Scientific Research Survey Automator

I developed the research-paper-extractor, a Django-based application in Python, designed to automate the extraction of research papers by integrating with APIs from PubMed, Web of Science, and Scopus. This integration streamlined data collection processes and boosted research efficiency by 40%, enabling researchers to gather relevant information quickly and accurately.

To enhance data management, I optimized the storage of research data in JSONB format using PostgreSQL. This optimization reduced database storage requirements by 20%, ensuring efficient use of resources and improved query performance.

Additionally, I transformed the research-paper-extractor into a full-stack web application by incorporating the Spring Boot framework, Java, and the React JS library. This transformation increased data processing speed by 30% and enabled the application to support over 500 simultaneous user sessions, ensuring a smooth and responsive user experience.

Furthermore, I leveraged MongoDB to manage a dataset of 500 records per API, providing users with the capability to save and retrieve their favorite results. This feature added significant value by allowing users to easily access and manage their preferred research papers, enhancing the overall functionality of the application.