Guidelines and Basics

The requirements of research funders and the rules of good scientific practice call for the sustainable and systematic handling of research data. Corresponding guidelines help researchers to deal with the requirements for RDM before starting a project.

On this page you will find out:

• What is Research Data?
• What is Research Data Management?
• Why should I practice RDM?
• Which guidelines provide orientation?
• Further links
• Where can I get support?

What is Research Data?

The term research data encompasses all data that is collected, observed, derived, simulated or otherwise generated in connection with research¹. At the TUHH, a wide variety of data is generated, both in terms of quantity and type, for example: measurement data, laboratory values, audios, videos, texts, images, training data and samples. Research data also includes methods, (electronic) lab books, software and simulations as the results of scientific work.

What is Research Data Management?

Research Data Management (RDM) is about the conscious and careful handling of research data throughout its entire life cycle. RDM accompanies the research process from the planning of a project to the subsequent use of the data. This includes the collection, processing, evaluation, backup, documentation, publication and long-term storage of the data.

Why should I practise Research Data Management?

Responsible, open handling of research data makes a significant contribution to ensuring good scientific practice. RDM promotes the transparency and reproducibility of research processes and research results, which not least benefits you as a researcher. After all, research data is both the basis and the result of scientific work.

This video reports on the careless handling of research data in the context of the moon landing².

Which guidelines provide orientation?

Guidelines or policies stipulate for all employees of an institution how research data should be handled and which procedures should be used for RDM. Such accompanying regulations also exist for funding organizations, editorial boards and publishers and now provide important guidance.

Guidelines of the TU Hamburg

In May 2024, the TUHH published a new statute on safeguarding good scientific practice and dealing with suspected cases of scientific misconduct, which sets out the responsible handling of research data. Specific guidelines on handling research data were adopted in May 2021. These guidelines inform researchers at TU Hamburg about how they can implement the requirements for research data management in their projects.

Guidelines and directives for research funding (as of February 2024)

More and more funding organizations expect transparent, public-oriented research data management. Above all, this means that research data should be made openly available with as few restrictions as possible. Many funding organizations also require information on the handling of data in the project and its possible further use after the project has been completed.

We therefore recommend that all applicants familiarize themselves with the guidelines and directives of the respective funding organization and formulate specific statements on their implementation in the application. We will be happy to support you in this task.

German Research Foundation / Deutsche Forschungsgemeinschaft (DFG)

In its Guidelines for Safeguarding Good Research Practice (Code of Conduct), the DFG provides guidelines for promoting scientific integrity. In these guidelines, it sets out three core requirements for RDM. Researchers should:

1. pursue cross-phase quality assurance (Guideline 17),
2. grant public access to research data (Guideline 13) and
3. archive research data for at least 10 years (Guideline 17).).

The DFG Guidelines on the Handling of Research Data (PDF, 131 KB) specify these requirements and provide information on submitting applications for RDM:

• Comments on RDM: A section describing the handling of research data is mandatory in applications. The Checklist for the Handling of Research Data (PDF, 30 KB) serves as a starting point. The level of detail required differs depending on the funding program. Documentation and a description of the research data are required at the end of the project.
• Public provision: Provided that no third-party rights are affected, all relevant data should be made available at a processing stage that enables meaningful subsequent and further use by third parties. The data should be published as soon as possible.
• Storage: The data should be archived in your own institution or in a relevant supra-regional research data infrastructure for at least 10 years.

Project-specific costs for data management can be applied for.

Further information:

• Website on the handling of research data
• Guidelines for the Handling of Research Data (PDF, 131 KB)
• Proposal Preparation Instructions (PDF, 283 KB) – Handling of research data on p. 5
• Checklist for the Handling of Research Data (PDF, 30 KB)

European Commission – Horizon Europe

The European Commission’s ninth research framework program, Horizon Europe (2020 – 2027), places the following requirements on research data management:

• Explanations on RDM: The application must explain on approximately one page how the handling of the data and other research results is planned with regard to the FAIR Data principles (findable, accessible, interoperable, reusable). At the beginning of the project, usually in month six, researchers must submit a Data Management Plan (DMP). This should be updated at the end of the project.
• Public provision: All research data required to validate results from text publications must be published in a trustworthy repository. Exceptions are possible for certain reasons. The data should be published as soon as possible, but no later than the publication of the associated text publication.
• Data management according to the FAIR principles: Persistent identifiers are required, metadata must be available under the Creative Commons CC-0 (or equivalent) license, data must be published under the CC-BY or CC-0 (or equivalent) license.

Individual tenders may have additional requirements. The costs for data management are chargeable.

Further information:

• Flyer Horizon Europe, Open Science³
• Application template for collaborative projects (PDF, 2,110 KB) – Information on data management on p. 9
• Programme Guidelines (PDF, 1.108 KB)
• Template Data Management Plan (DOCX, 101 KB)

VolkswagenStiftung / Volkswagen Foundation

The guiding principle of the Volkswagen Foundation is that the important research data generated in the projects it funds should be made available as openly as possible and that, at the same time, the scientists working on the data should receive as much academic credit as possible. In detail, the following measures are required:

• Comments on RDM: When applying for data-generating or data-using projects in disciplines without a clear workflow, the submission of a digital concept in the form of a data management plan is required. It is recommended that the DMP be used as a living document.
• Public provision: Data (potentially) relevant for research should be stored in public, non-commercial repositories. It is recommended to clarify at an early stage what the target repository is.
• Further requirements: Open data generated by the applicant must be shown in the CV for the review and decision-making process.

The Volkswagen Foundation provides additional funding for the processing of emerging/created research data for approved projects.

Weitere Informationen:

• Website – Funding opportunities of the Volkswagen Foundation
• Data Reuse – Additional funding for the preparation of research data
• Open Science Policy (PDF, 476 KB) – Open Data p. 4 f.
• Basic Data Management Plan (RTF, 112 KB)

Journal Policies

Journal guidelines are of particular importance. On the one hand, access to data that forms the basis of a publication is necessary in the context of content quality assurance through peer review procedures. On the other hand, editorial boards and publishers are increasingly promoting the open accessibility of scientific data.

Elsevier

Elsevier, Research data and Open Science research data

PLOS

PLOS Open Data policy und PLOS Open Data

Springer Nature

Springer, Research Data Policies

Taylor & Francis

Taylor & Francis, Basic data sharing policy

Wiley

Wiley’s Data Sharing Policies

Creating a Research Data Policy

In collaborative projects, it makes sense to set up a separate research data policy in order to establish a common standard for handling the data. This applies in particular if different types of research data are generated and specific requirements exist. TU Berlin has published a structured guideline for the creation of research data policies for research projects, which is intended to provide orientation and certainty³.

Do you need support? I will be happy to advise you.