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| "name": "ConnectomeBench2", |
| "description": "\n\t\n\t\t\n\t\tConnectomeBench2\n\t\n\nConnectomeBench2 is a unified benchmark for automated proofreading of connectomic neural-segmentation data. 401,170 samples across 4 species (mouse, fly, human, zebrafish) and 5 sample types (real merge edits, real split edits, synthetic adjacent / junction / synapse controls), with the associated mesh geometry and electron-microscopy (EM) renderings.\nDownstream trainers should treat this dataset as the single source of truth for sample identity, labels\u2026 See the full description on the dataset page: https://huggingface.co/datasets/jeffbbrown2/ConnectomeBench2.", |
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| "@type": "Person", |
| "name": "Jeff Brown", |
| "url": "https://huggingface.co/jeffbbrown2" |
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| "keywords": [ |
| "other", |
| "100K - 1M", |
| "parquet", |
| "Image", |
| "Text", |
| "3D", |
| "Datasets", |
| "Dask", |
| "Polars", |
| "Croissant", |
| "\ud83c\uddfa\ud83c\uddf8 Region: US", |
| "connectomics", |
| "proofreading", |
| "3d", |
| "electron-microscopy", |
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| ], |
| "license": "https://choosealicense.com/licenses/other/", |
| "url": "https://huggingface.co/datasets/jeffbbrown2/ConnectomeBench2", |
| "citeAs": "Brown, J., Farkas, T., Razgar, G., Boyden, E. S. ConnectomeBench2: A unified benchmark for automated connectomic proofreading. (2026, in submission). Brown J. and Farkas T. contributed equally as first authors.", |
| "datePublished": "2026-05-07", |
| "version": "1.0.0", |
| "rai:dataLimitations": "Sample distribution is skewed across species. Fly (45%) and mouse (37.5%) dominate, zebrafish (12.4%) and human (5.1%) are underrepresented. EM views are present on only 63% of samples; tasks that require EM should filter via `has_em` (controls = 100%, real edits = 23-38%). Real edits reflect human proofreader judgment and may carry their selection biases. Junction and synapse controls are synthetic and meant as task-specific negatives, not as a faithful sample of the natural false-merge / false-junction error distribution. Not recommended for: clinical or diagnostic use; tasks requiring representativeness of any specific neural circuit.", |
| "rai:dataBiases": "(1) Species imbalance: fly (45%) and mouse (37.5%) dominate; zebrafish (12.4%) and human (5.1%) are sparse, so models trained on the full mix may perform much worse on zebrafish/human. (2) Brain-region bias: each upstream connectome covers a specific brain region (mouse visual cortex, fly central brain, H01 cortex, zebrafish larval brain). Thus, the data is not representative of \"all neural tissue\". (3) Edit selection bias: real human proofreading edits reflect what proofreaders chose to fix, which is likely biased toward visually obvious or task-relevant segments and away from edits that are hard to spot. (4) Modality bias: EM is present on all synthetic controls but only ~30% of real edits, so EM-trained models see a different sample mix than geometry-only training; this can confound comparisons.", |
| "rai:personalSensitiveInformation": "No personally identifiable information. Source data is mesh geometry and electron-microscopy of brain tissue from non-human animals (fly, mouse, zebrafish) and one previously-published, de-identified human cortical biopsy sample (H01, Harvard/Lichtman lab). No demographic, clinical, or behavioral data is included.", |
| "rai:dataUseCases": "The captured construct is human proofreader decisions on real connectome volumes: real merge_edit/split_edit rows are actual curator-applied corrections; control rows follow documented synthetic recipes (see prov:wasGeneratedBy). The dataset validly trains/evaluates models for imitating proofreader decisions on volumes similar to the upstream sources. It does not capture the broader \"all segmentation errors\" construct, as real edits skew toward errors humans noticed, and controls reflect specific construction recipes, not natural error distributions. Not validated for: detecting errors human proofreaders missed; stand-alone proofreading deployment; neuron-type classification; any clinical use.", |
| "rai:dataSocialImpact": "Positive: accelerates connectomics research by reducing the manual proofreading bottleneck and standardizing evaluation of automated methods. Risks are minimal as data is neural mesh geometry, not subjects. Main indirect risk is over-reliance on automation reducing human review quality. Human-in-the-loop deployment is recommended.", |
| "rai:hasSyntheticData": true, |
| "prov:wasDerivedFrom": [ |
| "https://www.microns-explorer.org/cortical-mm3", |
| "https://flywire.ai/", |
| "https://h01-release.storage.googleapis.com/landing.html", |
| "https://fish1-release.storage.googleapis.com/index.html" |
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| "prov:wasGeneratedBy": "Real edits (merge_edit, split_edit) come from CAVE proofreading edit history of the upstream connectomes. Adjacent controls are sampled from segments adjacent to real edits (synthetic negatives for merge correction). Junction controls are sampled from putative junctions in proofread neurons (synthetic negatives for false-merge ID). Synapse controls are pre/post-synaptic root pairs where presynaptic and postsynaptic neurons differ (synthetic negatives for merge correction). All samples are rendered into: (a) 3 orthographic views (front/side/top) of the mesh as a 7-channel float16 array (silhouette + depth + xyz normals + per-segment masks), stored as compressed npz; (b) 4 EM slices (xy, xz, yz, oblique 'best') as 3-channel uint8 PNGs (raw EM + per-segment masks). Train/val/test split is by 50\u00b5m spatial cubes randomly assigned to splits to prevent leakage from spatial proximity. Pipeline scripts are available at https://github.com/timfarkas/ConnectomeBench2/tree/main/scripts/neurips/." |
| } |
