the use of AI in the project been considered?

Contact

Do you have suggestions for the checklist, or do you need advice on research ethics? Please contact UV’s internal research ethics committee (Norwegian).