The article discusses potential strategies for automation of assessment of data management plans so that researchers and funders can receive quick feedback on the quality and FAIRness of planned or executed actions. The results presented in the paper will have an impact on the new features developed in the DAMAP, opens an external URL in a new window tool that is offered by TU Wien as the TU Wien DMP Tool, opens an external URL in a new window.

The full paper can be found here: https://datascience.codata.org/articles/10.5334/dsj-2023-028, opens an external URL in a new window

Abstract

Most research funders require Data Management Plans (DMPs). The review process can be time-consuming since reviewers read text documents submitted by researchers and provide their feedback. Moreover, it requires specific expert knowledge in data stewardship, which is scarce. Machine-actionable Data Management Plans (maDMPs) and semantic technologies increase the potential for automatic assessment of information contained in DMPs. However, the level of automation and new possibilities are still not well-explored and leveraged. This paper discusses methods for the automation of DMP assessment. It goes beyond generating human-readable reports. It explores how the information contained in maDMPs can be used to provide automated pre-assessment or to fetch further information, allowing reviewers to better judge the content. We map the identified methods to various reviewer goals.

Source: Miksa, T, Suchánek, M, Slifka J, Knaisl V, Ekaputra FJ, Kovacevic F, Ningtyas AM, El-Ebshihy, A and Pergl, R. 2023. Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans. Data Science Journal, 22: 28, pp. 1–13. DOI: https://doi.org/10.5334/dsj-2023-028, opens an external URL in a new window

Contact

TU Wien
Center for Research Data Management
Favoritenstraße 16 (top floor), 1040 Vienna

research.data@tuwien.ac.at

Twitter: @RDMTUWien