# data[].attributes.description
Experiments generate up to 150 TB per day of data saved at the measurement facility. Such large datasets are impractical for users to take home.
 
Subsequent analysis needs to be performed remotely making it attractive for deployment as a cloud-like use case. Involving EOSC in the analysis and re-use of this data is an appropriate use case.

Serial crystallography is a beam-line technique for collecting information on the structure of a protein without growing large protein crystals. Instead, a large number of small protein crystals are held in a pulsed X-ray beam. In a second step, the series of produced images are used to reconstruct a precise 3-D image of the protein structure. Serial crystallography is the preferred technique for obtaining diffraction data of proteins at room temperature, where radiation damage from the X-ray beam starts rapidly. The standard software for analysing serial crystallography is “CrystFEL”.

The proposed workflow was rendered in a standard fashion, which would allow it to be easily adopted by arbitrary systems or also other containerized applications. The only requirements are an Apptainer installation on the system and a Docker or Singularity/Apptainer image of the application, as well as an adjustment of the configuration file for the wrapper script.

# data[].attributes.workflow-content.nodes[].data.description
Experiments generate up to 150 TB per day of data saved at the measurement facility. Such large datasets are impractical for users to take home.

Subsequent analysis needs to be performed remotely making it attractive for deployment as a cloud-like use case. Involving EOSC in the analysis and re-use of this data is an appropriate use case.

Serial crystallography is a beam-line technique for collecting information on the structure of a protein without growing large protein crystals. Instead, a large number of small protein crystals are held in a pulsed X-ray beam. In a second step, the series of produced images are used to reconstruct a precise 3-D image of the protein structure. Serial crystallography is the preferred technique for obtaining diffraction data of proteins at room temperature, where radiation damage from the X-ray beam starts rapidly. The standard software for analyzing serial crystallography is “CrystFEL”.

The proposed workflow was rendered in a standard fashion, which would allow to be easily adopted by arbitrary systems or also other containerized applications. The only requirements are an Apptainer installation on the system and a Docker or Singularity/Apptainer image of the application, as well as an adjustment of the configuration file for the wrapper scripts. 

# data[].attributes.workflow-content.nodes[].data.html-description
Experiments generate up to 150 TB per day of data saved at the measurement facility. Such large datasets are impractical for users to take home.

Subsequent analysis needs to be performed remotely making it attractive for deployment as a cloud-like use case. Involving EOSC in the analysis and re-use of this data is an appropriate use case.

Serial crystallography is a beam-line technique for collecting information on the structure of a protein without growing large protein crystals. Instead, a large number of small protein crystals are held in a pulsed X-ray beam. In a second step, the series of produced images are used to reconstruct a precise 3-D image of the protein structure. Serial crystallography is the preferred technique for obtaining diffraction data of proteins at room temperature, where radiation damage from the X-ray beam starts rapidly. The standard software for analyzing serial crystallography is “CrystFEL”.

The proposed workflow was rendered in a standard fashion, which would allow to be easily adopted by arbitrary systems or also other containerized applications. The only requirements are an Apptainer installation on the system and a Docker or Singularity/Apptainer image of the application, as well as an adjustment of the configuration file for the wrapper scripts.

# data[].attributes.workflow-content.nodes[].data.description
Raw data as saved by the facility. File format varies according to facility and
instrument - could be XTC files (LCLS), HDF5 files (Eiger) or CBF files (Pilatus).

Raw data: 10TB. 
Current data location: Raw data on dCache at DESY

# data[].attributes.workflow-content.nodes[].data.html-description
Raw data as saved by the facility. File format varies according to facility and instrument - could be XTC files (LCLS), HDF5 files (Eiger) or CBF files (Pilatus).

Raw data: 10TB. Current data location: Raw data on dCache at DESY

# data[].attributes.workflow-content.nodes[].data.description
Indexing is a crucial and demanding step in the workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
Indexing is a crucial and demanding step in the workflow.

# data[].attributes.workflow-content.nodes[].data.description
The VISA instance can be created from the EOSC service or directly at DESY's VISA platform. 
To access VISA remote desktop, just login with an account of your choosing (EGI Check-In, Helmholtz AAI or GitHub) and wait for your account to be elevated to create a VISA instance.

