ScaleBarOn and Muad'Data: simple Python tools for elemental imaging data visualization and comparative scaling

Elemental imaging (EI) techniques, such as laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS), produce spatially resolved elemental data comprising multiple elemental channels and associated spatial coordinates for each pixel. Although EI originated in geochemistry, it is increasingly applied in the life sciences, where between-specimen comparison of individual elements is central to experimental interpretation. However, most existing EI visualization software tools are designed for single specimen datasets, and comparative analysis between specimens is time-intensive and inefficient. In addition, large LA-ICP-TOF-MS datasets, often several gigabytes in size, hinder multi-specimen comparison, and slow data loading and sharing. Here, we describe a new framework for the visualization and comparison of EI data across multiple specimens, implemented using open-source Python tools tailored to biological EI workflows. ScaleBarOn enables scaled, multi-specimen visualization and image generation for individual elements, while Muad'Data supports single-specimen exploration, including multi-element overlays. To reduce computational burden and improve interoperability, multi-channel EI datasets are decomposed into single-element matrix files stored in lightweight, widely supported formats. This enables consistent scaling and efficient comparison of elemental distributions across large biological datasets, supporting reproducible and interpretable analysis.