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Key components of data publishing: using current best practices to develop a reference model for data publishing
Copyright policy ) Claire C. Austin; Theodora Bloom; Sünje Dallmeier-Tiessen; Varsha K. Khodiyar; Fiona Murphy; Amy Nurnberger; Lisa Raymond; +4 Authors
Claire C. Austin; Theodora Bloom; Sünje Dallmeier-Tiessen; Varsha K. Khodiyar; Fiona Murphy; Amy Nurnberger; Lisa Raymond; Martina Stockhause; Jonathan Tedds; Mary Vardigan; Angus Whyte;
Key components of data publishing: using current best practices to develop a reference model for data publishing
Additional Contributors: Tim Clark, Eleni Castro, Elizabeth Newbold, Samuel Moore, Brian Hole This is the revised version of: Bloom, Theodora et al.. (2015). Workflows for Research Data Publishing: Models and Key Components (Submitted Version). Zenodo. 10.5281/zenodo.20308 Abstract Purpose: 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 in order to provide a reference model for these stakeholders. Methods: 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. Results: The results of this examination have been used to derive a data publishing reference model comprised of 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. Conclusions: 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. 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.
- Ubiquity Press United States
- University of Leicester United Kingdom
- University Of Reading
- British Library United Kingdom
- Massachussets General Hospital Pathology Harvard Medial School United States
Microsoft Academic Graph classification: business.industry Process (engineering) Computer science Best practice Data publishing Data science Open data Workflow Publishing business Publication Reference model
Library and Information Sciences, Data publishing; Open data; Open Science; World Data System; Research Data Alliance, data publishing, open data, open science, Research Data Alliance, World Data System
Library and Information Sciences, Data publishing; Open data; Open Science; World Data System; Research Data Alliance, data publishing, open data, open science, Research Data Alliance, World Data System
Microsoft Academic Graph classification: business.industry Process (engineering) Computer science Best practice Data publishing Data science Open data Workflow Publishing business Publication Reference model
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1. Schmidt B, Gemeinholzer B, Treloar A (2015). Open Data in Global Environmental Research: The Belmont Forum's Open Data Survey. https://docs.google.com/document/d/1jRM5ZlJ9o4KWIP1GaW3vOzVkXjIIBYONFcd985q TeXE/ed
2. Vines TH, Albert AYK, Andrew RL, DeBarre F, Bock DG, Franklin MT, Kimberly J. Gilbert KJ, Moore JS, Renaut S,Rennison DJ (2014) The availability of research data declines rapidly with article age. Current Biology 24(1): 94-97
3. Hicks D, Wouters P, Waltman L, De Rijcke S, Rafols,I. (2015) Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548).http://www.nature.com/news/bibliometrics-the-leiden-manifesto-forresearch-metrics-1.17351 . Accessed 10 November 2015
4. Piwowar H, Vision T. (2013) Data reuse and the open data citation advantage. PeerJ Computer Science. https://peerj.com/articles/175/. Accessed 10 November 2015
5. Pienta AM, Alter GC, & Lyle JA (2010).The enduring value of social science research: The use and reuse of primary research data. http://hdl.handle.net/2027.42/78307. Accessed 10 November 2015
6. Borgman, CL (2015) Big data, little data, no data: Scholarship in the networked world.MIT Press, Cambridge MA
7. Wallis JC, Rolando E, Borgman CL (2013) If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology. PLoS ONE, 8(7), e67332.doi.org/10.1371/journal.pone.0067332
8. Peng RD (2011) Reproducible research in computational science. Science, 334(6060): 1226-1227.
9. Thayer KA, Wolfe MS, Rooney AA, Boyles AL, Bucher JR, and Birnbaum LS (2014) Intersection of systematic review methodology with the NIH reproducibility initiative. Environmental Health Perspectives. 122: A176-A177. http://ehp.niehs.nih.gov/wpcontent/uploads/122/7/ehp.1408671.pdf. Accessed 10 November 2015 [OpenAIRE]
10. George BJ, Sobus JR, Phelps LP, Rashleigh B, Simmons JE, Hines RN (2015) Raising the bar for reproducible science at the U.S. Environmental Protection Agency Office of [OpenAIRE]
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).20 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% views 1K downloads 1K citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).20 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% - 1Kviews1Kdownloads
citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!viewsViews provided by UsageCounts
Views provided by UsageCountsdownloadsDownloads provided by UsageCounts
Downloads provided by UsageCounts 20
Top 10%
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Top 10%
1K
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- Ubiquity Press United States
- University of Leicester United Kingdom
- University Of Reading
- British Library United Kingdom
- Massachussets General Hospital Pathology Harvard Medial School United States
- Publicis Groupe (France) France
- University of Reading Finland
- University of Leicester Finland
- Acentech (United States) United States
- Columbia University United States
- Ubiquity Press (United Kingdom) United Kingdom
- Massachussetts General Hospital Harvard Medical School Neurogenetics Unit United States
- Univera (United States) United States
- Ubiquity Press Switzerland
- Universia Spain
- Universeum Sweden
- Publicis Groupe (Switzerland) Switzerland
- Publicis Groupe (United States) United States
- German Climate Computing Centre Germany
- King’s University United States
- Carleton University Canada
- Dedan Kimathi University of Technology Kenya
- University of Reading United Kingdom
- Universitalia Italy
- German Climate Computing Center
- Woods Hole Oceanographic Inst United States
- Columbus University Panama
- Ubiquity Press Netherlands
- New York University
- University of Leicester, UK United Kingdom
- Ubiquity Press Germany
- Univé (Netherlands) Netherlands
- Univerza (Czechia) Czech Republic
- University of Leicester
- Publicis Groupe (Germany) Germany
- Castleton University United States
- Woods Hole Oceanographic Institution United States
- Ubiquity Press Czech Republic
- Carleton University - Canada
- Woods Hole Oceanographic Institution / Edgcombs lab
Additional Contributors: Tim Clark, Eleni Castro, Elizabeth Newbold, Samuel Moore, Brian Hole This is the revised version of: Bloom, Theodora et al.. (2015). Workflows for Research Data Publishing: Models and Key Components (Submitted Version). Zenodo. 10.5281/zenodo.20308 Abstract Purpose: 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 in order to provide a reference model for these stakeholders. Methods: 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. Results: The results of this examination have been used to derive a data publishing reference model comprised of 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. Conclusions: 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. 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.