Research Data Preservation Using Process Engines and Machine-Actionable Data Management Plans

Abstract

Scientific experiments in various domains require nowadays collecting, processing, and reusing data. Researchers have to comply with funder policies that prescribe how data should be managed, shared and preserved. In most cases this has to be documented in data management plans. When data is selected and moved into a repository when project ends, it is often hard for researchers to identify which files need to be preserved and where they are located. For this reason, we need a mechanism that allows researchers to integrate preservation functionality into their daily workflows of data management to avoid situations in which scientific data is not properly preserved.

In this paper we demonstrate how systems used for managing data during research can be extended with preservation functions using process engines that run pre-defined preservation workflows. We also show a prototype of a machine-actionable data management plan that is automatically generated during this process to document actions performed. Thus, we break the traditional distinction between platforms for managing data during research and repositories used for preservation afterwards. Furthermore, we show how researchers can easier comply with funder requirements while reducing their effort.