Considerable scientific work involves locating, analyzing, systematizing, and synthesizing other publications. Its results end up in a paper's "background" section or in standalone articles, which include meta-analyses and systematic literature reviews. The required research is aided through the use of online scientific publication databases and search engines, such as Web of Science, Scopus, and Google Scholar. However, use of online databases suffers from a lack of repeatability and transparency, as well as from technical restrictions. Thankfully, open data, powerful personal computers, and open source software now make it possible to run sophisticated publication studies on the desktop in a self-contained environment that peers can readily reproduce. Here we report a Python software package and an associated command-line tool that can populate embedded relational databases with slices from the complete set of Crossref publication metadata, ORCID author records, and other open data sets, for in-depth processing through performant queries. We demonstrate the software's utility by analyzing the underlying dataset's contents, by visualizing the evolution of publications in diverse scientific fields and relationships among them, by outlining scientometric facts associated with COVID-19 research, and by replicating commonly-used bibliometric measures of productivity, impact, and disruption.
翻译:大量科学工作涉及查找、分析、系统化和综合其他出版物,其结果最终出现在纸张的“背景”部分或独立的文章中,其中包括元分析和系统文献审查。所需的研究通过使用在线科学出版物数据库和搜索引擎(如科学网、斯科普斯和谷歌学者等)得到帮助。但是,在线数据库的使用缺乏重复性和透明度,也缺乏技术限制。感谢、公开的数据、强大的个人计算机和开放源码软件,使得现在有可能在一个可随时复制的自足环境中在桌面上进行复杂的出版物研究。在这里,我们报告一个Python软件包和相关的指令线工具,它能够将整套Crossref出版物元数据、ORCID作者记录和其他开放数据集的切片纳入嵌入式关系数据库,通过业绩查询进行深入处理。我们通过对不同科学领域和它们之间的关系的演变情况进行直观分析,通过概述共同的科学研究、重塑的生产力和重塑性研究,通过共同的重塑、重塑性研究、重塑性研究,从而证明软件的效用。