Short Answer

Below you will find a list of general guidelines and recommendations for research data management as well as a selection of guidelines for research data management by funding agencies.

Detailed Answer

General guidelines and recommendations for research data management

Data management is crucial to allow meaningful exchange of and access to data. Therefore, solid data management and the preparation of a data management plan (DMP) is required by many funding agencies. But what does good data management entail?

The basic considerations were defined in the FAIR data principles[1], that quickly evolved to be the golden standard in data management and data dissemination. The article and the more detailed webpage of the “GoFAIR”-initiative[2] provide a good overview about the goals that should be strived towards when dealing with research data.

A comprehensive overview about the steps throughout the data cycle and the associated questions that have to be considered within research data management is provided within the GFBio Data Life Cycle Tool or in the primer on data management provided by dataONE. A more detailed guidance towards data management best practices throughout the data life cycle can be found within the booklet "An Introduction to Data Management", co-produced by GFBio.

Guidelines for research data management by funding agencies

Specific guidelines on best practices in data management are available by many funding agencies. The guidelines and requirements of two major German and European funding agencies are can be accessed below:

Deutsche Forschungsgemeinschaft (DFG)

EU-Horizon2020 and European Research Council (ERC)

References

  1. Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) doi:10.1038/sdata.2016.18
  2. https://www.go-fair.org/fair-principles/

See also