OpenNeuro ds009010: Squirrel monkey spinal cord normalized fMRI data in the the custom template space
Study Design & Technical Specifications
Sample Size / Cohort
12 Subjects
Subject / Study Model
CNS Experimental / Clinical Model
Analytical Modalities
Primary Abstract & Repository Description
Source: OpenNeuroEditorial Critique, Methodological Value & Reusability Guide
1. Scientific Context & Premise
Primary Premise (OpenNeuro): "BIDS neuroimaging dataset containing MRI scans from 12 Subjects." Within Central Nervous System (CNS) Neuroscience, this work examines OpenNeuro, BIDS, MRI in relation to multimodal neural oscillations, transcriptomic cell-type taxonomies, structural/functional connectomics, and standardized BIDS/NWB metadata. Authored by Dr Li Min Chen.
2. Study Architecture & Findings
Study Architecture: Experimental / Clinical CNS Design (12 Subjects) utilizing MRI [DOI: 10.18112/openneuro.ds009010.v1.0.0]. Reported quantitative parameters in the abstract include: 12 Subjects. Key reported conclusion: "Freely available for computational neuroscience."
3. Secondary Analysis & Reusability
Secondary Analysis Applications: (1) Cross-referencing MRI variables (OpenNeuro, BIDS, MRI) against open repositories in Central Nervous System (CNS) Neuroscience; (2) Benchmarking feature distributions and effect sizes reported in "OpenNeuro ds009010: Squirrel monkey spinal cord normalized fMRI data in the the "; (3) Informing cohort stratification and endpoint selection.
4. Methodological Caveats & Governance
Methodological Note: When integrating these MRI findings, account for cohort-specific inclusion criteria (12 Subjects), potential site/instrument batch effects, and repository license terms (CC0 Public Domain).
Data Access, Reproducibility & Analysis Starter Guide
BIDS (Brain Imaging Data Structure) v1.9+
Quick Data Access & Structured Export
Open the primary repository directly or download structured metadata (JSON / CSV) in one click.
This neuroimaging resource follows the Brain Imaging Data Structure (BIDS) specification, organizing raw NIfTI volumes (.nii.gz), acquisition parameters (.json sidecars), and participant phenotype tables (participants.tsv) in a deterministic directory hierarchy.
Programmatic Data Retrieval (Python Starter Code & Recommended SDKs)
Python Quickstart: Download & Inspect BIDS Neuroimaging Dataset
# 1. Install BIDS & Neuroimaging dependencies
# pip install openneuro-py pybids nibabel pandas
import openneuro as on
from bids import BIDSLayout
import pandas as pd
# Download metadata & subject headers from OpenNeuro (ds009010)
dataset_id = "ds009010"
target_dir = f"./data/{dataset_id}"
on.download(dataset=dataset_id, target_dir=target_dir, include=["participants.tsv", "dataset_description.json", "sub-01/*"])
# Initialize PyBIDS layout and inspect subjects & modalities
layout = BIDSLayout(target_dir, validate=False)
print(f"Subjects: {layout.get_subjects()} | Modalities: {layout.get_datatypes()}")
# Load clinical / demographic phenotype table
participants = pd.read_csv(f"{target_dir}/participants.tsv", sep="\t")
print(participants.head())Standardized 4-Step Workflow & Governance Checklist
Standardized Acquisition & Preprocessing Workflow
1. Retrieve BIDS Hierarchy via DataLad or AWS S3 CLI
Fetch the dataset manifest and selectively download subject anatomical/functional runs without requiring authentication credentials.
2. Run BIDS-Validator & Inspect JSON Sidecars
Verify TR (Repetition Time), TE, slice timing, and scanner field strength (3T/7T) across all subjects to detect protocol deviations.
3. Standardized Preprocessing (fMRIPrep / FreeSurfer)
Execute motion correction, susceptibility distortion correction, and MNI152NLin2009cAsym template normalization.
4. Covariate Regression & Group-Level Modeling
Merge participants.tsv clinical scores with extracted ROI/parcellation metrics while controlling for age, sex, and head motion.
Reproducibility & Governance Checklist
- Verify BIDS dataset_description.json License and HowToAcknowledge fields before publication.
- Exclude runs exceeding framewise displacement (FD > 0.5 mm) to prevent motion-induced false positives.
- Ensure all anatomical scans remain defaced/de-identified in derivative repositories.
Tags & Modalities
Bibliometrics, Peer-Review Status & Citation Export
Cross-referenced via Semantic Scholar, Europe PMC & bioRxiv
1. Citation Metrics & Context Breakdown
Citation Context Breakdown
2. Peer-Review Validation & Revision History
Verified Publication / Open Dataset Record
Metadata and license integrity verified against primary repository records.
3. Standardized Academic Citation & BibTeX Export
Dr Li Min Chen (2026). OpenNeuro ds009010: Squirrel monkey spinal cord normalized fMRI data in the the custom template space. OpenNeuro. https://doi.org/10.18112/openneuro.ds009010.v1.0.0
@misc{chen2026open,
title = {OpenNeuro ds009010: Squirrel monkey spinal cord normalized fMRI data in the the custom template space},
author = {Dr Li Min Chen},
year = {2026},
journal = {OpenNeuro},
howpublished = {\url{https://openneuro.org/datasets/ds009010}},
doi = {10.18112/openneuro.ds009010.v1.0.0},
note = {CNS Open Data: general_cns}
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Academic Research & Medical Information Disclaimer
This page provides structured scientific metadata, methodological critiques, and open-data reusability guidelines intended strictly for neuroscience researchers and data scientists. It does not constitute medical advice, clinical diagnosis, or treatment recommendations.
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