Implementing AI-Driven Anomaly Detection in Research Data

You are tasked with developing a comprehensive plan to implement AI-driven anomaly detection in research data to ensure data integrity and identify potential errors. Begin by identifying the types of research data you are working with and the specific anomalies you need to detect. Next, explore various AI models suited for anomaly detection, such as supervised and unsupervised learning techniques. Provide a structured overview of each method, highlighting their strengths and weaknesses. Recommend tools, frameworks, or libraries that can assist in building anomaly detection models. Finally, outline a step-by-step implementation strategy, including data preprocessing, model training, validation, and integration into existing research workflows. Include considerations for scalability and continuous monitoring to enhance the reliability of the anomaly detection system.