{
 "ops_run": "bench-fleet D1 NIfTI conversion (nucleus_instance_3d)",
 "purpose": "NIfTI (.nii.gz) copies of this repo's image and segmentation volumes, added ALONGSIDE the originals (nothing deleted or overwritten), so the MedOtter scoring engine -- which reads NIfTI only, via nibabel -- can score the nucleus_instance_3d benchmark cohort.",
 "date": "2026-09-08",
 "tools": {
  "python": "3.12.3",
  "numpy": "2.5.3",
  "nibabel": "5.4.2",
  "h5py": "3.16.0",
  "tifffile": "2026.8.23"
 },
 "conventions": {
  "axis_order": "(Z, Y, X): the source page/voxel order is written unchanged. medotter_core.inference's VolumeLoader / InstanceVolumeLoader read np.asarray(img.dataobj) and do NOT reorient, so they see exactly the source array.",
  "affine": "identity (np.eye(4)), zooms (1, 1, 1). Neither source format carries a machine-readable voxel size (NIS3D states resolution as prose in Info.txt; the NucMM-Z HDF5 files carry none) and this benchmark's metrics (f1 / aji / pq over voxel IoU) consume no spacing, so no spacing is invented.",
  "values": "preserved exactly: every written file was reloaded and asserted np.array_equal against the source array (and, for segmentations, against the int32 cast the instance loader applies).",
  "dtypes": "images keep their stored dtype; segmentations use the smallest integer dtype that holds the cohort's maximum instance id, capped at int32 because InstanceVolumeLoader.load_gt casts to int32. No relabelling: instance ids are byte-identical."
 },
 "license": {
  "dataset_card": "medotter/knowledge/datasets/NucMM.data.final.yaml",
  "license": "MIT",
  "redistribution_allowed": "yes",
  "verdict": "NucMM (both halves, NucMM-Z included) is MIT per the authors' data-request form and their MIT-tagged release pytc/NucMM; the card's redistribution.allowed is 'yes'.",
  "origin_url": "https://huggingface.co/datasets/pytc/NucMM"
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