The Commercial Potential of Science

[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.【更新说明】本版本包含了1990年至2026年间全球发表的超6800万篇学术论文的商业化潜力预测数据。 本数据集提出了一种用于预测学术论文商业化潜力的新型指标。该指标可量化某篇论文被企业用于开发可市场化产品或工艺的概率。除商业化潜力指标外，本数据集还推出了科学潜力预测指标——即某篇论文对科学发展具有贡献价值的可能性，无论其是否具备商业化应用前景。 上述两类指标对于致力于以下研究的学者至关重要：1）具备商业化潜力的科研成果产出情况；2）从学术研究到市场创新的转化路径，以及影响科学发现商业化可行性的各类因素。 引用说明：若使用本数据集，请引用如下文献：Masclans, R., Hasan, S., & Cohen, W. M. (2025). 衡量科学的商业化潜力. 《战略管理期刊》(Strategic Management Journal), 46(9), 2199-2236. 数据集构成：本数据集涵盖了符合以下条件的超3000万篇论文的指标数据： 1. 发表年份：1990年至2026年 2. 发表主体：全球各高校 3. 学科领域：应用科学、自然科学与工程学领域 数据以单个CSV文件形式交付。文件中每一行对应一篇学术论文的信息，包含以下变量： - `doi`：数字对象标识符（Digital Object Identifier），即唯一论文标识，可用于匹配OpenAlex、Dimensions或Web of Science等其他数据源。 - `compot`：商业化潜力指数。 - `scipot`：科学潜力指数。 为构建商业化潜力指数，研究团队采用了SciBert（Beltagy等人，2019）——一款面向科学文本理解的大语言模型（Large Language Model）。我们通过深度学习神经网络对SciBert进行微调，以基于商业化应用潜力对学术论文进行分类。研究团队每年训练一个预测模型，利用学术论文的摘要文本，生成任意给定论文的商业化潜力的事前、样本外及训练期外预测结果。 许可与联系方式：本数据集及其组成部分采用知识共享署名-非商业性使用许可协议进行分发。 致谢：感谢杜克大学（Duke University）与考夫曼基金会（Kauffman Foundation）为本数据集的研发提供资助。