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://www.lib4ri.ch/sites/default/files/media/documents/2023_05_PhD-Training_RDM_PSI_web.pdf
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/workflows/backup-fork-of-full-field-tomography-at-psi-wip#workflow
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
