2016
Journal article
Open Access
Are scientific data repositories coping with research data publishing?
Assante M., Candela L., Castelli D., Tani A.
Data infrastructures Data Quality Scientific Data Repositories Computer Science (miscellaneous) Computer Science Applications Online Information Services Research Data Publishing
Research data publishing is intended as the release of research data to make it possible for practitioners to (re)use them according to "open science" dynamics. There are three main actors called to deal with research data publishing practices: researchers, publishers, and data repositories. This study analyses the solutions offered by generalist scientific data repositories, i.e., repositories supporting the deposition of any type of research data. These repositories cannot make any assumption on the application domain. They are actually called to face with the almost open ended typologies of data used in science. The current practices promoted by such repositories are analysed with respect to eight key aspects of data publishing, i.e., dataset formatting, documentation, licensing, publication costs, validation, availability, discovery and access, and citation. From this analysis it emerges that these repositories implement well consolidated practices and pragmatic solutions for literature repositories. These practices and solutions can not totally meet the needs of management and use of datasets resources, especially in a context where rapid technological changes continuously open new exploitation prospects.
Source: Data science journal 15 (2016). doi:10.5334/dsj-2016-006
Publisher: Committee on Data for Science and Technology of the International Council for Science., Paris
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BibTeX entry
@article{oai:it.cnr:prodotti:354435,
title = {Are scientific data repositories coping with research data publishing?},
author = {Assante M. and Candela L. and Castelli D. and Tani A.},
publisher = {Committee on Data for Science and Technology of the International Council for Science., Paris},
doi = {10.5334/dsj-2016-006},
journal = {Data science journal},
volume = {15},
year = {2016}
}
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