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KMi Researchers Win Vannevar Bush Best Paper Award at JCDL2019

Petr Knoth, Wednesday 05 June 2019 | Annotate
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KMi researchers Dasha Herrmannova, Nancy Pontika and Petr Knoth win Vannevar Bush Best Paper Award at the Joint Conference on Digital Libraries (JCDL 2019) for their paper titled: “Do Authors Deposit on Time? Tracking Open Access Policy Compliance”. JCDL 2019 is an A* conference (highest rank) and the world’s highest ranking venue for digital libraries research, within the top 4% of all computer science conferences according to the Computing Research & Education Conference Portal. JCDL has taken place this year at the University of Illinois at Urbana-Champaign, United States.

The paper, which uses data from CORE (core.ac.uk) to quantify the growing traction of open access has received media attention even prior to the presentation at JCDL 2019 and was featured in an article in Physics Today. Dasha Herrmannova says: “I am utterly astonished and still can’t get over the fact that we won the best paper award last night at this amazing conference.” Petr Knoth says: “It has been a pleasure to work with this amazing team. We went through many revisions on this paper as the work turned out to be more complicated than we originally anticipated, but it paid off.”

What is even more impressive is that this is the second Best Paper Award in a 12-month period at key digital libraries conferences for the Big Scientific Data and Text Analytics Group (BSDTAG). David Pride and Petr Knoth won the Best Paper Award at the best European DL conference - TPDL in Porto, Portugal in September 2018 for their paper: “Peer review and citation data in predicting university rankings, a large-scale analysis”.



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Drahomira Herrmannova

Research Associate

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Nancy Pontika

Project Officer - Open Access Aggregation

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CORE

The world's largest collection of open access research papers

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CORE - COnnecting REpositories

Linking semantically similar publications from Open Access repositories using text mining from full-text and representing the relations as Linked Data

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