Solar wind models are highly dependent on global magnetic fields at the solar surface as their inner boundary condition, and the lack of global field data is a significant problem plaguing solar wind modeling. Currently, only direct observations of the near-side magnetic field exist and far-side approximations are incapable of predicting growth of existing active regions or new magnetic flux emergence. To fill this data gap, we develop a method that calibrates far-side helioseismic images, which are calculated using near-side Doppler observations, to far-side magnetic flux maps. The calibration employs multiple machine-learning methods that use EUV 304 Å data as a bridge. These algorithms determine a relation 1) between the near-side AIA 304 Å data and HMI magnetic field data, and 2) between STEREO 304 Å data and far-side helioseismic images obtained from a newly developed time-distance helioseismic far-side imaging method. The resulting magnetic flux maps have been further calibrated using maps produced by a flux transport model. The various data products from this work — far-side acoustic maps, far-side STEREO EUV-derived magnetic flux maps, and near-real-time acoustically-driven far-side magnetic flux maps, along with maps of the associated uncertainties — are being made available to enable a synchronic global magnetic flux input into coronal and solar wind models.

The National Research Council published a report on Enhancing the Effectiveness of Team Science in 2015. This report identified 7 fundamental challenges that large research teams, such as the NASA DRIVE Science Centers (DSC), might face including: high diversity of membership, deep knowledge integration, large size, goal misalignment, permeable boundaries, geographic dispersion, and high task interdependence. In Phase I, the COFFIES DSC formed a Center Effectiveness Team (CET) to identify and help overcome these and other unique challenges, including those introduced by the COVID-19 pandemic. CET members have focused on finding and exploring novel ways to align and direct the Science Teams with the goal of enabling breakthrough science. We will present the CET initiatives and implementations, and review the lessons learned for future large-scale science collaborations.