MIM: A Minimum Information Model vocabulary and framework for Scientific Linked Data

Matthew Gamble, Carole Goble, Graham Klyne, Jun Zhao

Research output: Contribution to conferencePaperpeer-review

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Linked Data holds great promise in the Life Sciences as a platform to enable an interoperable data commons, supporting new opportunities for discovery. Minimum Information Checklists have emerged within the Life Sciences as a means of standardising the reporting of experiments in an effort to increase the quality and reusability of the reported data. Existing tooling built around these checklists is aimed at supporting experimental scientists in the production of experiment reports that are compliant. It remains a challenge to quickly and easily assess an arbitrary set of data against these checklists. We present the MIM (Minimum Information Model) vocabulary and framework which aims to provide a practical, and scalable approach to describing and assessing Linked Data against minimum information checklists. The MIM framework aims to support three core activities: (1) publishing well described minimum information checklists in RDF as Linked Data; (2) publishing Linked Data against these checklists; and (3) validating existing “in the wild” Linked Data against a published checklist. We discuss the design considerations of the vocabulary and present its main classes. We demonstrate the utility of the framework with a checklist designed for the publishing of Chemical Structure Linked Data using data extracted from Wikipedia as an example.
Original languageEnglish
Publication statusPublished - Oct 2012
Event2012 IEEE 8th International Conference on E-Science (e-Science) - Chicago, IL, USA
Duration: 8 Oct 201212 Oct 2012


Conference2012 IEEE 8th International Conference on E-Science (e-Science)


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