bidsschematools
Python tools for working with the BIDS schema.
Decision gist · record as of 2026-08-14
Yes, if you work with BIDS datasets or neuroimaging data structures. The package has low install friction, active maintenance, permissive licensing, and no known vulnerabilities. It is marked Beta but serves as the reference implementation for BIDS schema testing, suggesting stability for its intended use. Install it if you need to validate, parse, or export BIDS schema metadata.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low install friction with three lightweight runtime dependencies (acres, click, pyyaml).
- Active maintenance with a recent release 25 days ago and ongoing commits; the repository is not archived and has 366 stars.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) means you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
last release 2026-07-20 (25 days) · last repo commit 2026-08-10 · 366 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 172,396 downloads/mo, #10,338 on PyPI
Alternatives
Verify before relying
pip install bidsschematools
import bidsschematools
# Use the library to parse and work with BIDS schema objects- Whether the package is suitable for production use despite Beta status, or primarily for schema testing and development.
- What specific schema validation capabilities are available beyond reference parsing.
What it is and what it does
bidsschematools is a Python library for working with the Brain Imaging Data Structure (BIDS) schema, a standardized format for organizing neuroimaging datasets. It provides lightweight schema parsing, validation, and export functionality, serving as the reference implementation used for testing the BIDS schema itself. The library is designed for neuroimaging researchers and developers who need to programmatically interact with BIDS metadata structures.
The package includes both a Python API for direct schema manipulation and simple command-line bindings (such as `bst export`) for common tasks. It depends on three runtime libraries: acres, click for CLI support, and pyyaml for schema file handling. The project is actively maintained by the BIDS community and supports modern Python versions.
Use it for
- Validate neuroimaging datasets against the BIDS schema specification during data collection or preprocessing pipelines.
- Export BIDS schema definitions in different formats for documentation or integration with other neuroimaging tools.
- Programmatically parse and inspect BIDS schema structure in research workflows or data management systems.
- Test BIDS schema compliance in custom neuroimaging data processing scripts.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with BIDS datasets or neuroimaging data structures.
The package has low install friction, active maintenance, permissive licensing, and no known vulnerabilities. It is marked Beta but serves as the reference implementation for BIDS schema testing, suggesting stability for its intended use. Install it if you need to validate, parse, or export BIDS schema metadata.
Install
bidsschematools on PyPI
Before you install
Low install friction with three lightweight runtime dependencies (acres, click, pyyaml). Active maintenance with a recent release 25 days ago and ongoing commits; the repository is not archived and has 366 stars.
Requires Python 3.10 or later.
License in practice
MIT license (permissive) means you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions.
Quickstart
pip install bidsschematools
import bidsschematools
# Use the library to parse and work with BIDS schema objectsVerify before relying
- Whether the package is suitable for production use despite Beta status, or primarily for schema testing and development.
- What specific schema validation capabilities are available beyond reference parsing.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesacresclickpyyaml |
| Maintenance | Actively maintained 25 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 172,396 / month, #10,338 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Information Analysis |
Evidence: bidsschematools-1.2.7-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “BIDS schema parsing”
- bidsschematoolsParse, validate, and export the Brain Imaging Data Structure (BIDS)…
- pybidsPyBIDS provides a Python interface to query, validate, and interact…
- bids-validatorValidates that brain imaging datasets conform to the BIDS (Brain…
Give your agent the search over MCP, or paste the wish link into any chat.
More Information Analysis packages
A drop-in replacement for Python's standard `re` module that adds advanced regex features like nested sets, fuzzy matching, lookaround in conditionals, and full Unicode case-folding while maintaining backward compatibility.
pyarrow provides Python bindings to Apache Arrow's C++ libraries for efficient columnar data processing, serialization, and interoperability with pandas, NumPy, and other Python ecosystem tools.
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
Connects Python applications to Snowflake data warehouses using the DB API 2.0 specification, enabling SQL queries, data transfers, and warehouse operations.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
Snowpark Python provides APIs to query and process data directly in Snowflake without moving data to your local system, with support for both native Snowpark and pandas-compatible interfaces.
Install it if you use Snowflake and want to process data without moving it to your application layer.
See also pybids · bids-validator · mne-bids · pyxnat · nibabel · nilearn · dipy · nipype · dicom2nifti · trx-python