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name: datasets-interfaces description: "Use Nilearn dataset/template/atlas fetchers and public interfaces for cache-aware, BIDS-aware, and fMRIPrep-aware workflows without accidental network-heavy actions." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Nilearn Datasets and Interfaces

Use this sub-skill when the task involves nilearn.datasets fetchers/loaders, dataset cache choices, atlas/template selection, OpenNeuro/NeuroVault helpers, BIDS file discovery, fMRIPrep confound loading, or FSL design text loading.

Route Here

• No-network templates: Prefer local loaders such as load_mni152_template, load_mni152_brain_mask, load_fsaverage, and load_fsaverage_data when a task only needs a template, mask, or bundled fsaverage5 surface. See workflows.
• Download-aware datasets: Treat most fetch_* functions as network and cache operations. Require an explicit data_dir for reproducibility and confirm large downloads, external terms, or credentials before running. See API reference.
• Atlas selection: Choose deterministic label atlases, probabilistic map atlases, or coordinate sets based on the downstream masker/analysis contract. See data formats.
• OpenNeuro and NeuroVault: Use fetch_ds000030_urls, select_from_index, fetch_openneuro_dataset, fetch_neurovault, or fetch_neurovault_ids only with bounded filters and clear download intent. See troubleshooting.
• BIDS/fMRIPrep/FSL interfaces: Use get_bids_files, parse_bids_filename, load_confounds, load_confounds_strategy, and get_design_from_fslmat for local derivative inspection and confound extraction. See data formats.

Route Elsewhere

• Route first-level or second-level statistical model construction, contrasts, and BIDS GLM writing to ../glm-analysis/SKILL.md.
• Route decoding, connectome, or machine-learning examples that merely consume fetched datasets to ../ml-decoding-connectivity/SKILL.md.
• Route plotting, HTML reports, display objects, and figure export for dataset outputs to ../plotting-reporting/SKILL.md.
• Route low-level Niimg loading, masking, resampling, and signal cleaning after data discovery to ../data-io-signal/SKILL.md.
• Route surface-specific mesh/image object manipulation to ../surface-workflows/SKILL.md.

Quick Checklist

1. Decide whether a local load_* helper is sufficient before using any fetch_* downloader.
2. Set data_dir explicitly, or document the environment-variable/home cache fallback chosen by get_data_dirs.
3. Bound network fetches with subject counts, filters, atlas dimensions, or NeuroVault/OpenNeuro URL subsets.
4. Inspect returned Bunch keys before assuming whether maps, labels, lut, file paths, images, or DataFrames are present.
5. For fMRIPrep, pass the processed image path, not the TSV path; the associated confounds TSV/JSON must be in the same directory with supported BIDS names.

Bundled Script

Run python scripts/inspect_dataset_fetchers.py --help for options. The default python scripts/inspect_dataset_fetchers.py mode lists public dataset/interface entry points and exercises only safe local loaders when Nilearn and its runtime dependencies are importable; it does not trigger downloads.

References

• API reference
• Workflows
• Data formats
• Troubleshooting
• Fetcher inspection script