Information Integration for Machine Actionable Data Management Plans

Tomasz Miksa, Andreas Rauber, Roman Ganguly, Paolo Budroni


Data management plans are free-form text documents describing the data used and produced in scientific experiments. The complexity of data-driven experiments requires precise descriptions of tools and datasets used in computations to enable their reproducibility and reuse. Data management plans fall short of these requirements. In this paper, we propose machine-actionable data management plans that cover the same themes as standard data management plans, but particular sections are filled with information obtained from existing tools. We present mapping of tools from the domains of digital preservation, reproducible research, open science, and data repositories to data management plan sections. Thus, we identify the requirements for a good solution and identify its limitations. We also propose a machine-actionable data model that enables information integration. The model uses ontologies and is based on existing standards.

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The International Journal of Digital Curation. ISSN: 1746-8256
The IJDC is published by the University of Edinburgh
and is a publication of the Digital Curation Centre.