Individual skill
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.
PerCom-Skills/skills/percom-reproducibility/SKILL.md
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# 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.