Datasets:

pytc
/

zebrafinch-j0126

Tags:

biology

License:

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Cannot get the split names for the config 'default' of the dataset.

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/hdf5/hdf5.py", line 58, in _split_generators
                  with _safe_open_h5py(f, "r") as h5:
                       ~~~~~~~~~~~~~~~^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 390, in _safe_open_h5py
                  f = h5py.File(file, mode)
                File "/usr/local/lib/python3.14/site-packages/h5py/_hl/files.py", line 555, in __init__
                  fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr)
                File "/usr/local/lib/python3.14/site-packages/h5py/_hl/files.py", line 232, in make_fid
                  fid = h5f.open(name, flags, fapl=fapl)
                File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
                File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
                File "h5py/h5f.pyx", line 106, in h5py.h5f.open
              OSError: Unable to synchronously open file (file signature not found)
              
              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.

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[FFN paper], [data from the paper]

We downloaded and processed the data into formats that are easier to share.

Ground truth
- train: j0126-train-33vol.zip, 33 densely labeled subvolumes
  - im_raw/, seg_gt/: original image and instance label volumes (150x150x150 voxels each, except one 128x256x256 cube)
  - im_raw_4-32-32/, seg_gt_4-32-32/: the same volumes padded with [4,32,32] on both sides in zyx. The image padding is real EM context read from the source volume, so the network sees valid input there; the label padding is -1, an ignore value for the loss
  - seg_gt_4-32-32/gt_*_skeleton.h5: skeleton voxels of each padded label volume, carrying the id of the segment they belong to (for ERL/NERL scoring)
- validation: valid_12_skeletons.h5, manually traced skeletons for 12 neurons
- test: test_50_skeletons.h5, manually traced skeletons for 50 neurons
Masks, for the j0126 tutorial in pytorch_connectomics
- j0126-tissue-border-keep-mask.tar: the exclusion mask the reference decode runs under, at native mip 0 (9x9x20 nm xyz). tar -xf unpacks tissue_border_keep_mask_full.zarr, zyx 5700x10912x10664 uint8, 1=keep and 0=exclude. It is FFN's own tissue_classification thresholded to NOT(blood vessel | myelin | out-of-bounds), upsampled 2x in xy onto the mip-0 grid, and ANDed with the 0/255 border ring of the aligned EM. It removes 15.64% of the volume. Building it from scratch streams the whole tissue_classification layer; this file is the same artifact, already built.
- j0126-nucleus-instances-80nm.h5: 465 hand-proofread nucleus instances at 80 nm isotropic. Dataset main, zyx 1425x1365x1333 uint16, 0=background, one id per nucleus. Upsample by [4, 8, 8] (zyx) to reach mip 0. Non-neuronal detections (myelin, blood vessel) were dropped and false splits/merges corrected by hand.
Image and FFN result
- use cloud-volume to download from the neuroglancer link
- image: precomputed://gs://j0126-nature-methods-data/GgwKmcKgrcoNxJccKuGIzRnQqfit9hnfK1ctZzNbnuU/rawdata_realigned
- ffn-seg: precomputed://gs://j0126-nature-methods-data/GgwKmcKgrcoNxJccKuGIzRnQqfit9hnfK1ctZzNbnuU/ffn_segmentation

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