Abstract
The present study presents an extensive dataset meticulously curated from solar images sourced from the Solar and Heliospheric Observatory (SOHO), encompassing a range of spectral bands. This collaborative effort spans multiple disciplines and culminates in a robust and automated methodology that traverses the entire spectrum from solar imaging to the computation of spectral parameters and relevant characteristics. The significance of this undertaking lies in the profound insights yielded by the dataset. Encompassing diverse spectral bands and employing topological features, the dataset captures the multifaceted dynamics of solar activity, fostering interdisciplinary correlations and analyses with other solar phenomena. Consequently, the data's intrinsic value is greatly enhanced, affording researchers in solar physics, space climatology, and related fields the means to unravel intricate processes. To achieve this, an open-source Python library script has been developed, consolidating three pivotal stages: image acquisition, image processing, and parameter calculation. Originally conceived as discrete modules, these steps have been unified into a single script, streamlining the entire process. Applying this script to various solar image types has generated multiple datasets, subsequently synthesised into a comprehensive compilation through a data mining procedures. During the image processing phase, conventional libraries like OpenCV and Python's image analysis tools were harnessed to refine images for analysis. In contrast, image acquisition utilised established URL libraries in Python, facilitating direct access to original SOHO repository images and eliminating the need for local storage. The computation of spectral parameters involved a fusion of standard Python libraries and tailored algorithms for specific attributes. This approach ensures precise computation of a diverse array of attributes crucial for comprehensive analysis of solar images.