Choose the generic Apptainer image, which has Apptainer and git pre-installed. 

# data[].attributes.workflow-content.nodes[].data.html-description
The VISA instance can be created from the EOSC service or directly at DESY's VISA platform. To access VISA remote desktop, just login with an account of your choosing (EGI Check-In, Helmholtz AAI or GitHub) and wait for your account to be elevated to create a VISA instance.

Choose the generic Apptainer image, which has Apptainer and git pre-installed.

# data[].attributes.workflow-content.nodes[].data.description
Merging is another step within the workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
Merging is another step within the workflow.

# data[].attributes.workflow-content.nodes[].data.description
With ContainerizedApplication, behind the scenes, all CrystFEL commands are executed on the container. 
This is also true for the CrystFEL GUI, which is run within the container. 
Both can be setup to make use of a Slurm manager, if on is installed on the host system.
To start crystfel GUI from command line:
```
crystfel
```

If container was pulled manually, run instead:
```
apptainer exec --bind /dcache/ ./crystfel.sif crystfel
```

# data[].attributes.workflow-content.nodes[].data.html-description
With ContainerizedApplication, behind the scenes, all CrystFEL commands are executed on the container. This is also true for the CrystFEL GUI, which is run within the container. Both can be setup to make use of a Slurm manager, if on is installed on the host system. To start crystfel GUI from command line:

crystfel

If container was pulled manually, run instead:

apptainer exec --bind /dcache/ ./crystfel.sif crystfel

# data[].attributes.workflow-content.nodes[].data.description
External users can log in with Helmholtz AAI and use their home institution's credentials.

# data[].attributes.workflow-content.nodes[].data.html-description
External users can log in with Helmholtz AAI and use their home institution's credentials.

# data[].attributes.workflow-content.nodes[].data.description
Helper scripts to make easy use of containerized applications. 
Auto creation of wrapper scripts for application commands and pulling of specified container.

# data[].attributes.workflow-content.nodes[].data.html-description
Helper scripts to make easy use of containerized applications. Auto creation of wrapper scripts for application commands and pulling of specified container.

# data[].attributes.workflow-content.nodes[].data.description
CrystFEL container image that the bash installation script pulls.

Built and maintained by a CI/CD workflow on DESY's gitlab.

# data[].attributes.workflow-content.nodes[].data.html-description
CrystFEL container image that the bash installation script pulls.

Built and maintained by a CI/CD workflow on DESY's gitlab.

# data[].attributes.workflow-content.nodes[].data.description
CrystFEL is available under open source license.

# data[].attributes.workflow-content.nodes[].data.html-description
CrystFEL is available under open source license.

# data[].attributes.workflow-content.nodes[].data.description
First step in the serial crystallization workflow is running a hit finder (cheetah) on the recorded images.

# data[].attributes.workflow-content.nodes[].data.html-description
First step in the serial crystallization workflow is running a hit finder (cheetah) on the recorded images.

# data[].attributes.workflow-content.nodes[].data.description
Within the installation script, Apptainer created an .sif image from the CrystFEL Docker image, which can be found within the target directory. It is used to run the container for any wrapper commands that are executed.

If not relying on the ContainerizedApplication script, pull crystfel image from GitLab repository.
```
apptainer pull ./crystfel_image.sif docker://gitlab.desy.de:5555/thomas.white/crystfel/crystfel:latest
```

# data[].attributes.workflow-content.nodes[].data.html-description
Within the installation script, Apptainer created an .sif image from the CrystFEL Docker image, which can be found within the target directory. It is used to run the container for any wrapper commands that are executed.

If not relying on the ContainerizedApplication script, pull crystfel image from GitLab repository.

apptainer pull ./crystfel_image.sif docker://gitlab.desy.de:5555/thomas.white/crystfel/crystfel:latest

# data[].attributes.workflow-content.nodes[].data.description
MTZ files (<1GB) ready for structure analysis.
Option of exporting reduced datasets containing only hits (<1TB) that are useful for
researchers to take home for more detailed analysis or archiving. This gives a defined
output for the pipeline. Will consider delivery of MTZ files through a web page or email
rather than disk-based file retrieval.

