发表机构
Forschungszentrum Jülich GmbH; University of Siegen; Imperial College London（于利希研究中心有限公司; 锡根大学; 伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究旨在从大量能源系统研究中自动提取技术经济数据，通过特定方法汇编海量数据点与元数据条目形成FAIR数据库，能展示学术假设与实证数据差异及研究重点变化，还通过交互式仪表板方便用户使用。
AI 中文摘要
能源系统模型指导着具有社会重要性的决策，但其可信度取决于难以获取和审核的定量假设。元分析可提高透明度和建模实践，但出版物的快速增长使人工信息提取变得越来越不切实际。因此，数据库更新不频繁，各研究团队常重复工作。本文展示了自2010年以来从76000项能源系统研究中高度准确地自动提取定量信息。我们汇编了320万个结构化定量数据点以及2000万个相关元数据条目，涵盖广泛的技术、方法和系统特征。所得的FAIR数据库不仅为模型提供输入数据，还使能源系统文献本身可分析。我们展示了学术假设与实证观测数据的差异，以及研究重点在技术、地区和时间上的规模变化。为便于社区广泛使用，该数据库通过交互式仪表板提供，用户可根据特定研究需求过滤、分析和下载数据。
英文摘要
Energy system models guide societally important decisions, but their credibility rests on quantitative assumptions that are difficult to source and audit. Meta-analyses can improve transparency and modeling practices, but the rapid growth of publications makes manual information extraction increasingly impractical. Consequently, databases are updated infrequently and efforts are often duplicated across research groups. Here, we demonstrate the highly accurate automated extraction of quantitative information from 76,000 energy system studies published since 2010. We compile 3.2 million structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.
