The problem InfoBase solves

1. Funding: Researchers often face difficulties in obtaining funding for their projects. The competition for grants and funding can be intense, and even when funding is available, it may come with strict requirements and limitations. There is no systematic funding process to ensure fair distribution of the funds as big projects and parties tend to influence the result. Apart from generic funding there are not many sources of revenue that researchers can opt to.
   In order to solve this problem, we have added "Quadratic Funding" to our application. Quadratic funding ensures fair distribution of funds without being influenced by big investors and projects. it majorly rely on the public interest and sentiments which ensuresthe funding is not biased. In addition, our platfform allows researchers and users to build DAOs around their interest and earn through NFT subscriptions. A no-code token deployment system is provided in our app to ensure seamless user experience. Researchers with quadratic funding, can also earn from pay for view contents (like live stream recordings, consultancy video calls, conversations and discussions etc powered by Huddle01).
2. Interdisciplinary collaboration: Many research questions today require interdisciplinary collaboration, which can be challenging due to differences in terminology, methods, and approaches among disciplines.
   Our platform provides seamless audio and video communication to ensure a better and smoother communication bbetween researchers and peers.
3. Access to data: In many fields, researchers rely on access to large and diverse datasets to conduct their studies. However, obtaining such data can be difficult due to legal, ethical, or financial barriers.
   In order to overcome this problem we have came up with a data science component that properly curates research paper data and serves it to the user. This makes makes the task of the user easy and time efficient. We plan to generate ZK-proofs for the paper

Challenges I ran into

Extracting relevant information and the main idea from research papers and transforming them into a format that can be quantitatively compared with other papers in the IPFS database requires a sophisticated approach.

One of the primary challenges is information extraction, which involves identifying and extracting relevant information from research papers. This challenge is compounded by the fact that research papers have varying structures and formats, making it difficult to extract the same information across all papers. Additionally, research papers often contain large amounts of irrelevant or redundant information that can make it challenging to identify the most important concepts and ideas.

To address these challenges, a robust approach to information extraction must involve advanced techniques such as natural language processing (NLP), which is used in this product.

Firstly, we addressed the issue of irrelevant or redundant information within the papers by implementing a pre-processing step that involved the removal of stop words, followed by the lemmatization of the remaining text. This allowed us to distill the essence of the papers and focus only on the most important concepts and ideas.

Next, we tokenized the pre-processed text to generate a weighted dictionary that captured the key themes and concepts within each paper. The weighting was determined by the frequency of occurrence of each token, allowing us to prioritize the most significant aspects of each paper.

To facilitate a quantitative comparison of the papers, we used cosine similarity as a measure of their similarity. This allowed us to compute the degree of similarity between the weighted dictionaries for each pair of papers.

We were also caught up in HuddleSDK and LightHouseSDK but mentors helped us with it throughout