# data[].attributes.workflow-content.nodes[].data.html-description
MTZ files (<1GB) ready for structure analysis. Option of exporting reduced datasets containing only hits (<1TB) that are useful for researchers to take home for more detailed analysis or archiving. This gives a defined output for the pipeline. Will consider delivery of MTZ files through a web page or email rather than disk-based file retrieval.

# data[].attributes.workflow-content.nodes[].data.description
The new European X-ray Free-Electron Laser (European XFEL) is the first X-ray free-electron laser capable of delivering intense X-ray pulses with a megahertz interpulse spacing in a wavelength range suitable for atomic resolution structure determination. An outstanding but crucial question is whether the use of a pulse repetition rate nearly four orders of magnitude higher than previously possible results in unwanted structural changes due to either radiation damage or systematic effects on data quality. Here, separate structures from the first and subsequent pulses in the European XFEL pulse train were determined, showing that there is essentially no difference between structures determined from different pulses under currently available operating conditions at the European XFEL.

# data[].attributes.workflow-content.nodes[].data.html-description
The new European X-ray Free-Electron Laser (European XFEL) is the first X-ray free-electron laser capable of delivering intense X-ray pulses with a megahertz interpulse spacing in a wavelength range suitable for atomic resolution structure determination. An outstanding but crucial question is whether the use of a pulse repetition rate nearly four orders of magnitude higher than previously possible results in unwanted structural changes due to either radiation damage or systematic effects on data quality. Here, separate structures from the first and subsequent pulses in the European XFEL pulse train were determined, showing that there is essentially no difference between structures determined from different pulses under currently available operating conditions at the European XFEL.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Evaluation of serial crystallographic structure determination within megahertz pulse trains

# data[].attributes.workflow-content.nodes[].data.description
By using the ContainerizdApplication install script, the latest CrystFEL container will be downloaded and ease-of-use wrapper scripts for CrystFEL are created. They allow handling CrystFEL as if it was installed directly on the system.

```
git clone https://gitlab.desy.de/silvan.schoen/containerizedapplication.git crystfel
source ./crystfel/install_script.sh
```

# data[].attributes.workflow-content.nodes[].data.html-description
By using the ContainerizdApplication install script, the latest CrystFEL container will be downloaded and ease-of-use wrapper scripts for CrystFEL are created. They allow handling CrystFEL as if it was installed directly on the system.

git clone https://gitlab.desy.de/silvan.schoen/containerizedapplication.git crystfel
source ./crystfel/install_script.sh

# data[].attributes.workflow-content.nodes[].data.description
Based on a SciCat metadata catalogue, the user can choose one of his experiments during VISA instance creation.
Experimental or open data are then mounted from dCache directly into the VISA instance.

# data[].attributes.workflow-content.nodes[].data.html-description
Based on a SciCat metadata catalogue, the user can choose one of his experiments during VISA instance creation. Experimental or open data are then mounted from dCache directly into the VISA instance.

# data[].attributes.workflow-content.nodes[].data.description
On our e-learning portal we have additional background information on basic crystallography in our Wiki and e-learning courses on various X-ray science topics.

# data[].attributes.workflow-content.nodes[].data.html-description
On our e-learning portal we have additional background information on basic crystallography in our Wiki and e-learning courses on various X-ray science topics.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Learning material on basic crystallography 

# data[].attributes.description
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.

To find more ressources on Tomography please check out the data portal

# data[].attributes.workflow-content.nodes[].data.description
At PSI, data is made available through a Public Data Repository after the individual embargo period has passed, which can be up to three years.

Supports OAI-PMH in order to support harvesting of dataset metadata.

# data[].attributes.workflow-content.nodes[].data.html-description
At PSI, data is made available through a Public Data Repository after the individual embargo period has passed, which can be up to three years.

Supports OAI-PMH in order to support harvesting of dataset metadata.

# data[].attributes.workflow-content.nodes[].data.description
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.

To find more ressources on Tomography please check out the open data portal. 

# data[].attributes.workflow-content.nodes[].data.html-description
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.

To find more ressources on Tomography please check out the open data portal.

