SciTeX Dataset (scitex-dataset)

Unified access to neuroscience and scientific datasets

Full Documentation · pip install scitex-dataset

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Interfaces: Python ⭐⭐⭐ (primary) · CLI ⭐ · MCP ⭐⭐ · Skills ⭐⭐ · Hook — · HTTP —

Problem and Solution

#	Problem	Solution
1	Public dataset repositories balkanized -- OpenNeuro (BIDS) + DANDI (NWB) + PhysioNet (WFDB) + Zenodo (generic) + GEO / ChEMBL / ClinicalTrials — different APIs, auth, download tools	Unified fetcher -- stx.dataset.neuroscience.openneuro.fetch_all_datasets() same call shape across all; local FTS5 search across metadata
2	"Download this BIDS dataset" means reading DataLad docs first -- the barrier is tooling, not knowledge	One-line fetch -- no DataLad setup; the module handles auth, resumption, checksums transparently

Problem

Neuroscience datasets are scattered across multiple repositories -- OpenNeuro, DANDI Archive, PhysioNet, Zenodo -- each with its own API, data format, and query interface. Researchers waste time navigating incompatible APIs to discover relevant data. AI agents lack a unified way to search and evaluate datasets programmatically.

Solution

SciTeX Dataset provides a single Python API, CLI, and MCP (Model Context Protocol) server to discover and query metadata from major scientific data repositories. It focuses on fast metadata retrieval without downloading full datasets.

Repository	Description	Data Types
OpenNeuro	Open platform for sharing neuroimaging data	MRI, EEG, MEG, iEEG, PET
DANDI	BRAIN Initiative data archive	Electrophysiology, Ophys
PhysioNet	Physiological signal databases	ECG, EEG, clinical data
Zenodo	General scientific data repository (CERN)	Any research data

_{Table 1. Supported data repositories. Each source is queried via its public API; no authentication required for metadata access.}

Installation

Requires Python >= 3.10.

pip install scitex-dataset

MCP support: pip install scitex-dataset[mcp]

Quick Start

from scitex_dataset import fetch_all_datasets, format_dataset

# Fetch datasets from OpenNeuro
datasets = fetch_all_datasets(max_datasets=10)

# Format for analysis
for ds in datasets:
    formatted = format_dataset(ds)
    print(f"{formatted['id']}: {formatted['name']} ({formatted['n_subjects']} subjects)")

Four Interfaces

Python API

from scitex_dataset import fetch_all_datasets, format_dataset, search_datasets, sort_datasets
from scitex_dataset import neuroscience, database

# Fetch from specific sources
datasets = fetch_all_datasets(max_datasets=100)                    # OpenNeuro
dandi_ds = neuroscience.dandi.fetch_all_datasets(max_datasets=50)  # DANDI
phys_ds = neuroscience.physionet.fetch_all_datasets()              # PhysioNet

# Search and filter
eeg_datasets = search_datasets(datasets, modality="eeg", min_subjects=20)
popular = sort_datasets(datasets, by="downloads", descending=True)

# Local database for fast full-text search
database.build()                                        # index all sources
results = database.search("alzheimer EEG", min_subjects=20)

Full API reference

CLI Commands

scitex-dataset --help-recursive             # Show all commands

# Fetch from repositories
scitex-dataset openneuro -n 100 -o datasets.json -v
scitex-dataset dandi -n 50 -o dandi.json -v
scitex-dataset physionet -n 50 -v
scitex-dataset zenodo -q "neuroscience" -n 20

# Local database
scitex-dataset db build                     # index all sources
scitex-dataset db search "epilepsy EEG"     # full-text search
scitex-dataset db stats                     # show statistics

# Introspection
scitex-dataset list-python-apis -v          # list Python API tree
scitex-dataset mcp list-tools -v            # list MCP tools

Full CLI reference

MCP Server -- for AI Agents

AI agents can discover and query neuroscience datasets autonomously.

Tool	Description
dataset_openneuro_fetch	Fetch datasets from OpenNeuro
dataset_dandi_fetch	Fetch datasets from DANDI Archive
dataset_physionet_fetch	Fetch datasets from PhysioNet
dataset_zenodo_fetch	Fetch datasets from Zenodo
dataset_search	Filter datasets by modality, subjects, etc.
dataset_list_sources	List available data repositories
dataset_db_build	Build local search database
dataset_db_search	Full-text search across all sources
dataset_db_stats	Database statistics

_{Table 2. Nine MCP tools available for AI-assisted dataset discovery. All tools accept JSON parameters and return JSON results.}

scitex-dataset mcp start

Full MCP specification

Skills — for AI Agent Discovery

Skills provide workflow-oriented guides that AI agents query to discover capabilities and usage patterns.

scitex-dataset skills list              # List available skill pages
scitex-dataset skills get SKILL         # Show main skill page
scitex-dev skills export --package scitex-dataset  # Export to Claude Code

Skill	Content
quick-start	Basic usage
data-sources	OpenNeuro, DANDI, PhysioNet
cli-reference	CLI commands
mcp-tools	MCP tools for AI agents

Part of SciTeX

SciTeX Dataset is part of SciTeX. When used inside the SciTeX framework, dataset discovery integrates with reproducible research sessions:

import scitex
from scitex_dataset import fetch_all_datasets, format_dataset

@scitex.session
def main(logger=scitex.INJECTED):
    datasets = fetch_all_datasets(max_datasets=100, logger=logger)
    formatted = [format_dataset(ds) for ds in datasets]
    scitex.io.save(formatted, "openneuro_datasets.json")
    return 0

The SciTeX ecosystem follows the Four Freedoms for Research, inspired by the Free Software Definition:

Four Freedoms for Research

0. The freedom to run your research anywhere -- your machine, your terms.
1. The freedom to study how every step works -- from raw data to final manuscript.
2. The freedom to redistribute your workflows, not just your papers.
3. The freedom to modify any module and share improvements with the community.

AGPL-3.0 -- because we believe research infrastructure deserves the same freedoms as the software it runs on.

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