J.K. Boehmer
Source: https://repository.tudelft.nl/person/Person_39e488d0-2b14-4d6c-84fc-ba0084cbd0f0 Parent: https://repository.tudelft.nl/person/Person_12c2f967-4bb1-472e-a56d-67fac1875e47
JB
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J.K. Boehmer
info
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Abstract (1)
Journal article (1)
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2 records found
1
Are the FAIR Data Principles fair?
Journal article (2020) - Alastair Dunning , Madeleine de Smaele , Jasmin Boehmer
This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles. Simultaneously, it will analyse how easy it is for data archives to adhere to the principles. The research took place from November 2016 to January 2017, ...
This practice paper describes an ongoing research project to test the effectiveness and relevance of the FAIR Data Principles. Simultaneously, it will analyse how easy it is for data archives to adhere to the principles. The research took place from November 2016 to January 2017, and will be underpinned with feedback from the repositories. The FAIR Data Principles feature 15 facets corresponding to the four letters of FAIR - Findable, Accessible, Interoperable, Reusable. These principles have already gained traction within the research world. The European Commission has recently expanded its demand for research to produce open data. The relevant guidelines1are explicitly written in the context of the FAIR Data Principles. Given an increasing number of researchers will have exposure to the guidelines, understanding their viability and suggesting where there may be room for modification and adjustment is of vital importance. This practice paper is connected to a dataset(Dunning et al.,2017) containing the original overview of the sample group statistics and graphs, in an Excel spreadsheet. Over the course of two months, the web-interfaces, help-pages and metadata-records of over 40 data repositories have been examined, to score the individual data repository against the FAIR principles and facets. The traffic-light rating system enables colour-coding according to compliance and vagueness. The statistical analysis provides overall, categorised, on the principles focussing, and on the facet focussing results. The analysis includes the statistical and descriptive evaluation, followed by elaborations on Elements of the FAIR Data Principles, the subject specific or repository specific differences, and subsequently what repositories can do to improve their information architecture.
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How to prepare your River Studies Data for the future with 4TU.Centre for Research Data
Abstract (2018) - Jasmin Boehmer , Robin Duinker , M Bentum
Due to the latest funder requirements on data management and Open Data, documenting and managing research data during the research and preparing it for publication gained in importance. Making research data findable and accessible for decades via a trusted and certified data arch ...
Due to the latest funder requirements on data management and Open Data, documenting and managing research data during the research and preparing it for publication gained in importance. Making research data findable and accessible for decades via a trusted and certified data archive – such as 4TU.Centre for Research Data – and therefore enabling the interoperability and reusability of its content can positively impact the future of river studies.
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