4602770
doi
10.5281/zenodo.4602770
oai:zenodo.org:4602770
user-covid-19
Ilias Kanellos
Athena Research Center
Serafeim Chatzopoulos
Athena Research Center
Danae Pla Karidi
Athena Research Center
Theodore Dalamagas
Athena Research Center
BIP4COVID19: Impact metrics and indicators for coronavirus related publications
Thanasis Vergoulis
Athena Research Center
url:https://pages.semanticscholar.org/coronavirus-research
handle:www.biorxiv.org/content/10.1101/2020.04.11.037093v2
url:https://github.com/diwis/PaperRanking
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
COVID-19
coronavirus
scientometrics
bibliometrics
<p>This dataset contains impact metrics and indicators for a set of publications that are related to the <a href="https://en.wikipedia.org/wiki/Coronavirus_disease_2019">COVID-19 infectious disease</a> and the coronavirus that causes it. It is based on:</p>
<ol>
<li>Τhe <a href="https://pages.semanticscholar.org/coronavirus-research">CORD-19 dataset</a> released by the team of <a href="https://www.semanticscholar.org/">Semantic Scholar</a><sup>1</sup> and</li>
<li>Τhe curated data provided by the <a href="https://www.ncbi.nlm.nih.gov/research/coronavirus/">LitCovid hub</a><sup>2</sup>.</li>
</ol>
<p>These data have been cleaned and integrated with data from <a href="https://github.com/echen102/COVID-19-TweetIDs">COVID-19-TweetIDs</a> and from other sources (e.g., PMC). The result was dataset of 264,204 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:</p>
<ul>
<li><em><strong>Influence:</strong></em> Citation-based measure reflecting the total impact of an article. This is based on the PageRank<sup>3</sup> network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (<a href="https://github.com/diwis/PaperRanking">https://github.com/diwis/PaperRanking</a>) library<sup>4</sup>.</li>
<li><strong><em>Influence_alt:</em></strong> Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.</li>
<li><em><strong>Popularity:</strong></em> Citation-based measure reflecting the current impact of an article. This is based on the AttRank<sup>5</sup> citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.</li>
<li><em><strong>Popularity alternative:</strong></em> An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM<sup>6</sup> 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 current "hype" of an article. This measure was calculated using the PaperRanking (<a href="https://github.com/diwis/PaperRanking">https://github.com/diwis/PaperRanking</a>) library<sup>4</sup>.</li>
<li><em><strong>Social Media Attention: </strong></em>The number of tweets related to this article. Relevant data were collected from the <a href="https://github.com/echen102/COVID-19-TweetIDs">COVID-19-TweetIDs</a> dataset. In this version, tweets between 30/1/21-5/2/21 have been considered from the previous dataset. </li>
</ul>
<p>We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count).</p>
<p>The work is based on the following publications:</p>
<blockquote>
<ol>
<li>COVID-19 Open Research Dataset (CORD-19). 2020. Version 2021-03-08 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2021-03-08. doi:10.5281/zenodo.3715506</li>
<li>Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2021-03-08)</li>
<li>R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.</li>
<li>I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019</li>
<li>I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020)</li>
<li>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</li>
</ol>
</blockquote>
<p>A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found <a href="https://bip.covid19.athenarc.gr">here</a>. More details in our preprint <a href="https://www.biorxiv.org/content/10.1101/2020.04.11.037093v2">here</a>.<br>
<br>
<em>Funding: 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).</em></p>
<p><em><strong>Terms of use:</strong></em> These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</p>
Please cite: Thanasis Vergoulis, Ilias Kanellos, Serafeim Chatzopoulos, Danae Pla Karidi, Theodore Dalamagas. "BIP4COVID19: Releasing impact measures for articles relevant to COVID-19". bioRxiv 2020.04.11.037093; doi: https://doi.org/10.1101/2020.04.11.037093
Zenodo
2021-03-13
info:eu-repo/semantics/other
3723281
user-covid-19
39
1674431178.549405
23580288
md5:8f99b07ce67b8833f5e5617f8c32fa28
https://zenodo.org/records/4602770/files/articles_by_tweets.csv
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md5:8ea3c5e430cd8a597cf420a61d878d95
https://zenodo.org/records/4602770/files/articles_by_influence.csv
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md5:f867c984de7bfae2e11acf1ee581893f
https://zenodo.org/records/4602770/files/articles_by_influence_alt.csv
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md5:77fb82742dfc56be95846c855ecc44f4
https://zenodo.org/records/4602770/files/articles_by_popularity.csv
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md5:0140da3c67c7df297c92fe48b2cefab0
https://zenodo.org/records/4602770/files/articles_by_popularity_alt.csv
public
https://pages.semanticscholar.org/coronavirus-research
Cites
url
www.biorxiv.org/content/10.1101/2020.04.11.037093v2
Is supplement to
handle
https://github.com/diwis/PaperRanking
Cites
url
10.5281/zenodo.3723281
isVersionOf
doi