Speaker
Description
Data Management Plans (DMPs) are crucial for a structured research data management and often a mandatory part of research proposals. The manual creation of DMPs can be very time-consuming, since many researchers have to start from scratch, are unsure about the required content and may run the risk of not meeting the funder requirements. By using tools, DMPs can be effectively developed and managed. There are a variety of tools to support the development of DMPs: from discipline-agnostic DMP tools, which can be used to generate a generic draft DMP, to discipline-specific DMP tools, which support the creation of a DMP in a specific research field, such as psychology, biodiversity, engineering, or the life sciences.
Our aim is to develop a quick and easy to use DMP service for members of Saxon research institutions, building on existing work. In order to evaluate 18 of the existing DMP tools, we defined 32 requirement parameters covering aspects with regard to basic functions, technical aspects and user-friendliness. To further prioritize, a weight factor between zero (not relevant) and three (high priority) was assigned to every requirement parameter. The DMP tools were rated according to a fixed rating scheme from zero (poor) to ten (excellent), and then multiplied with the weight factor.
The evaluated tools satisfied between three and 28 of the requirement parameters. 11 tools covered at least half of the parameters. The highest total evaluation scores were reached by Data Stewardship Wizard (733.5), DMPTool (645.5) and RDMO NFDI4Ing (579.5). Experience from RDM consultancy showed that researchers find pre-fabricated text passages very helpful, which are automatically generated by the DMP tool based on their input. Accordingly, the corresponding requirement parameters were of high importance to us. The only tools generating pre-fabricated text passages while also providing the tool's source code were Data Stewardship Wizard, DataPLAN from NFDI4plants and the DMP tool of the TU Dresden Service Center Research Data. Moreover, we considered the machine-actionability of the DMP as an important requirement, because it can facilitate data findability, reusability, automated evaluation and monitoring. A machine-actionable DMP is machine- and human-readable and aims to be interoperable, automated and standardized. Seven DMP tools fulfill the requirement of providing machine-actionable DMPs, while being open source.
Our results can support tool developers to identify potential improvements and hosting institutions to select a tool suited to their specific needs. In a next step, we will check the feasibility of adapting each of the three tools according to our needs and estimate the respective workload. The most suitable DMP tool will then be customized for our requirements.
