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Tubban
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flyeye-escape-neuron-v1

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schemaVersion: int64
profile: string
kind: string
dataset: string
attribution: string
license: string
scientificBoundary: string
neuronCount: int64
edgeCount: int64
graphBytes: int64
graphHash: string
bodyIds: list<item: string>
  child 0, item: string
originalIndices: list<item: int64>
  child 0, item: int64
groups: list<item: struct<id: string, label: string, indices: list<item: int64>, count: int64>>
  child 0, item: struct<id: string, label: string, indices: list<item: int64>, count: int64>
      child 0, id: string
      child 1, label: string
      child 2, indices: list<item: int64>
          child 0, item: int64
      child 3, count: int64
motor: struct<dnL: list<item: int64>, dnR: list<item: int64>, mnL: list<item: null>, mnR: list<item: null>>
  child 0, dnL: list<item: int64>
      child 0, item: int64
  child 1, dnR: list<item: int64>
      child 0, item: int64
  child 2, mnL: list<item: null>
      child 0, item: null
  child 3, mnR: list<item: null>
      child 0, item: null
escapeTargets: list<item: struct<index: int64, positiveWeight: int64, negativeWeight: int64, edgeCount: int64, sour (... 32 chars omitted)
  child 0, item: struct<index: int64, positiveWeight: int64, negativeWeight: int64, edgeCount: int64, sourceIndex: in (... 20 chars omitted)
      child 0, index: int64
      child 1, positiveWeight: int64
      child 2, negativeWeight: int64
      child 3, edgeCount: int64
      child 4, sourceIndex: int64
      child 5, bodyId: string
binary: struct<arr
...
teOrder: string
  child 2, header: string
  child 3, magic: string
  child 4, rowMeaning: string
  child 5, version: int64
source: struct<repository: string, commit: string, graphHash: string, neuronCount: int64, edgeCount: int64>
  child 0, repository: string
  child 1, commit: string
  child 2, graphHash: string
  child 3, neuronCount: int64
  child 4, edgeCount: int64
extraction: struct<algorithm: string, maxHops: int64, topDirectEscapeDNs: int64, seedGroup: string, outputGroups (... 102 chars omitted)
  child 0, algorithm: string
  child 1, maxHops: int64
  child 2, topDirectEscapeDNs: int64
  child 3, seedGroup: string
  child 4, outputGroups: list<item: string>
      child 0, item: string
  child 5, preservesSignedWeights: bool
  child 6, preservesAllInternalEdgesAmongSelectedNodes: bool
pathwayProvenance: string
nodes: list<item: struct<id: string, label: string, neuronCount: int64>>
  child 0, item: struct<id: string, label: string, neuronCount: int64>
      child 0, id: string
      child 1, label: string
      child 2, neuronCount: int64
edges: list<item: struct<source: string, target: string, edgeCount: int64, positiveWeight: int64, negativeW (... 14 chars omitted)
  child 0, item: struct<source: string, target: string, edgeCount: int64, positiveWeight: int64, negativeWeight: int6 (... 2 chars omitted)
      child 0, source: string
      child 1, target: string
      child 2, edgeCount: int64
      child 3, positiveWeight: int64
      child 4, negativeWeight: int64
to
{'schemaVersion': Value('int64'), 'profile': Value('string'), 'kind': Value('string'), 'dataset': Value('string'), 'source': {'repository': Value('string'), 'commit': Value('string'), 'graphHash': Value('string')}, 'attribution': Value('string'), 'scientificBoundary': Value('string'), 'nodes': List({'id': Value('string'), 'label': Value('string'), 'neuronCount': Value('int64')}), 'edges': List({'source': Value('string'), 'target': Value('string'), 'edgeCount': Value('int64'), 'positiveWeight': Value('int64'), 'negativeWeight': Value('int64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schemaVersion: int64
              profile: string
              kind: string
              dataset: string
              attribution: string
              license: string
              scientificBoundary: string
              neuronCount: int64
              edgeCount: int64
              graphBytes: int64
              graphHash: string
              bodyIds: list<item: string>
                child 0, item: string
              originalIndices: list<item: int64>
                child 0, item: int64
              groups: list<item: struct<id: string, label: string, indices: list<item: int64>, count: int64>>
                child 0, item: struct<id: string, label: string, indices: list<item: int64>, count: int64>
                    child 0, id: string
                    child 1, label: string
                    child 2, indices: list<item: int64>
                        child 0, item: int64
                    child 3, count: int64
              motor: struct<dnL: list<item: int64>, dnR: list<item: int64>, mnL: list<item: null>, mnR: list<item: null>>
                child 0, dnL: list<item: int64>
                    child 0, item: int64
                child 1, dnR: list<item: int64>
                    child 0, item: int64
                child 2, mnL: list<item: null>
                    child 0, item: null
                child 3, mnR: list<item: null>
                    child 0, item: null
              escapeTargets: list<item: struct<index: int64, positiveWeight: int64, negativeWeight: int64, edgeCount: int64, sour (... 32 chars omitted)
                child 0, item: struct<index: int64, positiveWeight: int64, negativeWeight: int64, edgeCount: int64, sourceIndex: in (... 20 chars omitted)
                    child 0, index: int64
                    child 1, positiveWeight: int64
                    child 2, negativeWeight: int64
                    child 3, edgeCount: int64
                    child 4, sourceIndex: int64
                    child 5, bodyId: string
              binary: struct<arr
              ...
              teOrder: string
                child 2, header: string
                child 3, magic: string
                child 4, rowMeaning: string
                child 5, version: int64
              source: struct<repository: string, commit: string, graphHash: string, neuronCount: int64, edgeCount: int64>
                child 0, repository: string
                child 1, commit: string
                child 2, graphHash: string
                child 3, neuronCount: int64
                child 4, edgeCount: int64
              extraction: struct<algorithm: string, maxHops: int64, topDirectEscapeDNs: int64, seedGroup: string, outputGroups (... 102 chars omitted)
                child 0, algorithm: string
                child 1, maxHops: int64
                child 2, topDirectEscapeDNs: int64
                child 3, seedGroup: string
                child 4, outputGroups: list<item: string>
                    child 0, item: string
                child 5, preservesSignedWeights: bool
                child 6, preservesAllInternalEdgesAmongSelectedNodes: bool
              pathwayProvenance: string
              nodes: list<item: struct<id: string, label: string, neuronCount: int64>>
                child 0, item: struct<id: string, label: string, neuronCount: int64>
                    child 0, id: string
                    child 1, label: string
                    child 2, neuronCount: int64
              edges: list<item: struct<source: string, target: string, edgeCount: int64, positiveWeight: int64, negativeW (... 14 chars omitted)
                child 0, item: struct<source: string, target: string, edgeCount: int64, positiveWeight: int64, negativeWeight: int6 (... 2 chars omitted)
                    child 0, source: string
                    child 1, target: string
                    child 2, edgeCount: int64
                    child 3, positiveWeight: int64
                    child 4, negativeWeight: int64
              to
              {'schemaVersion': Value('int64'), 'profile': Value('string'), 'kind': Value('string'), 'dataset': Value('string'), 'source': {'repository': Value('string'), 'commit': Value('string'), 'graphHash': Value('string')}, 'attribution': Value('string'), 'scientificBoundary': Value('string'), 'nodes': List({'id': Value('string'), 'label': Value('string'), 'neuronCount': Value('int64')}), 'edges': List({'source': Value('string'), 'target': Value('string'), 'edgeCount': Value('int64'), 'positiveWeight': Value('int64'), 'negativeWeight': Value('int64')})}
              because column names don't match

