Analysing and Representing Data Management Dimensions in Research and Innovation Actions Görzig Heike1, Gernhardt Benjamin2, Engel Felix3, Vogel Tobias4, Moore Reagan5, Hemmje Matthias L6 1Faculty for Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, D-58097, Hagen, Germany, (E): Heike.Goerzig@fernuni-hagen.de 2Faculty for Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, D-58097, Hagen, Germany, (E): Benjamin.Gernhardt@fernuni-hagen.de 3Faculty for Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, D-58097, Hagen, Germany, (E): Felix.Engel@fernuni-hagen.de 4Faculty for Mathematics and Computer Science, University of Hagen, Universitätsstrasse 1, D-58097, Hagen, Germany, (E): Tobias.Vogel@fernuni-hagen.de 5University of North Carolina at Chapel Hill, 312 Lenoir Dr, Chapel Hill, NC, 27599, (E): rwmoore@renci.org 6Faculty for Mathematics and Computer Science, University of Hagen, Chair of Multimedia and Internet Applications, Universitätsstrasse 1, D-58097, Hagen, Germany, +49-2331-987-304, (E): Matthias.Hemmje@fernuni-hagen.de Online published on 3 November, 2016. Abstract This article outlines how the Knowledge-based Production Process Planning can be applied to develop data management policy rules (DMPRs). The process supports the creation and evolution of data management plans and their automated execution. The planning is guided by a policy refinement process where a methodological approach derives rules from natural language data management policies. Technologies from the semantic web are used to implement the approach. The article concludes with a cognitive walkthrough of an example to validate the proposed approach. Top Keywords RAGE, DMP, RDM, OAIS, OAI-ORE, Data curation, Automation, Data management policy rules, Function blocks. Top |