This project was constructed by a team of four senior students in Computer Science at Virginia Tech Shreya Mallamula, Adharsh Jayaseelan, Mohamed Kanu, and Seth Robertson for the Spring 2023 Hypertext/Multimedia Capstone. Special thanks to our client Dr. Escobar and our professor Dr. Fox for their guidance and feedback throughout the construction of this project.
## Getting started
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-[ ] [Set up project integrations](https://git.cs.vt.edu/shreyarm/womenclimatechange/-/settings/integrations)
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-[ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
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## Project Goals
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 machine learning 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 PostGreSQL database using PGAdmin. The next step is to train our own machine learning algorithm that we will generate using Dataiku. Dataiku is an easy to use platform that allows users to visually train machine learning algorithms, which felt like a good approach for our team that doesn’t have much experience using machine learning. We have downloaded publicly available datasets labeled with the most common names in America, France, China, and India, to start with training our algorithm. Following the application of this machine learning algorithm on our scraped dataset we will put the now labeled data 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”. The hope is that with more accurate visualizations, women will finally get the credit they deserve in all domains of academia.
