Chalmers e-Commons

Best practices for good research data management

This page contains guidelines and checklists on common research data management questions. The material provided here is meant to serve as guidance for Chalmers' guiding principles on good research practices.

What is Research Data Management?

Research Data Management (RDM) means maintaining well-organized and well-documented research data throughout the research data lifecycle. By organizing, structuring, and documenting research data in a well-thought-out way, it becomes easier to:

  • report on the research process according to good research practice

  • keep track of and follow legal requirements. 

  • reproducibility and verification of results benefits from good RDM, 

  • find, understand and re-use research data during and after a research project. 

  • It also allows for more efficient ways of archiving and sharing data (e.g. by publishing data) and thereby providing for possible re-use. 

This means Open Science practices and the reproducibility within research benefit from good RDM.

What do we mean by research data?

Research data refers to “data underpinning scientific research” (European Commission, 2024) and can have different types and formats – experiment results, measurements, survey responses, texts,  images and film can all be research data. Research data can be raw or processed in different ways.

Best Practices in Research Data Management

Chalmers sets down three guiding principles for good research practice. The principles are the following:

  1. All research activities at Chalmers shall establish and maintain a data management plan.

  2. The right to decide on the use of research data shall be retained within Chalmers and data shall as far as possible be stored under Chalmers control.

  3. All research data that is considered important for long-term documentation of research should be as FAIR as possible.

The policy document also contains principles for open access to scientific publications and for responsible internationalisation.

You can read more about managing research data throughout its lifecycle on our research data lifecycle page.