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Abstract Geosciences generate an ever-growing volume of complex data, much of it unlabeled, presenting significant analytical challenges. While artificial intelligence (AI) offers potential solutions, the current dominant paradigms, supervised and unsupervised learning, exhibit key limitations. Supervised training typically requires large labeled datasets and is often task specific, limiting its ability to generalize. Classical unsupervised approaches (i.e. clustering), while capturing regular collocated patterns from unlabeled data, are also task specific and do not learn long range multiscale dependencies. Consequently, there is a need to leverage massive datasets to learn general purpose representations that support a wide range of scientific and operational tasks. Transformer architectures, despite their proven success in natural language processing, weather forecasting, and genome data analysis, remain largely underutilized in atmospheric and oceanographic data analytics. Training transformer models requires massive datasets, specialized AI expertise, and significant computing resources. In this article, we argue for a paradigm shift toward self-supervised learning (SSL) to leverage the capabilities of scalable transformer encoders for large scale data analysis and downstream product development in geoscience. SSL allows models to learn robust, generalizable representations directly from massive unlabeled data through pretext tasks. The self-attention mechanism in transformers is particularly effective for capturing complex spatiotemporal relationships. By leveraging pretrained models, researchers without extensive AI expertise or computational resources can access advanced capabilities, thereby unlocking the scientific potential of vast archives of observational and model data. We outline the potential of SSL with transformers and propose a concrete community based framework aimed at accelerating its adoption. Realizing this vision will require sustained institutional support, computing infrastructure, and interdisciplinary expertise. 

The double-spin-polarization observable E for γ p → pπ0 has been measured with the CEBAF Large Acceptance Spectrometer (CLAS) at photon beam energies Eγ from 0.367 to 2.173 GeV (corresponding to center-ofmass energies from 1.240 to 2.200 GeV) for pion center-ofmass angles, cos θc.m. π0 , between − 0.86 and 0.82. These new CLAS measurements cover a broader energy range and have smaller uncertainties compared to previous CBELSA data and provide an important independent check on systematics. These measurements are compared to predictions as well as new global fits from The George Washington University, Mainz, and Bonn-Gatchina groups. Their inclusion in multipole analyses will allow us to refine our understanding of the single-pion production contribution to the Gerasimov-Drell- Hearn sum rule and improve the determination of resonance properties, which will be presented in a future publication.