Reputable neuroscience repo patterns#

This page summarizes what high-trust neuroscience and scientific Python repositories look like, and what Kahlus should copy.

EEG / BCI / neural decoding#

MNE-Python#

  • Repo: mne-tools/mne-python

  • Role: canonical EEG/MEG/ECoG analysis and visualization toolkit.

  • Docs pattern: Sphinx documentation, extensive examples, API reference, publication-ready figure tutorial.

  • Figure lesson: use real data containers, channel metadata, sensor locations, time/frequency units, and reproducible plotting code.

MOABB#

  • Repo: NeuroTechX/moabb

  • Role: reproducible EEG/BCI benchmarking.

  • Docs pattern: benchmark results, dataset summaries, examples, API, citation instructions.

  • Figure lesson: benchmark visualizations must be tied to explicit datasets, paradigms, splits, and evaluation strategies.

Braindecode#

  • Repo: braindecode/braindecode

  • Role: PyTorch decoding models for raw EEG/ECoG/MEG with MNE/MOABB integration.

  • Docs pattern: landing page, quickstart, tutorial gallery, model zoo, API, citation.

  • Figure lesson: deep EEG figures should distinguish dataset loading, preprocessing/windowing, architecture, and evaluation.

CEBRA#

  • Repo: AdaptiveMotorControlLab/cebra

  • Role: latent embeddings for joint behavioral and neural analysis.

  • Docs pattern: Sphinx/PyData theme, installation, usage, demos, figures, API docs, citations.

  • Figure lesson: a serious method repo gives readers a path from paper figures to executable demos and APIs.

NeuroML / computational neuroscience#

NeuroML documentation and pyNeuroML#

  • Repos: NeuroML/Documentation, NeuroML/pyNeuroML, NeuroML/libNeuroML

  • Role: standard model descriptions, simulation, visualization, and analysis of neural models.

  • Docs pattern: Sphinx/ReadTheDocs, specification pages, tutorials, software pages, provenance guidance.

  • Figure lesson: morphology, voltage traces, F-I curves, and network diagrams should be generated from model files and simulation protocols.

fMRI / neuroimaging#

Nilearn#

  • Role: fMRI/statistical neuroimaging plotting and machine learning.

  • Docs pattern: example gallery, plotting API, decoding/connectivity tutorials.

  • Figure lesson: brain images need atlas/template/projection provenance, threshold rules, and clear statistical meaning.

Data and reproducibility infrastructure#

SpikeInterface, AllenSDK, DANDI, BIDS/MNE-BIDS#

  • Role: acquisition/analysis pipelines, public datasets, metadata standards.

  • Lesson: serious neuroscience code emphasizes data provenance, metadata, file standards, and reproducible examples over decorative visuals.

What Kahlus should copy#

  • Sphinx/MyST docs with a clean landing page.

  • Figure pages that include generated assets, captions, and provenance.

  • API reference generated from code once public interfaces stabilize.

  • A tutorial path: install → prepare data → run baseline → inspect ridge diagnostics.

  • A methods path: data contracts → split manifests → leakage audit → baselines → claims.

  • Citation and evidence pages.

What Kahlus should avoid#

  • Fake EEG waveforms generated directly by image prompts.

  • Generic brain clip art.

  • Topomaps without sensor positions.

  • Claims not tied to exact run artifacts.

  • Docs that look like a pitch deck but cannot reproduce a figure.