Skill UI

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

The EPSL Workflow Router is a comprehensive guide that helps authors navigate the entire manuscript lifecycle for high-impact letters in Earth and Planetary Science. It routes users through critical stages—from initial topic selection and literature positioning to data analysis, writing style, and final submission—ensuring the work maintains conciseness and focuses on a single, decisive, process-level advance.

A comprehensive guide for executing and reporting rigorous data analysis for Environmental Science & Technology (ES&T) manuscripts. It details essential norms, including explicit QA/QC reporting (blanks, recoveries, LOD/LOQ), handling uncertainty, selecting appropriate statistics for environmental data (e.g., left-censored data), ensuring mass/energy balance closure, and guaranteeing reproducibility through master workflows.

A comprehensive guide for environmental science manuscripts, detailing the standards required for data reporting and ensuring research reproducibility. It instructs authors on assembling Supporting Information (SI), writing data-availability statements, and depositing raw data, code, and protocols into recognized public repositories (e.g., Zenodo, GenBank).

This skill acts as the central orchestrator for preparing Environmental Science & Technology (ES&T) manuscripts. It guides users through the complex submission pipeline by determining the optimal article type (e.g., Research Article, Critical Review, Policy Analysis) and the current stage of writing, ensuring the content maintains clear environmental significance. It routes users to specific sub-skills like data analysis, literature positioning, and writing style.

A comprehensive guide for establishing academic rigor in AI and data science research. It outlines best practices for achieving reproducibility, emphasizing the mandatory release and documentation of raw data, trained models, and qualitative findings. Key tools include datasheets, model cards, and detailed provenance logging, ensuring that research claims are fully auditable and consistent.

A comprehensive guide detailing the transition of an accepted FSE paper into a journal-ready (PACMSE) camera-ready format. This process involves critical steps including full de-anonymization, applying the final ACM template, completing detailed journal metadata (ORCID, CCS), integrating required revisions, and permanentizing open science artifacts with DOIs.

A comprehensive guide for researchers ensuring the highest level of open-science reproducibility for academic submissions (e.g., FSE). It details how to map every research claim (numbers, techniques) to verifiable evidence, pin technical provenance (SHAs, model IDs), manage data availability statements, and achieve defined degrees of reproducibility (Turnkey, Scripted).

A comprehensive audit checklist for preparing research submissions for FSE. This guide covers critical steps including meeting two-stage deadlines (registration and submission), adhering to strict ACM formatting and page budgets, ensuring heavy double-anonymous anonymity by scrubbing all metadata leaks, and fulfilling open science requirements with Data Availability statements. Essential for authors ensuring their manuscript meets high academic standards.

A comprehensive tool for authors aiming to submit high-significance, novel research to journals like Geology (GSA). It guides authors on compressing broad geoscientific findings into concise letters, ensuring the work meets strict standards for novelty, general significance, and adherence to specific journal house style and data deposition policies.

This guide outlines the best practices and norms for executing and reporting empirical analyses in governance and comparative political science. It covers essential methods including cross-national inference, clustering uncertainty, robustness checks, multi-method triangulation, and ensuring reproducibility, guiding researchers to conduct rigorous, publication-ready studies.

This guide details the necessary steps for achieving and reporting reproducibility in Human-Robot Interaction (HRI) studies. Since HRI involves unique elements like human participants and physical environments, reproducibility focuses on making the study protocol, robot behavior specifications, analysis code, and de-identified data transparent. Learn how to properly assemble a comprehensive replication package and ethically disclose limitations to enhance academic credibility in top-tier HRI publications.