A survey dataset of 1,000 battery industry professionals with LLM-assisted free-text categorization

Résumé fourni par la source

The 2024 Battery Talent Census has created the largest global primary dataset on battery workforce and talent trends to date. The Census offers insights into workforce development trends, including skills and training gaps, compensation, benefits, workforce mobility, student perspectives, and worker sentiment. These findings have the potential to shape current and future discourse on workforce training, higher education curricula, workforce policy, and pay equity. This work curates the entire Census dataset and presents a frontier large language model (LLM) driven data analysis pipeline that provides reproducible categorization of free-form text responses. The usage of this custom pipeline is demonstrated in the context of automating Census data analysis for a dataset consisting of tens of thousands of unique text responses. The same pipeline is broadly applicable for automating the categorization of survey data of any size, with the potential for broader application to larger survey datasets.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.