Skill UI

This guide helps researchers determine the primary focus of their work—whether it lies in Information Processing (algorithms, estimation) or Sensor Platforms, Tools, and Design Methods (hardware, deployment). It provides a structured methodology for selecting the most appropriate academic venue (e.g., SenSys, MobiSys, INFOCOM) based on the project's technical contribution and maturity.

A comprehensive project management guide detailing the entire lifecycle of a high-stakes sensing systems research submission. It provides a detailed backward planning timeline, covering critical milestones from initial venue and track selection, hardware freezing, field deployment logistics, double-blind review, and final submission to major conferences like SenSys. It emphasizes managing the critical path of field work and data artifacts.

This guide provides structured rules for authors writing sensor-systems or information-processing papers. It emphasizes leading with the physical sensing problem, requiring real-hardware measurements for energy, latency, and ground truth, and adhering to strict academic formatting constraints. Use this when revising submissions to ensure technical depth and practical deployability.

A comprehensive guide on achieving scientific reproducibility for complex computer architecture and system simulations. It details how to manage and pin all dependencies—including simulator commit hashes, local patches, workload binaries, hardware states, and post-processing scripts—to ensure that every reported figure and number can be accurately regenerated, regardless of time or environment changes.

Provides an in-depth strategic guide to the academic conference submission pipeline, using ISCA as a detailed case study. It meticulously details the two-round review structure, the critical rebuttal/revision window, and offers actionable strategies for authors to navigate reviewer feedback, committee deliberations, and the final decision-making process in top-tier architecture venues.

A comprehensive guide detailing the required structure and evidence for packaging research artifacts for academic evaluation tracks (e.g., ISSTA). It outlines how to achieve specific ACM badges—Available, Functional, Reusable, and Results Reproduced—by ensuring the tool is containerizable, has a clear entry point, and allows key results to be independently regenerated by reviewers.

This guide clarifies what constitutes an 'artifact' for pure-theory venues like ITCS. It emphasizes that the core deliverable is not code or functional components, but the mathematical argument itself. Successful evaluation relies on providing complete, self-contained proofs within the submitted PDF, along with citing dependencies precisely. It details how to handle minor computational content to ensure static, verifiable checks.

This guide outlines the standards for making pure theoretical claims independently verifiable. It demands complete, self-contained proofs for all central lemmas, explicit definitions for every symbol, and precise citation for all borrowed results. It ensures that the submitted paper, the full preprint version, and the mathematical arguments are perfectly consistent, addressing the highest level of academic rigor in theoretical computer science.

This comprehensive guide helps authors assemble and audit the Supporting Information (SI) for a Journal of the American Chemical Society (JACS) manuscript. It ensures every reported chemical result and structure is fully reproducible by an independent chemist, covering detailed procedures, full characterization data (NMR, HRMS, IR), stereochemistry, and crystallographic details (CIF/CCDC).

This guide details the rigorous, reproducible empirical strategies required to identify causal parameters in applied econometrics, meeting high standards (e.g., JAE). It covers various designs—including DID, IV, panel data, and time-series—and mandates that every assumption and diagnostic test must be explicitly stated, tested, and reproducible using deposited code/data.

This guide outlines the mandatory data and code deposit policy for articles published in the Journal of Applied Econometrics (JAE). Authors must submit a complete, non-confidential set of input data (plain ASCII/CSV), programs that replicate every result, and a detailed README file. It emphasizes strict adherence to archival format rules, ensuring the research is fully reproducible for the academic community.

A comprehensive orchestrator skill guiding the entire lifecycle of an applied econometrics manuscript targeting the Journal of Applied Econometrics (JAE). It ensures the paper maintains an applied, real-data focus and rigorously follows the necessary steps, including literature positioning, identification strategy, reproducible data analysis, and mandatory data archival deposit. Designed to prevent common pitfalls by integrating data integrity checks at every stage.