Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies
Score
71首次大规模自动化提取能源系统文献中的技术经济数据，方法新颖且数据量巨大。
方法描述详细，结果展示清晰，但作为预印本尚未经同行评审。
为能源系统建模者提供可直接使用的数据库和工具，显著减少手动收集数据的工作量。
2025年7月发布，对应最新研究成果，能源系统建模领域对数据透明度需求迫切。
来源为arXiv预印本，未经同行评审，但作者团队背景可靠。
Base 78.5 × T2 Source weight ×0.9 = 71
Curation line 70Selected
Why selected
The paper presents an innovative method with large-scale data and high practical value, but the source is a single arXiv preprint, not yet peer-reviewed.
AI commentary
「This is a model work applying NLP techniques to scientific literature mining, offering important insights for both energy modeling and AI information extraction. It is especially valuable for researchers in energy system studies, techno-economic analysis, or scientific meta-analysis, showing how massive literature can be turned into structured knowledge bases.」
Quick overview
This paper presents a highly accurate automated extraction method that extracts 3.2 million structured quantitative data points and 20 million associated metadata entries from 76,000 energy system studies published since 2010, covering a broad spectrum of technologies, methodological approaches, and system characteristics. The resulting FAIR database not only provides input data for models but also makes the energy system literature itself analyzable, revealing divergences between academic assumptions and empirical data, as well as how research priorities vary across technologies, regions, and time. An interactive dashboard enables community users to filter, analyze, and download data according to their specific research needs.
- Automated extraction of quantitative data from 76,000 energy system studies
- Construction of a FAIR database with 3.2 million data points and 20 million metadata entries
- Revealing divergences between academic assumptions and empirical data
- Providing an interactive dashboard for community use
Full text
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