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name: datasets description: "Load, preprocess, integrate, transform, and debug SpikingJelly neuromorphic datasets and data utilities." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

datasets

Use this sub-skill when the task is about SpikingJelly data: loading a built-in neuromorphic dataset, preparing manual download files, choosing event versus frame samples, integrating events to frames, applying dataset transforms, or using data utility helpers.

Route here for

• Built-in spikingjelly.datasets dataset wrappers such as DVS128Gesture, CIFAR10DVS, CIFAR10DVSTEBNSplit, NMNIST, NCaltech101, ASLDVS, DVSLip, ESImageNet, HARDVS, NAVGestureSit, NAVGestureWalk, Bullying10kClassification, SpikingHeidelbergDigits, and SpikingSpeechCommands.
• Dataset-base and builder questions around NeuromorphicDatasetFolder, NeuromorphicDatasetConfig, EventBuilder, FrameFixedNumberBuilder, FrameFixedDurationBuilder, and FrameCustomIntegrateBuilder.
• Download/manual-data boundaries: downloadable(), resource_url_md5(), expected root/download contents, archive extraction, raw events_np / events_h5 creation, and processed frame directories.
• Event/frame layout: event dictionaries with t, x, y, p; frame archives containing frames; fixed-number, fixed-duration, and custom integration workflows.
• Dataset utilities: load_aedat_v3, load_ATIS_bin, load_npz_frames, integrate_events_*, pad_sequence_collate, padded_sequence_mask, split_to_train_test_set, create_sub_dataset, save_as_pic, and play_frame.
• Dataset transforms, especially random_temporal_delete and RandomTemporalDelete, including their batch_first time-axis convention.

Do not handle here

• Model construction, training loops, optimizers, distributed launch, epoch scheduling, or data-loader-to-training recipes. Use ../training-and-scaleout/.
• SNN state reset, step_mode, recurrent state, surrogate gradients, encoders, monitors, or layer semantics. Use ../core-snn/.
• ANN-to-SNN conversion, calibration data selection for conversion, or converted-model timestep behavior. Use ../ann2snn/.
• CUDA/CuPy/Triton backend performance, precision, memory optimization, profiling, or deployment speed. Use ../performance-and-analysis/.
• Exchange/deployment formats or hardware runtime export. Use ../deployment-exchange/.

Read first

• references/data-workflows.md for dataset-class coverage, root layouts, builder selection, event/frame integration contracts, transforms, utilities, and the verified DVS128Gesture signature.
• references/troubleshooting.md for manual-download failures, corrupted/existing extracted dirs, split/layout mistakes, frame-shape mismatches, fixed-duration collation, and preprocessing configuration.

Skill-owned script

• scripts/dataset_tiny_fixture_smoke.py creates temporary event fixtures, runs the fixed-number and custom frame builders, and checks variable-length padding plus temporal-delete transform behavior. It performs no downloads and does not require a real dataset.

Operating workflow

1. Identify the dataset class and whether its constructor uses train, split, train_test_val, or no split argument.
2. Check downloadable() before assuming a dataset can be fetched automatically. For manual datasets, place every resource_url_md5() file under root/download before instantiating the dataset.
3. Choose data_type='event' for raw event dictionaries, or data_type='frame' with exactly one frame strategy: frames_number plus split_by, duration, or custom_integrate_function.
4. Verify the expected layout: root/download, root/extract, default raw root/events_np or dataset-specific root/events_h5, then the processed root selected by the builder.
5. For fixed-duration frame data, plan for variable T; use pad_sequence_collate and padded_sequence_mask before handing batches to training code.
6. Run the no-download smoke script when you need a quick sanity check of builder contracts before debugging a real dataset tree.

Cross-links

• If the data are loaded and the question becomes “how do I train or scale this?”, switch to ../training-and-scaleout/.
• If the question is about temporal state, step_mode, reset_net, or sequence tensor semantics after loading data, switch to ../core-snn/.
• If the data are being used only for ANN2SNN calibration or conversion validation, switch to ../ann2snn/ after the dataset contract is clear.