  We present the VIS30K dataset, a collection of 29,689 images that represents
30 years of figures and tables from each track of the IEEE Visualization
conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage
of the scientific literature in visualization not only reflects the progress of
the field but also enables researchers to study the evolution of the
state-of-the-art and to find relevant work based on graphical content. We
describe the dataset and our semi-automatic collection process, which couples
convolutional neural networks (CNN) with curation. Extracting figures and
tables semi-automatically allows us to verify that no images are overlooked or
extracted erroneously. To improve quality further, we engaged in a peer-search
process for high-quality figures from early IEEE Visualization papers. With the
resulting data, we also contribute VISImageNavigator (VIN,
visimagenavigator.github.io), a web-based tool that facilitates searching and
exploring VIS30K by author names, paper keywords, title and abstract, and
years.
