# Percom Reproducibility > Use when strengthening IEEE PerCom reproducibility and open-data evidence for human-subjects sensing, covering the dataset-availability statement, de-identified datasets with IRB/consent handling, sensing provenance (devices, sampling, labeling), cross-subject reproducibility, honest degrees of reproducibility, and consistency between what the paper says and what the dataset contains. - Skill: `thedixitjain/percom-reproducibility` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/percom-reproducibility` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/percom-reproducibility/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/percom-reproducibility --- # PerCom Reproducibility Use this before submission and again before camera-ready. In pervasive computing, reproducibility turns on **the sensing data**: a PerCom result is only as trustworthy as the dataset behind it, how it was collected, and whether it generalizes across people. The goal is that a competent reader could rebuild your pipeline and reach your conclusions — and, where ethics permit, on your actual data. ## Evidence map - Map each recognition/system claim and reported number to a **verifiable location** — a paper section, a table generated from logged data, or a script in the artifact. - For recognizers, give enough of the features, model, hyperparameters, and **evaluation split** (leave-one-subject-out / leave-one-session-out) that a reader could re-run it. - For datasets, report subjects and their selection, sensors and placement, sampling rates, labeling protocol and inter-annotator agreement, and preprocessing (filtering, windowing, normalization). - Keep the **dataset-availability statement** truthful and specific: what is shared, where it will live after acceptance, and — if something cannot be shared — exactly why (privacy, IRB, consent). - Keep the paper and the dataset **consistent**: a number in the PDF that no script reproduces from the released data is the contradiction reviewers read as carelessness. ## Dataset-availability statement audit | Claim in the paper | Weak availability answer | PerCom-ready answer | |---|---|---| | "We collected data from N participants" | "Dataset available on request" | De-identified dataset + datasheet, or a documented restricted-access path with the ethics reason | | "Our recognizer generalizes across users" | "Code will be released" | Runnable pipeline with a **LOSO** reproduction script and a README demo | | "We labeled activities" | Nothing about protocol | Labeling protocol, annotator agreement, and the label files | | "We deployed in a smart space" | "Testbed is proprietary" | Sensor list, placement, and sampling; simulated/sample data if the raw cannot ship | "Available on request" reads as *not available*; convert every such line into a concrete, de-identified dataset or an explicit, justified exception with a request path. ## Sensing provenance floor ```text [Devices] device models + firmware, sensor types, sampling rates, placement on body/space [Labels] labeling protocol, who labeled, inter-annotator agreement, label schema [Preprocess] filtering, windowing, normalization, resampling -- the exact pipeline, not prose [Splits] leave-one-subject-out / session-out defined so a reader reproduces the same folds [Ethics] IRB/approval status, consent scope, and the de-identification performed [Compute] hardware, training time, number of runs so a reader can size a reproduction [Randomness] seeds for any stochastic step; say what is and is not deterministic ``` ## Degrees of reproducibility (state the one you achieved) - **Turnkey:** one documented command regenerates each table/figure (including the LOSO result) from released data. - **Scripted:** scripts exist but require documented manual steps or restricted-data access. - **Descriptive:** prose detailed enough that a competent reader could rebuild the pipeline. For PerCom, aim turnkey for anything a reviewer might rerun quickly (inference on a bundled sample, a plot from logged features); full raw human-subjects data may stay **scripted with restricted access** when consent/IRB forbids public release — but say so honestly rather than promising turnkey behavior that cannot legally run. ## Vignette: a wearable HAR study Consider a study collecting wrist-IMU data from participants doing daily activities. Its reproducibility spine: the collection protocol and device/sampling details; the de-identified extracted dataset with a datasheet; the labeling protocol with annotator agreement; the feature and model code; the **leave-one-subject-out** evaluation scripts that regenerate the F1 table; and one honest sentence about the parts (raw video used for labeling, re-identifiable timestamps) that cannot be shared and why. ## Consistency and camera-ready pass - Before submission: every scored number traces to the artifact; the availability statement matches reality; the review package is anonymized (no testbed, lab, or owner strings). - Before camera-ready: swap anonymized links for a permanent, DOI-issuing, de-identified deposit (IEEE DataPort / Zenodo), and align the statement with what you actually release (`percom-artifact-evaluation`). ## Output format ```text [Claim inventory] evidence location> [Dataset availability] concrete / vague / restricted-with-reason / missing [Provenance gaps] [Cross-subject reproduction] LOSO script present and matching the paper? yes/no [Reproducibility level] turnkey / scripted / descriptive, stated honestly [Paper fixes] [Dataset fixes] ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `PerCom-Skills/skills/percom-reproducibility/SKILL.md`