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 458,321 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. <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. <em><strong>Popularity:</strong></em> Citation-based measure reflecting the current impact of an article. This is based on the AttRank⁵ 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. <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 RAM⁶ 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, tweets between 1/1/22-7/1/22 have been considered from the previous dataset. 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). The work is based on the following publications: COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-01-17 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-01-17. doi:10.5281/zenodo.3715506 Chen Q, Allot A, &amp; Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-01-17) 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 I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) 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 peer-reviewed publication here (also here there is an outdated preprint version).<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> <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评价参数。本数据集依托两项数据源构建：一是由Semantic Scholar（语义学者）团队发布的CORD-19数据集¹，二是LitCovid数据库提供的经人工整理的数据集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。经处理后，本数据集最终包含458,321篇唯一标识的学术文章及其关联元数据（如底层引用网络）。 我们基于本数据集为每篇文章计算了以下影响力评价指标： **影响力（Influence）**：基于引用的评价指标，用于反映单篇文章的整体影响力。该指标依托PageRank³网络分析方法构建，在引用网络场景中，通过文章在全局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。 **替代影响力（Influence_alt）**：另一项基于引用的整体影响力评价指标，即单篇文章的总引用量，其计算依托BIP4COVID19数据集内文章间的引用网络完成。 **热度（Popularity）**：基于引用的评价指标，用于反映单篇文章的当前影响力。该指标基于AttRank⁵引用网络分析方法构建。类似PageRank的传统方法会对新近发表的文章存在偏见（新文章需要时间积累首篇引用），而AttRank通过引入注意力机制缓解了这一问题——该机制类同于带时间限制的优先依附模型，可精准捕捉研究者对近期受关注论文的阅读偏好，因此更适用于反映单篇文章当前的“热度”。 **替代热度（Popularity alternative）**：另一项基于引用的当前影响力评价指标（在BIP4COVID19数据集26版本之前，该指标为官方提供的基础热度指标）。该指标基于RAM⁶引用网络分析方法构建。如前文所述，传统方法对新发表文章存在偏见，而RAM通过“时间感知”策略缓解该问题，因此同样适用于反映单篇文章当前的“热度”。本指标通过PaperRanking库（https://github.com/diwis/PaperRanking）⁴计算得到。 **社交媒体关注度（Social Media Attention）**：与单篇文章相关的推文数量。相关数据从COVID-19-TweetIDs数据集采集，本版本使用了该数据集2022年1月1日至2022年7月1日期间的推文数据。 本数据集提供5个CSV格式文件，所有文件包含完全一致的字段信息，但各自按照不同的影响力指标进行排序。所有CSV文件均采用制表符分隔，且共享相同的列字段：PubMed_id、PMC_id、DOI、influence_score、popularity_alt_score、popularity score、influence_alt score、tweets count。 本项工作依托以下出版物完成： 1. COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-01-17. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-01-17. doi:10.5281/zenodo.3715506 2. Chen Q, Allot A, Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-01-17) 3. R. Motwani, L. Page, S. Brin, T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. 4. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 5. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020) 6. Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 依托本数据集构建的COVID-19文献探索网页工具可通过此处访问。更多细节可查阅同行评议论文（此处同时提供过时的预印本版本）。 <em>资助说明：本研究获得项目“从大数据管理迈向数据科学”（项目编号MIS 5002437/3）的资助，该项目隶属于“强化研究与创新基础设施”行动，由“竞争力、创业与创新”运营计划（NSRF 2014-2020）资助，并由希腊与欧盟（欧洲区域发展基金）共同出资。</em> <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。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）进行发布。