Birmingham Environment for Academic Research : Case studies volume 3

This collection of case studies was brought together to showcase the extent and diversity of research that is supported by the University of Birmingham’s Environment for Academic Research (BEAR). BEAR is a collection of contemporary IT resources designed to help research. The following case studies demonstrate how BEAR services such as the Research Data Store (RDS), BEAR software and the University supercomputer BlueBEAR are integral to the progression of important research across disciplines. BlueBEAR is a key component of BEAR, providing compute power and specialist applications free to enable staff and students to delve deeper into their research. Upgraded in 2023, the cluster includes many large memory nodes and a GPU service alongside standard compute nodes. Alongside BlueBEAR, the RDS is a popular choice amongst researchers to securely store their working research data. As of publication, more than 5000 researchers across all five colleges were actively using BlueBEAR and/or the RDS. In this volume, we showcase case studies representing diverse research from every college. From estimating snow coverage to modelling second language acquisition, we show how BEAR services are enabling exciting and important research across the university

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series and the regionalisation of the ΔSNOW model

Time series of daily Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the ΔSNOW model (Winkler et al., 2021) and regionalised parameters. An extensive description of the dataset and the method to generate it can be found in the data descriptor manuscript that has been submitted to the journal Earth System Science Data. Dataset: A total of 11,0071 time series of modelled SWE and estimated snow density at the point scale, spanning 1950-2022, at daily resolution. "NH-SWE_dataset_MAP.png" shows a Northern Hemisphere map with the location of all stations in the NH-SWE dataset and their elevation in meters. Files: The dataset is provided in two different formats: Individual .csv files for each station in the NH-SWE dataset at "NH_SWE_dataset_vector_files.zip" Full-dataset .csv matrices with dates as rows and NH-SWE stations as columns at "NH_SWE_dataset_matrix_files.zip" Metadata: "NH_SWE_METADATA.csv" Includes information on NH-SWE stations location (ID, country, station name, coordinates, elevation), data source, length of time series, model parameters and the climate variables used to estimate them, and average snow climatology such as average maximum snow depth, average peak SWE and average maximum snow cover duration. More details and units in the "README_fileformats.txt" file. ΔSNOW model parameter regionalisation: The code to obtain the ΔSNOW model parameters based on climate variables for all the stations in the NH-SWE dataset is shared in "DeltaSNOW_parameter_regionalisation.zip". The method is extensively described in the data descriptor manuscript by Fontrodona-Bach et al., (2023) submitted to Earth System Science Data. More details in the "README_regionalisation.txt" file. Data use: Free, provided adequate citation of both the data descriptor manuscript and the zenodo record. See "README_datausage.txt" Version history: v1: Initial upload. The ΔSNOW model regionalisation was missing. v2: Manuscript submission version. Updated dataset and includes the ΔSNOW model regionalisation code

NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in-situ snow depth time series

Time series of Snow Water Equivalent (SWE) and Snow Density over the Northern Hemisphere, based on in-situ station observations of snow depth converted to SWE using the DeltaSNOW model (Winkler et al., 2021). A total of 11,003 time series of SWE at the point scale, spanning 1950-2022, at daily resolution. Data descriptor manuscript submitted to Earth System Science data

Birmingham Environment for Academic Research:Case studies volume 3

This collection of case studies was brought together to showcase the extent and diversity of research that is supported by the University of Birmingham’s Environment for Academic Research (BEAR). BEAR is a collection of contemporary IT resources designed to help research. The following case studies demonstrate how BEAR services such as the Research Data Store (RDS), BEAR software and the University supercomputer BlueBEAR are integral to the progression of important research across disciplines.BlueBEAR is a key component of BEAR, providing compute power and specialist applications free to enable staff and students to delve deeper into their research. Upgraded in 2023, the cluster includes many large memory nodes and a GPU service alongside standard compute nodes. Alongside BlueBEAR, the RDS is a popular choice amongst researchers to securely store their working research data. As of publication, more than 5000 researchers across all five colleges were actively using BlueBEAR and/or the RDS. In this volume, we showcase case studies representing diverse research from every college. From estimating snow coverage to modelling second language acquisition, we show how BEAR services are enabling exciting and important research across the university