Guidelines on research data management at the FSU Jena

Guidelines and Principles

Guidelines on research data management at the FSU Jena
Image: Roman Gerlach

Over the past years, funding agencies and other initiatives released a number of principles and guidelinesExternal link on how to manage research data. When conducting research projects, funding agencies increasingly expect compliance with these guidelines and principles. While planning your research project and writing your proposal we recommend to study the relevant guidelines and clearly state how research data management will be implemented in your project.

Friedrich Schiller University Jena

On December 20, 2016, the senate of Friedrich Schiller University Jena agreed upon a policy on the handling of research data as well as on guidelines and recommendations for research data management. Both documents were developed by a working group on research data management which consists of representatives of the university's central service facilities (URZ de, ThULBExternal link, SFT, Legal Office de), the scientific community, the vice-president for research, and the research data management helpdesk.

The policy on the handling of research datapdf, 170 kb · de provides a first overview on general recommendations and best practices while working with research data. The guidelines and recommendationspdf, 316 kb · de complement the policy and specify the general recommendations.

Deutsche Forschungsgemeinschaft (DFG)

In July 2019, the DFG published the Code of Conduct "Guidelines for Safeguarding Good Research Practice"External link (coming into effect 01.08.2019). It replaces the memorandum on Safeguarding Good Scientific Practice (1998, 2013, with English part) which had been in effect until then. Already in September 2015, the DFG senate adopted new Guidelines on the Handling of Research Data. The DFG website Handling of Research DataExternal link provides a comprehensive compilation of principles, support and funding. For some disciplines, also subject-specific recommendations can be found on this website.

On March 14, 2022, the German Research Foundation specified the requirementsExternal link for handling research data in funding proposals. Specific information on the handling of research data, based on a catalog of questionsExternal link, (as of December 2021) is now made mandatory by the DFG.

European Commission - Horizon 2020

At the end of 2013 the European Commission launched the Pilot to open up publicly funded research dataExternal link ("Open Research Data Pilot"). This pilot was extended in 2017 covering all thematic areas now (see Open Reseach Data in H2020External link). Open data has become the default, although grantees may opt out under certain conditions. The two most important guidelines are the Guidelines on FAIR Data Management in Horizon 2020External link (PDF, version 3.0, 26 July 2016) and the Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020External link (PDF, version 3.2., 21 March 2017).

The creation of a Data Management Plan (DMP) for ERC projectsExternal link is mandatory from 2021 onwards. The DMP must be provided at the latest 6 months after the start of the project.

European Commission - Horizon Europe

The new EU framework program Horizon Europe requires immediate open access to publications that have undergone a peer-review process. Embargo periods of six or twelve months, after which a publication had to be freely accessible, as was still the case in the Horizon 2020 program, are no longer envisaged.

In Horizon Europe, free access to research data is also to be guaranteed in principle, in accordance with the principle "as open as possible - as restricted as necessary". In dealing with research data, researchers are to be guided by the FAIR principles. This includes, the mandatory creation of a data management plan in each project and the storage and provision of data in a relevant repository. All other guidelines are escribed in the Horizon Europe Program GuideExternal link

FAIR Data Principles

The so-called "FAIR Data PrinciplesExternal link"  which have been published in 2016 have reached a high level of acceptance and general approval. Besides the main points presented in the abridged version below further information on this topic can be found on the web sites of FORCE11External link as well as in the article "The FAIR Guiding Principles for scientific data management and stewardship" by Wilkinson, M.D. et al. (2016)External link.


F1. (meta)data are assigned a globally unique and eternally persistent identifier.
F2. data are described with rich metadata.
F3. metadata clearly and explicitly include the identifier of the data it describes.
F4. (meta)data are registered or indexed in a searchable resource.


A1 (meta)data are retrievable by their identifier using a standardized communications protocol.
A1.1 the protocol is open, free, and universally implementable.
A1.2 the protocol allows for an authentication and authorization procedure, where necessary.
A2 metadata are accessible, even when the data are no longer available.


I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (meta)data use vocabularies that follow FAIR principles.
I3. (meta)data include qualified references to other (meta)data.


R1. (meta)data are richly described with a plurality of accurate and relevant attributes.
R1.1. (meta)data are released with a clear and accessible data usage license.
R1.2. (meta)data are associated with detailed provenance.
R1.3. (meta)data meet domain-relevant community standards.


The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has published a Data Management PolicyExternal link in January 2020. It provides a framework for all IPBES entities, including technical support units and experts. The policy provides guidance for developing data management plans and a suggested workflow for long term storage as well as promotes the usage of open-source software.

It also makes sense for research projects - especially in collaborative research - to set-up a research data policy in order to establish a common standard on how to deal with the generated research data - especially when different types of research data are generated and different specific needs in handling research data exist. The project Research Data Policies for Research Projects at the TU Berlin (part of the three-year DFG joint project FDNextExternal link) has compiled a guidelineExternal link for this purpose (in German), which is intended to provide security of action and orientation. It comprises three parts:

  1. Preliminary considerations and reasons for a research data policy, stakeholders, project-related framework conditions, the process of creation from drafting to adoption to publication and implementation
  2. Content and structure of a research data policy, conceptual considerations for the structure, catalog with topics and guiding questions
  3. Checklist for a systematic approach