  NASA JPL scientists working on the micro x-ray fluorescence (microXRF)
spectroscopy data collected from Mars surface perform data analysis to look for
signs of past microbial life on Mars. Their data analysis workflow mainly
involves identifying mineral compounds through the element abundance in
spatially distributed data points. Working with the NASA JPL team, we
identified pain points and needs to further develop their existing data
visualization and analysis tool. Specifically, the team desired improvements
for the process of creating and interpreting mineral composition groups. To
address this problem, we developed an interactive tool that enables scientists
to (1) cluster the data using either manual lasso-tool selection or through
various machine learning clustering algorithms, and (2) compare the clusters
and individual data points to make informed decisions about mineral
compositions. Our preliminary tool supports a hybrid data analysis workflow
where the user can manually refine the machine-generated clusters.
