Supplementary materials for From Preprocessing to Validation: A Systematic Review of Signal Processing and Machine Learning Practices for Reproducible Plant Bioelectrical Signal Analysis

This repository contains the complete supplementary dataset for the systematic literature review: "From Preprocessing to Validation: A Systematic Review of Signal Processing and Machine Learning Practices for Reproducible Plant Bioelectrical Signal Analysis". Directory Structure and Contents: 01_search: Contains the exact source-specific search queries, search timeline, and chronological yields for the nine databases searched on October 27-28, 2025. 02_screening: Contains the full 427-record screening ledger, the screening decision codebook, and PRISMA summary tables. 03_quality_assessment: Contains the 10-item quality assessment checklist based on the Parsifal instrument, score details for the 69 full-text assessed studies, and lists of excluded papers with specific reasons. 04_included_studies: Contains the registry of the 57 final included unique studies, including their chronologies and metadata. 05_modelling_extraction: Contains the core and full feature-extraction datasets mapping plant species, stimuli, and sensor configurations. 06_validation: Contains the reconciled 42-row evaluation-level validation audit and the 57-study study-level validation roll-up categorizing studies into the rigorous leakage-aware taxonomy. 07_segmentation: Contains the 57-study segmentation and temporal-unit audit, analyzing how raw plant bioelectrical signals are windowed, stratified, and partitioned. 08_reproducibility: Contains the verified public-resource audit confirming data/code availability status, specific URLs, and resource types for the 8 sharing papers in the corpus. 09_performance: Contains the descriptive performance-context database mapping reported accuracies and metric selections. 10_synthesis: Contains the source data for the synthesis figures (synthesis flowchart and validation categories) and the manuscript claims crosswalk linking every quantitative claim in the paper to the underlying dataset rows. All data tables are provided in standard CSV format alongside detailed Markdown codebooks defining all variables and coding categories. For questions or reuse, please cite the main systematic review manuscript and this Zenodo DOI.