---

name: data-preparation description: "Prepare nnU-Net v2-compatible datasets, path variables, dataset.json metadata, conversions, inference input naming, labels/regions/ignore-label conventions, and validation troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

data-preparation

Use this sub-skill when the task is to make data acceptable to nnU-Net v2 before planning/preprocessing.

Route here for

• Setting nnUNet_raw, nnUNet_preprocessed, nnUNet_results, or optional nnUNet_extTrainer.
• Creating nnUNet_raw/DatasetXXX_Name folders with imagesTr, labelsTr, optional imagesTs, and dataset.json.
• Naming training or inference inputs with _0000, _0001, ... channel suffixes and a consistent file ending.
• Authoring or checking dataset.json, including labels, regions, regions_class_order, ignore labels, and reader/writer overrides.
• Converting Medical Segmentation Decathlon or old nnU-Net v1 datasets into nnU-Net v2 format.
• Diagnosing validation errors before nnUNetv2_plan_and_preprocess.

Do not handle here

• Planning fingerprints, experiment planners, or preprocessing outputs: use planning-preprocessing.
• Trainer classes, folds, losses, schedules, or training commands: use training-configuration.
• Prediction, ensembling, evaluation, or postprocessing after a trained model exists: use inference-evaluation.

Start with these references

• Dataset layout, naming, dataset.json, regions, ignore labels, and inference input format: references/data-formats.md.
• Storage path environment variables and lazy path failure behavior: references/path-setup.md.
• MSD/v1 conversion command patterns and preflight validation: references/conversion-and-validation.md.
• Common failure modes and fixes: references/troubleshooting.md.

Bundled helper

Run the lightweight validator before planning/preprocessing:

python sub-skills/data-preparation/scripts/validate_dataset_json.py /path/to/nnUNet_raw/Dataset123_MyDataset

Add --check-files to verify that imagesTr and labelsTr filenames match dataset.json channel count and file ending. The helper checks metadata and names only; it does not inspect image geometry or label voxel values.