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Title: J2J-GR: Journal-to-Journal References by Greek Researchers
Authors: Pispiringas, Leonidas
Dervos, Dimitris A.
Evangelidis, Georgios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Bibliography metadata
Journal-to-journal references
Data visualization
Journal evaluation
Issue Date: 21-Oct-2019
Volume: 11815
First Page: 83
Last Page: 95
Volume Title: Model and Data Engineering
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: The Hellenic Academic Libraries Link (HEAL-Link, operates since 1998. Its members include all the academic and/or research institutions operating under the auspices of the Hellenic Ministry of Education, plus the Academy of Athens, the National Library, and the Institute of Educational Policy. The present paper reports on the research effort undertaken in order to facilitate the decision-making process and the development of a HEAL-Link strategy plan. The focus is on scientific journals the Greek academic/research community publish their works with, and on the journals they reference. It is assumed that when a researcher makes a reference to an article in a scientific journal, the latter is considered to comprise a valuable source of information. The more references made to articles published with a given journal, the higher the value of the latter as a source of scientific information to the HEAL-Link user community. In order to exploit the aforementioned research goal, bibliographic metadata from nearly 63,000 research publications have been collected and pre-processed. The publications involve at least one (co-)author affiliated to a Greek academic/research institution. They span over a period of nine years (2010–2018), and nearly 10,000 journals. The bibliographic data include metadata on subject (discipline) area(s). The findings are made public via a Web application ( The latter utilizes interactive graphs that facilitate the interpretation of the relevant bibliography data, and it is seen to comprise a springboard for conducting further data analytic and mining tasks.
ISBN: 978-3-030-32064-5
ISSN: 0302-9743
Other Identifiers: 10.1007/978-3-030-32065-2_6
Appears in Collections:Department of Applied Informatics

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