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Ground-based magnetometers used to measure magnetic fields on the Earth’s surface (B) have played a central role in the development of Heliophysics research for more than a century. These versatile instruments have been adapted to study everything from polar cap dynamics to the equatorial electrojet, from solar wind-magnetosphere-ionosphere coupling to real-time monitoring of space weather impacts on power grids. Due to their low costs and relatively straightforward operational procedures, these instruments have been deployed in large numbers in support of Heliophysics education and citizen science activities. They are also widely used in Heliophysics research internationally and more broadly in the geosciences, lending themselves to international and interdisciplinary collaborations; for example, ground-based electrometers collocated with magnetometers provide important information on the inductive coupling of external magnetic fields to the Earth’s interior through the induced electric field (E). The purpose of this white paper is to (1) summarize present ground-based magnetometer infrastructure, with a focus on US-based activities, (2) summarize research that is needed to improve our understanding of the causes and consequences of B variations, (3) describe the infrastructure and policies needed to support this research and improve space weather models and nowcasts/forecasts. We emphasize a strategic shift to proactively identify operational efficiencies and engage all stakeholders who need B and E to work together to intelligently design new coverage and instrumentation requirements. 

Models for space weather forecasting will never be complete/valid without accounting for inter-hemispheric asymmetries in Earth’s magnetosphere, ionosphere and thermosphere. This whitepaper is a strategic vision for understanding these asymmetries from a global perspective of geospace research and space weather monitoring, including current states, future challenges, and potential solutions. 

Magnetometers are a key component of heliophysics research providing valuable insight into the dynamics of electromagnetic field regimes and their coupling throughout the solar system. On satellites, magnetometers provide detailed observations of the extension of the solar magnetic field into interplanetary space and of planetary environments. At Earth, magnetometers are deployed on the ground in extensive arrays spanning the polar cap, auroral and sub-auroral zone, mid- and low-latitudes and equatorial electrojet with nearly global coverage in azimuth (longitude or magnetic local time—MLT). These multipoint observations are used to diagnose both ionospheric and magnetospheric processes as well as the coupling between the solar wind and these two regimes at a fraction of the cost of in-situ instruments. Despite their utility in research, ground-based magnetometer data can be difficult to use due to a variety of file formats, multiple points of access for the data, and limited software. In this short article we review the Open-Source Python library GMAG which provides rapid access to ground-based magnetometer data from a number of arrays in a Pandas DataFrame, a common data format used throughout scientific research. 

Abstract There are known limitations in methods of detecting positive selection. Common methods do not enable differentiation between positive selection and compensatory covariation, a major limitation. Further, the traditional method of calculating the ratio of nonsynonymous to synonymous substitutions (dN/dS) does not take into account the 3D structure of biomacromolecules nor differences between amino acids. It also does not account for saturation of synonymous mutations (dS) over long evolutionary time that renders codon-based methods ineffective for older divergences. This work aims to address these shortcomings for detecting positive selection through the development of a statistical model that examines clusters of substitutions in clusters of variable radii. Additionally, it uses a parametric bootstrapping approach to differentiate positive selection from compensatory processes. A previously reported case of positive selection in the leptin protein of primates was reexamined using this methodology.