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We share the story of how we made this paper, the first executable paper in Heliophysics, through cross-disciplinary collaboration to highlight the benefits of our process. Executable papers are interactive documents that put a publication’s text inline with the code used in the research in a containerized environment with the data and dependencies needed to run the code. This approach enables readers to reproduce every step taken to arrive at the publication’s conclusions and to easily build upon and extend the work—all important components of open science. Open science is, broadly speaking, transparent and accessible knowledge that is shared and developed through collaborative networks. In this work, we present an adaptable workflow to compare magnetosphere models to spacecraft observations. It is one example of many other workflows that can be developed through collaborations between software developers and scientists in a move towards open science. Most of the authors are members of the Python in Heliophysics Community (PyHC), an international, multi-organizational community that serves as a knowledge base for performing Heliophysics research in the Python programming language. PyHC promotes the executable paper format as a supplemental tool to improve the reproducibility of publications and support open science. A key takeaway is that our collaboration made such a complex task an easy feat in the end. Additionally, the executable version of our paper makes it trivial for others to reproduce our work, and it gives them a better launching point to extend it. These facts underscore the success of our approach. In highlighting this new open science approach, we hope to be an example to our field and encourage this way of doing science.

Heliophysics model outputs are increasingly accessible, but typically are not usable by the majority of the community unless directly collaborating with the relevant model developers. Prohibitive factors include complex file output formats, cryptic metadata, unspecified and often customized coordinate systems, and non-linear coordinate grids. Some pockets of progress exist, giving interfaces to various simulation outputs, but only for a small set of outputs and typically not with open-source, freely available packages. Additionally, the increasing array of tools built upon these sporadic interfaces are typically model-specific. We present Kamodo’s model-agnostic satellite flythrough capabilities as the solution to the utilization barrier for heliophysics model outputs. Developed at the Community Coordinated Modeling Center, these flythrough capabilities are built in Python upon a network of model-agnostic interfaces developed in collaboration with model developers, providing interpolation results the community can trust. Kamodo’s flythrough capabilities present the user with a growing variety of flythrough tools based upon a rapidly expanding library of heliophysics model outputs in several domains, currently including a variety of Ionosphere-Thermosphere-Mesosphere and global magnetosphere model outputs. Each capability is designed to be easily accessible via simplistic model-agnostic syntax, with the entire package freely available in the cloud on Github. Here, we describe the tools developed, include several sample applications for common science questions, demonstrate interoperability with selected packages, and summarize ongoing developments.

Findability, Accessibility, Interoperability, and Reusability (FAIR) data are essential to heliophysics and all scientific research. The principles of FAIR data ensure the reusability and findability of data, as well as its long-term care. The goal is that data are accessible for the ongoing discovery and verification process and can be used on their own or with newly generated data in future studies leading to innovations. With the onset in the previous decades of NASA and other agencies requiring mission data to be open to the public, heliophysics has already made great strides toward FAIR data and benefited from these efforts. Continued improvements in our metadata, data archives, and data portals and the addition of DOIs for data citation will ensure data will be FAIR, enabling further scientific discoveries, reproducibility of results, longitudinal studies, and verification and validation of models. Currently, not all the data collected are findable and on open networks or archives, and not all data on archives have DOIs. Within this study, we make recommendations to prioritize resources needed to satisfy FAIR data principles, treating them as a fundamental research infrastructure rather than a simple research product.• Data collection, preparation, archiving, and accessibility need to be a priority.• Data collection, preparation, archiving, and accessibility need dedicated and sustained funding support.• Data need to be accessible through investment in infrastructure: tools to access and read the data and personnel to maintain these data and IT infrastructure.• Data need to be collected in sustained ways to enable further science and, specifically, model validation efforts.