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.

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.

The anonymization or pseudonymization of personal data aims to safeguard the privacy of the data subjects. Subjects may not be (re-)identifiable based on the research data. Absolute anonymization is achieved, e.g., when data is only collected in aggregated form. In the case of pseudonymization, the personal characteristics recorded during collection (e.g., name, age, city) are substituted with analogous characteristics or placeholders (e.g., Max Mustermann).

The FAIR principles delineate the optimal preparation of data for open re-use. Research data should be findable, accessible, interoperable and re-usable. The GO FAIR Initiative is developing guidelines for concrete implementation.

Licenses regulate the granting of additional right of use for copyrighted data, i.e., they specify how the data may be used and by whom. Creative Commons licences (CC) are often utilized for sharing research data.

Metadata can be defined as “data about data.” Metadata contain structured information, e.g., on the technical format and context in which the data was created. The implementation of shared subject-, topic-, or collaborative project-specific metadata standards increase the findability and reusability of research data. Examples of well-known interdisciplinary metadata standards include the Common European Research Information Format (CERIF) or DataCite for the bibliographic description of data. Subject-specific standards are listed by the respective NFDI consortia (see Further Information).

The abbreviation NFDI stands for National Research Data Infrastructure (National Research Data Infrastructure). The NFDI aims to establish common standards and to enhance the possibilities of utilizing research data for science and society. Various thematic sections (e.g., ELSA - Ethical, Legal and Social Aspects or edutrain - Training & Education) and discipline-centered consortia are engaged in this undertaking. Six of the NFDI consortia are based in the humanities and social sciences.

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

Personal data is defined as any information that relates to an identified or identifiable natural person. It is subject to specific safeguards against unauthorized processing as outlined by data protection regulations.

A repository is a database or data archive for the storage and publication of digital research data. The primary purpose of such a repository is to preserve the data for a defined period of time and to ensure its availability, citability, and reusability. There are generic, interdisciplinary as well as subject-specific repositories (see Publication and Archiving). The service re3data.org offers a worldwide overview.

The research data life cycle describes the stages of handling of research data in the course of every research project: from planning to collecting and analyzing data to its publication and archiving, and the (re-)use of data.