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Your tasks: Ethical aspects

Ethics refers to moral principles and norms that help us identify right from wrong within a particular context. Ethical issues/concerns typically arise when these principles conflict. Navigating through such concerns often requires one to compare the benefits of an action with its potential harmful consequences. When it comes to research involving human participants, such ethical concerns may appear when accessing, using, or sharing data of a sensitive nature, for example health or personal data. Ethics, however, goes beyond the issue of compliance with legal obligations, and the collection and use of data.

The Open Data Institute narrows ‘ethics’ in the RDM context to:

“A branch of ethics that evaluates data practices with the potential to adversely impact on people and society – in data collection, sharing and use.”

Which aspects of RDM might raise ethical issues?


Ethical issues refer to moral principles and standards that guide human conduct and define what is considered right or wrong within a particular context.


  • There are different aspects in the management of research data that can raise ethical issues. It is important to distinguish between ethical issues and legal behaviour.
    • Ethical standards may vary across cultures, disciplines, and professional organisations. Researchers are expected to adhere to these ethical principles even if certain practices are not explicitly prohibited by law. Often these standards are collected in declarations and guidelines, which may be backed by laws.
    • Legal behaviour, on the other hand, refers to compliance with applicable laws, regulations, and policies. Legal requirements provide a baseline level of conduct that researchers must meet to avoid legal sanctions. However, legal compliance does not necessarily guarantee ethical behaviour. Some actions may be legally permissible but raise ethical concerns, while others may be ethically unquestionable but explicitly prohibited by specific legislation.
  • Ethical issues arise most often in research on or involving humans affecting human dignity and autonomy. These issues are partly addressed by the General Data Protection Regulation (see also the RDMkit data protection page) There are additional considerations connected to health research. Under the viewpoint of RDM you should especially consider:
  • Other ethical questions are arising from the impact of research outcomes, including data on the interest of communities or individuals
    • Fair management of intellectual property rights - also see e.g. Access and Benefit-sharing (ABS)
    • Publication of research data that might impact the reputation of communities or individuals
    • Publication of research data that might impact economical interests of communities or individuals
    • Publication of research data that might impact security of society, communities or individuals
  • There are also general research ethics considerations that are relevant in the context of research data, including:
    • What are the reasons justifying the exclusion/inclusion of research data in a particular context?
    • Is the data source accurate and trustworthy?
    • How can bias in practices of research data management be identified and minimised/avoided?
    • Assessment of models and algorithms used with respect to possible bias
    • Can the research data be misinterpreted?
    • Prevention of withholding of research data
    • Prevention of manipulation and fraud of research data
    • Assessment of who is excluded or included to data access and why
    • How can harm to other beings and the environment be identified and mitigated in a timely manner?


  • Assess potential ethical implication through an ethics review
    • Your local ethics committee can help you to review the ethical implications of the project or might guide you to more relevant bodies and resources
  • In order to address challenges when working with human data (see also the RDMkit page on human data)
    • Used standardised consent forms (see e.g. GA4GH Regulatory and Ethics toolkit) with clauses that can be represented in a machine actionable way using the Data Use Ontology (DUO) and Informed Consent Ontology (ICO))
    • Before you start collecting/processing data, be transparent about these in the consent form and also about the cases when withdrawal is no longer possible due to anonymization, aggregation, or other data processing. Anticipate the possibility of consent/data withdrawal and implement administrative and technical processes.
    • Data should be anonymized whenever possible (this is a non-reversible process), pseudonymisation (this is a reversible process) enhances data protection in cases where this is not possible (see also Recommendations on shaping technology according to GDPR provisions and the RDMkit data protection page)
    • Data analysis approaches that have potential to cause stigmatisation should be considered in advance and be discussed as part of an ethics review
    • Create processes for incidental findings before you start collecting data, include the way the participant wants you to deal with it in the consent form
  • In order to manage the impact of data collection and sharing:
    • The management of intellectual property rights connected with the data, if any, should be planned early on (RDMkit data management plan page), be part of collaboration/consortium agreements, and make use of standard licensing terms
    • The Nagoya Protocol regulates access to genetic resources and conditions for transfer of genetic resources and traditional knowledge across counties. For implementations into national law please consult the ABS Clearing House (also see RDMkit compliance monitoring & measurement)
    • Laws & Regulations concerning biosecurity, data export control and national interests might be linked from the RDMkit national resources pages
    • In order to ensure conformity with ethical research principles, the following should also be considered for data management:
      • Follow general research ethics laws and guidelines (see below)
      • Minimise suffering of animals in research to the absolute minimum, following the guidelines and laws relevant to your location as a baseline. A sound documentation and management of research outputs is an essential cornerstone to reduce unnecessary repetitions of experiments. Consider the use of specialised LIMS systems to capture relevant metadata
      • Reflect on potential future implications of the outcome of research and data capture and sharing for society and environment (RRI Toolkit, RRI self reflection tool) involving stakeholders of the research is an important measure in order to receive feedback. If data is presented to stakeholders, this should happen in an accessible format and might require pre-processing, visualisation and guidance.
      • Transparency and reproducibility of the research project underpin the scientific rigour of the project and reduce unnecessary duplication of efforts. Good RDM following the FAIR principles is a cornerstone of these efforts. Continuous tracking of provenance from e.g. research subjects to samples to data and semantic annotation of processes (documentation and metadata) can enhance the trustworthiness and value of research findings.
      • Automated sharing of research data after a specific period or milestones and deliverables in a project can be a good mechanism to enhance the openness of a project (data management coordination)

How can I identify regulations, guidelines and laws connected to ethics in my research context?


  • In all cases, your institution’s Data Protection Officer (DPO) is the person to refer to when considering ethical and legal aspects of data management.
  • When looking for recommendations and regulations, it is best to start from the local, that is, starting what is applicable within your discipline, and nationally. Then (if applicable), EU policies, directives and regulations are to be explored, as well as global recommendations (for example, from the UNESCO).
  • The solutions given below do not attempt to be exhaustive and highlight only the most relevant ones.


Further materials

Related pages

More information

Skip tool table
Tool or resource Description Related pages Registry
BBMRI-ERIC's ELSI Knowledge Base The ELSI Knowledge Base is an open-access resource platform that aims at providing practical know-how for responsible research. Human data GDPR compliance
Data Use Ontology (DUO) DUO allows to semantically tag datasets with restriction about their usage. Human data Standards/Databases Training
GA4GH Regulatory and Ethics toolkit Framework for Responsible Sharing of Genomic and Health-Related Data Human data
Informed Consent Ontology (ICO) The Informed Consent Ontology (ICO) is an ontology for the informed consent and informed consent process in the medical field. Human data Standards/Databases
RRI self reflection tool The Self-Reflection Tool provides questions and statements addressing all stakeholder groups (policy makers, education representatives, civil society organisations, industry and business, and the research community).
RRI Toolkit The RRI Toolkit helps stakeholders across Europe put Responsible Research and Innovation into practice.