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Keywords: Python  or data management 

and

Content provider: ALBA  or Paul Scherrer Institute (PSI) 


ETH Lib4RI: Research Data Management – The Basics

Research data management best practices in the ETH Lib4RI, the Library for the Research Institutes within the ETH Domain: Eawag, Empa, PSI & WSL

Keywords: data catalogue, data management, metadata, SciCat

Resource type: slides

ETH Lib4RI: Research Data Management – The Basics https://pan-training.eu/materials/eth-lib4ri-research-data-management-the-basics Research data management best practices in the ETH Lib4RI, the Library for the Research Institutes within the ETH Domain: Eawag, Empa, PSI & WSL data catalogue, data management, metadata, SciCat research scientists postdocs PhD students ExPaNDS and PaNOSC project members PaN users PaN Community researchers
Full-field Tomography at PSI

This workflow has some details on the instrument the data is produced from (TOMCAT beamline) and the infrastructure PSI has concerning their data. If you are more interested in the science and want to reproduce the data and not bother with the surrounding details/context, please refer to the...

Keywords: synchrotron, imaging, Jupyter notebooks, Python, Pulmonary arterial hypertension

Resource type: workflow

Full-field Tomography at PSI https://pan-training.eu/materials/full-field-tomography-at-psi This workflow has some details on the instrument the data is produced from (TOMCAT beamline) and the infrastructure PSI has concerning their data. If you are more interested in the science and want to reproduce the data and not bother with the surrounding details/context, please refer to the Pulmonary arterial hypertension research workflow. Full-field Tomography at PSI Tomography datasets often present large volumes (100 GBs - few TBs) difficult to compress and transfer. The tomographic reconstruction is highly demanding on compute (GPU) and storage resources for the intermediate and/or final result. In addition, the optional image segmentation step may be demanding on computer memory. The offline analysis (after experiment) could be performed remotely by users at home making it attractive for deployment as a cloud-like use case. Finally, this technique is applied at many facilities and in different scientific domains - therefore a portable result is more useful. This entire process is illustrated with a typical experiment. synchrotron, imaging, Jupyter notebooks, Python, Pulmonary arterial hypertension research data scientist life scientists