🏵️Overcoming Data Processing Bottlenecks in Energy Storage plus AI Expansion

Energy storage research is often slowed by the tedious processing of experimental data using proprietary or lab-made tools, hindering reproducibility and comparability. The lack of transparent, shared methodologies creates a significant data processing limitation. While open-source software like Python and R are gaining traction, dedicated open tools for MATLAB users, a popular language in the field, have been lacking. Emerging open-source initiatives, often built with MATLAB, are addressing these bottlenecks by providing comprehensive toolkits for data import, structuring, analysis, and visualization. These platforms emphasize data traceability through metadata and promote interoperability, fostering a more collaborative and efficient research ecosystem. This shift towards open tools promises greater transparency, reproducible science, and accelerated innovation in energy storage systems.