Hyperface Data Paper

C+ 73 completed
Data Tool
unknown / json · small
80
Files
28,458
LOC
1
Frameworks
7
Languages

Pipeline State

completed
Run ID
#363417
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
70.00
Framework unique
Isolation
Last stage change
2026-05-10 03:35:17
Deduplication group #52011
Member of a group with 3 similar repo(s) — canonical #10509 view group →
Top concepts (2)
Project DescriptionTesting
Repobility — same analyzer, your code, free for public repos · /scan/

AI Prompt

Create a Python library for analyzing the Hyperface fMRI dataset. I need tools to handle the data, which is structured according to BIDS standards and includes raw data, fMRIPrep, and FreeSurfer derivatives. Specifically, I need scripts to run a comprehensive Quality Assurance pipeline. This pipeline should include functions to generate motion parameter plots, compute and plot tSNR maps, and calculate inter-subject correlation. The output should also include generating interactive HTML reports for both motion and tSNR analysis. The core functionality should be built using Python and utilize pytest for testing.
python pytest fmri neuroimaging data-analysis json html qa bids
Generated by gemma4:latest

Catalog Information

A Python library that provides tools for analyzing the Hyperface fMRI dataset.

Description

This library offers a comprehensive suite of functions for loading, preprocessing, and visualizing the Hyperface fMRI dataset. It includes utilities for cleaning raw signals, extracting time‑series features, and applying dimensionality reduction techniques. Machine‑learning pipelines are provided to train classifiers on brain activity patterns, enabling researchers to explore cognitive states. The code leverages popular scientific libraries to ensure reproducibility and performance. It is designed for neuroscientists and data scientists who need a ready‑to‑use toolkit for fMRI analysis.

الوصف

توفر هذه المكتبة مجموعة شاملة من الوظائف لتحميل ومعالجة وتصور مجموعة بيانات Hyperface للدماغ باستخدام التصوير بالرنين المغناطيسي الوظيفي. تتضمن الأدوات تنظيف الإشارات الخام واستخراج ميزات السلاسل الزمنية وتطبيق تقنيات تقليل الأبعاد. كما توفر خطوط أنابيب التعلم الآلي لتدريب المصنفات على أنماط النشاط الدماغي، مما يمكّن الباحثين من استكشاف الحالات المعرفية. يعتمد الكود على مكتبات علمية مشهورة لضمان القابلية للتكرار والأداء العالي. صُممت لتلبية احتياجات علماء الأعصاب وعلماء البيانات الذين يحتاجون إلى أداة جاهزة للتحليل fMRI. تبرز هذه المكتبة بقدرتها على التعامل مع بيانات معقدة وتقديم رؤى دقيقة حول النشاط الدماغي. كما توفر واجهة برمجية سهلة الاستخدام تسمح بتكامل سريع مع مشاريع البحث الحالية. تُعد هذه الأداة خياراً مثالياً لمن يسعون إلى تحليل بيانات fMRI بكفاءة وموثوقية.

Novelty

4/10

Tags

fmri-analysis brain-imaging data-preprocessing machine-learning visualization feature-extraction statistical-analysis neuroscience

Technologies

matplotlib numpy pandas scikit-learn scipy

Claude Models

claude-opus-4.6 claude-opus-4.5 claude (unknown version)

Quality Score

C+
73.2/100
Structure
76
Code Quality
74
Documentation
64
Testing
65
Practices
68
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Code linting configured (ruff (possible))
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • 591 duplicate lines detected \u2014 consider DRY refactoring
  • 2 'god files' with >500 LOC need decomposition

Security & Health

6.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
BSD-2-Clause
License
6.5%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

json
74.2%
python
20.9%
html
2.4%
yaml
1.5%
markdown
0.5%
toml
0.4%
shell
0.0%

Frameworks

pytest

Concepts (2)

Repobility · code-quality intelligence · https://repobility.com
CategoryNameDescriptionConfidence
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auto_descriptionProject Description![DOI](https://doi.org/10.5281/zenodo.19038401)80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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