Research Data Management

In your project, you will most likely generate your own research data, e.g., through interviews, surveys, or archival research. Please reflect on and describe the methods and sources of your data collection (e.g., semi-structured interviews) and processing (e.g., transcription software). If you collect personal data, it must be anonymized or at least pseudonymized in accordance with the relevant discipline-specific research standards. You also need to ascertain whether you need informed consent and/or an ethics vote at an early stage of your project (see Legal and Ethical Aspects).

If you want to (re-)use existing data, be it from your own previous projects or from third parties, please refer to the information on (re-) use of data below.

Responsible RDM includes a comprehensible documentation of the research data in order to make it findable, accessible, interoperable, and re-usable in line with the FAIR principles. You should therefore describe your data with metadata and assign persistent identifiers (e.g., a DOI). On the pages of the relevant NFDI consortia, you can check whether there are discipline-specific metadata standards suitable for your project (see the links under Further Information). Persistent identifiers are often assigned automatically by repositories and journals upon publication or archiving.

Remember to document not only the data generated and used in your project, but also the methods and tools employed (e.g., subject-specific software or generative AI models) and ideally include them in the metadata.

Data storage refers to the backup of data during the course of the project. Data must be stored in such a way that it is protected against both data loss and unauthorized access.

If you are working on your project individually, you fulfill the standard requirements by storing the data on the Viadrina server and, where appropriate, in the Viadrina cloud. Our recommendation: Define a coherent folder structure and easy-to-find file names.

In a project team or collaborative project, the partners need to agree on the storage of the (shared) data and its organization. This might require consultation with the computing centers of the research institutions involved.

Many funding organizations require the publication of project data. Journals require the simultaneous publication of the data on which the article to be published is based. The advantage for you as a researcher: Data publications are considered as independent, scholarly publications that can increase your visibility in the scientific community (see the DFG Guidelines for Preparing Publications Lists).

We recommend that you publish your data in subject-specific, internationally recognized, and peer-reviewed data journals or repositories. At forschungsdaten.info you will find data journals and repositories in the humanities, social sciences, and economics.

For publication, the data must be available in freely accessible data formats, linked with descriptive metadata and findable via a persistent identifier (e.g., DOI or URN).

We recommend CC0- or CC BY licensing. When deciding on the appropriate license, check the requirements of your funding organization, if applicable, and clarify any legal or ethical issues (e.g., copyright and right of use within a project team) in advance.

Remember to register your data publication in the research information system of the Viadrina.

(Long-term) archiving refers to the long-term storage of research data after the conclusion of the project. As a general rule, research data must be stored for a period of at least ten years following the publication of project results or data or after the end of the project. If your project is funded by a third party, check in advance whether your funding organization stipulates an extended timeframe.

As with data publications, research data can be archived in subject-specific or generic repositories and must be made identifiable with descriptive metadata and persistent identifiers. You can find repositories in the humanities-, social sciences, and economics at forschungsdaten.info.

Remember to designate a contact person for the archiving period and, if necessary, after its expiration.

Re-using data can usefully expand your own data basis. In addition, it is a necessary prerequisite for the verification and validation of research results.

You can find data sets licensed for re-use in generic and subject-specific repositories, relevant journals and portals in the humanities, social sciences, and economics.

Remember to check the licensing when re-using data – are you allowed to use it? – and cite the data correctly, i.e., identify the source and author. This equally applies to your own data from previous projects.

Copyright, right of use, and data protection aspects play a central role when dealing with research data: Who owns the data and who determines its (re-)use? These questions need to be clarified within a project team, in relation to other researchers and, in particular, in relation to other participants such as interview partners.

If you collect personal data in your project, you must obtain informed consent from your participants, e.g., interview partners. If you collect sensitive data and/or work with vulnerable groups, you must furthermore obtain an ethics vote from the Ethics Commission of the Viadrina. 

Such a vote is a prerequisite for the submission of applications to funding organizations and for publication in journals. Make sure that you adhere to the relevant discipline-specific research standards for anonymization and/or pseudonymization.

Ethical dimensions also play a role with regard to compliance with good scientific practice. You need to ensure correct citation of all sources and the identification of all authors and contributors to a given (data) publication

You can find further information on legal and ethical issues in RDM at forschungsdaten.info (pages only available in German).

