Evaluation design and reporting in machine-learning state-of-health estimation on the NASA lithium-ion battery dataset — a systematic meta-research review of open-access studies
Pre-registered protocol (v1.2, 14 October 2026) and supporting materials for a systematic meta-research review of evaluation design and metric reporting in machine-learning state-of-health estimation studies using the NASA PCoE lithium-ion battery dataset. Includes the protocol, the dedicated search export (OpenAlex, 14 October 2026), the screening log for 3,157 records, the pre-registered random processing order of 398 candidates and the 25% verification sample, the blank extraction form and codebook, and the screening code. Deposited before full-text data extraction. A parallel OSF registration was submitted on the same day; its archiving failed because of an OSF-side file-copy error and is being resolved.
Authors
- Jeng Batacandolo (ORCID: https://orcid.org/0009-0009-9301-5917)
Institutions
- Capiz State University (PH)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-14
- DOI
- https://doi.org/10.5281/zenodo.23359171
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00