cff-version: 1.2.0
abstract: "<p>The paper investigated the use of deep learning for deriving the number of active sweat glands and the sweat rate per gland from a(n in-silico) discrete sweat sensing device. The study was completely in silico. This dataset includes the trained neural networks that were evaluated for this study (.keras, version in READ ME), the synthetic datasets that were used for training and testing (.parquet) and the results of the tests (.xlsx). The latter contains more results than presented in the paper (including the precision and recall).</p>"
authors:
  - family-names: Haakma
    given-names: Jelte
    orcid: "https://orcid.org/0009-0001-5396-1935"
  - family-names: Turco
    given-names: Simona
  - family-names: Peri
    given-names: Elisabetta
    orcid: "https://orcid.org/0000-0002-1231-9372"
  - family-names: Mischi
    given-names: Massimo
title: "Models, datasets, and raw results of &#34;Measurement of sweat gland activity by discrete sweat sensing, statistics, and deep learning&#34;"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/f62008b2-4c3a-42c6-bf3a-c55e37a9598c.v1
license: CC BY 4.0
date-released: 2025-06-27