Historical Origin

Since the 1980s, an increasing formalization and standardization of ethical requirements can be observed in professional associations and working groups in social and cultural anthropology. Corresponding ethical declarations summarize the role of researchers in terms of their responsibility and commitment.

In the United States, the first Codes of Ethics and Institutional Review Boards (IRB) were developed as a response to inhumane experiments and research in the medical and psychological fields (e.g., during World War II and the post-war period). Ethical principles from these disciplines were transferred to social science research (Dilger, 2020, p. 286).

In Germany, ethical guidelines for ethnographic disciplines began to emerge in the 2000s:

• In 2005, the Society for Medical Anthropology issued guidelines for ethical self-reflection¹ see: https://link.springer.com/referenceworkentry/10.1007/978-3-662-58685-3_70-1.
• In 2007, the Council for Social and Economic Data (RatSWD) established ethical principles²see: https://www.konsortswd.de/wp-content/uploads/RatSWD_Output9_Forschungsethik.pdf.
• In 2009, the „Frankfurt Declaration on Ethics in Ethnology“ was adopted³see: https://www.dgska.de/wp-content/uploads/2016/07/DGV-Ethikerklaerung.pdf.

From these guidelines, the following fundamental principles can be derived:

Fundamental Principles of Research Ethics

1. Scientific Quality and Integrity of Researchers: Researchers must adhere to legally prescribed measures of good scientific practiceGood scientific practice (GSP) represents a standardized code of conduct established in the guidelines of the German Research Foundation (DFG). These guidelines emphasize the ethical obligation of every researcher to act responsibly, honestly, and respectfully, also in order to strengthen public trust in research and science. They serve as a framework for guiding scientific work processes. Read More. This includes maintaining accuracy and neutrality (e.g., avoiding political biases or influences), preventing plagiarism and deception, and ensuring that findings are comprehensible and transparent through detailed documentation.
   
2. Non-Maleficence (Avoiding Harm): Researchers should protect both the study participants and themselves. No one involved should suffer legal, health-related, material, social, or other harm from the research. This includes a conscious sensitivity to sensitive topics and appropriate anonymization and pseudonymization strategies to protect personal dataPersonal data includes: 'any information relating to an identified or identifiable natural person (data subject); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier, or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural, or social identity of that natural person(…)” (EU GDPR Article 4 No. 1, 2016; BDSG §46 para. 1, 2018; BlnDSG §31, 2020). Read More (see Anonymization and Pseudonymization article). Researchers should also be mindful of their own psychological and health limits, as well as those of their team members, which is particularly relevant in research involving violence or conflict zones.
   
3. Voluntary and Informed Consent: Participants must be informedInformed consent refers to the agreement of research participants to take part in a study based on the basis of comprehensive and understandable information. The design of an informed consent must address both ethical principles and data protection requirements. Read More about the goals of the research in an anticipatory and comprehensible manner. They should then be able to freely decide whether to participate or not (see Informed Consent article).

These ethical principles intersect with both legal requirements and scientific-ethical considerations. They apply beyond the immediate research situation and encompass all phases of the research data lifecycleThe research data lifecycle model represents all the phases that research data can go through, from the point of collection to their reuse. These phases are linked to specific tasks and may vary (Forschungsdaten.info, 2023). Generally, the research data lifecycle includes the following stages: Read More, including documentation, archiving, and reuse.

Data Ethics in Research Data Management

An ethical approach is essential in every phase of the research data lifecycle for effective data management, and this can be summarized under the term data ethics. The Open Data Institute defines data ethics as:

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

(Open Data Institute, 2023)

Thus, it is essential to critically assess how one’s own handling of generated data in research data managementResearch data management is aimed at handling research data in a responsible and well-considered manner. The idea is to carefully organize, maintain and process research data using specific measures and strategies. The goal is to store data long-term and make it accessible and reusable by others, in line with good scientific practice. This enables easier verification of scientific findings, secures evidence, and allows for further evaluations and analyses of the data. Read More can affect the people involved. Data practices (i.e., data generation, analysis, evaluation, knowledge production, decisions on data sharing, and potential reuse by third parties) must always be conducted in a way that prevents harm or negative consequences for participants. For each decision within these practices, possible impacts on participants must be considered, as highlighted in various articles in this portal (see also Archiving and Data Reuse).

The FAIR PrinciplesThe FAIR Principles were first developed in 2016 by the FORCE11 community (The Future of Research Communication and e-Scholarship). FORCE11 is a community of researchers, librarians, archivists, publishers, and research funders aiming to bring about change in modern scientific communication through the effective use of information technology, thereby supporting enhanced knowledge creation and dissemination. The primary goal is the transparent and open presentation of scientific processes. Accordingly, data should be made findable, accessible, interoperable, and reusable (FAIR) online. The objective is to preserve data long-term and make it available for reuse by third parties in line with Open Science and Data Sharing principles. Precise definitions by FORCE11 can be found on their website see: https://force11.org/info/the-fair-data-principles/. Read More – which were established in response to Open DataOpen data are data that are openly and freely accessible online and may be reused by third parties without restriction. This requires that they are provided with an open license (Opendefinition, 2023). Read More (i.e., unrestricted access to all data) – recognize certain restrictions on data access to protect individuals legally (see Introduction to Research Data Management, Data in Ethnographic Research, Data Protection, Rights and Licenses). However, as a key tool in research data management, the FAIR principles do not sufficiently consider ethical aspects of the data generation process (Imeri & Rizzolli, 2022).

For this reason, the Global Indigenous Data Alliance (GIDA)The Global Indigenous Data Alliance (GIDA) is a network of researchers, data practitioners, and political activists dedicated to ensuring that Indigenous groups: Read More introduced the CARE Principles in 2019 as a complement to the FAIR Principles. These principles focus more on historically rooted research contexts (e.g., postcolonial contexts) and the associated power asymmetries between researchers and participants in data management.

The CARE acronym stands for:

• C – Collective Benefit: Participants should have access to and be able to reuse the collected data (e.g., via online repositories).
• A – Authority to Control: Control over the representation of data should be shaped by the communities themselves.
• R – Responsibility: Researchers have a duty to establish and maintain respectful relationships with participants.
• E – Ethics: Ethical aspects should be considered throughout the research process, aiming to prevent harm, uphold rights and interests, and foster the involvement of the community itself.

The fundamental principles of research ethics, data ethics, and the CARE principles provide guidance and support for ethical questions and can serve as tools for ethnographic research and data management. However, in social and cultural anthropology, these ethical reflections are already well-established within academic debates and theoretical frameworks.

Research Ethics in Social and Cultural Anthropology

Ethical reflections are deeply embedded in social and cultural anthropology, as evidenced by discussions surrounding the „crisis of representation“ (the so-called „Writing Culture“ debate), the positionality of ethnographers, and postcolonial theoretical approaches (Dilger, 2020, p. 292). Ethnographic research is inherently reciprocal, participatory, and collaborative, with an open and flexible approach to research design. The establishment of close, trusting, and respectful relationships with research participants is an integral part of the discipline’s methodology and is therefore included in academic training (ibid.). Similarly, a respectful and considerate approach to research data should be firmly rooted and established within this disciplinary tradition.