# data[].attributes.workflow-content.nodes[].data.html-description
PSI data processing.

# data[].attributes.workflow-content.nodes[].data.description
The raw data is stored by the data acquisition system on our file system, together with the metadata generated in the automatic processing and analysis steps. Everything is packaged together in one dataset.

# data[].attributes.workflow-content.nodes[].data.html-description
The raw data is stored by the data acquisition system on our file system, together with the metadata generated in the automatic processing and analysis steps. Everything is packaged together in one dataset.

# data[].attributes.workflow-content.nodes[].data.description
The principal investigator can trigger an action to store the datasets in our long term storage tape archive system at CSCS.

# data[].attributes.workflow-content.nodes[].data.html-description
The principal investigator can trigger an action to store the datasets in our long term storage tape archive system at CSCS.

# data[].attributes.workflow-content.nodes[].data.description
Full field tomography is a well established and widely used imaging technique. Synchrotron full feld tomography allows imaging specimens with high resolution in space (3D) and time (4D).

3D tomographic datasets are reconstructed from 2D projections using optimised software based on Fourier methods and a user-friendly interface (i.e., an ImageJ plug-in).

#### Full-field Tomography at PSI

The TOmographic Microscopy and Coherent rAdiology experimentTs (TOMCAT) beamline is a typical user facility at PSI and therefore, it is necessary to make both data and analysis scripts accessible to external users. In the following section, we describe the scientific methodology together with the analysis workflow.

#### Scientific Methodology

The TOMCAT beamline at PSI is operated by the X-ray Tomography Group and offers cutting-edge technology and scientific expertise for exploiting the benefits of synchrotron radiation for fast, non-destructive, high resolution, quantitative investigations on a large variety of samples. Absorption-based and phase-contrast imaging are routinely performed with isotropic voxel sizes ranging from 0.16 to 11 μm (fields-of-view (h x v) of 0.4 x 0.3 mm2 and 22 x 3–7 mm2, respectively) in an energy range of 8–45 keV. Phase contrast is obtained with simple edge-enhancement, propagation-based techniques or through grating interferometry.

Typical acquisition times are on the order of seconds to a few minutes. However, dynamic processes can be followed in 4D (3D space + time) using the ultra-fast endstation, which provides sub-second temporal resolution for extended time periods thanks to the in-house developed GigaFRoST system. A laser-based heating system and a cryojet and cryo-chamber are available as standard installations and are compatible with both the standard and ultra-fast endstations. It is also possible to bring specialized, user-defined instrumentation to TOMCAT.

A temporal resolution of a few (< 5) minutes can also be achieved with the hard X-ray full-field microscope setup delivering a pixel size of 65 nm for microscopic samples (~75x75 μm2 field-of-view).

# data[].attributes.workflow-content.nodes[].data.html-description
Full field tomography is a well established and widely used imaging technique. Synchrotron full feld tomography allows imaging specimens with high resolution in space (3D) and time (4D).

3D tomographic datasets are reconstructed from 2D projections using optimised software based on Fourier methods and a user-friendly interface (i.e., an ImageJ plug-in).

Full-field Tomography at PSI

The TOmographic Microscopy and Coherent rAdiology experimentTs (TOMCAT) beamline is a typical user facility at PSI and therefore, it is necessary to make both data and analysis scripts accessible to external users. In the following section, we describe the scientific methodology together with the analysis workflow.

Scientific Methodology

The TOMCAT beamline at PSI is operated by the X-ray Tomography Group and offers cutting-edge technology and scientific expertise for exploiting the benefits of synchrotron radiation for fast, non-destructive, high resolution, quantitative investigations on a large variety of samples. Absorption-based and phase-contrast imaging are routinely performed with isotropic voxel sizes ranging from 0.16 to 11 μm (fields-of-view (h x v) of 0.4 x 0.3 mm2 and 22 x 3–7 mm2, respectively) in an energy range of 8–45 keV. Phase contrast is obtained with simple edge-enhancement, propagation-based techniques or through grating interferometry.

Typical acquisition times are on the order of seconds to a few minutes. However, dynamic processes can be followed in 4D (3D space + time) using the ultra-fast endstation, which provides sub-second temporal resolution for extended time periods thanks to the in-house developed GigaFRoST system. A laser-based heating system and a cryojet and cryo-chamber are available as standard installations and are compatible with both the standard and ultra-fast endstations. It is also possible to bring specialized, user-defined instrumentation to TOMCAT.

