Skill UI

A comprehensive guide for authors targeting the IEEE International Conference on Data Engineering (ICDE). This skill helps assess venue fit, re-frame manuscripts from journal/arXiv styles, and structure papers for top CS conferences. It details required evidence (system architecture, realistic workloads) and best practices for positioning contributions in data engineering, database systems, and analytics.

This guide helps researchers strategically position their database, data management, or analytics papers for the International Conference on Very Large Data Bases (VLDB). It provides deep insights into conference positioning, required evidence (scale, systems depth), optimal structure, and how to compare the work against top venue standards. Use it before submission to refine your narrative and meet the archival standards of a flagship database conference.

This guide provides a rigorous methodological checklist for conducting data analysis suitable for top-tier academic journals, such as JMIS. It covers advanced techniques across econometrics (addressing identification and endogeneity), behavioral modeling (SEM/PLS, common-method bias), and ML/analytics, ensuring that research findings meet the highest standards of robustness and theoretical grounding.

This guide provides comprehensive advice for positioning computer science manuscripts, particularly those involving software repository mining, developer behavior, and empirical methods, for the Mining Software Repositories (MSR) conference. It covers required elements like dataset construction, validity threat analysis, reproducible workflows, and aligning the work with the specific scope of software analytics venues, helping authors strategically re-frame their research for maximum impact.

This guide details best practices for achieving high standards of reproducibility in visual analytics papers, focusing on open materials, verifiable code, and rigorous documentation. It covers mapping figures to data sources, preregistering studies, documenting data provenance, and defining the level of reproducibility achieved (e.g., turnkey, scripted).

This skill performs the crucial ETL process of mapping OpenMed-extracted, terminology-coded clinical facts (conditions, drugs, measurements) into the OMOP Common Data Model (CDM) v5.4 tables. It is used downstream after entity extraction and source coding, transforming raw NLP findings into standardized, OMOP-conformant records for OHDSI analytics.

This skill extracts crucial Social Determinants of Health (SDOH) — such as housing instability, food insecurity, and transportation barriers — from unstructured clinical notes. It maps the identified text spans to standardized ICD-10-CM Z-codes (Z55–Z65). It is essential for health-equity analytics, building risk stratification tools, and identifying coding gaps by converting narrative text into queryable, discrete data points.

This skill standardizes free-text medication mentions extracted by OpenMed to RxNorm RxCUIs using the public RxNav API. It resolves complex relationships between brand names, generic ingredients, strengths, and dose forms, providing a stable, canonical code. Ideal for clinical interoperability, data analytics, de-duplication, and building US Core Medication resources.

This tool provides a statistically rigorous framework for analyzing a user's private LinkedIn post performance data. It moves beyond simple averages, utilizing median/MAD and permutation testing to determine if observed patterns are genuine or statistical noise. It guides users through three steps: descriptive analysis, pattern mining, and sizing future A/B experiments, ensuring hypotheses are testable and avoiding over-conclusion. Works only with your own exported data.

A comprehensive guide to product analytics, covering event tracking, funnels, cohort retention, north star metrics, OKRs, and dashboards. Examples include PostHog implementation and Python code for retention analysis. Ideal for product managers and data analysts seeking data-driven decisions.

Send custom logs to an Azure Monitor Log Analytics workspace with the Python Logs Ingestion API. Covers authentication, Data Collection Rules, batching, async clients, and error handling.

Query Azure Monitor logs and metrics from Python using LogsQueryClient and MetricsQueryClient with authentication, batching, async, and pandas support.