Introduction
In this guide, we explain the University of Freiburg’s policy on the handling of research data (gray boxes with quotation marks) and provide you as a researcher at the University of Freiburg with detailed explanations, practical tips, and further information (yellow boxes) and examples.
All researchers at the University of Freiburg commit to responsible handling of research data. The Central Data Facility supports all researchers at the University of Freiburg in implementing the university guidelines.
The Policy
Let’s go through the Policy on the handling of research data at the University of Freiburg section by section:
Policy on the handling of research data at the University of Freiburg
I. Preambel
The University of Freiburg recognizes the fundamental importance of research data, its creation and processing context, as well as its documentation and publication, for maintaining high-quality research and scientific integrity. The University of Freiburg strives to meet the highest possible standards. It further recognizes that accurate, easily discoverable and accessible research data are essential foundations of any data-driven research project. They are necessary for the traceability, validability, and reproducibility of research processes and results. Research data thus have a long-term benefit for science and the potential for extensive subsequent use and dissemination in society. This policy is intended to give scientists guidance in dealing with research data and to contribute to a sustainable research environment. In conjunction with „The University of Freiburg’s Open Access Resolution“, it is intended to contribute to spreading and living the idea of “Open Science” and “Open Data”.
What is a Policy?
A policy is an official guideline or directive. It establishes binding rules and expectations that all stakeholders are expected to follow.
At a glance
- A policy is NOT a recommendation, but binding.
- The policy on the handling of research data at the University of Freiburg was adopted by the University of Freiburg rectorate on September 21, 2022 and updated in January 2026.
- It builds on the DFG principles of good scientific practice.
What is the purpose of this policy?
The policy is intended to ensure that research data at the University of Freiburg are handled responsibly, transparently and sustainably. This is a prerequisite for establishing trust in data-driven research, for which the University of Freiburg wants to become known. Through its provisions, the policy aims to provide researchers at the university with a framework.
To build this trust, the policy names the following principles:
- Data shall, insofar as this is not restricted by further rights, be open.
- Data shall be produced in a verifiable process or taken from other sources.
- Research data must be prepared for reuse. This must also be possible for people from society who do not belong to research.
- DFG Guidelines on Principles of Good Scientific Practice
II. Scope
This policy is addressed to all members of the University of Freiburg who deal with research data, both as independent researchers and in their function as teachers and persons responsible for the supervision of scientists in the early career phase. It was adopted by the rectorate on 09/21/2022 and updated on 01/21/2026. This policy should also be taken into account in the case of externally funded projects. Specific agreements with third-party funders regarding data management take precedence over this policy.
Who does this policy apply to?
The policy applies to all members and affiliates who conduct research or enable research at the University of Freiburg.
This policy affects you if you are among others
- Professor at the University of Freiburg
- Working as a research associate
- Involved in research as a research assistant
- Writing a final thesis or dissertation in which research data are included in the work
- Supervising student research projects as a lecturer
- Guiding young scientists in your research group
- Involved in third-party funded projects (e.g., DFG, EU)
- Conducting research as a visiting scientist at the University of Freiburg
However, the policy does not apply absolutely, but can be influenced or even partially overridden by further rules. An example of such an override could be the obligation for longer archiving than the ten years specified in this policy.
- The policy incorporates categories of the State Higher Education Act (LHG) of Baden-Württemberg. From the definition in the LHG, among other things, the groups mentioned above result. It can be assumed that the policy addresses everyone who works with university systems through which research is processed.
- The Policy on the Handling of Research Data sets rules that apply within the university. It thus creates a kind of internal law that must be observed, but cannot override regulations that come from outside. Examples are funding conditions from third-party funders, requirements from professional societies or general laws. Such requirements will not be in total opposition to the Policy on the Handling of Research Data, but deviations in detail can occur, which are to be found through comparison and examination. An example would be if third-party funders expect longer archiving of research data.
- Are you unsure whether you are affected? In case of doubt: Yes, you are affected. If you have questions, contact the Central Data Facility.
- State Higher Education Act (LHG) Baden-Württemberg
III. Handling research data
Research data refers to all data that is generated during the research process or results from it. Depending on the research question and the methods applied, such data are produced or collected, processed, analyzed, and ultimately published and/or archived. Consequently, research data appear in different media types, levels of aggregation, and formats across all academic disciplines. To enable the provision and reuse of research data, it is essential to document their context of origin and the tools used.
