Key components of data publishing: using current best practices to develop a reference model for data publishing

被引：19

作者：

Austin C.C. ^{[1
,2
]}

Bloom T. ^{[3
]}

Dallmeier-Tiessen S. ^{[4
]}

Khodiyar V.K. ^{[5
]}

Murphy F. ^{[6
]}

Nurnberger A. ^{[7
]}

Raymond L. ^{[8
]}

Stockhause M. ^{[9
]}

Tedds J. ^{[10
]}

Vardigan M. ^{[11
]}

Whyte A. ^{[12
]}

机构：

[1] Research Data Canada, Toronto

[2] Carleton University, Ottawa

[3] BMJ, London

[4] CERN, Geneva

[5] Nature Publishing Group, London

[6] University of Reading, Reading

[7] Columbia University, New York

[8] Woods Hole Oceanographic Institution, Woods Hole

[9] German Climate Computing Centre (DKRZ), Hamburg

[10] University of Leicester, Leicester

[11] University of Michigan/ICPSR, Leicester

[12] Digital Curation Centre, Edinburgh

来源：

International Journal on Digital Libraries | 2017年 / 18卷 / 2期

关键词：

Data publishing; Open data; Open Science; Research Data Alliance; World Data System;

D O I：

10.1007/s00799-016-0178-2

中图分类号：

学科分类号：

摘要：

The availability of workflows for data publishing could have an enormous impact on researchers, research practices and publishing paradigms, as well as on funding strategies and career and research evaluations. We present the generic components of such workflows to provide a reference model for these stakeholders. The RDA-WDS Data Publishing Workflows group set out to study the current data-publishing workflow landscape across disciplines and institutions. A diverse set of workflows were examined to identify common components and standard practices, including basic self-publishing services, institutional data repositories, long-term projects, curated data repositories, and joint data journal and repository arrangements. The results of this examination have been used to derive a data-publishing reference model comprising generic components. From an assessment of the current data-publishing landscape, we highlight important gaps and challenges to consider, especially when dealing with more complex workflows and their integration into wider community frameworks. It is clear that the data-publishing landscape is varied and dynamic and that there are important gaps and challenges. The different components of a data-publishing system need to work, to the greatest extent possible, in a seamless and integrated way to support the evolution of commonly understood and utilized standards and—eventually—to increased reproducibility. We therefore advocate the implementation of existing standards for repositories and all parts of the data-publishing process, and the development of new standards where necessary. Effective and trustworthy data publishing should be embedded in documented workflows. As more research communities seek to publish the data associated with their research, they can build on one or more of the components identified in this reference model. © 2016, Springer-Verlag Berlin Heidelberg.

引用

页码：77 / 92

页数：15

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