Research Data Life Cycle

Important foundations are laid during the preparatory phase of a research project — including those related to research data management (RDM). The goal is to ensure that research data are handled in a structured, efficient, and compliant manner right from the start.

The first step is to familiarize yourself with the applicable guidelines for handling research data. These include institutional policies, requirements set by funding organizations, and relevant legal and ethical frameworks such as data protection, copyright, and participant consent regulations. Existing funding programs and their specific RDM requirements should also be considered.

Another key aspect is the planning of the necessary RDM resources: Which data will be collected? How will they be stored, documented, and, if applicable, shared? Many funding bodies require a data management plan that systematically addresses these questions.

Clarifying legal and ethical issues at an early stage — as well as defining potential collaborations with external partners — provides a secure foundation for the further course of the project. It is also worthwhile to review existing research data, as reusing suitable datasets can save effort and enable other research to build upon your work.

Overall, careful preparation from an RDM perspective helps ensure the quality and reusability of scientific data, while meeting the standards of good scientific practice and the expectations of funding organizations.

During the implementation phase of a research project, research data management (RDM) plays a crucial role in ensuring the efficient handling of both newly generated and reused data.

An essential step involves procuring the RDM resources defined during the planning phase. These include appropriate storage solutions, data organization software, and documentation tools. With the help of these resources, data can be collected systematically, stored securely, and structured in a comprehensible manner.

Continuous training in RDM is equally important. Particularly when new tools or methods are introduced, researchers need to become familiar with their effective use. Training and advisory services — for example, those offered by the University of Mannheim or research funding organizations — support researchers in staying up to date and applying best practices in data management.

Sound RDM during the project phase not only facilitates efficient teamwork but also lays the foundation for the subsequent reuse of data — whether in follow-up projects or by other researchers.

At the end of a research project, the focus shifts to the sustainable management of the data used. The results must be prepared in a way that ensures long-term accessibility, comprehensibility, and reusability.

First, determine whether, and under what conditions, the research data can be reused — for instance, by other researchers or in follow-up projects. In this context, legal, ethical, and copyright considerations play a crucial role.

The next step involves selecting appropriate channels for data publication — that is, platforms or repositories through which the data can be made publicly available. At the same time, an archive should be chosen that guarantees the secure and long-term preservation of the data.

For both publication and archiving, the data must be properly prepared. This includes comprehensive documentation using so-called metadata, which describe, for example, how and under what conditions the data were generated. Technical formats and structures should also be adapted to ensure readability and usability.

A thorough project completion process ensures that the research data continue to hold scientific value beyond the project itself — remaining understandable, reusable, and well documented.