Research data include, among others, measurement data, laboratory values, audiovisual information, texts, survey data, objects from collections or samples that arise, are developed or evaluated in scientific work. Methodological test procedures, such as questionnaires, software and simulations can also represent central results of scientific research and should therefore also be included under the term research data. This also includes unstructured texts.
- DFG Guidelines on Handling of Research Data
When dealing with research data, the entire lifecycle process must be taken into account, during which it is collected or reused, processed, analyzed, edited, archived and, if applicable, published.
For the reuse of data, and especially for the purpose of replication, information must be provided on how they arose or how they were generated (metadata). If this additional information is not stored with the data itself, at least a link must be set where this metadata can be found. Metadata can be archived as independent digital objects. For structuring metadata, so-called metadata standards are often used. A distinction can be made here between generic and subject-specific or discipline-specific standards.
Consider the entire data life cycle
When managing data, the model is often used in which research data are assumed to have a life cycle. There are variants of the model. What they have in common is mapping research data to phases. Each phase can be organized differently, but the main goal should be to automate the transition from one phase to the next as much as possible or to carry it out according to a stringent procedure. This additionally ensures that data do not disappear unintentionally. If data are to be deleted, this should be a planned step. The principles of good scientific practice as well as the policies that prescribe archiving of 10 years must be observed.
Research data arise, are reused, processed, stored and possibly published. It should be considered and documented early on how research data should be handled during the course of the project. Data storage itself should be taken into account in project planning, as well as ensuring the reuse of data after the end of the project.
The tool catalogue with services helps with concrete implementation.
- Metadata and metadata standards as descriptive information about data
- Phases of the research data life cycle
- List of services in the CDF tool catalogue
The FAIR principles (Findable, Accessible, Interoperable, Reusable) are to be addressed early in this process, unless there are legal or ethical reasons against them and they are technically feasible (3). It is particularly important to preserve the integrity and context of research data. Research data must be stored in a correct, complete, unaltered and reliable manner and be reusable in the long term.
Comply with FAIR principles
FAIR stands for four properties that research data should have:
| Letter | Term | Meaning for You |
|---|---|---|
| F | Findable | Data must be provided with clear descriptions (metadata) so that others can find them. |
| A | Accessible | Data should be openly accessible – or it must be clearly described why not. |
| I | Interoperable | Data should be stored in formats that can be read by different programs. |
| R | Reusable | Data must be provided with a license so that others can use them further. |
- Explanations of metadata: Metadata are referred to as data about data, which can have various functions. They can perform rather bibliographic tasks. A metadata schema with greater distribution is Dublin Core, with which core information about digital objects can be captured. In addition, further metadata can be annotated that give more subject-specific information about a digital object.
In accordance with intellectual property rights and provided that no third-party rights, legal provisions or other property rights prohibit it, research data shall be provided with a free license for subsequent use and shall be made openly available.
To make data reusable, they must not only be technically accessible. It is just as important to legally enable their reuse.
Grant open licenses
Research data should, if possible, be published with open licenses (e.g., Creative Commons) so that other researchers can legally use the data further. To ensure citability when data are reused, data should be provided with a DOI (Digital Object Identifier). Exceptions: If third-party rights, data protection or confidentiality stand in the way.
- Information on licenses: CDF guide
- Information on data protection is available from the Data Protection Officer of the UFR
- Information on protective rights and patents is provided by the ZFT
Research data should be deposited in a suitable repository or archiving system. It is recommended to examine primarily subject-specific or methodologically suitable repositories provided that they meet at least the Open Access requirements of the University`s services stated in this policy.
There are a variety of repositories in which data can be stored. The global directory re3data offers a search by discipline for this. The National Research Data Infrastructure (NFDI) offers subject-specific storage options. Contact there can be established by the Central Data Facility. The UFR offers FreiData with InvenioRDM as a publication platform for research data and other digital objects from research. FreiData can be used by all members and affiliates of the university with a university account.
In addition to at least descriptive metadata, data should be provided with persistent identifiers.
