Guidelines and policies

Research funding agencies, scientific communities, universities, and other organizations have released guidelines and policies on the handling of research data. When conducting research projects, research funding agencies increasingly expect researchers to know and to comply with these guidelines and policies. All researchers who are applying for funding are therefore advised to study the guidelines and to explicitly state how they will be implemented in the research project.

Julius-Maximilians-Universität Würzburg (JMU)

The JMU has enacted Guidelines for Safeguarding Good Scientific Practice and Procedures Concerning Scientific Misconductthat are mandatory for all members of the University. With the latest revision of the guidelines, which were approved by the Senate on 31.05.2022, the JMU also implements the DFG Code of 2019 in a legally binding manner.

On 20.05.2017, the JMU has enacted Guidelines for Handling Research Data. The guidelines are addressed to all scientists at the JMU. They provide recommendations for data management before the start of and during a research project as well as for archiving research data at the end of the project.

The JMU signed the Sorbonne Declaration of Research Data Rights on 27.01.2020 in Paris. The signatories advocate for openly accessible and reusable research data following the internationally established FAIR Data Principles. They also call on state governments to create the policy and legal framework for open research data and to provide the necessary funding for technological infrastructure and research data management. The Sorbonne Declaration was signed by nine associations representing more than 160 top international universities. One of the associations is German U15, which represents 15 leading research-intensive universities in Germany and of which the JMU is a member.

Further Download
Press release of the German U15 (English version)

German Research Foundation (DFG)

In July 2019, the DFG published the Code Guidelines for Safeguarding Good Scientific Practice, which all universities and research institutions must implement to receive funding from the DFG. Accordingly, all researchers who wish to submit a research proposal to the DFG have to follow this guideline when dealing with research data.

Of the 19 guidelines formulated in the Code, the following are particularly relevant to research data management and data archiving:

Guideline 7: Cross-phase quality assurance
Guideline 10: Legal and ethical frameworks
Guideline 11: Methods and standards
Guideline 12: Documentation
Guideline 13: Providing public access to research results
Guideline 17: Archiving

In 2015, the DFG defined Guidelines for Handling Research Data (German version). These build on the Principles for Handling Research Data (German version) that the Alliance of Science Organizations enacted in 2010.

Subject-specific recommendations

Since the requirements for handling research data sometimes differ greatly between scientific subjects, there are increasingly initiatives within the communities to issue subject-specific recommendations.

In 2015, the DFG published Guidelines for Handling Research Data (German version) and called on DFG committees to specify these guidelines for their respective scientific discipline. Subject-specific recommendations for some disciplines can be found on the DFG website.

Biodiversity

Data Management Policy of the Intergovernmental Science-Policy Platform and Biodiversity and Ecosystems Services (IPBES)

High-engery physics

Policies on the access to Large Hadron Collider data at CERN

Life sciences

Guideline for Good Clinical Practice (GCP)
Principles of Good Laboratory Practice (GLP)

FAIR Data Principles

The FAIR Data Principles by Wilkinson et al. (2016) have become an important guide for handling research data in the scientific community. The four principles aim to ensure that data can be re-used to obtain the maximum value for the research community. FAIR means that data should be made findable, accessible, interoperable, and reusable whenever possible.

F1. (meta)data are assigned a globally unique and persistent identifier
F2. data are described with rich metadata (defined by R1 below)
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