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Data & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
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Showing 385–408 of 12,625 skills
- 468 Index B94157eaInteractive Mermaid visualization showing skill directory structureVotes: 0GitHub stars: 9
- 459 Image Processing A48cfd15This reference covers accessing raw pixel data, image rendering, and creating new images in OMERO.Votes: 0GitHub stars: 9
- 423 Getting Started C71d2255This guide provides end-to-end examples for common Data Commons workflows.Votes: 0GitHub stars: 9
- 418 Geofred 0b5745ffGeoFRED endpoints provide access to geographic/regional economic data and shape files for mapping.Votes: 0GitHub stars: 9
- 366 Feature Detection 24ce0862Feature detection identifies persistent signals (chromatographic peaks) in LC-MS data. Feature linking combines features across multiple samples for quantitative comparison.Votes: 0GitHub stars: 9
- 333 Emg 87dce7c7Electromyography (EMG) measures electrical activity produced by skeletal muscles during contraction. EMG analysis in NeuroKit2 focuses on amplitude estimation, muscle activation detection, and temporal dynamics for psychophysiology and motor control research.Votes: 0GitHub stars: 9
- 326 Eeg 7a3a715aAnalyze electroencephalography (EEG) signals for frequency band power, channel quality assessment, source localization, and microstate identification. NeuroKit2 integrates with MNE-Python for comprehensive EEG processing workflows.Votes: 0GitHub stars: 9
- 256 Data Management 5d2448b0PathML provides efficient data management solutions for handling large-scale pathology datasets through HDF5 storage, tile management strategies, and optimized batch processing workflows. The framework enables seamless storage and retrieval of images, masks, features, and metadata in formats optimized for machine learning pipelines and downstream analysis.Votes: 0GitHub stars: 9
- 193 Consensus Peaks F30a172cGeniml provides tools for building genomic "universes" — standardized reference sets of consensus peaks from collections of BED files. These universes represent genomic regions where analyzed datasets show significant coverage overlap, serving as reference vocabularies for tokenization and analysis.Votes: 0GitHub stars: 9
- 179 Common Patterns 83f7475eUse when exploring available data without loading expression matrices.Votes: 0GitHub stars: 9
- 178 Common Issues 16fecf7bTroubleshooting guide for frequently encountered matplotlib problems.Votes: 0GitHub stars: 9
- 136 Chartjs Generator 7791ec3fThe **Chart.js Generator** skill creates professional, interactive data visualizations using the Chart.js library, supporting all eight major chart types (line, bar, pie, doughnut, radar, polar area, bubble, scatter). This skill generates complete MicroSim packages with HTML, CSS, and documentation, optimized for embedding in educational content, reports, and dashboards built with MkDocs Material theme.Votes: 0GitHub stars: 9
- 1156 Workflows 4633ece8This document provides complete, end-to-end examples of common workflows using ESM3 and ESM C. Each workflow includes setup, execution, and analysis code.Votes: 0GitHub stars: 9
- 1084 Tokenizers Cb33dfe1Tokenizers convert text into numerical representations (tokens) that models can process. They handle special tokens, padding, truncation, and attention masks.Votes: 0GitHub stars: 9
- 1084 Tokenizers 59857435Tokenizers convert genomic regions into discrete tokens for machine learning applications, particularly useful for training genomic deep learning models.Votes: 0GitHub stars: 9
- 1073 Theoretical Foundations D0de6e6eThis document explains the mathematical and statistical principles underlying scvi-tools.Votes: 0GitHub stars: 9
- 1059 Targets Pathways 1317f6ceDrugBank provides comprehensive information about drug-protein interactions including targets, enzymes, transporters, and carriers. This data is essential for understanding drug mechanisms, identifying repurposing opportunities, and predicting off-target effects.Votes: 0GitHub stars: 9
- 102 Best Practices 8a7ed9cdBefore implementing parallel computing with Dask, explore these alternatives: - Better algorithms for the specific problem - Efficient file formats (Parquet, HDF5, Zarr instead of CSV) - Compiled code via Numba or Cython - Data sampling for development and testingVotes: 0GitHub stars: 9
- 1005 Spec Builder Patterns 2282ecac**PURPOSE:** Build Vega-Lite specs from scratch without templates. Use when analyze_data.py suggests chart types beyond the 6 basic templates. **PRINCIPLE:** Vega-Lite specs are just JSON. Build them programmatically based on data patterns.Votes: 0GitHub stars: 9
- 067 Assumptions And Diagnostics C15cb5ffThis document provides comprehensive guidance on checking and validating statistical assumptions for various analyses.Votes: 0GitHub stars: 9
- 064 Arrays 341f9a2dDask Array implements NumPy's ndarray interface using blocked algorithms. It coordinates many NumPy arrays arranged into a grid to enable computation on datasets larger than available memory, utilizing parallelism across multiple cores.Votes: 0GitHub stars: 9
- 051 Api Reference B7a3c128This document provides detailed API information, advanced examples, and troubleshooting guidance for working with scikit-bio.Votes: 0GitHub stars: 9
- 036 Analysis Cookbook 570d6343Common analysis patterns using Polars for session tracing data.Votes: 0GitHub stars: 9
- 033 Alignment 9fbdfde5Bio.Align provides tools for pairwise sequence alignment using various algorithms, while Bio.AlignIO handles reading and writing multiple sequence alignment files in various formats.Votes: 0GitHub stars: 9