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Applications and methods of artificial intelligence (AI) are becoming powerful tools for scientific investigations in the space sciences, particularly in the analysis and interpretation of the plethora of spacecraft and ground-based observations of the near-Earth and faraway space. Applications of Statistical Methods and Machine Learning in the Space Sciences was a virtual conference held during 17-21 May 2021 to bring together experts in AI and in the various subfields of space sciences to further explore the utility of AI, machine learning, and statistical analysis techniques while sharing the current status of applications in these fields. The conference concluded by emphasizing the scope of AI techniques available to the space science community for addressing outstanding problems with great success as revealed in the number of research works presented. 

In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task.