A persistent identifier, English persistent identifier (PID), is defined as a permanent (persistent), digital identifier consisting of alphanumeric characters, which is assigned to a dataset (or another digital object) and points to it – usually as a link. As a de facto standard for research data, the DOI (Digital Object Identifier) has established itself in recent years.
- Persistent identifiers (PIDs)
In particular, the University of Freiburg recommends the use of ORCID IDs for individuals, and of DOIs for publication of data sets (if applicable, in addition to discipline- or repository-specific identifiers and metadata).
DOI is the abbreviation for Digital Object Identifier. An identifier makes it possible to uniquely reference digital objects. The University of Freiburg recommends this scheme because it has the widest distribution. ORCID has a similar function for natural persons. All publishers at the University of Freiburg who are members must register an ORCID for themselves. This ORCID should be linked with your own account in FreiDok plus. This way publications in FreiDok plus and in the ORCID profile are synchronized. Publications that appear during membership at the university are simultaneously linked with it as an institution.
- Information on persistent identifiers: UB website
- FreiDok plus offers extended ORCID functions: UB News
- Information on ORCID: CDF Guide
Research data intended for re-use should be made available in citable form. This includes ensuring the appropriate context, which may include the research software and workflow environments used. This task may be outsourced to appropriate professional services. It should be guaranteed that citation rules are observed and terms regarding publication and use are met.
The origin of reused data is thus clearly traceable and the corresponding source is honored.
Through targeted research data management, data that arise and are used in the research process become citable. This extends to software and further data for the environment in which research happens. In addition to the data itself, the research software (e.g., web service or tool with version number) and the operating environment should be described. Research software refers to software that is developed or used in the context of scientific research to address scientific questions. It can be used in various phases, e.g., for data analysis, simulations, for visualization of results, workflow automation, reproducibility of experiments. Examples include R, Python, MATLAB, image analysis software for microscopy, statistics programs. An operating environment is the combination of software, system resources and configurations that are needed to execute a program. These include, for example, operating system, libraries, runtime environments, memory and processor resources. Examples include Windows, Linux, MacOS, Java, Docker, Galaxy.
Citing data includes bringing them into a form that makes them intellectually accessible. Such a package of research data should be registered with a DOI. At this step, the involved persons are named, all equipped with an ORCID. These do not necessarily have to be the same persons who published a corresponding paper.
Research data and documents shall be retained and kept accessible for as long as required by internal policies, professional guidelines, or the requirements of research funders under applicable legal and contractual provisions (e.g., EU requirements regarding the collection of personal data). The minimum retention period for research data and documents is ten years after publication of the data, publication of the relevant work or after project completion.
At the faculties and scientific institutions of the University of Freiburg there are RDM contact persons who inform about data retention when it comes to project completion or departure of a professor/PI. Researchers are encouraged to inform themselves already when applying for projects about what possibilities the UFR offers to keep project data over this period. Funds for this should be considered in the application; the DFG provides funds for this. The Central Data Facility also informs about storage options and storage costs as well as the application of funds, and about all aspects of this step in research data management.
- DFG information on application for project-specific costs
If research data and associated documents are to be deleted or destroyed after the storage period has expired or for legal or ethical reasons, this may only be done taking into account any legal or ethical considerations. The deletion must be traceable and documented. When deciding whether to retain or delete data, the interests and contractual provisions of third-party funders and other parties involved, in particular contributors and collaboration partners, must be taken into account. Aspects of security and confidentiality must be considered.
After the retention period expires, the University Archive is to be included, e.g., when it comes to deleting data.
- Information on the University Archive
IV. Responsibilities
The responsibility for research data management during and after the duration of research projects and undertakings lies with the University of Freiburg and its researchers and should be in accordance with the recommendations for safeguarding good scientific practice. The implementation of these recommendations is supervised in the „Regulations of the Albert Ludwig University on Safeguarding Academic Integrity“.
