Szczegóły publikacji
Opis bibliograficzny
Teaching reproducible research in industrial electronics with project TIER / Jerzy BARANOWSKI, Waldemar BAUER, Kacper JARZYNA // W: ICELIE 2025 [Dokument elektoniczny] : 2025 IEEE 12th International Conference on E-Learning in Industrial Electronics (ICELIE) : 14–17 October 2025, Madrid, Spain. — Wersja do Windows. — Dane tekstowe. — [Spain : IEEE], [2025]. — ( IEEE International Conference on E-learning in Industrial Electronics ; ISSN 2997-7304 ). — Print on Demand (PoD) ISBN: 979-8-3315-0802-9. — e-ISBN: 979-8-3315-0801-2. — S. [1–6]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [6], Abstr. — Publikacja dostępna online od: 2025-11-27
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166408 |
|---|---|
| Data dodania do BaDAP | 2026-03-04 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICELIE64733.2025.11244783 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | IEEE International Conference on E-learning in Industrial Electronics |
Abstract
The rapid adoption of Industry 4.0 technologies-digital twins, IoT sensors, and AI-driven analytics-has amplified long-standing reproducibility challenges in industrial-electronics R&D. This paper demonstrates how the Project TIER Protocol (Version 4.0) can be tailored to electronics laboratory courses, detailing a standardized folder hierarchy, “one-click” Master Script workflow, and integration with Git for full auditability. We analyze domain-specific hurdles, including cultural resistance to data sharing, tooling and infrastructure barriers, and curricular gaps in data literacy and experiment design. A concrete case study-a TIER-compliant DC-motor vibration dataset with preregistration, metadata schemas, and analysis scripts-is presented to illustrate end-to-end feasibility. Finally, we offer practical recommendations for phased adoption, including lightweight lab-exercise templates and faculty workshops, to equip students and researchers with rigorous, transparent workflows essential for credible, AI-augmented industrial-electronics research.
