Research Data and Code for Adaptive Closed-Loop Digital Evidence Acquisition

This dataset contains the computational research data, experimental results, configuration information, analysis outputs, and research prototype code supporting the study “Adaptive Closed-Loop Digital Evidence Acquisition in Dynamic Distributed Evidence Environments: A Prediction-Error-Driven Control Framework.” The materials include results from the principal controlled experiments, integrated ablation experiments, controlled action-competition experiments, and dynamic evidence-topology experiments.

The dataset supports examination of prediction-error feedback, acquisition-behaviour adaptation, acquisition-induced disturbance, dynamic topology updating, and subsequent acquisition decisions. The experiments were conducted in a controlled deterministic simulation environment using synthetic representations and do not contain real-world forensic case data, personally identifiable information, or live digital evidence.