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Methods to Evaluate Lifecycle Models for Research Data Management

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      Abstract

      Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.

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          How FAIR can you get? Image Retrieval as a Use Case to calculate FAIR Metrics

          A large number of services for research data management strive to adhere to the FAIR guiding principles for scientific data management and stewardship. To evaluate these services and to indicate possible improvements, use-case-centric metrics are needed as an addendum to existing metric frameworks. The retrieval of spatially and temporally annotated images can exemplify such a use case. The prototypical implementation indicates that currently no research data repository achieves the full score. Suggestions on how to increase the score include automatic annotation based on the metadata inside the image file and support for content negotiation to retrieve the images. These and other insights can lead to an improvement of data integration workflows, resulting in a better and more FAIR approach to manage research data.
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            Author and article information

            Journal
            31 January 2019
            1901.11267
            10.1515/dmpt-2016-00

            http://creativecommons.org/licenses/by/4.0/

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            cs.DL

            Information & Library science

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