Tarazona-Alvarado M, Sierra-Porta D
Universidad Industrial de Santander, Escuela de Física. Car 27 #9, Bucaramanga, 680001, Santander, Colombia.
Universidad Tecnológica de Bolívar, Facultad de Ciencias Básicas, Parque Industrial y Tecnológico Carlos Vélez Pombo Km 1 Vía Turbaco, Cartagena de Indias, 130010, Bolívar, Colombia.
Data Brief. 2023 Oct 26;51:109728. doi: 10.1016/j.dib.2023.109728. eCollection 2023 Dec.
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.
本研究展示了一个广泛的数据集，该数据集是精心整理自太阳和日球层天文台（SOHO）的太阳图像，涵盖了一系列光谱波段。这项合作努力跨越多个学科，最终形成了一种强大的自动化方法，该方法贯穿从太阳成像到光谱参数及相关特征计算的整个光谱范围。这项工作的意义在于该数据集所产生的深刻见解。该数据集涵盖不同的光谱波段并采用拓扑特征，捕捉了太阳活动的多方面动态，促进了与其他太阳现象的跨学科关联和分析。因此，数据的内在价值得到极大提升，为太阳物理学、空间气候学及相关领域的研究人员提供了揭示复杂过程的手段。为实现这一目标，开发了一个开源的Python库脚本，整合了三个关键阶段：图像采集、图像处理和参数计算。这些步骤最初被设想为离散的模块，现已统一到一个脚本中，简化了整个过程。将此脚本应用于各种太阳图像类型生成了多个数据集，随后通过数据挖掘程序将这些数据集综合成一个全面的汇编。在图像处理阶段，利用了OpenCV等传统库和Python的图像分析工具对图像进行优化以便分析。相比之下，图像采集使用了Python中已有的URL库，便于直接访问SOHO原始存储库图像，无需本地存储。光谱参数的计算涉及标准Python库与针对特定属性的定制算法的融合。这种方法确保了对各种对太阳图像综合分析至关重要的属性进行精确计算。
