jonasmortelmansvliz/ImagingValidationApp: v1.0.1

Imaging sensors are widely used in marine observatories to collect biodiversity observations, but image-based data typically require manual validation before further use. We present an R Shiny application developed within the VLIZ observatory framework to support the validation of biodiversity observations derived from imaging sensors. The application allows users to browse large sets of images organised in folders, visually inspect individual images, and assign validation or annotation labels using a predefined taxonomy. Images can be labeled individually or in bulk. Validated results are stored in a simple tabular format with user attribution and can be exported for integration into downstream workflows. The application is sensor-independent and can be applied to image data from different imaging platforms, providing a practical tool for routine validation of imaging-based biodiversity observations. ImagingValidationApp is an interactive Shiny application designed to: (1) Browse .tif plankton images in subfolders; (2) Select images and assign labels based on taxonomy; (3) Track progress for each subfolder and overall dataset and (4) Export validated labels to CSV files for downstream analysis The app dynamically generates label buttons based on a taxonomy file and color-codes them according to type (e.g., detritus, phytoplankton, copepod) Dynamic taxonomy buttons: Buttons are generated from taxonomy.csv, with type-specific colors Progress tracking: Overall dataset and per-subfolder progress bars with a playful copepod icon Caching: Efficient loading of .tif images using base64 encoding Export: Save labeled datasets to CSV files Validate All: Option to validate all images in a folder with a single click User tracking: Labels are saved along with the username for multiple annotators