Skill UI

This comprehensive guide outlines the high standards and scope required for publishing in Food Chemistry. It emphasizes rigorous analytical chemistry, detailed method validation (LOD/LOQ, precision), quantified compositional results, and strong chemical mechanism interpretation. Use this tool to assess whether your manuscript meets the necessary scientific rigor for this specialized journal.

A comprehensive guide for authors preparing manuscripts for Global Change Biology (GCB). It details the requirements for scientific exhibits, mandating a mechanism-first approach that clearly links environmental drivers to biological responses. Covers graphical abstract construction, ensuring visible uncertainty, and maximizing data accessibility for publication.

Provides comprehensive guidelines for designing scientific exhibits, including figures, tables, maps, and Highlights, for interdisciplinary, policy-relevant manuscripts. It emphasizes making data self-contained, focusing on the human/policy dimensions rather than just biophysical trends, and ensuring accessibility across various formats (grayscale, print).

This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.

A comprehensive guide for ensuring the reproducibility of data mining research for top-tier conferences like ICDM. It addresses the unique challenges of triple-blind review and strict page limits by detailing necessary artifacts, such as configuration files, seed lists, and anonymized packages. Learn how to achieve Rerunnable, Rebuildable, or Attested reproducibility tiers to solidify your scientific claims.

A comprehensive guide for researchers submitting academic papers to ensure high standards of reproducibility and verifiable evidence. It covers critical areas such as data-availability statements, artifact management, provenance pinning (for LLMs and mining), and mapping claims directly to evidence, adhering to rigorous academic standards.

This guide outlines the rigorous requirements for building a comprehensive replication package and proof appendix for manuscripts submitted to journals following the International Economic Review (IER) style. It details what must be included in the data deposit (datasets, programs, README) and how to structure a self-contained proof appendix, ensuring the work meets the highest standards of reproducibility and scientific rigor required for publication.

This comprehensive guide provides best practices for designing, running, and reporting computational experiments for academic journals. It teaches authors how to make performance claims scientifically defensible by addressing potential referee criticisms (e.g., tuning artifacts, seed luck). It covers advanced statistical techniques (performance profiles, nonparametric tests) and details the full structure required for a complete, verifiable GitHub data deposit.

A comprehensive guide to designing rigorous, reproducible, and fair experimental protocols for computational science research, particularly for top-tier academic journals. It covers critical elements such as selecting standard benchmark instances, establishing strong baselines, ensuring fair parameter tuning, reporting hardware constraints, and performing robust statistical validation, helping researchers avoid common pitfalls and maximize the scientific validity of their claims.

This framework provides a comprehensive methodology for designing, auditing, and reporting empirical measurements about large-scale systems like the Internet. It guides users to ensure measurements are scientifically rigorous, by matching claims to evidence, quantifying coverage bias, designing for temporal changes, and adhering to strict ethical guidelines and data provenance standards.

This guide outlines best practices for ensuring the scientific reproducibility and artifact availability of network measurement research. It distinguishes between analysis reproducibility (recreating figures from released data) and method reproducibility (re-running the measurement). It mandates thorough documentation of provenance, including vantage points, timing, and tool versions, ensuring research claims are verifiable and robust.

This comprehensive aid helps researchers determine the suitability of their surgical trials, outcomes studies, or perioperative research for JAMA Surgery. It details required scientific rigor, including comparative, patient-centered outcomes, robust risk adjustment, and advanced statistical methods. It also covers specific reporting standards (CONSORT, STROBE, PRISMA) and the current JAMA Network house style, ensuring manuscripts are submission-ready.