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Datasets:
SLOP011
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flywire-fafb-connectome

Tasks:
Graph Machine Learning
Modalities:
Tabular
Formats:
parquet
Size:
10M - 100M
Tags:
connectome
neuroscience
drosophila
graph-neural-network
gnn
benchmark
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Datasets
Dask
Polars
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pre
int64
720,575,941B
720,575,941B
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post
int64
720,575,941B
720,575,941B
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syn_count
int32
1
2.41k
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gaba
float32
0
1
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ach
float32
0
1
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glut
float32
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1
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oct
float32
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1
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ser
float32
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da
float32
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1
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FlyWire FAFB v783 Connectome — GNN-ready package

The complete proofread wiring diagram of an adult female Drosophila melanogaster brain — 139,255 neurons and their synaptic connections — repackaged as a ready-to-train graph dataset. Companion to the flywire-gnn Python package.

This is a dataset packaging of two public, no-auth sources:

File Contents Source
connections.parquet (474 MB) 15,091,983 unique directed neuron→neuron pairs at ≥1 synapse: pre, post, syn_count, and 6 synapse-weighted neurotransmitter probabilities (gaba, ach, glut, oct, ser, da) FlyWire v783 release (Zenodo, CC-BY-4.0): doi:10.5281/zenodo.10676866
nodes.parquet (2.9 MB) 139,255 proofread neurons: root_id + annotations flow, super_class, cell_class, cell_sub_class, cell_type, hemibrain_type, hemilineages, top_nt, top_nt_conf, side, nerve, vfb_id, fbbt_id Supplementary Data 5 of Schlegel et al. 2024: doi:10.1038/s41586-024-07686-5
meta.json dataset statistics, feature schema, task definition this package

Benchmark task: node classification on super_class

9 coarse classes (optic / central / sensory / visual_projection / ascending / descending / visual_centrifugal / motor / endocrine). Every neuron is labeled. The default benchmark graph uses directed edges at ≥5 synapses (the published FAFB "connection" threshold) → 2,700,513 unique directed edges.

  • Node features (174-dim, wiring-derived): log1p(out_degree), log1p(in_degree), log1p(out/in synapses), log1p of out/in synapse counts across 79 neuropils, synapse-weighted mean NT profiles (in & out, 6 each).
  • Edge attributes: log1p(syn_count) + 6 weighted NT probabilities.
  • Splits: 70/15/15 train/val/test, stratified by label, seed 42.

Baseline results (real runs, CPU, seed 42)

Model Test accuracy Macro-F1 Best val acc (epoch) Epochs Wall time
MLP (feat. only) 0.9851 0.7115 0.9860 (200) 200 212 s
GCN 0.9166 0.4702 0.9202 (180) 200 1414 s
GraphSAGE 0.9812 0.7563 0.9820 (150) 150 783 s

What the benchmark shows: wiring-profile features alone nearly saturate accuracy (98.5%); message passing with mean aggregation (GraphSAGE) matches features and adds robustness on the rare classes (best macro-F1); GCN's normalized averaging over-smooths and trails. Report your model's accuracy and macro-F1 to compare on the same splits.

License & citation

Connectivity data is CC-BY-4.0. If you use this dataset, cite:

  1. Dorkenwald et al., Neuronal wiring diagram of an adult brain, Nature 2024. doi:10.1038/s41586-024-07558-y
  2. Schlegel et al., Whole-brain annotation and multi-connectome cell typing of Drosophila, Nature 2024. doi:10.1038/s41586-024-07686-5

Annotations redistributed here are from the Schlegel et al. 2024 supplementary data; the connectivity graph is derived from the FlyWire v783 public release. This package is not affiliated with the FlyWire Consortium. Explore interactively at codex.flywire.ai.
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