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Introduction to DXRL: Deep X-ray Lithography at the Elettra synchrotron in Trieste

Introduction to DXRL by Benedetta Marmiroli: Deep X-ray Lithography at the CERIC Austrian partner facility of the Graz University of Technology at the Elettra synchrotron in Trieste, Italy ---DXRL webpage on the CERIC website:...

Keywords: CERIC, Central European Research Infrastructure Consortium, deep x-ray lithography, DXRL

Resource type: video

Introduction to DXRL: Deep X-ray Lithography at the Elettra synchrotron in Trieste https://pan-training.eu/materials/introduction-to-dxrl-deep-x-ray-lithography-at-the-elettra-synchrotron-in-trieste Introduction to DXRL by Benedetta Marmiroli: Deep X-ray Lithography at the CERIC Austrian partner facility of the Graz University of Technology at the Elettra synchrotron in Trieste, Italy ---DXRL webpage on the CERIC website: https://www.ceric-eric.eu/lab-instrument/dynamic-light-scattering/ ---DXRL webpage on the Elettra website: https://elettra.eu/elettra-beamlines/dxrl.html CERIC, Central European Research Infrastructure Consortium, deep x-ray lithography, DXRL researchers scientists engineers
The FAIR Experiment

Video recording of the workshop exploring the FAIR Experiment at PaN facilities (02/10/2020)

Keywords: FAIR, research data, data management, metadata, wp2-ExPaNDS

Resource type: video

The FAIR Experiment https://pan-training.eu/materials/the-fair-experiment Video recording of the workshop exploring the FAIR Experiment at PaN facilities (02/10/2020) FAIR, research data, data management, metadata, wp2-ExPaNDS facility staff instrument scientist
FAIR for facilities

Video recording of the workshop providing an overview of FAIR to PaN facilities (01/10/2020)

Keywords: FAIR, research data, wp2-ExPaNDS

Resource type: video

FAIR for facilities https://pan-training.eu/materials/fair-for-facilities Video recording of the workshop providing an overview of FAIR to PaN facilities (01/10/2020) FAIR, research data, wp2-ExPaNDS PaN Community instrument scientist facility staff
CrystFEL tutorial

This tutorial covers most of the main steps of using CrystFEL, and is based on processing some freely available LCLS data. The aim of this page is to give an overall idea of how the programs fit together and equip you with the knowledge you need to get started with processing a serial...

Keywords: CrystFEL, data processing, crystallography, structural biology

Resource type: tutorial

CrystFEL tutorial https://pan-training.eu/materials/crystfel-tutorial This tutorial covers most of the main steps of using CrystFEL, and is based on processing some freely available LCLS data. The aim of this page is to give an overall idea of how the programs fit together and equip you with the knowledge you need to get started with processing a serial crystallography data set on your own. CrystFEL, data processing, crystallography, structural biology Photon Community crystallography
Delivering data services to EOSC

Wiki page recording the ExPaNDS training workshop on data services for EOSC (06/04/2021)

Keywords: OAI-PMH, metadata, harvesting, SciCat, ICAT, B2FIND, OpenAIRE, research data, wp3-ExPaNDS

Resource type: wiki

Delivering data services to EOSC https://pan-training.eu/materials/delivering-data-services-to-eosc Wiki page recording the ExPaNDS training workshop on data services for EOSC (06/04/2021) OAI-PMH, metadata, harvesting, SciCat, ICAT, B2FIND, OpenAIRE, research data, wp3-ExPaNDS data curator research data scientist
Python Laser Image Visualization

Tool showing pictures from different cameras (directories) in a grid and a stepwise counter-based scroll functionality. Most of the layout and (future) filter options are defined by command line to allow an easy integration into an workflow based on CWL, OWL (or Knime). In future developments...

Keywords: Python, Laser, Visualization, Cameras, Laser Ion Acceleration, Qt5

Resource type: software, git

Python Laser Image Visualization https://pan-training.eu/materials/python-laser-image-visualization Tool showing pictures from different cameras (directories) in a grid and a stepwise counter-based scroll functionality. Most of the layout and (future) filter options are defined by command line to allow an easy integration into an workflow based on CWL, OWL (or Knime). In future developments most of the parameters can be changed interactive and saved to a json file which can be used to describe the next workflow inputs, so that an interactive workflow development is possible. Python, Laser, Visualization, Cameras, Laser Ion Acceleration, Qt5 PaN Community Photon Community