Information Integration for Machine Actionable Data Management Plans

Tomasz Miksa,Roman Ganguly,Paolo Budroni,Andreas Rauber

doi:10.2218/ijdc.v12i1.529

Tomasz Miksa, Roman Ganguly + Show 2 more

Open Access

https://doi.org/10.2218/ijdc.v12i1.529

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Abstract

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.

Highlights

Data Management Plans (DMPs) are a compulsory document accompanying project proposals or project deliverables submitted to research funders or further funding bodies
We propose machine-actionable data management plans that cover the same themes as standard DMPs, but particular sections are completed with information obtained from existing tools. maDMPs can be considered as automatically collected metadata about experiments
There is a need for automatically created machine-actionable description of experiments that could improve the quality of data management plans and reduce effort of their preparation without influencing research practices

Summary

Introduction

Data Management Plans (DMPs) are a compulsory document accompanying project proposals or project deliverables submitted to research funders or further funding bodies. We propose machine-actionable data management plans (maDMPs) that cover the same themes as standard DMPs, but particular sections are completed with information obtained from existing tools.

Results

Conclusion