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 158,419 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 2020-08-23 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-08-23. doi:10.5281/zenodo.3715506 Chen Q, Allot A, &amp; Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2020-08-23) 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. This dataset version contains a minor update of the tweet counts. <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.本数据集收录了与新型冠状病毒肺炎（COVID-19）及其致病冠状病毒相关的一系列学术出版物的影响力指标与统计量。 本数据集基于语义学者（Semantic Scholar）团队发布的CORD-19数据集¹以及LitCovid知识库（LitCovid hub）提供的精选标注数据²构建。我们对上述数据进行清洗，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158,419篇独立学术文章及相关元数据（如引文网络底层结构）的数据集。 我们基于本数据集为每篇文章计算了以下三类影响力指标： <strong>影响力（Influence）</strong>：基于引文网络的综合影响力指标，依托PageRank³网络分析方法构建。在引文网络场景中，该指标通过单篇文章在整体网络中的中心性评估其重要性，通过PaperRanking库（https://github.com/diwis/PaperRanking）⁴计算得到。 <strong>热度（Popularity）</strong>：基于引文网络的即时影响力指标，依托RAM⁵引文网络分析方法构建。传统PageRank类方法对新近发表的文章存在偏见（新文章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，更适合捕捉单篇文章当前的学术关注度，同样通过PaperRanking库⁴计算得到。 <strong>社交媒体关注度</strong>：指与该文章相关的推文数量，相关数据采集自COVID-19-TweetIDs数据集。本版本仅纳入该数据集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，三者包含完全一致的信息，但分别按照不同的影响力指标进行排序。所有文件均采用制表符分隔，且包含相同字段：PubMed_id、PMC_id、DOI、热度得分（popularity_score）、影响力得分（influence_score）、推文计数（tweets count）。 本工作依托以下研究成果完成： 1. 新型冠状病毒肺炎开放研究数据集（COVID-19 Open Research Dataset, CORD-19）. 2020. 版本2020-08-23，获取自https://pages.semanticscholar.org/coronavirus-research. 访问时间2020-08-23. doi:10.5281/zenodo.3715506 2. Chen Q, Allot A, & Lu Z. (2020) 追踪最新冠状病毒研究进展. 《自然》579:193（版本2020-08-23） 3. R. Motwani, L. Page, S. Brin, T. Winograd. 1999. PageRank引文排名：为网络带来秩序. 技术报告. 斯坦福信息实验室 4. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: 基于影响力的学术出版物排名：综述与实验评估. TKDE 2019 5. Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, Kristina Lerman. 2011. 动态引文网络中的时间感知排名. 见：数据挖掘研讨会（ICDMW）. 373–380 可通过指定链接访问基于本数据集构建的新冠文献探索Web用户界面，详细信息可查阅我们的预印本。本数据集版本对推文计数进行了小幅更新。 <strong>使用条款</strong>：本数据集按"as is"提供，不附带任何形式的明示或默示担保。本数据集采用知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International license）进行发布。