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：大量科学工作涉及定位、分析、系统化以及综合其他出版物，其成果最终呈现于论文的“背景”章节或独立文章（包括荟萃分析和系统性文献综述）。此类研究借助在线科学出版数据库和搜索引擎（如Web of Science、Scopus和Google Scholar）得以开展。然而，在线数据库的使用存在可重复性不足、透明度欠缺以及技术限制等问题。幸运的是，开放数据、高性能个人计算机和开源软件如今已使在桌面端运行自包含、可被同行轻松复现的复杂出版研究成为可能。本文报告一个Python软件包及其关联的命令行工具，该工具能够将Crossref出版元数据、ORCID作者记录及其他开放数据集的切片填充至嵌入式关系数据库中，并通过高性能查询实现深度处理。我们通过分析底层数据集内容、可视化不同科学领域出版物的演变及其相互关系、概述与COVID-19研究相关的科学计量学事实，以及复现生产力、影响力和颠覆性等常用文献计量指标，展示了该软件的实用性。