Skill UI

deepTools is a comprehensive command-line toolkit designed for processing and analyzing high-throughput sequencing data. It handles core genomics tasks including BAM to bigWig conversion, quality control assessment (PCA, correlation), normalization, and generating publication-quality visualizations (heatmaps, profiles) for ChIP-seq, RNA-seq, and ATAC-seq data. Essential for reproducible genomic research workflows.

A comprehensive tool to scan all connected digital marketing platforms (e.g., Google Ads, Meta, TikTok) for statistically significant deviations from established performance baselines. It detects performance drops (traffic, conversions, ROAS) or unexpected growth opportunities, classifying anomalies by severity (Critical, Warning, Info) and providing probable root causes and immediate action plans. Essential for proactive campaign management and cost control.

This guide outlines the mandatory steps and components required to assemble a robust, reproducible data and code replication package for submissions to the American Economic Journal: Applied Economics (AEJ: Applied). It ensures compliance with the stringent AEA Data and Code Availability Policy, covering data provenance, master scripts, and version control. This package must be built iteratively, not at the acceptance stage.

This guide details the best practices for preparing and submitting supplementary materials for CSCW submissions. It clarifies the difference between main papers, appendices, and separate files, emphasizing that anything needed to *trust* the method belongs here. Crucially, it provides deep guidelines on data anonymization, metadata scrubbing, and version control for review rounds.

A guide detailing the rigorous standards for data availability and code reproducibility required for academic manuscripts, particularly in demography. It emphasizes depositing data in FAIR-aligned repositories with persistent identifiers (DOI), providing master scripts with full version control, and documenting restricted data access conditions to ensure scientific transparency.

A guide for integrating behavioral theory into rigorous empirical studies. It outlines the necessary steps to move beyond descriptive findings of irrationality by constructing a statistically identifiable behavioral model (e.g., incorporating loss aversion parameters) and testing it against rational baselines using controlled, incentive-compatible experimental designs. Essential for submitting papers requiring model explanation of behavioral biases.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.