[Updated! This version contains commercial potential predictions for over 68 million scientific articles published worldwide between 1990 and 2026.] This dataset introduces a novel index designed to predict the commercial potential of scientific articles. The index captures the probability that an article will be used by firms for the development of marketable products or processes. In addition to commercial potential, the dataset also introduces an index to predict scientific potential—the likelihood that an article will be relevant for the advance of science, regardless its commercial application. The indices are crucial for researchers focused on understanding 1) the production of science with commercial potential and 2) the pathway from academic research to market innovations and the factors that influence the commercial viability of scientific discoveries. Citation Information: If you use this dataset, please cite the article: “Masclans, R., Hasan, S., & Cohen, W. M. (2025). Measuring the Commercial Potential of Science. Strategic Management Journal, 46(9), 2199-2236.” Components of the Dataset: The dataset encompasses indices for over 30 million articles that meet the following criteria: Publication year: 1990 to 2026 Published under universities worldwide Articles in the applied and natural sciences and engineering fields Data is delivered via a single csv file. Each row contains information for a scientific article, with the following variables: ‘doi’: Digital Object Identifier—unique article identifier that can be used to match to other data sources, such as OpenAlex, Dimensions, or Web of Science. ‘compot’: commercial potential index. ‘scipot‘: scientific potential index. To develop the commercial potential index, we employed SciBert (Beltagy et al., 2019), a Large Language Model for scientific understanding. We fine tune SciBert with deep neural networks to classify scientific articles based on their potential for commercial application. We trained one predictive model per year using the text of an academic article’s abstract to generate ex-ante, out-of-sample, and out-of-training-time-period predictions of any given scientific article’s commercial potential. Licensing and Contact Information: The dataset and its components are distributed under a Creative Commons Attribution Non-Commercial license. Acknowledgments: We thank Duke University and the Kauffman Foundation for funding the creation of this dataset.