A temporal resolution of a few (< 5) minutes can also be achieved with the hard X-ray full-field microscope setup delivering a pixel size of 65 nm for microscopic samples (~75x75 μm2 field-of-view).

# data[].attributes.workflow-content.nodes[].data.description
Remote access to a flexible HPC facility is available for subsequent, advanced post-processing and
data quantification. A suite of analytical and iterative reconstruction routines are provided;
additional ad-hoc tools can be installed by a user.

# data[].attributes.workflow-content.nodes[].data.html-description
Remote access to a flexible HPC facility is available for subsequent, advanced post-processing and data quantification. A suite of analytical and iterative reconstruction routines are provided; additional ad-hoc tools can be installed by a user.

# data[].attributes.workflow-content.nodes[].data.description
#### Abstract

In pulmonary arterial hypertension, plexiform lesions are associated with severe arterial obstruction and right ventricular failure. Exploring their structure and position is crucial for understanding the interplay between hemodynamics and vascular remodeling. The aim of this research was to use synchrotron-based phase-contrast micro-CT to study the three-dimensional structure of plexiform lesions. Archived paraffin-embedded tissue samples from 14 patients with pulmonary arterial hypertension (13 idiopathic, 1 with known BMPR2-mutation) were imaged. Clinical data showed high-median PVR (12.5 WU) and mPAP (68 mmHg). Vascular lesions with more than 1 lumen were defined as plexiform. Prior radiopaque dye injection in some samples facilitated 3-D rendering. Four distinct types of plexiform lesions were identified: 1) localized within or derived from monopodial branches (supernumerary arteries), often with a connection to the vasa vasorum; 2) localized between pulmonary arteries and larger airways as a tortuous transformation of intrapulmonary bronchopulmonary anastomoses; 3) as spherical structures at unexpected abrupt ends of distal pulmonary arteries; and 4) as occluded pulmonary arteries with recanalization. By appearance and localization, types 1–2 potentially relieve pressure via the bronchial circulation, as pulmonary arteries in these patients were almost invariably occluded distally. In addition, types 1–3 were often surrounded by dilated thin-walled vessels, often connected to pulmonary veins, peribronchial vessels, or the vasa vasorum. Collaterals, bypassing completely occluded pulmonary arteries, were also observed to originate within plexiform lesions. In conclusion, synchrotron-based imaging revealed significant plexiform lesion heterogeneity, resulting in a novel classification. The four types likely have different effects on hemodynamics and disease progression.

# data[].attributes.workflow-content.nodes[].data.html-description
Abstract

In pulmonary arterial hypertension, plexiform lesions are associated with severe arterial obstruction and right ventricular failure. Exploring their structure and position is crucial for understanding the interplay between hemodynamics and vascular remodeling. The aim of this research was to use synchrotron-based phase-contrast micro-CT to study the three-dimensional structure of plexiform lesions. Archived paraffin-embedded tissue samples from 14 patients with pulmonary arterial hypertension (13 idiopathic, 1 with known BMPR2-mutation) were imaged. Clinical data showed high-median PVR (12.5 WU) and mPAP (68 mmHg). Vascular lesions with more than 1 lumen were defined as plexiform. Prior radiopaque dye injection in some samples facilitated 3-D rendering. Four distinct types of plexiform lesions were identified: 1) localized within or derived from monopodial branches (supernumerary arteries), often with a connection to the vasa vasorum; 2) localized between pulmonary arteries and larger airways as a tortuous transformation of intrapulmonary bronchopulmonary anastomoses; 3) as spherical structures at unexpected abrupt ends of distal pulmonary arteries; and 4) as occluded pulmonary arteries with recanalization. By appearance and localization, types 1–2 potentially relieve pressure via the bronchial circulation, as pulmonary arteries in these patients were almost invariably occluded distally. In addition, types 1–3 were often surrounded by dilated thin-walled vessels, often connected to pulmonary veins, peribronchial vessels, or the vasa vasorum. Collaterals, bypassing completely occluded pulmonary arteries, were also observed to originate within plexiform lesions. In conclusion, synchrotron-based imaging revealed significant plexiform lesion heterogeneity, resulting in a novel classification. The four types likely have different effects on hemodynamics and disease progression.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Distinct types of plexiform lesions identified by synchrotron-based phase-contrast micro-CT

# data[].attributes.workflow-content.nodes[].data.description
Raw and derived datasets are stored at PSI, along with the experiment metadata, and can later be accessed using the data catalogue.

