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 460,225 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 2022-01-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-01-25. doi:10.5281/zenodo.3715506 Chen Q, Allot A, &amp; Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-01-25) 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文献的影响力指标与统计量。本数据集基于：Semantic Scholar团队发布的CORD-19数据集¹，以及LitCovid数据库提供的精选数据²。我们对这些数据进行了清洗，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460,225篇独立文献的数据集，附带相关元数据（如引文网络基础数据）。 我们利用该数据集为每篇文献计算了以下影响力指标： * **影响力（Influence）**：基于引文的文献总影响力指标，依托PageRank³网络分析方法构建。在引文网络语境下，该指标通过文献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完成计算。 * **替代影响力（Influence_alt）**：另一项基于引文的文献总影响力指标，即单篇文献的引文数量，其计算基于BIP4COVID19数据集中文献间的引文网络。 * **流行度（Popularity）**：基于引文的文献当前影响力指标，依托AttRank⁵引文网络分析方法构建。类似PageRank的传统方法易对新近发表文献产生偏见（新文献需要时间获得首次引用），而AttRank通过引入注意力机制缓解了这一问题，该机制类似带时间限制的偏好依附模型，可精准捕捉研究者对近期受关注文献的阅读偏好，因此更适合体现文献当前的“热度”。 * **替代流行度（Popularity alternative）**：另一项基于引文的文献当前影响力指标（该指标为BIP4COVID19数据集26版之前的基础流行度指标），依托RAM⁶引文网络分析方法构建。同样针对传统方法对新文献的偏见问题，RAM通过“时间感知”方法进行优化，因此同样适合体现文献当前的“热度”，该指标通过PaperRanking库⁴完成计算。 * **社交媒体关注度（Social Media Attention）**：与该文献相关的推文数量，相关数据采集自COVID-19-TweetIDs数据集。本版本中，我们采用了该数据集2022年1月1日至2022年7月1日期间的推文数据。 本数据集包含5个以制表符分隔的CSV文件，所有文件包含完全一致的字段信息，但条目排序依据的影响力指标各不相同。所有文件共享以下列字段：PubMed_id、PMC_id、DOI、influence_score、popularity_alt_score、popularity score、influence_alt score、tweets count。 本工作基于以下学术成果： 1. COVID-19 Open Research Dataset (CORD-19). 2020. Version 2022-01-25 Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2022-01-25. doi:10.5281/zenodo.3715506 2. Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193 (version 2022-01-25) 3. R. Motwani, L. Page, S. Brin and 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, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380 可通过指定链接访问基于本数据集构建的COVID-19文献探索网页用户界面。更多细节可参阅我们的同行评议论文（此处另有过时的预印本版本）。 **资助说明**：本工作得到项目“从大数据管理迈向数据科学”（项目编号MIS 5002437/3）的支持，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，由希腊与欧盟（欧洲区域发展基金）共同出资。 **使用条款**：本数据按"现状"提供，不附带任何形式的保证。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）进行分发。