← All datasets
Imaging Mass Cytometry

Human kidney

S-BIAD3694:discovery:Batch16:L110:kidney

bronze ✓ Verified publication
Imaging Mass Cytometry
print(sdata) 
SpatialData object
├── Images
│     ├── 'img_Batch16_021_image': DataTree[cyx] (35, 1622, 490), (35, 811, 245), (35, 405, 122), (35, 202, 61), (35, 101, 30)
│     ├── 'img_Batch16_022_image': DataTree[cyx] (35, 991, 545), (35, 495, 272), (35, 247, 136), (35, 123, 68), (35, 61, 34)
│     ├── 'img_Batch16_023_image': DataTree[cyx] (35, 536, 342), (35, 268, 171), (35, 134, 85), (35, 67, 42), (35, 33, 21)
│     ├── 'img_Batch16_024_image': DataTree[cyx] (35, 815, 684), (35, 407, 342), (35, 203, 171), (35, 101, 85), (35, 50, 42)
│     ├── 'img_Batch16_025_image': DataTree[cyx] (35, 312, 479), (35, 156, 239), (35, 78, 119), (35, 39, 59), (35, 19, 29)
│     ├── 'img_Batch16_026_image': DataTree[cyx] (35, 574, 568), (35, 287, 284), (35, 143, 142), (35, 71, 71), (35, 35, 35)
│     ├── 'img_Batch16_027_image': DataTree[cyx] (35, 549, 662), (35, 274, 331), (35, 137, 165), (35, 68, 82), (35, 34, 41)
│     ├── 'img_Batch16_028_image': DataTree[cyx] (35, 1528, 616), (35, 764, 308), (35, 382, 154), (35, 191, 77), (35, 95, 38)
│     ├── 'img_Batch16_029_image': DataTree[cyx] (35, 1904, 571), (35, 952, 285), (35, 476, 142), (35, 238, 71), (35, 119, 35)
│     ├── 'img_Batch16_030_image': DataTree[cyx] (35, 1351, 634), (35, 675, 317), (35, 337, 158), (35, 168, 79), (35, 84, 39)
│     ├── 'img_Batch16_031_image': DataTree[cyx] (35, 804, 792), (35, 402, 396), (35, 201, 198), (35, 100, 99), (35, 50, 49)
│     └── 'img_Batch16_032_image': DataTree[cyx] (35, 2738, 538), (35, 1369, 269), (35, 684, 134), (35, 342, 67), (35, 171, 33)
├── Labels
│     ├── 'img_Batch16_021_cells_labels': DataTree[yx] (1622, 490), (811, 245), (405, 122), (202, 61), (101, 30)
│     ├── 'img_Batch16_022_cells_labels': DataTree[yx] (991, 545), (495, 272), (247, 136), (123, 68), (61, 34)
│     ├── 'img_Batch16_023_cells_labels': DataTree[yx] (536, 342), (268, 171), (134, 85), (67, 42), (33, 21)
│     ├── 'img_Batch16_024_cells_labels': DataTree[yx] (815, 684), (407, 342), (203, 171), (101, 85), (50, 42)
│     ├── 'img_Batch16_025_cells_labels': DataTree[yx] (312, 479), (156, 239), (78, 119), (39, 59), (19, 29)
│     ├── 'img_Batch16_026_cells_labels': DataTree[yx] (574, 568), (287, 284), (143, 142), (71, 71), (35, 35)
│     ├── 'img_Batch16_027_cells_labels': DataTree[yx] (549, 662), (274, 331), (137, 165), (68, 82), (34, 41)
│     ├── 'img_Batch16_028_cells_labels': DataTree[yx] (1528, 616), (764, 308), (382, 154), (191, 77), (95, 38)
│     ├── 'img_Batch16_029_cells_labels': DataTree[yx] (1904, 571), (952, 285), (476, 142), (238, 71), (119, 35)
│     ├── 'img_Batch16_030_cells_labels': DataTree[yx] (1351, 634), (675, 317), (337, 158), (168, 79), (84, 39)
│     ├── 'img_Batch16_031_cells_labels': DataTree[yx] (804, 792), (402, 396), (201, 198), (100, 99), (50, 49)
│     └── 'img_Batch16_032_cells_labels': DataTree[yx] (2738, 538), (1369, 269), (684, 134), (342, 67), (171, 33)
├── Shapes
│     ├── 'img_Batch16_021_cell_boundaries': GeoDataFrame shape: (4557, 2) (2D shapes)
│     ├── 'img_Batch16_022_cell_boundaries': GeoDataFrame shape: (3608, 2) (2D shapes)
│     ├── 'img_Batch16_023_cell_boundaries': GeoDataFrame shape: (1130, 2) (2D shapes)
│     ├── 'img_Batch16_024_cell_boundaries': GeoDataFrame shape: (3491, 2) (2D shapes)
│     ├── 'img_Batch16_025_cell_boundaries': GeoDataFrame shape: (868, 2) (2D shapes)
│     ├── 'img_Batch16_026_cell_boundaries': GeoDataFrame shape: (2082, 2) (2D shapes)
│     ├── 'img_Batch16_027_cell_boundaries': GeoDataFrame shape: (2323, 2) (2D shapes)
│     ├── 'img_Batch16_028_cell_boundaries': GeoDataFrame shape: (6117, 2) (2D shapes)
│     ├── 'img_Batch16_029_cell_boundaries': GeoDataFrame shape: (5925, 2) (2D shapes)
│     ├── 'img_Batch16_030_cell_boundaries': GeoDataFrame shape: (4591, 2) (2D shapes)
│     ├── 'img_Batch16_031_cell_boundaries': GeoDataFrame shape: (3597, 2) (2D shapes)
│     └── 'img_Batch16_032_cell_boundaries': GeoDataFrame shape: (8261, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (46550, 35)
with coordinate systems:
    ▸ 'img_Batch16_021', with elements:
        img_Batch16_021_image (Images), img_Batch16_021_cells_labels (Labels), img_Batch16_021_cell_boundaries (Shapes)
    ▸ 'img_Batch16_022', with elements:
        img_Batch16_022_image (Images), img_Batch16_022_cells_labels (Labels), img_Batch16_022_cell_boundaries (Shapes)
    ▸ 'img_Batch16_023', with elements:
        img_Batch16_023_image (Images), img_Batch16_023_cells_labels (Labels), img_Batch16_023_cell_boundaries (Shapes)
    ▸ 'img_Batch16_024', with elements:
        img_Batch16_024_image (Images), img_Batch16_024_cells_labels (Labels), img_Batch16_024_cell_boundaries (Shapes)
    ▸ 'img_Batch16_025', with elements:
        img_Batch16_025_image (Images), img_Batch16_025_cells_labels (Labels), img_Batch16_025_cell_boundaries (Shapes)
    ▸ 'img_Batch16_026', with elements:
        img_Batch16_026_image (Images), img_Batch16_026_cells_labels (Labels), img_Batch16_026_cell_boundaries (Shapes)
    ▸ 'img_Batch16_027', with elements:
        img_Batch16_027_image (Images), img_Batch16_027_cells_labels (Labels), img_Batch16_027_cell_boundaries (Shapes)
    ▸ 'img_Batch16_028', with elements:
        img_Batch16_028_image (Images), img_Batch16_028_cells_labels (Labels), img_Batch16_028_cell_boundaries (Shapes)
    ▸ 'img_Batch16_029', with elements:
        img_Batch16_029_image (Images), img_Batch16_029_cells_labels (Labels), img_Batch16_029_cell_boundaries (Shapes)
    ▸ 'img_Batch16_030', with elements:
        img_Batch16_030_image (Images), img_Batch16_030_cells_labels (Labels), img_Batch16_030_cell_boundaries (Shapes)
    ▸ 'img_Batch16_031', with elements:
        img_Batch16_031_image (Images), img_Batch16_031_cells_labels (Labels), img_Batch16_031_cell_boundaries (Shapes)
    ▸ 'img_Batch16_032', with elements:
        img_Batch16_032_image (Images), img_Batch16_032_cells_labels (Labels), img_Batch16_032_cell_boundaries (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("uNkq8YUATdyMC5DY0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)
Install (Python 3.12+) Docs →
Python · lamindb

Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("uNkq8YUATdyMC5DY")   # stem uid: Lamin resolves it to the latest version

sdata = artifact.load()   # cache, then load into memory
group = artifact.open()   # stream without downloading
path  = artifact.cache()  # sync into the local cache, returns the path
Install (Python 3.12+) Docs →
Shell 
# sddb client: download the .zarr into data/
sddb-cli download uNkq8YUATdyMC5DY0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/uNkq8YUATdyMC5DY

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid uNkq8YUATdyMC5DY
Install (Python 3.12+) Docs →

License: CC0 1.0

View on Lamin ↗ Flag a data issue
Part of collection
bia-s-biad3694→
67 datasets · publication

Metadata

Technology
Imaging Mass Cytometry
Organism
human
Tissue
kidney
Modality
protein
Observations (cells)
46,550
Tier
bronze
Chemistry
IMC
Staining
Antibodies
Size
328 MB
Sample ID
S-BIAD3694:discovery:Batch16:L110:kidney
License
CC0 1.0
Published
Aug 18, 2026

Cite this dataset

doi.org/10.1038/s41467-026-77210-y ↗
BibTeX 
@article{bia:S-BIAD3694,
  doi = {10.1038/s41467-026-77210-y},
  title = {S-BIAD3694:discovery:Batch2:L108:kidney}
}

Source: BioImage Archive ↗ · License: CC0 1.0 ↗ · Reprocessed to SpatialData by SpatialData-DB