Data Collection: Gather data from multiple sources such as laboratory experiments, clinical studies, and field research. Data Cleaning: Address missing values, outliers, and inconsistencies to ensure data quality. Data Transformation: Convert data to a common format, which may involve mapping values to standardized units of measurement and terminologies. Data Annotation: Label data with metadata to provide context such as experimental conditions, methodologies, and chemical properties. Data Storage: Store standardized data in databases that support easy retrieval and analysis.
