2026-10-11T06:45:40Z https://data.mendeley.com/oai

oai:data.mendeley.com/24k73vk3ws.1 2026-09-30T07:45:05Z

Priadarshi, Pushpraj Dataset and analysis code for: Bioleaching of Waste Printed Circuit Boards - Mechanisms, Process Optimization, and the Emerging Role of AI/ML Mendeley Data This dataset underlies the statistical analysis in the review "Bioleaching of Waste Printed Circuit Boards: Mechanisms, Process Optimization, and the Emerging Role of AI/ML." It comprises 1,479 biological copper-recovery datapoints extracted from 49 peer-reviewed bioleaching studies, spanning four mechanism families (chemolithotrophic, fungal, cyanogenic, and heterotrophic/enzymatic). Each row records copper recovery (%) alongside seven process variables — leaching time, initial pH, pulp density, temperature, stirring speed, particle size, and inoculum size — plus mechanism/family classification. Imputed columns (suffixed _clean) fill gaps in pulp density and other variables using a documented hierarchical imputation method; raw (non-imputed) columns are retained for transparency. analysis_corrected.py is the full, deterministic analysis pipeline used to generate every statistical result in the manuscript: mechanism-family classification, ANOVA/Kruskal-Wallis tests, Tukey HSD, Spearman correlations (pooled and study-level), pulp-density imputation, multivariate regression with cluster-robust standard errors, a complete-case sensitivity regression, PCA, and a robustness-verdict summary table. Running this script against the included CSV reproduces the manuscript's results exactly (independently verified cell-by-cell and pixel-by-pixel). Environmental Engineering Waste Management Bioleaching Dataset doi:10.17632/24k73vk3ws.1 oai:data.mendeley.com/24k73vk3ws.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/24k73vk3ws 2026-09-30T07:45:05Z