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Creative Informatics Data Management Plan

Authors: Susan Lechelt; Anna Orme; Melissa Terras; Nicola Osborne;

Creative Informatics Data Management Plan

Abstract

Creative Informatics is a research and development programme based in Edinburgh, which aims to bring the city’s world-class creative industries and tech sector together. We are a collaboration between University of Edinburgh, Edinburgh Napier University, Creative Edinburgh and CodeBase. The Creative Informatics programme is in part funded by the Arts and Humanities Research Council (AHRC). Projects funded by the AHRC and other UKRI funding bodies are required to submit Data Management Plans that describe the types of data that they will gather and use, and how they will manage this data in line both with current data legislation and with their institution’s data management policies. Because the Creative Informatics programme includes two interrelated but distinct strands of work – the academic research strand and the administrative strand – that at times manage data in different ways, we have chosen to write two separate Data Management Plans, one for each strand. This document comprises the two Data Management Plans that we have submitted to the AHRC and the University of Edinburgh’s Research, Knowledge Exchange and Impact (RKEI) office. We are sharing this document as an exemplar, and in the interests of transparency for all those we work with directly, as collaborators and as research participants. We would like to note that the two Data Management Plans included here are living documents, subject to at least annual review, and may evolve along with legislation changes or changes to the Creative Informatics programme structure. If substantial changes are made to the Data Management Plans in this document, new versions may be published to make those changes transparent.

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Keywords

data management, creative informatics, data management plan

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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