The dataset encompasses the processed point clouds (.pts format), a panoramic tour, and a video flythrough of registered point clouds capturing a 273 m long reach of the englacial portal channel in the glacier, Austre Broggerbreen, Svalbard, in March 2017. Point clouds were derived from 27 Terrestrial Laser Scanning (TLS) surveys, to characterise the morphology of the channel in three-dimensions and enable extraction of features reflective of hydrological flow conditions. The panoramic tour shows a greyscale image of the scan reflectivity values at each survey location, whereby the lighter the pixel colour, the greater the intensity of the laser beam return. This panoramic tour enables the viewer to self-navigate through the channel to see the morphological features within it. The video flythrough of the point cloud provides a visualisation of the point cloud data, travelling from the portal exit to the extent of the scanned reach. The point cloud has been coloured to reflect differences in height above the portal exit. Funding source Knowledge Economy Skills Scholarship (KESS II) under Project AU10003, a pan-Wales higher-level skills initiative led by Bangor University of behalf of the HE sector in Wales. It is part funded by the Welsh Government's European Social Fund (ESF) convergence programme for West Wales and the Valleys. Funding was awarded to TDLI-F and JEK, with support from Deri Jones & Associates Ltd. Additional support is acknowledged from Aberystwyth University (Department of Geography and Earth Sciences).

This dataset consists of long time series of subglacial water pressures, obtained from a pressure sensor installed in a borehole that connected to the basal drainage system of Kongsvegen, Svalbard. The glacier has been in a quiescent state since its last surge circa 1948, and has undergone a gradual acceleration during the last decade. The data series runs from 2018-10-19 to 2024-08-12. Data acquisition was funded by NERC Urgency Grant NE/R018243/1 REBUS (Resolving Enthalpy Budget to Understand Surges) and RCN Grant 301837 MAMMAMIA (Multi-scale, multi-method assessment of mechanisms for ice acceleration).