Skill UI

This guide details the optimal structure for writing a high-quality literature review, especially for quantitative economics journals. It advises users to avoid chronological descriptions and instead organize the review around the core method lineage (how a quantity is measured, forecasted, or decomposed) and the specific application gap in China's data context. This ensures the literature review logically and powerfully leads to the paper's unique contribution.

This guide provides a sophisticated framework for structuring the related work section when submitting research to KDD. It instructs authors on how to position their work within the deep academic lineage of data mining, comparing their novelty not just to prior KDD volumes, but also to major ML flagships (ICML, NeurIPS) and specialized venues (WWW, ICDM). Learn to articulate mechanism-level differences and avoid common citation traps.

This comprehensive guide equips authors with a structured framework for writing the Related Work section for top-tier NLP conferences (e.g., NAACL). It emphasizes tracking methodological, data, and task lineages, ensuring citation precision across Findings, workshops, and preprints, and culminating in a single, falsifiable "delta sentence" to precisely position the novel contribution.

Query live genomic surveillance data via GenSpectrum LAPIS API to identify circulating viral lineages, growth rates, and mutations. Essential for determining current pathogen states like dominant SARS-CoV-2 variants or influenza clades, ensuring accuracy beyond static knowledge cutoffs. Supports multiple pathogens including SARS-CoV-2, Influenza, and RSV.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.