Libraries deal with large amounts of data in the digital environment. Librarians manipulate, update and integrate data on e-journals & e-books every year to the new knowledge base or in their intended library software. Data need to be cleaned, transformed and refined before uploading. OpenRefine is a useful data wrangling tool to filter, clean and transform the data before migration. The paper exercises largescale data cleaning, extraction and analysis of publication data (81,729) downloaded from Scopus during 2016-2020 in the field of Mathematics where at least one author is affiliated with an Indian institute or University. The result shows that 76,712(93.86%) documents have DOIs; sharp increase in ORCID from 4.27% (2016) to 26.25% (2020). The paper also shed a light on gender analysis and the gravity of its disparity in the field of Mathematics. Based on first author analysis, the result reveals that 73% are male authors whereas 27% are female based on the study of over half-lakh papers on Mathematics, where at least one author is from India. There is extreme inequality in gender distribution in the scientific research publications in mathematics.

图书馆处理数字环境中的大量数据。图书馆员每年都会对电子期刊和电子书上的数据进行操作、更新和整合到新的知识库或他们打算使用的图书馆软件中。上传前需要对数据进行清理、转换和提炼。OpenRefine 是一个有用的数据整理工具，可在迁移前过滤、清理和转换数据。该论文对 2016-2020 年期间从 Scopus 下载的数学领域的出版数据 (81,729) 进行大规模数据清理、提取和分析，其中至少有一位作者隶属于印度研究所或大学。结果显示，76,712（93.86%）份文档具有 DOI；ORCID 从 4.27%（2016 年）急剧增加到 26.25%（2020 年）。该论文还阐明了性别分析及其在数学领域的差距的严重性。根据第一作者分析，根据对超过 50 万篇数学论文的研究，结果显示 73% 是男性作者，而 27% 是女性，其中至少有一位作者来自印度。数学类科研出版物的性别分布极度不平等。