With the emergence of the global Open Science (OS) movementSince the early 2000s, the Open Science movement has advocated for an open and transparent approach to science in which all stages of the scientific knowledge process are made openly accessible online. This means that not only the final results of research, such as monographs or articles, are shared publicly, but also materials that accompanied the research process, such as lab notebooks, research data, software used, and research reports. This approach aims to promote public participation in science and knowledge, engaging interested audiences. It also seeks to encourage creativity, innovation, and new collaborations, while enabling the verification of findings in terms of quality, accuracy, and authenticity – a process intended to democratize research. Components of Open Science include Open Access and Open Data, which provide the infrastructure for sharing interim research results. Read More in the early 2000s, expectations for responsible research and 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 (GWP) have evolved. GWP constitutes a standardized code embedded in the guidelines of the German Research Foundation (DFG) and commits researchers to honest, responsible, and ethically and legally sound scientific work (DFG, 2022). Increasing emphasis is also placed on demands for Open AccessOpen Access refers to the free, costless, unrestricted, and barrier-free access to scientific knowledge and materials. For third parties to reuse these materials legally, the creators must grant usage rights through a licensing agreement. Free CC licenses, for example, specify exactly how data and materials may be reused. Read More – free and unrestricted access to research data – and consequently 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, enabling the reuse of data by third parties. Open Science principles are enshrined in DFG guidelines and recommendations, aiming to make academic research accessible to diverse publics, strengthen trust in science, and foster creativity, innovation, and collaboration.

According to 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, first published by the FORCE11 community (The Future of Research Communication and e-Scholarship) in 2016, scientific findings should circulate transparently and openly even during their development (Force, 2021). Data should be findable, accessible, interoperableInteroperability is the ability of a system to work seamlessly with other systems. In interoperable systems, data can be automatically combined and exchanged with other datasets, making data machine-readable, interpretable, and comparable in a simplified and accelerated manner. Interoperability is one of the main criteria of the FAIR Principles (Forschungsdaten.info, 2023). Read More, and reusable – structured, documented, and stored accordingly. Unrestricted access to knowledge is intended to enable broader participation in academic discourse and promote the democratization of research.

Source: FAIR Principles (based on Paulina Halina Sieminska), Anne Voigt with CoCoMaterial, 2023, licensed under CC BY-SA 4.0

A core focus in this context is research data management (RDM). RDM is a key concept of responsible and good scientific practice and encompasses the handling of research data concerning its organization, maintenance, and processing through specific measures and strategies. The goal is to preserve data in the long term in accordance with the FAIR principles and make it accessible to third parties so that scientific claims can be verified, evidence secured, and further evaluations or analyses conducted. This aligns with the imperative of Data SharingData sharing refers to the act of sharing or distributing data. According to research requirements, data should be made as open as possible and as confidential as necessary (European Commission, 2021). Particularly with regard to the reuse and handling of sensitive, personal data, it is crucial to carefully assess whether and in what form archiving and sharing data with other researchers and the public is possible and appropriate. The imperative of data sharing enjoys broad consensus within the Open Science movement but should be critically considered and weighed from a social and cultural anthropological perspective. Read More – the sharing and dissemination of data. According to Open Science'Open Science encompasses strategies and practices aimed at making all components of the scientific process openly accessible and reusable on the internet. This approach is intended to open up new possibilities for science, society, and industry in handling scientific knowledge” (AG Open Science, 2014, translation by Saskia Köbschall). Read More principles, research data should be presented and made available „as open as possible and as closed as necessary“ (European Commission, 2021).

In recent years, research data management has gained increasing importance, leading more universities, research institutions, and funding bodies to establish their own research data policies. These policies and guidelines provide support on RDM-related questions and should be considered in implementation. While Germany does not yet have standardized regulations for handling research data, funding programs from the DFG or the EU may require compliance with specific documentation, such as a data management planA data management plan (DMP) describes and documents the handling of research data and materials during and after the project period. The DMP specifies how data and materials are generated, processed, stored, organized, published, archived, and, if applicable, shared. Additionally, it outlines responsibilities and rights. As a 'living document' (a dynamic document that is continuously revised and updated), the DMP is regularly reviewed and adjusted as needed throughout the course of the project. Read More.

A recommended structured approach to RDM is provided by 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 model, which serves as a practical tool. This model categorizes the different „life stages“ of data and associates them with specific tasks that arise before, during, and after data collection. These include research planning, data collection, data processing and analysis, data publication, archiving, and reuse. The lifecycle metaphor highlights data as „living“ entities that continue to have relevance beyond the original research project, potentially through reuseData reuse, often referred to as secondary use, involves re-examining previously collected and published research datasets with the aim of gaining new insights, potentially from a different or fresh perspective. Preparing research data for reuse requires significantly more effort in terms of anonymization, preparation, and documentation than simple archiving for storage purposes. Read More.

The FAIR principles, university guidelines, and the research data lifecycle offer recommendations for successful research data management but only marginally address ethical aspectsResearch ethics addresses the relationship between researchers, the research field, and the subjects/participants of the research. This relationship is critically examined against the backdrop of vulnerabilities and power asymmetries created by the research process (Unger, Narimani & M’Bayo, 2014, p.1-2). Due to the processual and open-ended nature of ethnographic research, ethical questions arise throughout the research process in various ways, depending on the research context and methods. However, research ethics does not end with leaving the field; it also encompasses issues related to data archiving, data protection, and sharing research data with participants (see, for example, ethics guidelines by the DGSKA or the position paper on archiving, provision, and reuse of research data by the dgv). Read More and challenges. To complement these principles, the Research Data Alliance – which aims to expand technical and social infrastructures for data sharing – introduced the CARE principlesThe CARE principles were established by the Global Indigenous Data Alliance (GIDA) in 2019. They complement the FAIR principles and are used as a tool to focus more strongly on research contexts and their historical embeddedness, as well as on power asymmetries in the field. The acronym stands for Collective Benefit (common good), Authority to Control (control of research participants over their own representation), Responsibility (responsibility on the part of researchers) and Ethics (consideration of ethical aspects). The CARE principles are intended to emphasize and take into account the fair, respectful and ethical treatment of research participants and the data generated from research with regard to data sharing. The CARE principles are therefore relevant in all phases of the research data life cycle and research data management. Read More in 2019. These are particularly relevant for social and cultural anthropological research (see article on research ethics; Research Data Alliance, 2016).

Evidence in Data Affairs

Introduction to Research Data Management