Today, the Polymathic AI team released two of its open-source training dataset collections to the public — a colossal 115 terabytes in total from dozens of sources — for the scientific community to use to train AI models and enable new scientific discoveries. The datasets include data from dozens of sources, including astrophysics, biology, fluid dynamics, acoustics and chemistry. The Polymathic AI team provides further information about the datasets in two papers accepted for presentation at the leading machine learning conference, NeurIPS, to be held in December in Vancouver, Canada.

Scientists have a new way to use data from high-energy particle smashups to peer inside protons. Their approach uses quantum information science to map out how particle tracks streaming from electron-proton collisions are influenced by quantum entanglement inside the proton. The results reveal that quarks and gluons, the fundamental building blocks that make up a proton’s structure, are subject to so-called quantum entanglement. 

Researchers report the reconciled mass balance of Antarctic Ice Sheet (AIS) and its regions from 1996 to 2021 based on 16 independent solutions provided by scientists from 8 Chinese research institutions. This is the first publication of such collaborative research efforts, which is not included in previous publications of the Ice sheet Mass Balance Inter-comparison Exercise (IMBIE) team. The results show that AIS has lost a total ice mass of 3213±253 Gt during this period, an equivalent of 8.9±0.7 mm of Global Sea Level Rise.