Processed Batch Experiment Dataset for Metal Immobilization from Actual Acid Mine Drainage Using Cow-Derived Biochar: Synthesis, Characterization, and Treatment Performance

This Mendeley Data repository contains the raw and processed batch experiment dataset generated to test the hypothesis that cow-derived biochar can effectively immobilise dissolved metals in actual acid mine drainage (AMD) under controlled laboratory conditions. The dataset is organised into clearly defined directories that reflect successive stages of data generation, processing, and interpretation, enabling transparent reuse and reproducibility.
Raw Data (Batch Experiment and Physicochemical Measurements)
The Raw_Data directory contains the original analytical outputs generated during batch treatment experiments using untreated AMD and cow-derived biochar. These files include ICP-OES measurements of dissolved metal concentrations, recorded in triplicate to assess analytical precision, along with associated physicochemical parameters such as pH and electrical conductivity. These data represent the unprocessed experimental observations and form the basis for all subsequent calculations. Users may reuse these files to apply alternative statistical analyses, adsorption models, or kinetic interpretations without reliance on pre-computed results.
Processed Data (Calculated Removal Efficiencies and Summary Tables)
The Processed_Data directory contains derived datasets generated from the raw measurements, including mean metal concentrations, standard deviations, and percentage removal or immobilisation efficiencies. These processed tables describe the treatment performance of cow-derived biochar across experimental conditions and provide benchmark values for comparison with other remediation materials. While these summaries facilitate rapid interpretation, all calculations are traceable to the raw data to allow independent verification.
Metadata and Experimental Context
The Metadata directory provides structured contextual information ensuring that secondary users can correctly interpret the dataset, reproduce the experimental setup where needed, and integrate the data into comparative or meta-analytical studies.