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FlyEye Escape Neuron v1

A compact, reproducible neuron-level Drosophila escape-circuit graph derived from the pinned MaleCNS source used by FlyEye.

Dataset summary

Graph	Neurons	Edges	Bytes
pinned source subset used by builder	70,000	798,715	9,864,604
FlyEye escape-neuron-v1	3,376	78,797	959,092

The compact graph contains:

126 LC4 / looming seed neurons;
26 selected direct escape descending-neuron targets;
directed LC4-to-output pathways within a maximum 3-hop corridor;
all signed internal edges between retained neurons;
original source indices;
MaleCNS body IDs;
source and derived graph hashes;
deterministic extraction metadata.

Files

graph.bin — FLYGRAPH v1 binary
manifest.json — IDs, groups, provenance and scientific boundary
report.json — extraction/compression report
escape-fast-v1.json — lightweight aggregate profile
CITATION.cff
DATA_LICENSE.md

FLYGRAPH v1 binary format

8 bytes   magic "FLYGRAPH"
u32 LE    version
u32 LE    neuronCount
u32 LE    edgeCount
u32[n+1]  rowOffsets
u32[e]    presynapticIndices
f64[e]    signed weights

Rows are postsynaptic neurons.

Scientific boundary

The retained neuron-level edges are real signed edges selected from the pinned MaleCNS-derived graph. The pathway selection itself is task-specific.

This dataset does not claim that FlyEye's camera encoding, thresholds, aggregate runtime dynamics, or behavioral readouts are measured biological physiology. Those components are modeled unless separately validated.

Licensing and attribution

MaleCNS-derived graph assets retain the upstream CC BY 4.0 attribution recorded in the manifest.

Source attribution:

Janelia FlyEM MaleCNS — https://male-cns.janelia.org/download/

FlyEye software and extraction tooling are licensed separately under MIT.

Reproducibility

The source repository pins the upstream graph source and contains the graph builder, CI workflow, graph integrity tests, JavaScript/TypeScript loaders and a Python SDK.

Source:

https://github.com/tubban1/fly-eye

Citation

If you use this dataset or the FlyEye Developer Kit in research, cite the archived software release:

FlyEye Contributors (2026). FlyEye Developer Kit: reusable Drosophila connectome graphs and runtime tooling (v0.5.0-alpha.9). Zenodo. https://doi.org/10.5281/zenodo.22829797

@software{flyeye_developer_kit_2026,
  author  = {{FlyEye Contributors}},
  title   = {FlyEye Developer Kit: reusable Drosophila connectome graphs and runtime tooling},
  year    = {2026},
  version = {0.5.0-alpha.9},
  doi     = {10.5281/zenodo.22829797},
  url     = {https://doi.org/10.5281/zenodo.22829797}
}

Related resources:

Live demo: https://fly.fde.fan
Source: https://github.com/tubban1/fly-eye
GitHub release: https://github.com/tubban1/fly-eye/releases/tag/v0.5.0-alpha.9
Zenodo archive: https://doi.org/10.5281/zenodo.22829797

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Total file size:

1.07 MB