Skill UI

This skill guides the execution and reporting norms for advanced data analysis in communication studies, specifically adhering to the rigorous standards of the Journal of Communication (JoC). It emphasizes transparency, reproducibility, honest reporting of uncertainty (beyond p-values), rigorous robustness checks, and proper handling of computational data, ensuring that analyses withstand expert and peer review.

This guide provides criteria for submitting research to the Journal of Tianjin University of Sport. The journal highly values mechanism-driven studies in exercise physiology, biochemistry, biomechanics, and sports medicine. Submissions must demonstrate rigorous experimental design, detailed measurements, statistical validity, and clear mechanistic interpretation, moving beyond simple descriptive reports.

This guide provides an advanced framework for conducting rigorous causal inference and policy evaluation, essential for academic journals. It covers state-of-the-art methods including DiD, RDD, Synthetic Control, and experimental analysis. The core focus is moving beyond simple p-values to estimate effect magnitudes in tangible 'decision units' (e.g., dollars per household, percentage points). It mandates comprehensive stress-testing, including pre-trend checks, placebo outcomes, and detailed subgroup heterogeneity analysis, ensuring results are immediately actionable by regulators.

This guide enforces rigorous standards for reporting clinical trial statistics, moving beyond simple P values. It mandates the use of confidence intervals, absolute and relative effect sizes, clear designation of the primary analysis population (ITT), and proper handling of multiplicity and missing data, aligning with standards set by top-tier medical journals.

A detailed guide for crafting medical structured abstracts, specifically adhering to the New England Journal of Medicine (NEJM) guidelines. This process ensures the abstract is concise (≤250 words), contains four mandatory sections (Background, Methods, Results, Conclusions), and reports primary outcomes using effect size and 95% CI, rather than relying solely on P-values. It is crucial for late-stage manuscript polish.

This comprehensive guide outlines best practices for creating key clinical display items, including Table 1 (baseline characteristics), Kaplan-Meier curves, Forest Plots, and CONSORT diagrams. It emphasizes statistical rigor (e.g., using standardized differences instead of P values for baseline), ensuring numbers-at-risk, and maintaining consistency across all reporting elements for high-impact medical publications.

This guide details best practices for rigorous statistical reporting in clinical trials, following guidelines from top medical journals like NEJM. It emphasizes using confidence intervals (CIs) over bare P-values, defining primary analysis populations (ITT), controlling for multiplicity, and reporting absolute risks (NNT). It helps researchers ensure their findings are statistically robust, transparent, and defensible.

A comprehensive guide based on high-impact journal requirements (like PNAS), detailing how to report quantitative scientific claims rigorously. It emphasizes moving beyond simple P values to include effect sizes, confidence intervals, sample size justification, distinction between biological and technical replicates, and ensuring full code and data reproducibility.

A comprehensive guide detailing the rigorous data analysis and reporting standards required for top-tier psychological science journals. It emphasizes moving beyond p-values to report effect sizes with confidence intervals, ensuring full disclosure of exclusions, and maintaining a clear separation between confirmatory and exploratory findings to enhance research credibility and reproducibility.

SymPy is a powerful Python library designed for symbolic mathematics. Unlike numerical solvers, it performs exact calculations using mathematical symbols, allowing users to model and solve complex problems across various domains. Core capabilities include symbolic calculus (derivatives, integrals, limits), advanced algebra, solving systems of equations, linear algebra (determinants, eigenvalues), and specialized modules for physics and number theory. It can also generate executable code in languages like C and Fortran, making it invaluable for academic research and scientific computing.

This tool de-identifies sensitive free-text columns (e.g., names, dates, identifiers) in local structured datasets (CSV, JSONL, Parquet) using advanced OpenMed models. It ensures strict privacy by generating a completely redacted dataset and a separate PHI-free aggregate summary, allowing for secure data sharing and analysis without compromising source privacy or logging sensitive values.

This skill maps clinical concept spans (disorders, anatomy, procedures, etc.) extracted by OpenMed to SNOMED CT concept IDs (SCTID). It performs complex, out-of-process terminology resolution using standard FHIR R4 endpoints ($lookup, $translate, ValueSet/$expand) against a user-supplied, licensed terminology server (e.g., Ontoserver, UTS). Ideal for generating rich, hierarchy-aware clinical codes, performing subsumption queries (ECL), or translating codes between clinical standards (like ICD-10 to SNOMED CT).