Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050

TL;DR

This paper discusses the evolving role of data science and machine learning in heliophysics, emphasizing the need for rigorous methods and new knowledge representation to support scientific discovery from 2020 to 2050.

Contribution

It proposes a framework for integrating data science and machine learning into heliophysics research, highlighting the importance of evolving discovery methods and knowledge representation.

Findings

Data science will become increasingly vital in heliophysics.

Machine learning methods must be applied rigorously for effective discovery.

A new approach to knowledge representation is needed to keep pace with data and technology changes.

Abstract

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.