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 the CORD-19 dataset released by the team of Semantic Scholar in response to the relevant, ongoing pandemic: COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-03-13. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed 2020-03-18. doi:10.5281/zenodo.3715506 These data have been cleaned and integrated with data from other sources (e.g., PMC). The result was a subset of the COVID-19 dataset (23,222 unique articles). We constructed the underlying citation network and utilized it to produce, for each article, the values of the following impact measures, using the <em>PaperRanking</em> (https://github.com/diwis/PaperRanking) library¹: <em><strong>Citation-based influence</strong></em>: This is based on the PageRank² network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the network. Since it considers the whole network, it is an indicator of the impact in the <em>long term</em>. <em><strong>Citation-based popularity</strong></em>: This is based on the RAM³ 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 impact of a publication in the <em>short term</em>. The work is based on the following publications: 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 R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. <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）及致病冠状病毒相关的一批学术文献的影响力计量指标与统计特征。本数据集基于Semantic Scholar团队为应对持续蔓延的COVID-19大流行发布的《COVID-19开放研究数据集（COVID-19 Open Research Dataset, CORD-19）》2020版，版本号为2020-03-13，数据获取地址为https://pages.semanticscholar.org/coronavirus-research，访问时间为2020年3月18日，DOI为10.5281/zenodo.3715506。 本数据集已完成数据清洗，并整合了其他来源（如PubMed Central, PMC）的数据，最终得到该COVID-19数据集的一个子集，包含23222篇唯一学术文献。我们构建了底层引文网络，并借助PaperRanking库（https://github.com/diwis/PaperRanking）¹为每篇文献计算了以下影响力指标： - **基于引文的影响力（Citation-based influence）**：该指标基于PageRank²网络分析方法。在引文网络的语境下，它通过文献在网络中的中心性评估其重要性。由于该指标考量了整个网络结构，因此是衡量学术文献长期影响力的指标。 - **基于引文的流行度（Citation-based popularity）**：该指标基于RAM³引文网络分析方法。类似PageRank的传统方法会对新近发表的文献存在偏见（新文献需要时间才能获得首次引用），而RAM通过“时间感知”（time-awareness）机制缓解了这一问题，因此更适合捕捉学术文献的短期影响力。 本工作基于以下研究成果： I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou：科研文献的影响力排序：综述与实验评估，TKDE 2019 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序. 数据挖掘研讨会（ICDMW）, 373–380 R. Motwani, L. Page, S. Brin, T. Winograd. 1999. PageRank引文排序：为网络带来秩序. 斯坦福信息实验室技术报告. **使用条款**：本数据集按“现状”提供，不附带任何形式的明示或默示担保。本数据集基于知识共享署名4.0国际许可协议（Creative Commons Attribution 4.0 International）发布。