Skill UI

This guide outlines the mandatory requirements for authors publishing quantitative or mixed-method research in academic journals. It details the process of creating a complete replication package—including raw data, master scripts, seeds, and extensive README documentation—and depositing it in a trusted data repository (like Dataverse). It ensures scholarly rigor by guaranteeing that published findings are fully reproducible and traceable.

A comprehensive framework for ensuring reproducibility in Web Search and Data Mining (WSDM) research. It addresses unique challenges associated with behavioral data, covering data provenance, temporal splitting discipline, bias assumption reporting, statistical rigor (seeds, variance), and the 'honesty ladder' for non-reproducible results, helping researchers publish robust and verifiable work.

Populate Dataverse tables with realistic sample records so a freshly-scaffolded code app shows real-looking data on first launch. Generates contextually appropriate rows from each table's schema and inserts them in dependency order, with idempotent re-runs and solution-scoped inserts.

This robust fixture is designed to execute predefined tasks using a specified seed path and current working directory. It intelligently separates dispatch metadata within YAML frontmatter from the main content body, ensuring the router reliably loads execution parameters while ignoring potential interference from the body text. Ideal for structured testing and controlled execution workflows.

Compare multiple Japanese NLP libraries, models, or datasets across selected criteria using a comparison table. Supports both specific tool comparison (seed mode) and topic-based comparison (topic mode). Uses bundled data and web search to find relevant resources. Ideal for evaluating alternatives before choosing the best tool for your project.

Stress-test core claims for Journal of Systems Engineering submissions: parameter sensitivity, initial conditions, alternative models and metrics, extreme scenarios, Monte Carlo repetitions, random seeds, software environments, data/code statements, and explicit failure boundaries.