# data[].attributes.workflow-content.nodes[].data.html-description
Raw and derived datasets are stored at PSI, along with the experiment metadata, and can later be accessed using the data catalogue.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
search/archive/retrieve and publish your data

# data[].attributes.workflow-content.nodes[].data.description
The public data repository allows B2FIND and openAIRE to harvest the corresponding metadata in order to make the PSI data findable by the global community. This entire process is illustrated with a typical experiment (Synchrotron Imaging of Complex Vascular Lesions in Human Pulmonary Hypertension: Pathology Distribution in 3D Space).

# data[].attributes.workflow-content.nodes[].data.html-description
The public data repository allows B2FIND and openAIRE to harvest the corresponding metadata in order to make the PSI data findable by the global community. This entire process is illustrated with a typical experiment (Synchrotron Imaging of Complex Vascular Lesions in Human Pulmonary Hypertension: Pathology Distribution in 3D Space).

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Synchrotron Imaging of Complex Vascular Lesions in Human Pulmonary Hypertension: Pathology Distribution in 3D Space

# data[].attributes.workflow-content.nodes[].data.description
The cross section image reconstructed with the jupyter notebook that uses the TomoPy library.
![cross section image](https://i.imgur.com/Iio54Jo.png)

# data[].attributes.workflow-content.nodes[].data.html-description
The cross section image reconstructed with the jupyter notebook that uses the TomoPy library.

# data[].attributes.workflow-content.nodes[].data.description
Using TomoPy (TomoPy is an open-source Python package for tomographic data processing and image reconstruction.), the notebook will reconstruct an image. 

# data[].attributes.workflow-content.nodes[].data.html-description
Using TomoPy (TomoPy is an open-source Python package for tomographic data processing and image reconstruction.), the notebook will reconstruct an image.

# data[].attributes.workflow-content.nodes[].data.description
Following the data acquisition step, group members will analyse the data. Increasingly this is done with Jupyter notebooks provided through a JupyterHub running on the PSI HPC cluster.

# data[].attributes.workflow-content.nodes[].data.html-description
Following the data acquisition step, group members will analyse the data. Increasingly this is done with Jupyter notebooks provided through a JupyterHub running on the PSI HPC cluster.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Repository and various recipes for installing a Tomorec Jupyter kernel, downloading publically available data, and executing a sample analysis Juptyer notebook

# data[].attributes.description
Cartography or kinetic experiments can generate up to 3 TB per day of raw data saved at the measurement facility. Such large datasets are impractical for users to take home. Subsequent analysis needs to be performed remotely on a performant platform, making it attractive for deployment as a cloud-like use case. Even for a smaller amount of data, the possibility to perform offline analysis remotely is very attractive, in particular if mail-in and remote controlled modes are to be developed at Synchrotron facilities.

# data[].attributes.workflow-content.nodes[].data.description
Raw data as saved by the facility. File format: a series of NeXus files (HDF5), sharing the same root_name. Each file contains the Eiger4M images and Sample and Beamline metadata of a sub-region of the total mapping region. Altogether, the list of files describes the whole mapped region.

# data[].attributes.workflow-content.nodes[].data.html-description
Raw data as saved by the facility. File format: a series of NeXus files (HDF5), sharing the same root_name. Each file contains the Eiger4M images and Sample and Beamline metadata of a sub-region of the total mapping region. Altogether, the list of files describes the whole mapped region.

# data[].attributes.workflow-content.nodes[].data.description
Using Foxtrot application, each of the raw file making the entire map is treated by an editable specific macro, which converts the stacks of raw images into stacks of physically meaningful parameters, according to the specific analysis that has to be performed. For example, there is a macro which fits the radially grouped scattering pattern with a Gaussian function and delivers the q position, Delta-q extension and maximum intensity of the peak. For each of the resulting parameters, the corresponding sub-maps are computed onto a normalized grid, taking into account the actual reported positions of the two scanning motors. All the sub-maps are then exported to an output NeXus file (<0.2GB).

