Skill UI

Anndata is a Python package for handling annotated data matrices, storing experimental measurements alongside observation and variable metadata. It supports single-cell genomics analysis, managing h5ad and Zarr files efficiently. Users can read, write, concatenate, and subset data with sparse matrix support. It integrates with the scverse ecosystem including Scanpy and scvi-tools. Ideal for biological data workflows requiring efficient storage and manipulation.

Generate inline SVG graphics using DAX measures that return SVG markup strings for Power BI reports. Supports table, matrix, card, image, and slicer visuals. Includes templates for common chart types and guidance on using UDF libraries.

This guide outlines a systematic methodology for conducting comprehensive literature reviews in economics. It moves beyond simple summarizing by teaching researchers how to build an evidence matrix, ensuring systematic coverage by sweeping databases (JEL codes, keywords, working papers). It is essential for preparing high-impact academic reviews that must prove exhaustive coverage and synthesize complex, often conflicting, findings.

A comprehensive guide for authors in spatial economics on conducting rigorous robustness checks. It systematically addresses common spatial threats—such as the Modifiable Areal Unit Problem (MAUP), spatial weight matrix sensitivity, and spatial autocorrelation—to ensure published results are scientifically sound and not artifacts of the data definition or model specification. Essential for high-level academic submissions.

This guide outlines a systematic methodology for gathering, reading, and synthesizing large bodies of academic research for comprehensive economic surveys (JEL style). It moves beyond simple summarization by enforcing structured data collection into a detailed evidence matrix. The process ensures maximum coverage through advanced search techniques (e.g., snowballing, JEL code sweeps) and focuses on synthesizing connections, identifying consensus, and articulating scholarly tensions across different studies.

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.