Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
spaCR example data: Make Masks on a FIB-SEM volume
One focused-ion-beam scanning electron microscopy (FIB-SEM) volume of a HeLa cell, 128 x 256 x 256 voxels (Z, Y, X; 8-bit), with a 3-D instance label map of every mitochondrion (uint16), for the 3-D mask editor on spaCR's Make Masks "Load test data" menu. One menu entry opens the volume unsegmented; the other opens it with these labels loaded for correction.
Files
hela_fibsem.tif: the volume, an ImageJ hyperstack (axes ZYX, unit nm, 15 nm spacing).ground_truth_masks/hela_fibsem.tif: the labels, the same shape and calibration.manifest.csv: what a complete copy holds, with source and crop.
Source
EMPIAR-10982, Seven benchmark datasets of instance segmentation of
mitochondria: 6 diverse volume EM + 1 TEM (100 images) datasets, folder
data/mito_benchmarks/hela_cell (Narayan K, Conrad RW; NCI/FNLCR).
https://www.ebi.ac.uk/empiar/EMPIAR-10982/ - DOI 10.6019/EMPIAR-10982
The crop is the central 128 sections (Z 64-191) of the 256^3 benchmark volume at its full field. The image is unchanged; the labels are renumbered 1..n and stored as uint16. EMPIAR lists a 150 angstrom (15 nm) pixel and no separate section thickness, so 15 nm is written on all three axes. The Z value is an assumption: check it before measuring physical volumes.
Built by tools/build_fibsem_example_dataset.py in the spaCR repository.
Licence
CC0 1.0, as EMPIAR releases its entries.
Citation
Conrad R, Narayan K. Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset. Cell Systems 14(1):58-71.e5 (2023). doi:10.1016/j.cels.2022.12.006
Narayan K, Conrad RW. EMPIAR-10982 (2022). doi:10.6019/EMPIAR-10982
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