Compare open neuroimaging, electrophysiology, and multi-omics cohorts side-by-side across license permissiveness, sample size, and FAIR schemas—or compute required statistical sample sizes and match adequately powered repositories.
| Specification Dimension | Slot #1 Dandiset 002051: Highly Multiplexed 3D Fluorescence Imaging of Mouse Brain Tissue Using Amplification by Cyclic Extension (ACE)DANDI ArchiveBroad CNS / Neuroimaging | Slot #2 Dandiset 002050: Shared and task-specific choice codes coexist across cortical populationsDANDI ArchiveBroad CNS / Neuroimaging | |
|---|---|---|---|
| Resource Format & Date | Open Dataset 11/5/2026 | Open Dataset 11/5/2026 | Open Dataset 11/4/2026 |
Cohort Sample Size (N) | Multi-cohort / See abstract | Multi-cohort / See abstract | Multi-cohort / See abstract |
Modalities & Data Schemas | Electrophysiology (NWB) | Electrophysiology (NWB) | Electrophysiology (NWB) |
Reuse License & AI Training | CC-BY 4.0 (Open Data) Commercial & ML Permissive | CC-BY 4.0 (Open Data) Commercial & ML Permissive | CC-BY 4.0 (Open Data) Commercial & ML Permissive |
Recommended Software Stack | PyNWB & IPFX (Allen Institute) | PyNWB & IPFX (Allen Institute) | PyNWB & IPFX (Allen Institute) |
| Scientific Summary & Value | BRAIN Initiative neurophysiology dataset deposited to DANDI Archive. | BRAIN Initiative neurophysiology dataset deposited to DANDI Archive. | BRAIN Initiative neurophysiology dataset deposited to DANDI Archive. |
| Deep Brief & Primary Source |
# Reproducible Cross-Dataset Harmonization Blueprint
# Selected CNS Open Data IDs: dandi-002051, dandi-002050, dandi-002049
import pandas as pd
import numpy as np
from scipy import stats
cohort_configs = [
{"slot": 1, "id": "dandi-002051", "source": "DANDI Archive", "disease": "general_cns", "license": "CC-BY 4.0 (Open Data)"},
{"slot": 2, "id": "dandi-002050", "source": "DANDI Archive", "disease": "general_cns", "license": "CC-BY 4.0 (Open Data)"},
{"slot": 3, "id": "dandi-002049", "source": "DANDI Archive", "disease": "general_cns", "license": "CC-BY 4.0 (Open Data)"}
]
def zscore_harmonize_cohort(df: pd.DataFrame, feature_cols: list[str], covariate_cols: list[str]) -> pd.DataFrame:
"""Standardizes multimodal biomarker features within each repository prior to pooled meta-analysis."""
aligned = df.copy()
for col in feature_cols:
if col in aligned.columns:
aligned[f"{col}_z"] = stats.zscore(aligned[col].astype(float), nan_policy="omit")
return aligned
print(f"Initialized harmonization pipeline for {len(cohort_configs)} CNS cohorts.")Select up to 4 resources to compare side-by-side.