Skill UI

Provides comprehensive guidelines for authors submitting to the Journal of Econometrics (JoE). It details the required best practices for data citation (using the Elsevier `[dataset]` format), ensuring methodological reproducibility (e.g., Monte Carlo simulations), and preparing estimators as runnable code packages (R, Python, etc.). This guide is essential for meeting rigorous academic publication standards and passing peer review.

This guide outlines comprehensive best practices for ensuring the reproducibility of Machine Learning and Pattern Recognition research. It covers fixing random seeds, locking hardware/software dependencies (Python, CUDA, etc.), managing data splits, tracking pre-trained model versions, and structuring the required artifact package for external review. It is essential for building trust in published scientific results.

Track physical units and propagate measurement uncertainty in scientific Python calculations using pint and uncertainties, from unit conversion and GUM budgets to Monte Carlo and dimensional sanity checks.

Astropy is the essential Python package for astronomical research and data analysis. It provides a comprehensive suite of tools for handling celestial coordinate transformations (e.g., ICRS, Galactic), managing physical units and quantities, processing FITS files, performing complex cosmological calculations, and ensuring precise time handling. It is indispensable for any project involving scientific data in the field of astronomy and astrophysics.

Biopython is a comprehensive Python library designed for computational molecular biology. It provides robust tools for handling biological sequences (DNA, RNA, Protein), structural analysis (PDB), sequence alignment, phylogenetic tree construction, and interfacing with major public databases like NCBI (Entrez). Ideal for researchers performing advanced bioinformatics tasks.

Cirq is a powerful, open-source Python framework developed by Google Quantum AI. It allows users to design, simulate, and execute complex quantum circuits. It supports advanced features including parameterized circuits, noise modeling, circuit transformation, and seamless integration with multiple quantum hardware backends (e.g., IonQ, Google Quantum, Pasqal).

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.

Matplotlib is the foundational Python library for creating static, animated, and high-quality visualizations. This comprehensive skill guide covers everything from basic line plots and scatter graphs to advanced concepts like multi-subplot layouts, heatmaps, and 3D visualization. It details both the convenient pyplot interface and the powerful object-oriented API, ensuring you can generate publication-ready charts with professional customization.

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

Scanpy is a powerful, scalable Python toolkit built on AnnData for comprehensive single-cell RNA-seq data analysis. It supports the complete biological workflow, including quality control, normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker gene identification, and sophisticated cell type annotation, enabling the generation of publication-quality figures.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.