Juno UVS: Brightness maps of OI 1356 Å and OI 1304 Å emissions at Ganymede

The dataset provides the following data on a surface grid of Ganymede, as calculated in Duling et al. (2026) (Figure 2): OI 1356 Å brightness as observed by Juno UVS OI 1356 Å and OI 1304 Å integrated brightness as observed by Juno UVS OI 1356 Å reflected sunlight, estimated by scaling CII 1335 Å brightness OI 1356 Å and OI 1304 Å integrated reflected sunlight, estimated by scaling CII 1335 Å brightness OI 1356 Å brightness, estimated reflected sunlight subtracted OI 1356 Å and OI 1304 Å, estimated reflected sunlight subtracted exposure times All data are in units of Rayleigh, besides the exposure times, which are in units of millisecond. This data is derived from the calibrated Juno UVS data (Trantham 2014) at https://pds-atmospheres.nmsu.edu/PDS/data/jnouvs_3001/ . Values of zero Rayleigh indicate pixels where no photon was detected, either due to being not within the detection window or due to zero detected emission. Regions actually covered by the observation can be identified by using the exposure times as a mask.In the data corrected by reflected sunlight negative brightness values appear. Those are a consequence of low photon count rates, i.e. the strong Poisson noise in the data, and correctly average out over larger areas. In particular, negative values are calculated for pixels where high CII 1335 Å brightness values suggest more reflected sunlight than actually detected in the OI 1356 Å and OI 1304 Å lines. The coordinate system is based on body fixed IAU_Ganymede with latitude $\theta$ and western longitude $\lambda$, with 0° being the Jupiter-facing meridian. We provide the data in three types of file format: *.csv (ASCII):The first row gives the longitudes, the first column the latitudes, the remaining table gives the data. *.npz (Python numpy) import numpy as np data = np.load('file.npz') longitude = data['longitude'] latitude = data['latitude'] brightness = data['brightness'] *.nc (NetCDF)