Women Climate Change

Abstract

For decades women have been underrepresented in academia regardless of subject or profession. This project aims to shed light on women’s achievements specifically in the intersection of Climate Change and Disease by generating a replicable matching algorithm that can be applied to label large datasets with the sex of their authors. This data will then be turned into a variety of visualizations that will help more accurately depict women’s involvement in academia. The team utilized an open source MIT web scraping tool to scrape PubMed, an online directory of research papers to formulate the dataset for this project. The scraped data was left in CSV format, which we then piped into a Python file to conduct the processing. We have downloaded publicly available datasets labeled with the most common names in Canada, the USA, Mexico, Brazil, France, Finland, Australia, and India to create our labeled names repository. The Python routine we used holds the labeled names repository as its backend and looks for matches between the names in the input files, the author’s names and the names in the labeled directory. Following the application of this matching algorithm on our scraped dataset, the now labeled data was placed into Tableau to generate our visualizations. It was mentioned earlier that this project specifically aims to highlight women’s accomplishments in the field of Climate Change and Disease, but our overarching goal with this project is to design a replicable approach that can be easily applied to other fields such as “Agriculture” or “Occupational Therapy”. Through this project we aim to help women get the accreditation they deserve in a variety of fields, with the start being climate change and disease. This project will also provide researchers/data analysts with an easy to use tool in the future to quickly label named datasets more accurately than current tools on the market.