Addressing Heterogeneity, Bias, and Analytical Challenges of Datasets in Alzheimer's Disease Research – A Comprehensive Review

Despite the above limitations, current AD datasets have contributed to significant advancements. Future research should focus on: Standardization: Supporting uniform data gathering and processing techniques across research initiatives. Data Integration: Formulating effective strategies for integrating multi-omics, neuroimaging, and clinical data to explain the complex relationships of variables driving AD. Advanced Analytics: Implementing complex machine learning methodologies to address class imbalance, missing data, and high dimensionality while ensuring model interoperability and generalizability. Open Science: Promoting open data sharing to enhance collaborative research and optimize data value. This review underlines the necessity for continuous initiatives to enhance data quality, address methodological challenges, and support for open science principles to expedite AD research.