Stacy Brian, Kitzmüller Lucas, Wang Xiaoyu, Mahler Daniel Gerszon, Serajuddin Umar
World Bank, Development Data Group, 1818 H St NW, Washington, DC 20433, USA.
European Bank for Reconstruction and Development (EBRD), Office of the Chief Economist, 5 Bank St, London E14 4BG, United Kingdom.
PNAS Nexus. 2025 Jun 19;4(6):pgaf196. doi: 10.1093/pnasnexus/pgaf196. eCollection 2025 Jun.
Data-driven research is key to producing evidence-based public policies, yet little is known about where data-driven research is lacking and how it can be expanded. We propose a method for tracking academic data use by country of subject in English-language social science and medicine articles, applying natural language processing to a large corpus of academic articles. The model's predictions produce country estimates of the number of articles using data that are highly correlated with a human-coded approach, with a correlation of 0.99. Analyzing more than 140,000 academic articles, we find that high-income countries are the subject of ∼50% of all papers using data, despite only making up around 17% of the world's population. Finally, we classify countries by whether they could most benefit from increasing their production or use of data, with the former applying to many poorer countries and the latter to many wealthier countries.
数据驱动的研究是制定循证公共政策的关键，但对于数据驱动研究在哪些方面存在不足以及如何扩展，我们却知之甚少。我们提出了一种方法，通过对英语社会科学和医学文章按学科和国家追踪学术数据的使用情况，将自然语言处理应用于大量学术文章语料库。该模型的预测得出了使用数据的文章数量的国别估计值，这些估计值与人工编码方法高度相关，相关性为0.99。通过分析140,000多篇学术文章，我们发现高收入国家虽仅占世界人口的约17%，却成为了约50%所有使用数据的论文的主题。最后，我们根据各国是最能从增加数据产出还是使用中受益来对其进行分类，前者适用于许多较贫穷国家，后者适用于许多较富裕国家。
