The binary anomaly detection (classification) of ionospheric signal amplitude in prior research demonstrated potential for development and further advancement. Further data quality improvement is integral for advancing development of machine learning (ML) based ionospheric amplitude anomaly detection. This paper presents the transition from binary to multi-class classification of ionospheric amplitude datasets. The dataset comprises 19 transmitter-receiver pairs and 383,041 manually labeled amplitude instances. The target variable was reclassified from a binary classification (normal and anomalous data points) to a six-class classification that distinguishes between daytime undisturbed signals, nighttime signals, solar flare effects, instrument errors, instrumental noise, and outlier data points. Furthermore, in addition to the dataset, we developed a freely accessible web-based tool designed to facilitate the conversion of MATLAB data files to TRAINSET-compatible formats, thereby establishing a completely free and open data pipeline from the WALDO world data repository to data labeling software. This novel dataset facilitates further research in ionospheric amplitude anomaly detection, concentrating on further identification of the optimal model combinations for effective and efficient anomaly detection in ionospheric amplitude data. Potential outcomes of employing anomaly detection techniques on ionospheric amplitude data may be extended to other space weather parameters in the future, such as ELF/LF datasets and other relevant datasets.