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Data & Analytics · By NeuroAIHub
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency… | NeuroAIHub/ | 1.1k | — | ~2.3k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 2 | Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical… | NeuroAIHub/ | 1.1k | — | ~2.6k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 3 | Submission-grade Nature/high-impact journal figure workflow for Python or R. | NeuroAIHub/ | 1.1k | 1 repo | ~1.3k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 4 | Domain-validated pipeline guidance for calcium imaging data analysis: motion correction, ROI extraction, neuropil correction, spike inference, and quality control | NeuroAIHub/ | 1.1k | — | ~4.4k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 5 | Domain-validated pipeline and parameter guidance for event-related potential analysis, from preprocessing through statistical testing | NeuroAIHub/ | 1.1k | — | ~2.8k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 6 | Domain-validated guidance for fMRI preprocessing decisions: motion correction, slice timing, spatial normalization, smoothing, confound regression, and quality control | NeuroAIHub/ | 1.1k | — | ~4.7k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 7 | Domain-specific statistical modeling guidance for cognitive science and neuroscience, encoding when and how to apply mixed models, correction methods, Bayesian approaches, and effect size reporting | NeuroAIHub/ | 1.1k | — | ~5.3k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 8 | Generate and share anonymized skill usage statistics to help the community understand which skills are most valuable | NeuroAIHub/ | 1.1k | — | ~3.2k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 9 | Domain-specific visualization best practices for cognitive and neuroscience data, encoding plot type selection, color standards, and publication formatting | NeuroAIHub/ | 1.1k | — | ~4.6k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 10 | Expert guidance for designing EEG paradigms optimized to isolate specific ERP components, with domain-validated timing, trial count, and control condition parameters | NeuroAIHub/ | 1.1k | — | ~5k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 11 | Domain-validated guidance for building hierarchical Bayesian cognitive models with Stan/PyMC: prior specification, model structure, MCMC diagnostics, and posterior predictive checks | NeuroAIHub/ | 1.1k | — | ~5.6k | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
| 12 | Create or update the canonical Markdown inventory of task-relevant research data. | NeuroAIHub/ | 1.1k | — | ~579 | Automated safety check: Pass | AGPL-3.0 | 6 days ago |
