An Evolving Solar Data Environment

TL;DR

This paper discusses the rapid growth of solar data, introduces new tools to support evolving analysis workflows, and highlights the availability of large datasets through common APIs for the research community.

Contribution

It presents recently deployed tools and outlines future development paths for solar data analysis in response to increasing data volume.

Findings

Major datasets from SDO, Hinode, IRIS are accessible via common APIs.

Tools support multiple programming languages including IDL and Python.

Facilitates research into stellar chromospheres and UV spectra.

Abstract

The rapid growth of solar data is driving changes in the typical workflow and algorithmic approach to solar data analysis. We present recently deployed tools to aid this evolution and layout the path for future development. The majority of space-based datasets including those from the multi-petabyte Solar Dynamics Observatory and the Hinode and Interface Region Imaging Spectrograph (IRIS) missions are made available to the community through a common API with support in IDL (via SolarSoft), Python/SunPy and other emerging languages. Stellar astronomers may find the IRIS data particularly useful for research into stellar chromospheres and for interpreting UV spectra.