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Dataset Open Access

# BIP4COVID19: Impact metrics and indicators for coronavirus related publications

Thanasis Vergoulis; Ilias Kanellos; Serafeim Chatzopoulos; Danae Pla Karidi; Theodore Dalamagas

### Dublin Core Export

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<dc:creator>Thanasis Vergoulis</dc:creator>
<dc:creator>Ilias Kanellos</dc:creator>
<dc:creator>Serafeim Chatzopoulos</dc:creator>
<dc:creator>Danae Pla Karidi</dc:creator>
<dc:creator>Theodore Dalamagas</dc:creator>
<dc:date>2020-03-21</dc:date>
<dc:description>This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on the CORD-19 dataset released by the team of Semantic Scholar in response to the relevant, ongoing pandemic:

COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-13. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-03-18. doi:10.5281/zenodo.3715506

These data have been cleaned and integrated with data from other sources (e.g., PMC). The result was a subset of the COVID-19 dataset (23,222 unique articles). We constructed the underlying citation network and utilized it to produce, for each article, the values of the following impact measures, using the PaperRanking (https://github.com/diwis/PaperRanking) library1:

Citation-based influence: This is based on the PageRank2 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the network. Since it considers the whole network, it is an indicator of the impact in the long term.
Citation-based popularity: This is based on the RAM3 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the impact of a publication in the short term.

The work is based on the following publications:

I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019
Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380
R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.

<dc:description>We acknowledge support of this work by the project "Moving from Big Data Management to Data Science" (MIS 5002437/3) which is implemented under the Action "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).</dc:description>
<dc:identifier>https://zenodo.org/record/3723282</dc:identifier>
<dc:identifier>10.5281/zenodo.3723282</dc:identifier>
<dc:identifier>oai:zenodo.org:3723282</dc:identifier>
<dc:relation>doi:10.5281/zenodo.3723281</dc:relation>
<dc:relation>url:https://zenodo.org/communities/covid-19</dc:relation>
<dc:relation>url:https://zenodo.org/communities/zenodo</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:subject>COVID-19</dc:subject>
<dc:subject>coronavirus</dc:subject>
<dc:subject>scientometrics</dc:subject>
<dc:subject>bibliometrics</dc:subject>
<dc:title>BIP4COVID19: Impact metrics and indicators for coronavirus related publications</dc:title>
<dc:type>info:eu-repo/semantics/other</dc:type>
<dc:type>dataset</dc:type>
</oai_dc:dc>

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