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EVENTS: A dataset on the history of top-prestigious events in five computer science communities

: Fathalla, Said; Lange, Christoph

Postprint urn:nbn:de:0011-n-5200203 (757 KByte PDF)
MD5 Fingerprint: e0ac0b751b98bb9eb261ec7f2bee0174
Erstellt am: 1.12.2018

González-Beltrán, A.:
Semantics, Analytics, Visualization. 3rd International Workshop, SAVE-SD 2017 : Perth, Australia, April 3, 2017, and 4th International Workshop, SAVE-SD 2018, Lyon, France, April 24, 2018, Revised Selected Papers
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 10959)
ISBN: 978-3-030-01378-3 (Print)
ISBN: 978-3-030-01379-0 (Online)
ISBN: 978-3-030-01380-6
International Workshop "Semantics, Analytics, Visualisation - Enhancing Scholarly Data" (SAVE-SD) <3, 2017, Perth>
International Workshop "Semantics, Analytics, Visualisation - Enhancing Scholarly Data" (SAVE-SD) <4, 2018, Lyon>
Deutsche Forschungsgemeinschaft DFG
AU 340/9-1; OSCOSS
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()
scientific event dataset; Scholarly Communication; digitization; metadata analysis

Information emanating from scientific events, journal, organizations,institutions as well as scholars become increasingly available online. Therefore, there is a great demand to assess, analyse and organize this huge amount of data produced every day, or even every hour. In this paper, we present a dataset (EVENTS) of scientific events, containing historical data about the publications, submissions, start date, end date, location and homepage for 25 top-prestigious event series (718 editions in total) in five computer science communities. The dataset is publicly available online in three different formats (i.e., CSV, XML, and RDF). It is of primary interest to the steering committees or program chairs of the events to assess the progress of their event over time and compare it to compe ting events in the same field, and to potential authors looking for events to publish their work. In addition, we shed light on these events by analyzing their metadata over the last 50 years. Our transferable analysis is based on exploratory data analysis.