We recommend that you acquaint yourself with discipline-specific guidelines and standards for handling research data at the onset of your project. In addition to the various discipline-specific associations, some of the DFG review boards have published subject-specific recommendations on the handling of research data, which offer a valuable initial orientation. The six NFDI consortia, which are relevant for the departments represented at the Viadrina, also provide subject-specific information as well as concrete templates and standards (see Further Information).

As researcher, you are responsible for the management of the research data you collect and (re-)use. In project teams, the responsibility for RDM lies with the project leader. The Viadrina provides the necessary infrastructure.

Data protection law does not apply to research data that has already been collected in anonymous form (e.g., aggregated) or subsequently anonymized. Such anonymization is always the goal. While maintaining the research purpose, anonymization should take place as early as possible in the research process, but no later than publication or project completion. This is laid down as a binding requirement in the Brandenburg Data Protection Act (§25, para. 2 BBDSG) (cf. § 25, para. 3 BDSG).

For anonymization, personal data must be modified in such a way that individual characteristics can no longer be attributed to a specific or identifiable natural person (so-called absolute anonymization) or can only be attributed with a disproportionate amount of time, cost, and effort (so-called de facto anonymization). – See Pseudonymization.

The FAIR principles aim to optimize the processing of data for open reuse. Research data should be identifiable, accessible, interoperable, and reusable. Guidelines for concrete implementation are being developed by the GO FAIR initiative, for example.

The research data lifecycle describes the stages of handling research data in research projects: from planning, collecting, and analyzing to publishing, archiving, and reusing data.

Licenses regulate additional rights of use for copyright-protected data, i.e., they specify how the data may be used and by whom. Creative Commons (CC) licenses are usually used for research data.

Metadata are “data about data.” They contain structured information about, for example, the technical format and original context of research data. Shared metadata standards specific to a subject, topic, or joint project increase the findability and reusability of the research data described. Examples of well-known interdisciplinary metadata standards include the Common European Research Information Format (CERIF) and DataCite for the bibliographic description of data. Subject-specific standards are listed by the respective NFDI consortia (see Further information).

The Nationale Forschungsdateninfrastruktur (NFDI[JN1] ; National Research Data Infrastructure) aims to develop common standards and make research data more usable for science and society. Various thematic sections (e.g., ELSA – Ethical, Legal, and Social Aspects or edutrain – Training & Education) and discipline-driven consortia are working on this. Six of the NFDI consortia are based in the humanities and social sciences. These can be found in the further information linked at the bottom of this page.

Persistent identifiers are used to uniquely and permanently address digital resources. Two well-known examples are DOI (Digital Object Identifiers) and URN (Uniform Resource Names).

According to the General Data Protection Regulation (Art. 4 No. 1 GDPR), personal data is any information that relates to an identified or identifiable individual. A person is identifiable if they can be recognized directly or indirectly by means of an identifier or a specific characteristic, i.e. an expression of their physical, physiological, genetic, psychological, economic, cultural, or social identity. Examples of such characteristics include the name, a membership number, or location data. When assessing whether a person is likely to be identifiable, one needs to take into account all the means reasonably likely to be used considering objective factors such as costs and time as well as the technology available at the time (Recital 26 GDPR). – See the entries on anonymization and pseudonymization.

In qualitative research in particular, anonymization (see the corresponding entry) is often not possible or would prevent achieving the research purpose. In this case, pseudonymization must be used as a workaround. Pseudonymization refers to the replacement of identifiable characteristics with identification codes or, for example, fictitious names. A key allows the data to be assigned to real persons. To ensure that this is only possible for individuals authorized for research purposes, this additional information must be stored separately and securely (Art. 4 No. 5 GDPR). As soon possible in the research process, the data must be anonymized, i.e., the key and all personal data must be deleted. Any processing and, where applicable, publication of research data that is only pseudonymized requires the consent of the data subjects and particularly careful consideration of research ethics by the researchers.

A repository is a database or data archive for storing and publishing digital research data. Its primary purpose is preserving the data for a defined period of time and keeping it available, citable, and reusable. There are generic, interdisciplinary repositories as well as subject-specific repositories (see section Subject-specific). The re3data.org service provides an overview of repositories worldwide.