DataDesc: A framework for creating and sharing technical metadata for research software interfaces

The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers to reproduce, validate, and expand upon study findings. The analysis of open-source code aids in the comprehension, comparison, and integration of approaches. Often, however, no further use occurs because relevant software cannot be found or is incompatible with existing research processes. This results in repetitive software development, which impedes the advancement of individual researchers and entire research communities. In this article, the DataDesc (Data Description) framework is presented—an approach to describing data models of software interfaces with machine-actionable metadata. In addition to a specialized metadata schema, an exchange format and support tools for easy collection and the automated publishing of software documentation are introduced. This approach practically increases the FAIRness, i.e., findability, accessibility, interoperability, and reusability, of research software as well as effectively promotes its impact on research. •The DataDesc framework focuses on research software interfaces and their data models•A metadata schema maps input and output content, formats, value ranges, and structures•Tools enable the collection, exchange, and publication of machine-actionable metadata•DataDesc reduces annotation efforts and promotes software reuse and integration Combining and integrating software and data is critical to answering complex questions in many areas of research, but identifying compatible software is a time-consuming task. Frameworks, such as DataDesc, that facilitate the discovery and integration of software could promote reuse and scientific exchange between research groups, thus reducing resources wasted by redundant software development. The authors emphasize the importance of research software but point out that it is often difficult to reuse and integrate within individual software workflows. To facilitate this, they advocate specifically documenting software interfaces and their data models as machine-actionable metadata. For this, they present the DataDesc metadata schema and support it with tools for collecting, exchanging, and publishing the information. The authors show how the reusability and interoperability of research software can be increased while minimizing annotation efforts.