USDA (Unified Scientific Discovery Accelerator)

Small portion of the data

Inspiration

The main reason that inspired us to work on Unified Scientific Discovery Accelerator(USDA) was that we wanted to simplify the process of finding the right works,research papers for the topic on which we wanted to work 😊 . Because finding the right reference to start off with your work is very important.

What it does

Unified Scientific Discovery Accelerator(USDA) basically asks for the user to enter the name of disease on which he/she wants to research or work on. Then USAD through it's system provides the user with the relevant and appropriate existing research-works related to his query disease.This simplifies the process of filtering the research works for the user.

How we built it

We built USDA using a combination of advanced search algorithms, a rich database of scientific papers, and a user-friendly interface 😊. We integrated machine learning techniques to improve the accuracy of research suggestions and used a scalable back end to handle large datasets. The platform uses data extraction methods to pull research works related to a specific disease and present them in a user-friendly format.

Challenges we ran into

The main challenge was to handle the dataset and performing queries on it. Because it was the first time that we were working on graph database. We also encountered challenges with integrating machine learning models to accurately categorize and suggest the right research.

Accomplishments that we're proud of

1.We are proud of how quickly we were able to develop a platform that has the potential to accelerate scientific discovery 😄.

The integration of machine learning model in our project was one of our biggest achievement.
Understanding how graph database such as Neo4j works and it's visual representation was also pretty new experience 😄.

What we learned

We learned the importance of proper data handling and how important it is to ensure that data fed into machine learning models is accurate and clean. We learnt about the real challenges researchers or students face when looking for relevant information and how we can help solve this problem.In short we learnt about how combining proper data and machine learning model helps in building a good platform that can help the users 🙂.

What's next for USDA (Unified Scientific Discovery Accelerator)

Next, we plan to integrate additional AI-driven features, such as personalized research suggestions based on a user’s past research interests. We also aim to expand our database to include more diseases and related studies, enhancing the platform's value and hope we achieve it.