Skip main navigation

New offer! Get 30% off one whole year of Unlimited learning. Subscribe for just £249.99 £174.99. New subscribers only. T&Cs apply

Find out more

Research Data Management

Research Data Management with Dr. Hendrik Gessner
Speedcurve Performance Analytics
© Photo by Luke Chesser on Unsplash

What is research data management?

Research data are among the most important resources of science. Accordingly, it is essential to deal with them systematically and responsibly. Within the framework of research data management, “our own work processes concerning the generation and handling of research data” are organized and continuously controlled “as efficiently and goal-oriented as possible”. (Meyermann, 2020) Research data management thus complements research from the initial planning stage all the way to archiving, reuse or deletion of the data.

As part of research data management, researchers develop methods and guidelines, which they apply to their research activities associated with research data. This results in a strategy for handling data. This strategy helps to manage, control and standardize the handling of data in the subsequent research process.

By documenting the strategy with the planned methods and guidelines, an initial data management plan is created. It includes technical, organizational, structural, legal and ethical aspects of data handling for the duration of a project. But even more far-reaching aspects, such as the sustainability of the data, can be considered right from the start.

Tasks of research data management

Research data management is included in all steps of the research process. The central tasks of research data management are (Corti, et al):

  • Planning the handling of research data at the beginning of a research project and, if necessary
  • presenting the planned measures in funding proposals
  • Defining a folder structure and file naming conventions
  • Documentation of research data and tagging with metadata
  • Backup and long-term archiving of research data
  • IT security and access control for research data
  • Long-term archiving of research data
  • Publication of research data
  • Discovery and reuse of existing research data
  • Consideration of data protection and copyright law when handling research data


A proper strategy for research data management simplifies working with the data during the research project as well as afterwards. It serves as a compass for all participants managing research processes and their results. In the planning phase of the research it takes time to develop guidelines and methods. Ultimately, this effort will pay off on several levels. Retrieving the data and reviewing the processes is much easier if the analyses and results can be reproduced. The prospects of reusing the data will increase.

Most importantly, research data management makes research more comprehensible, more reproducible and facilitates the validation of results in terms of good scientific practice. For researchers, this can contribute to additional scientific recognition and reputation. [Piwowar, et al] Research funding agencies and publishers are placing increasing relevance on professional research data management for researchers. They demand asystematic and well-planned handling of generated data during the project period as well as access to research data after completion of the research project, i.e. the proactive management of research data.

Research data management facilitates the following aspects:

  • better findability of data, e.g. by meaningful naming
  • clarity, e.g. no scattered storage of data in different versions on different computers
  • knowledge preservation – data are accessible independently of individual people, projects or institutions
  • transfer of data to future projects
  • facilitation of cooperation
  • long-term reproducibility of results instead of new generation of data (retention of primary and secondary data)
  • prevents data loss, e.g. due to defective hardware or software or to the loss of initial versions of files
  • (semi-)automatic processing enabled by metadata
  • transfer and reuse of data by using informed consents formulated accordingly, e.g. without stating, that data will be deleted after the project has expired
  • optimised use of resources, e.g. cost savings through reuse instead of new data collection
  • compliance with requirements imposed by funding agencies
  • citation of research data
  • referencing
  • increasing the relevance of one’s own work by improved visibility
© This work by Hendrik Gessner is licensed under CC BY 4.0.
This article is from the free online

Openness in Science and Innovation

Created by
FutureLearn - Learning For Life

Reach your personal and professional goals

Unlock access to hundreds of expert online courses and degrees from top universities and educators to gain accredited qualifications and professional CV-building certificates.

Join over 18 million learners to launch, switch or build upon your career, all at your own pace, across a wide range of topic areas.

Start Learning now