📂 Data Contents image/mIF.ome.tif A multiplex immunofluorescence (mIF) image in OME-TIFF format. Serves as the base image for segmentation and analysis. segmentation/segmentation_mask.tif Segmentation mask labeling individual cells. Same dimensions as the input image. Generated using Cellpose nuclei model + 5px expansion. manual_artefact_annotations/artefacts.geojson Hand-curated annotations of tissue and image artefacts. Useful for excluding artefactual regions from downstream quantification. phenotyping/celltype_matrix.csv Matrix of celltype identities/phenotypes. Follows scimap phenotyping matrix scheme. phenotyping/gates.csv Definitions of gating strategies used to assign cell types (e.g., thresholds per marker). Enables reproducible phenotyping logic. quantification/quant.csv Spatially resolved quantification (e.g., mean marker intensities per cell). proteomics/DIANN_pg_matrix.csv Protein group abundance matrix generated from DIA-MS using DIA-NN. Rows = proteins; columns = regions collected from tissue. proteomics/DIANN_metadata.csv Sample metadata for the proteomics matrix, e.g., region name, acquisition info, batch. proteomics/collection_shapes.geojson Spatial outlines of tissue regions collected for LC-MS (e.g., laser capture shapes). Enables mapping proteomics data back to tissue context. 🧪 Intended Use This dataset is designed for testing openDVP functionalities. Developing and demonstrating spatial multi-omic integration pipelines. Quality control single-cell segmentation, artefact removal, and phenotyping methods. Linking spatial image data with region-specific proteomic measurements.