Responsibility means fulfilling these obligations. Currently, a network of RDM contact persons is being built up at the University of Freiburg, through which unclear situations are evaluated decentralized and, if necessary, brought closer to a decision. Information about this will soon be available on the CDF website.
a. Responsibilities of the researchers
i. Researchers collect, document, store and archive research data and the related documentation so that access or proper deletion is possible. This includes agreement on procedures andresponsibilities in collaborative research projects. Such information should be part of a data management plan (DMP) that documents the acquisition, aggregation, editing, retention, use, and publication of the data used and describes the requirements for integrity and confidentiality of the data. Researchers shall prepare a DMP for each research project and maintain and keep it current during the conduct of the project. Where appropriate, they shall document the availability of research data in representations of their projects, e.g., in a research information system or other publicly accessible project descriptions.
ii. Researchers handle research data in a way that complies with the principles and requirements of this guideline. They ensure already during project planning whether open source software can be an equivalent alternative to programs whose source code is not disclosed. In particular, the use of software available under free licenses is advised
iii. Researchers plan, as far as possible, the further use of the data, especially after project completion. This includes both the determination of rights of use and exploitation after the end of the project, including the allocation of corresponding licenses, as well as the regulation of data storage and archiving in case of leaving the University of Freiburg.
iv. Researchers plan, as far as possible, the further use of the data, especially after project completion. This includes both the determination of rights of use and exploitation after the end of the project, including the allocation of corresponding licenses, as well as the regulation of data storage and archiving in case of leaving the University of Freiburg.
v. Researchers understand the handling of research data as an integral part of scientific training. They incorporate research data management into their teaching.
The handling of research data during a project can be described in a structured manner in a data management plan (DMP). Some funding agencies require a DMP mandatorily. Information and advice on this is offered by the Central Data Facility.
Open source software should be preferred over commercial software. For popular services there are many European alternatives. Information on software for RDM is available in the CDF Tool Catalog. The Central Data Facility can provide further recommendations on software.
- Information on data management plan (DMP): CDF Guide
- Information on European alternatives
- Information on RDM tools and services is available in the CDF tool catalogue
b. Responsibilities of the University of Freiburg
- i. The University of Freiburg supports its organizational units, provides adequate funding and resources for research promotion, services, operation of organizational units, infrastructures and staff qualification. The University assigns the Central Data Facility with the coordination of institutional research data management. To this end, it participates in cross-cutting exchanges with other institutions, research funding agencies, and is a member of the National Research Data Infrastructure Germany.
ii. The University of Freiburg promotes compliance with the recommendations on good scientific practice. To this end, it provides templates for DMPs, conducts monitoring, and offers qualification measures as well as support and advice. This is done in accordance with current policies, contracts with third-party funders, internal bylaws, codes of conduct, and other relevant guidance documents.
- iii. The University of Freiburg develops mechanisms and provides services to store, securely retain, and publish research data to ensure access to research data during and after the completion of research projects.
iv. The University of Freiburg provides access to the services and infrastructure described above so that researchers can comply with the requirements of third-party funders and other legal entities and fulfill their responsibilities as described in this policy.
The Central Data Facility advises on all aspects mentioned in the RDM policy. It works closely with the other service providers at the UFR for this.
V. Validity
This policy shall become effective upon adoption by the directory board on 09/21/2022 and was updated on 01/21/2026. It will be reviewed every three years at the end of each year to determine if it needs to be updated.
References/Appendix
- Open-Science-Policy der Universität Freiburg. (2024). DOI: 10.6094/UNIFR/245816
- Hiemenz, B., & Kuberek, M. (2019). Strategischer Leitfaden zur Etablierung einer institutionellen Forschungsdaten-Policy. DOI: 10.14279/DEPOSITONCE-8412
- Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. DOI: 10.1038/sdata.2016.18
- Open Source Initiative (OSI): opensource.org/licenses
- Persistenter Identifikator zur eindeutigen Bezugnahme auf Wissenschaftler*innen: orcid.org
- Persistenter Identifikator zur eindeutigen Bezugnahme auf digitale Objekte aller Art: doi.org
- Deutsche Forschungsgemeinschaft. (2021, Dezember 7). Gute wissenschaftliche Praxis
- Ordnung der Albert-Ludwigs-Universität zu Sicherung der Redlichkeit in der Wissenschaft : Jahrgang 32
- Central Data Facility: unifreiburg.de/cdf
- NFDI | Nationale Forschungsdateninfrastruktur e. V. : nfdi.de