BIP4COVID19: Impact metrics and indicators for coronavirus related publications

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: Τhe CORD-19 dataset released by the team of Semantic Scholar¹ and Τhe curated data provided by the LitCovid hub². These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 117,972 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: <em><strong>Influence:</strong></em> Citation-based measure reflecting the total impact of an article. This is based on the PageRank³ 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 (https://github.com/diwis/PaperRanking) library⁴. <em><strong>Popularity:</strong></em> Citation-based measure reflecting the current impact of an article. This is based on the RAM⁵ 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 (https://github.com/diwis/PaperRanking) library⁴. <em><strong>Social Media Attention: </strong></em>The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, only tweets between Jan. 21^st and Mar. 31^st have been considered from the previous dataset. The rest will be included during next updates. We provide three 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, popularity_score, influence_score, tweets count). The work is based on the following publications: COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-06-09 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-06-09. doi:10.5281/zenodo.3715506 Chen Q, Allot A, &amp; Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2020-06-09) R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. 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 A Web user interface that uses these data to facilitate the COVID-19 literature exploration, can be found here. More details in our preprint here. <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.本数据集收录了一批与新型冠状病毒肺炎（COVID-19）及其致病冠状病毒相关的学术文献的影响力指标与统计量。本数据集基于以下资源构建：语义学者（Semantic Scholar）团队发布的CORD-19数据集¹，以及LitCovid知识库提供的标注数据²。 我们对上述数据进行了清洗，并整合了COVID-19-TweetIDs数据集及其他来源（如PubMed Central（PMC））的数据，最终得到包含117,972篇独立文献的数据集，附带相关元数据（如文献引用网络）。 我们基于该数据集为每篇文献计算了以下影响力指标： <em><strong>影响力（Influence）：</strong></em> 基于引用的指标，用于反映单篇文献的整体影响力。该指标基于PageRank³网络分析方法构建，在引用网络场景中，通过文献在整体网络中的中心性评估其重要性。本指标通过PaperRanking库（https://github.com/diwis/PaperRanking）⁴计算得到。 <em><strong>热度（Popularity）：</strong></em> 基于引用的指标，用于反映单篇文献的即时影响力。该指标基于RAM⁵引用网络分析方法构建。传统方法如PageRank对新近发表的文献存在偏倚（新文献需要时间获得首次引用），而RAM通过“时间感知”机制缓解了这一问题，因此更适合捕捉单篇文献当前的受关注热度。本指标同样通过PaperRanking库（https://github.com/diwis/PaperRanking）⁴计算得到。 <em><strong>社交媒体关注度：</strong></em> 指与该文献相关的推文数量，相关数据采集自COVID-19-TweetIDs数据集。本版本仅使用了该数据集2020年1月21日至3月31日期间的推文数据，剩余数据将在后续更新中补充。 本数据集提供三份CSV格式文件，三者包含完全相同的信息，但条目排序依据分别为不同的影响力指标。所有文件均采用制表符分隔，且拥有统一的字段：PubMed_id、PMC_id、DOI、popularity_score、influence_score、tweets count。 本工作基于以下公开成果构建： COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-06-09 获取自https://pages.semanticscholar.org/coronavirus-research，2020年6月9日访问。doi:10.5281/zenodo.3715506 Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, 《自然》579:193 (version 2020-06-09) R. Motwani, L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. 技术报告，斯坦福信息实验室。 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. 数据挖掘研讨会（ICDMW）, 373–380 基于本数据集实现的COVID-19文献探索网页用户界面已上线，更多细节可查阅我们的预印本。 <em><strong>使用条款：</strong></em> 本数据集按“现状”提供，不附带任何形式的担保。数据采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International）进行分发。