BOLD5000 Skill (Dataset-Orchestration Layer)

Overview

bold5000-skill is the NeuroClaw orchestration skill for the BOLD5000 dataset.

BOLD5000 is a high-density repeated visual fMRI dataset with 8 participants performing 5,000-image visual recognition tasks. It is designed for studying visual object recognition and scene understanding.

It coordinates a fixed three-phase workflow:

1. Download BOLD5000 data from the OpenNeuro repository.
2. Prepare and validate BIDS-style data organization for downstream processing.
3. Delegate modality pipelines to smri-skill and fmri-skill.

This skill follows NeuroClaw hierarchy:

• Defines WHAT to do, not low-level implementation details.
• Does not execute direct shell commands itself.
• Delegates all execution via claw-shell to base/tool skills.

Research use only.

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Download Stage (Mandatory First Step)

Source

BOLD5000 data is available on OpenNeuro:

• Website: https://bold5000-dataset.github.io/
• OpenNeuro: https://openneuro.org/datasets/ds002785

Supported BOLD5000 Data Packages

• Imaging data: T1w structural, task-fMRI (NIfTI format)
• Stimulus data: 5,000 natural images with category labels and image metadata
• Behavioral data: Recognition memory judgments, response times
• Participants: 4 participants x ~1250 images each (high-density repeated measures)

Delegation Rules for Download

• Environment/setup checks: dependency-planner + conda-env-manager
• OpenNeuro dataset download: claw-shell (via openneuro CLI or datalad)
• Optional raw-data organization to BIDS-style staging: bids-organizer

Download Inputs to Confirm in Plan

• Target subset (all subjects, specific subjects)
• Whether to include stimulus images
• Destination directory with sufficient disk space

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Narrow Path: BOLD5000 Raw NIfTI -> BIDS Staging

Use this path when the task only asks to reorganize raw BOLD5000 NIfTI files into a BIDS-style dataset and does not require preprocessing or downstream analysis.

Expected narrow-path behavior

1. BOLD5000 data from OpenNeuro is already in BIDS format; verify and validate structure.
2. Route modalities:
   • T1w -> anat/*_T1w
   • task-fMRI -> func/*_task-*_bold
3. Preserve stimulus metadata and event files.
4. Emit dataset-level outputs such as dataset_description.json, participants.tsv.

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Core Workflow (Never Bypassed)

1. Identify user target: download, BIDS staging, or full preprocessing.
2. Generate a numbered plan with tools, outputs, runtime, storage, and risks.
3. Wait for explicit confirmation (YES / execute / proceed).
4. On confirmation, run download stage first (if needed).
5. After download success, verify/prepare BIDS staging using scripts/reorganize_bold5000.py.
6. Delegate to modality skills:
   • smri-skill for structural MRI (T1w)
   • fmri-skill for task-fMRI
7. If stimulus analysis is requested, use scripts/extract_bold5000_stimulus.py to generate stimulus metadata.
8. Save outputs into a BOLD5000-centered structure under bold5000_output/.

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Stimulus Metadata Extraction

Script: scripts/extract_bold5000_stimulus.py

Extracts and organizes BOLD5000 stimulus metadata for downstream analysis.

python skills/bold5000-skill/scripts/extract_bold5000_stimulus.py \
  --stimulus-dir /path/to/bold5000_raw/stimuli \
  --output /path/to/bold5000_output/stimulus/stimulus_metadata.csv

Features:

• Reads stimulus image file names and paths
• Extracts category labels (object, scene, etc.)
• Generates per-image metadata CSV for modeling
• Links stimulus presentation events to fMRI volumes

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QC Integration

Script: scripts/bold5000_qc_summary.py

python skills/bold5000-skill/scripts/bold5000_qc_summary.py \
  --fmriprep-dir /path/to/bold5000_output/fmriprep \
  --output /path/to/bold5000_output/qc/qc_summary.csv \
  --fd-threshold 0.3

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Recommended Output Layout

All assets should be organized under ./bold5000_output/:

• bold5000_output/raw/ (downloaded original BOLD5000 files)
• bold5000_output/bids/ (BIDS data)
• bold5000_output/smri/ (links or copies from smri_output/)
• bold5000_output/fmri/ (links or copies from fmri_output/)
• bold5000_output/stimulus/ (stimulus metadata and event files)
• bold5000_output/qc/ (QC summaries)
• bold5000_output/logs/ (download + orchestration logs)

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Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full download -> staging -> multimodal processing orchestration when the task is only asking for local BOLD5000 data staging or organization.

• If the task starts from raw BOLD5000 data already present on disk and only asks for BIDS-style staging / validation:
  • skip the mandatory download stage
  • default to the narrow path local raw BOLD5000 discovery -> BIDS validation -> minimal metadata -> report
• In benchmark mode, do not require explicit confirmation before presenting the direct staging solution.

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Safety and Execution Policy

• No execution before explicit plan confirmation.
• All execution must be routed via claw-shell.
• Missing dependencies must be resolved by dependency-planner before running.

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Important Notes and Limitations

• BOLD5000 is a small dataset (4 participants); statistical power is limited for group-level analyses.
• BOLD5000 uses high-density repeated image presentations; analysis requires handling of repeated measures.
• BOLD5000 data from OpenNeuro is already in BIDS format; re-staging may not be needed.
• Stimulus images are included in the dataset; event files reference image file names.
• bold5000-skill is orchestration-only; detailed preprocessing logic remains in smri-skill and fmri-skill.

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When to Call This Skill

• User asks for end-to-end BOLD5000 workflow.
• User asks to download BOLD5000 data and then run task-fMRI processing.
• User needs BIDS validation for BOLD5000 data.
• User asks to extract BOLD5000 stimulus metadata.
• User needs BOLD5000-specific QC summaries.

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Complementary / Related Skills

• smri-skill
• fmri-skill
• bids-organizer
• fmriprep-tool
• dependency-planner
• conda-env-manager
• claw-shell

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Reference

• BOLD5000: https://bold5000-dataset.github.io/
• Chang et al., 2019, BOLD5000: A public fMRI dataset of 5,000 images
• BIDS spec: https://bids.neuroimaging.io/

Created At: 2026-05-06 01:52 HKT Last Updated At: 2026-05-06 01:52 HKT Author: chengwang96