Session 17-B
Fireside Chat: Unleashing Global Research Capabilities across Industry through Data Science
For over two centuries, scientific publishing has been constrained by traditional models, slow technology adoption, and misaligned incentives. In this context, AllSci emerges as a transformative platform that “atomizes” scientific content into fundamental units – research questions, hypotheses, results, and methods. This modular approach aims to dismantle barriers to content access, speed up the publication process, and promote the publication of all results, including null findings and replication studies. AllSci leverages advancements in machine learning and LLM-based knowledge extraction techniques to reengineer the way scientific data is processed, analyzed, and disseminated. By incorporating natural language processing (NLP), large-scale text mining, and advanced semantic analysis, AllSci enhances the functionality of search and discovery in research, fostering greater innovation.
Applying data science principles such as deep learning, ensemble methods, and unsupervised learning, AllSci ensures comprehensive indexing and clustering of scientific literature. Join us to discover how AllSci applies cutting-edge data science methodologies to revolutionize scientific publishing, making research more accessible and aligned with contemporary needs for knowledge creation.
Speaker
Dr. Tammy Hanna
Director, Product Management, AllSci
Dr. John Talburt
Chair of Information Quality and Director of Information Quality Graduate Program, UA Little Rock
dr. shaurya rohatgi
Applied Scientist, AllSci
derek reisfield
CFO, AllSci
