Guidelines for good research data management

Research data management initiatives

Many national and international initiatives provide comprehensive guidelines for research data management. These are intended to support harmonised scientific work.

Good scientific practice

Good scientific practice covers the basic principles and rules for ethical, transparent, and responsible scientific work. It aims to ensure research integrity and prevent misconduct such as fabricating data or plagiarism. Good scientific practice also relates to the data generated during research and how they are handled. Research data management is an essential part of good scientific practice.

Important principles are:

1. Honesty: accuracy in data collection, analysis and presentation.
2. Documentation: an understandable and complete record of research results.
3. Transparency: disclosure of methods, sources and conflicts of interest.
4. Authenticity: independent research and proper citation of third-party work.
5. Responsibility: Ensuring the quality and ethical integrity of research.

These principles are often specified in the guidelines of scientific institutions such as the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG).

 

Plattform Material Digital (PMD)

The Platform MaterialDigital is a German initiative to promote digital technologies for materials research and development. It aims to support digitalisation in the field of materials science and to create a central access point for data, tools, and methods. Core principles of PMD:

1. Data infrastructure: development of a standardised, interoperable system for the management and exchange of material data.
2. Digitalisation of materials knowledge: integration of artificial intelligence (AI), machine learning and simulation in materials research. For more information, read our article Digitalisation – from AI to Industry 4.0.
3. Collaboration: fostering knowledge exchange between research institutions, industry and other stakeholders.
4. Sustainability: supporting the development of resource-efficient and sustainable materials.

The platform is supported by the German Federal Ministry of Education and Research (BMBF) as part of a comprehensive strategy to enhance digitalization in the field of science. It aims to bring together materials scientists, computer scientists, and engineers to collaborate on the digital transformation of this discipline.  

Nationale Forschungsdateninfrastruktur (NFDI)

Key objectives of the NFDI:

1. Improve data management: develop and provide standards, tools and services for research data management.
2. Provide accessible data: enabling open and secure access to research data (considering legal and ethical requirements).
3. Facilitate networking: provide an interdisciplinary platform for collaboration between researchers and institutions.
4. Ensure sustainability: long-term preservation and recycling of research data.

The NFDI consists of subject-specific consortia covering different scientific disciplines or topics (e.g. medicine, social sciences, materials sciences). Each consortium develops customised solutions for its community. The overall coordination ensures that systems and standards are compatible. The NFDI aims to strengthen basic data-driven research, promote Germany’s international competitiveness, and implement the principles of FAIR data.   In addition to these initiatives, many institutions provide guidance on research data management to their staff and students. For example, some provide a comprehensive research data management checklist as a reference to keep track of the elements that make up good research data. Others offer free online courses to understand how to collect and sustainably manage digital research data. These measures serve as harmonised research data management tools to provide the scientific community with reusable data in the long term.   Further information:

1. Longwood research data management „Research Data Management Checklist“ (last access: 02.2025)
2. University of Edinburgh, Research Data Service, „Research Data MANTRA” (last access: 02.2025)