There is significant consensus that alteration of tissue microenvironment in kidney tissue (e.g. infiltration of inflammatory cells, cardiomyocyte death, fibrosis) is involved in several kidney pathologies. In my first project, the aim is to set up a computational infrastructure to analyse spatial transcriptomics data of kidney tissue. I will optimise the computational methodology to study spatial transcriptomics data from kidney samples from healthy donors to ensure that morphological quality of cells and integrity of RNA is preserved for future experiments with samples from diseased donors. To do this, I will among other things generate spatial transcriptomics data and run FASTQ files through Snakemake/Nextflow pipelines through docker. In this project I am in need of storage and computational resources to store and run my pipelines and data to perform proper evaluation. My current first sample is about 100 GiB large, and I will look at about 25 of these samples (so about 2500 GiB in total). The pipelines themselves (storage + results) take up about 1000 GiB of space. Moreover, I will in relation to the project download public repositories of single-nucleus RNAseq data to process in the pipeline, which will take up an additional 1000 GiB of data. As I am only at the start of my project (I began analysis about a month ago) and already have used about 1000 GiB of data, I feel that having 10000 GiB of space to work with would be beneficial for me. I have previously worked on my local computer and using cloud services but due to the size of the data and pipelines I do not see this strategy as sustainable anymore. Classification: Bioinformatics, Computational Biology, Transcriptomics, Medicine, Biomedical Science