Because of the rapid increase in literature around COVID-19, it is hard to keep up with the newest publications and find articles that are relevant for your work. That's why we have developed discovid.ai - a search engine specialized on the CORD-19 corpus (a collection of over 52,000 scholarly articles about COVID-19 and related viruses).
Our search engine supports complex boolean queries (AND, OR, NOT, etc.) and phrase queries to help you find exactly what you are looking for. But since you are not always sure what you are even looking for, we also show related publications to each article - so you can iteratively click your way through related research to browse the current literature and discover new insights!
Implemented in cooperation with TECO research group of KIT.
- allows complex boolean queries and phrase queries
- shows related research based on topic-wise similarity
- user-friendly interface
Research, Literature Review (Epidemiology, Healthcare, etc.)
There's a rapid increase in literature around COVID-19 that is hard to keep up with. This search engine helps you to stay up-to-date and find relevant articles.
We have trained a topic model on CORD-19 that allows us to recommend related papers based on topic-wise similarity. Since the model learns latent relationships within the corpus, this might help to discover new insights and relevant research.