English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

As Physics-Informed Neural Networks and other methods for full-vector-fieldconstruction or analysis become more prominent, a need has developed for alarge set of simulated active regions for training, validation and testingpurposes. We use a state-of-the-art magnetohydrostatic extrapolation method todevelop a public dataset of over five thousand data cubes based on theSpaceweather HMI Active Region Patch (SHARP) library of active regionmagnetogram images. Each cube resolves the magnetic field vector and plasmaforcing at approximately 100,000 scattered points that are adaptively clusterednear the high-flux regions of the domain. This paper describes the methodologyof construction of the Plasma-prescribed Active Region Static Extrapolation(PARSE) dataset, as well as its structure and how to access it.

The Plasma-prescribed Active Region Static Extrapolation (PARSE)  Dataset: A Machine-Learning-Ready Collection of Magnetohydrostatic Coronal  Active Regions