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: replication
Parasites per vacuole, for spaCR's Replication Assay module. Every parasite object of twelve control wells of the Toxoplasma MTOC screen, as the screen's own Mask and Measure runs segmented and measured them.
Download it from inside spaCR with Load test data... in the
Replication module, or with spacr-download replication. The
settings file ships beside the data, so after loading, Run is the next step.
Size
spacr-example-replication.tar: 168,263,680 bytes (168 MB), an uncompressed tar.
SHA-256 04bacb3116e44317e4569ab51a93abf97e74cc2bbd219c9d6389872f7f8dea8f.
Provenance
- Screen: Toxoplasma MTOC screen, plate 1 (acquired 2025-05-30, CellVoyager, 40x water).
- Plate folder on the lab NAS:
/nas_mnt/data/MTOC_Screen/mtocScreen_20250530_111302/plate1. - Wells: rows r3, r4, r5, r6, r7, r8 of columns c1, c2 (twelve wells), every field of each.
nc= column 1,pc= column 2: the screen's negative and positive control columns, labelled as the screen's own analysis settings label them. They are the controls of an MTOC-recruitment screen, not of a replication experiment, so no replication difference between them is claimed.
Contents
table in measurements/measurements.db |
rows |
|---|---|
pathogen |
11,416 |
cell |
4,561 |
Every row of the chosen wells is kept, unfiltered; nothing is resampled.
Recorded paths that named the NAS were replaced by merged/<file_name>.npy.
Two merged fields, one per control column, so the objects can be inspected:
merged/plate1_H01_17_1.npymerged/plate1_G02_5_1.npy
Each is a 7-plane uint16 stack (height x width x 7):
- 0 nucleus stain (the screen's nucleus_channel)
- 1 the screen's measured target (its channel_of_interest)
- 2 parasite stain (the screen's pathogen_channel)
- 3 cell stain (the screen's cell_channel)
- 4 cell mask
- 5 nucleus mask
- 6 pathogen mask
settings/replication_settings.csv holds the module settings; its src is
filled in with the download location when spaCR unpacks it.
Notes
The pathogen mask was segmented from the parasite stain (plane 2) and splits most vacuoles into their parasites, not always cleanly: the module groups parasites into vacuoles by clustering their centroids inside each host cell (vacuole_key = spatial), and its non_power_of_two bucket reports how often the result is off the 1-2-4-8-16 ladder. Read that bucket before reading the distribution. min_parasite_area is 400 px, the pathogen_min_size the screen's Measure run used; the table still holds the smaller objects, so lowering it is a real choice.
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