PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification
This work addresses specific data analysis needs for NASA JPL scientists in mineral identification, representing an incremental improvement to an existing tool.
The researchers tackled the challenge of improving mineral identification for NASA scientists analyzing Mars microXRF data by developing an interactive tool that combines manual selection and machine learning clustering, enabling hybrid workflows for refining clusters and comparing data points.
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.