# data[].attributes.workflow-content.nodes[].data.html-description
Using Foxtrot application, each of the raw file making the entire map is treated by an editable specific macro, which converts the stacks of raw images into stacks of physically meaningful parameters, according to the specific analysis that has to be performed. For example, there is a macro which fits the radially grouped scattering pattern with a Gaussian function and delivers the q position, Delta-q extension and maximum intensity of the peak. For each of the resulting parameters, the corresponding sub-maps are computed onto a normalized grid, taking into account the actual reported positions of the two scanning motors. All the sub-maps are then exported to an output NeXus file (<0.2GB).

# data[].attributes.workflow-content.nodes[].data.description
Contains metadata related to the data reduction steps (essentially a reminder of the macro parameters). A python script then extracts the individual sub-maps as ASCII files.

# data[].attributes.workflow-content.nodes[].data.html-description
Contains metadata related to the data reduction steps (essentially a reminder of the macro parameters). A python script then extracts the individual sub-maps as ASCII files.

# data[].attributes.workflow-content.nodes[].data.description
A second python script concatenates the sub-maps into a final Image Ascii file, one per parameter.

# data[].attributes.workflow-content.nodes[].data.html-description
A second python script concatenates the sub-maps into a final Image Ascii file, one per parameter.

# data[].attributes.workflow-content.nodes[].data.description
Can be readily imported by, e.g., ImageJ.

# data[].attributes.workflow-content.nodes[].data.html-description
Can be readily imported by, e.g., ImageJ.

# data[].attributes.description
This training provides an introduction for the acceleration of intense ion pulses on a few micrometers to 10 MeV energies is possible when light pulses from a short-pulse laser are focused on thin solid-state targets. In the interaction, the laser energy is primarily transferred to relativistic electrons whose collective fields result in the acceleration of the ions.

During the experiment camera images were taken from various positions. All camera images are taken at the same timestep, labeled with an associated counter position and stored in related directories. The visualization tool **laserVizTool** shows the selected camera positions for the same counter value. The use a filters on the images is also possible. For the demonstration of the tool a dataset is provided. 

# data[].attributes.workflow-content.nodes[].data.description
The acceleration of intense ion pulses on a few micrometers to 10 MeV energies is possible when light pulses from a short-pulse laser are focused on thin solid-state targets. In the interaction, the laser energy is primarily transferred to relativistic electrons whose collective fields result in the acceleration of the ions. The work on the laser ion acceleration at the HZDR is designed to diagnose and understand the time scales of only a few 10 femtosecond processes, to explore new regimes and, ultimately, to improve control of the beam parameters so that these unrivaled compact accelerators for applications.

The first step to control laser-plasma driven ion beams is a deep understanding of the interaction of the laser with the plasma. In our experiments laser pulses of the Draco Laser are focused onto targets and hence relativistic intensities of up to 1021 W/cm2 are reached which correspond to possible acceleration fields in the range of MV/µm. In order to gain profound insights of the processes inside the hot plasmas we use different additional probe pulses which are synchronized to the high intensity main laser pulse. Depending on whether surface or volume effects are analyzed, the probe pulses are in the optical wave length range or in X-rays are used. However, the complex dynamic of the laser plasma interaction can often only be understood using extensive large scale computer simulation. Together with the computational radiation physiks group we investigate the influence of the different experimental parameters, try to identify new acceleration mechanisms in order to be able to even predict experimental results in the future.

In the step we provide:
* Basic information
* Publication on beam profiles

# data[].attributes.workflow-content.nodes[].data.html-description
The acceleration of intense ion pulses on a few micrometers to 10 MeV energies is possible when light pulses from a short-pulse laser are focused on thin solid-state targets. In the interaction, the laser energy is primarily transferred to relativistic electrons whose collective fields result in the acceleration of the ions. The work on the laser ion acceleration at the HZDR is designed to diagnose and understand the time scales of only a few 10 femtosecond processes, to explore new regimes and, ultimately, to improve control of the beam parameters so that these unrivaled compact accelerators for applications.

