  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.
