Skill UI

This guide helps researchers determine if their problem constitutes a rigorous Operations Research (OR) or Management Science (MS) methodological contribution. It provides a comprehensive fit test covering method novelty, mathematical rigor (proofs, guarantees), and significance to the OR community. Furthermore, it guides users on selecting the precise editorial area (e.g., Optimization, Stochastic Models, Simulation) for submission.

This guide provides comprehensive standards for packaging ubiquitous computing sensing artifacts and datasets for academic publication. It covers best practices for ensuring reproducibility, emphasizing cross-subject evaluation (LOSO), necessary de-identification protocols for human-subjects data, and structuring the submission package for rigorous peer review.

A comprehensive guide for designing and auditing rigorous empirical evaluations for sensing systems, particularly Human Activity Recognition (HAR). It emphasizes best practices such as leave-one-subject-out cross-validation, using real human subjects under diverse conditions, reporting event-level metrics (e.g., Macro-F1) over raw accuracy, establishing fair baselines, and mitigating data leakage (e.g., subject or session leakage). This ensures that reported results are robust, generalizable, and defensible against academic skepticism.

A comprehensive guide for researchers in Pervasive Computing on ensuring the verifiable and reproducible nature of sensing data-driven claims. It mandates strict documentation practices covering dataset availability statements, sensing provenance (devices, labeling, preprocessing), cross-subject evaluation (LOSO), and consistency between published results and source data, adhering to ethical guidelines.

This guide provides a detailed model of the PerCom conference review process, covering double-blind review, the three-TPC-member evaluation model, the early-rejection gate, and the bounded rebuttal stage. It clarifies how authors should structure their submissions to succeed in this single-round, non-revisionary academic environment, differentiating it sharply from journal cycles.

Provides comprehensive guidelines for meeting high-level scientific publication standards, detailing the mandatory deposition of raw data (sequences, structures, datasets) and analysis code into approved public repositories. Learn how to obtain persistent identifiers (DOI/Accession) and ensure the reproducibility and integrity of your research findings for journals like PNAS.

This comprehensive checklist guides researchers through finalizing scientific figures and tables for submission to PNAS. It enforces rigorous standards covering optimal column sizing, minimum font size, displaying underlying data (points and 'n'), colorblind accessibility, and maintaining data integrity. Use this guide before submission to ensure your visualizations meet high journal standards.

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 for maintaining consistency and completeness of references for PNAS Nexus submissions. It details the critical differences between the format-neutral requirements at submission time and the final numbered style required for publication, emphasizing the proper citation of datasets, software, and preprints.

This guide outlines the mandatory standards for open science publication, detailing how researchers must deposit all data, code, and raw materials (images, sequences) in public, archived repositories (like Zenodo, GEO, PDB). It emphasizes the need for DOIs/accession numbers and prohibits relying solely on "available on request" statements for primary findings.

This guide ensures authors meet the rigorous standards for submitting figures and tables to PNAS Nexus. It covers critical aspects including managing the page budget (graphical-element count), optimizing sizing for print legibility, visualizing raw data points (vs. means), ensuring colorblind-safe palettes, and maintaining complete data integrity by retaining raw, unprocessed source images.

This guide provides comprehensive standards for achieving statistical rigor in scientific research reporting. It mandates that every quantitative claim must include effect sizes, uncertainty, sample size (n), and justified statistical tests. Key areas covered include distinguishing biological vs. technical replication, conducting power analyses, implementing multiple comparison corrections, and utilizing Registered Reports to prevent publication bias, ensuring maximum transparency and reliability in scientific findings.