Tomasz Miksa, P. Walk, Peter Neish, Simon Oblasser, Hollydawn Murray, Tom Renner, Marie-Christine Jacquemot-Perbal, João Cardoso, T. Kvamme, M. Praetzellis, M. Suchánek, Rob W.W. Hooft, Benjamin Faure, H. Moa, A. Hasan, Sarah Jones
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引用次数: 5
Abstract
This paper presents the application profile for machine-actionable data management plans that allows information from traditional data management plans to be expressed in a machine-actionable way. We describe the methodology and research conducted to define the application profile. We also discuss design decisions made during its development and present systems which have adopted it. The application profile was developed in an open and consensus-driven manner within the DMP Common Standards Working Group of the Research Data Alliance and is its official recommendation. TOMASZ MIKSA PAUL WALK PETER NEISH SIMON OBLASSER HOLLYDAWN MURRAY TOM RENNER MARIE-CHRISTINE JACQUEMOT-PERBAL JOÃO CARDOSO TROND KVAMME MARIA PRAETZELLIS MAREK SUCHÁNEK ROB HOOFT BENJAMIN FAURE HANNE MOA ADIL HASAN SARAH JONES
期刊介绍：
The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.
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