Virtual Codeathon Opportunity. Apply today!
The National Library of Medicine (NLM) wants to ensure that results retrieved using the PubMed search “Best Match” ranking algorithm provide access to citations of all relevant literature. To this end, we are hosting a virtual codeathon, Investigating Accurate and Equitable Representation in PubMed Search Results, and invite you to apply by February 28th!
This codeathon will bring together a diverse group of collaborators with expertise in natural language processing, search and retrieval, artificial intelligence, and data visualization. We encourage both coders and non-coding subject matter experts to apply. We will assign applicants to codeathon teams of 5-10 people based on their interests and skills. These teams will work together to uncover any inequities in PubMed “Best Match” search and suggest ways to mitigate them. The event will be cooperative rather than competitive, and teams will share ideas and technical advice.
After the event, we will make the team products publicly available through the NCBI Codeathons GitHub Organization. We encourage participants to share their work online and at conferences and will assist in manuscript publication.
Background Information: PubMed, NLM’s flagship database of more than 30 million citations and abstracts of biomedical and life sciences literature, is used every day by more than two million people—making it one of the most frequently used U.S. government websites. Recently, PubMed search provided a “Best Match” sort feature as the default to rank the search results based on search terms and other relevant properties (Source: Best Match: New relevance search for PubMed).
NLM supports the National Institutes of Health’s (NIH) commitment to promote diversity, equity, inclusion, and accessibility in biomedical research. One of the foundations of a sound, innovative scientific research process is the ability to examine previous studies that have been conducted in an area of interest. Finding and accessing information free from bias is critical to scientific discovery, the inclusion and promotion of creative and diverse thought, and the achievement of innovative progress for the betterment of human health.