A comprehensive quality assessment framework for scientific events.

Bibliometrics Metadata analysis Quality assessment Recommendation Scientific events

Journal

Scientometrics
ISSN: 0138-9130
Titre abrégé: Scientometrics
Pays: Switzerland
ID NLM: 7901197

Informations de publication

Date de publication:
2021
Historique:
received: 03 07 2020
pubmed: 11 11 2020
medline: 11 11 2020
entrez: 10 11 2020
Statut: ppublish

Résumé

Systematic assessment of scientific events has become increasingly important for research communities. A range of metrics (e.g., citations, h-index) have been developed by different research communities to make such assessments effectual. However, most of the metrics for assessing the quality of less formal publication venues and events have not yet deeply investigated. It is also rather challenging to develop respective metrics because each research community has its own formal and informal rules of communication and quality standards. In this article, we develop a comprehensive framework of assessment metrics for evaluating scientific events and involved stakeholders. The resulting quality metrics are determined with respect to three general categories-events, persons, and bibliometrics. Our assessment methodology is empirically applied to several series of computer science events, such as conferences and workshops, using publicly available data for determining quality metrics. We show that the metrics' values coincide with the intuitive agreement of the community on its "top conferences". Our results demonstrate that highly-ranked events share similar profiles, including the provision of outstanding reviews, visiting diverse locations, having reputed people involved, and renowned sponsors.

Identifiants

pubmed: 33169040
doi: 10.1007/s11192-020-03758-1
pii: 3758
pmc: PMC7609385
doi:

Types de publication

Journal Article

Langues

eng

Pagination

641-682

Informations de copyright

© The Author(s) 2020.

Références

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Neuron. 2010 Jan 28;65(2):280-90
pubmed: 20152133
Sci Data. 2016 Mar 15;3:160018
pubmed: 26978244
Curr Biol. 2017 Dec 4;27(23):3699-3705.e3
pubmed: 29174894

Auteurs

Sahar Vahdati (S)

Department of Computer Science, University of Oxford, Oxford, UK.
Institute for Applied Informatics (InfAI), Dresden, Germany.

Said Fathalla (S)

University of Bonn, Bonn, Germany.
Faculty of Science, University of Alexandria, Alexandria, Egypt.

Christoph Lange (C)

RWTH Aachen University, Aachen, Germany.
Fraunhofer FIT, Sankt Augustin, Germany.

Andreas Behrend (A)

Institute for Telecommunications (INT), TH Köln, Germany.

Aysegul Say (A)

University of Bonn, Bonn, Germany.

Zeynep Say (Z)

University of Bonn, Bonn, Germany.

Sören Auer (S)

L3S Research Center, Leibniz University of Hannover, Hannover, Germany.
TIB Leibniz Information Centre for Science and Technology, Hannover, Germany.

Classifications MeSH