2022/02/21 by Shao Zhang, Zhang, Shao, Yuting Jia +9
Computer Science · Decision Sciences · Earth and Planetary Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Geological Modeling and Analysis #Human-Computer Interaction (cs.HC) #Research Data Management Practices #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2202.10163
openalex publication_date 2022/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Geoscientists, as well as researchers in many fields, need to read a huge amount of literature to locate, extract, and aggregate relevant results and data to enable future research or to build a scientific database, but there is no existing system to support this use case well. In this paper, based on the findings of a formative study about how geoscientists collaboratively annotate literature and extract and aggregate data, we proposed DeepShovel, a publicly-available AI-assisted data extraction system to support their needs. DeepShovel leverages the state-of-the-art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc. in a human-AI collaboration manner. A follow-up user evaluation with 14 researchers suggested DeepShovel improved users' efficiency of data extraction for building scientific databases, and encouraged teams to form a larger scale but more tightly-coupled collaboration.