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\begin{document}

\title{Augmenting traditional networks with data buys can support science, as
well as operations}



\author[1]{Jennifer Gannon}%
\author[2]{Noé Lugaz}%
\affil[1]{CPI}%
\affil[2]{University of New Hampshire}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
Science is fueled by data. Throughout history, scientists have operated
sensors-from astronomical observatories to particle accelerators-that
accumulate observations for analysis or to evaluate a hypothesis.
However, as available technologies have increased both the volume of
data and the efficiency of data storage and transmission, a new model of
data access has emerged. The concept of a data buy is where an entity
purchases access to a set of data or a data stream, instead of operating
the sensors themselves. But why might a data consumer, whether a
researcher or an end-user, prefer this kind of data access over the more
traditional methods of running a network themselves? The simple answer,
in some cases, is efficiency and, possibly, cost. Space weather
forecasting and analysis has a growing private sector, and the extension
to data gathering can be considered as a natural next step in the
maturation of the field and the growing public-private partnerships.
Operational applications require consistent, clean, and (in some cases)
real-time data access that can be hard to support through the existing
model of sensor deployment. Even in scientific applications, where
access to raw information can be critical to discovery, there are
benefits to the data buy model. Consistent access to a trusted data set
allows more time to be spent on the scientific analysis, instead of
maintaining machines that require consistent development, maintenance,
and monitoring. The outsourcing of data infrastructure and pipelines can
be particularly beneficial when the sensors are in distributed networks,
spread over wide areas, and when there is a need to provide local data
in observational gaps in existing networks. In the ideal case, a data
buy can supplement the traditional observational networks in a
beneficial and symbiotic way. It is important to note that data buys
should not replace traditional observational networks, nor compete for
funding with future observatories and infrastructure that the scientific
community has deemed necessary (for example, through decadal survey
processes).%
\end{abstract}%



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