The first step to control laser-plasma driven ion beams is a deep understanding of the interaction of the laser with the plasma. In our experiments laser pulses of the Draco Laser are focused onto targets and hence relativistic intensities of up to 1021 W/cm2 are reached which correspond to possible acceleration fields in the range of MV/µm. In order to gain profound insights of the processes inside the hot plasmas we use different additional probe pulses which are synchronized to the high intensity main laser pulse. Depending on whether surface or volume effects are analyzed, the probe pulses are in the optical wave length range or in X-rays are used. However, the complex dynamic of the laser plasma interaction can often only be understood using extensive large scale computer simulation. Together with the computational radiation physiks group we investigate the influence of the different experimental parameters, try to identify new acceleration mechanisms in order to be able to even predict experimental results in the future.

In the step we provide:

Basic information

Publication on beam profiles

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
All-optical structuring of laser-driven proton beam profiles

# data[].attributes.workflow-content.nodes[].data.description
This dataset contains the experimental raw data, the analyzed data and corresponding scripts as well as figures for the "Proton beam quality enhancement by spectral phase control of a PW-class laser system" publication.

Still not a usable dataset, but in the future you can download a suitable dataset, unzip it and investigate the contents.

1. Download dataset
2. Unzip dataset
3. Evaluate images and associated metadata

# data[].attributes.workflow-content.nodes[].data.html-description
This dataset contains the experimental raw data, the analyzed data and corresponding scripts as well as figures for the "Proton beam quality enhancement by spectral phase control of a PW-class laser system" publication.

Still not a usable dataset, but in the future you can download a suitable dataset, unzip it and investigate the contents.

Download dataset

Unzip dataset

Evaluate images and associated metadata

# data[].attributes.workflow-content.nodes[].data.description
An HZDR-internal GitLab Project at the moment, for access contact o.knodel@hzdr.de. When access is granted, you can checkout the project with ` git clone git@gitlab.hzdr.de:fwcc/data-management/user-project-documentation/laserviztool.git`.

# LaserViz QT-based GUI

## Install

Qt5 System libraries (on CentOS):

``` qt5-qtbase-devel.x86_64 0:5.9.2-3.el7```

Simple install with pipenv:

```pipenv install -e .```

(you can point pipenv to a specific version of python by doing `pipenv --python 3.7.3 install -e .`)

and start the app with:

```pipenv run python laserVisualization/vizGUI.py```

## Manual

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. 

Command line execution with predefined layout:

```python laserVisualization/vizGUI.py --config config.json```

Command line parameters:

```
➜ python laserVisualization/vizGUI.py -h                                                  
usage: cl_vizGUI.py <command> [<args>]

                    Laser vizGUI commands for initial configuration   

optional arguments:
  -h, --help       show this help message and exit
  --config CONFIG  location of the config.json (default: config.json)
```

The GUI requires a config file as provided [here](https://gitlab.hzdr.de/o.knodel/laserviztool/blob/master/config.json):

```json
{
  "datestring": "2019_03_28",
  "extensionpattern": "original.png",
  "columns": 4,
  "basedir": "/Users/knodel/Desktop/laserdata/",
  "openbis": false,
  "user": "knodel39",
  "space": "FWKT_ION",
  "project": "2019",
  "type": "DEFAULT_EXPERIMENT",
  "sample_type": "EXPERIMENTAL_STEP_FWKT_ION",
  "dataset_type": "FWKT_ION_SHOT_IMAGE",
  "cameras": [
    ["AKO_Bulli", 1],
    ["FF_after_DD", 0],
    ["TransmittedLight_2w", 0],
    ["Keramik_Bulli", 0],
    ["ReflectedLight_1w", 0],
    ["ReflectedLight_2w", 0],
    ["TPS_0deg", 0],
    ["Szintillator_45_deg", 0],
    ["tiefendosis", 1],
    ["Lanex", 0]
  ]
}
```

# data[].attributes.workflow-content.nodes[].data.html-description
An HZDR-internal GitLab Project at the moment, for access contact o.knodel@hzdr.de. When access is granted, you can checkout the project with git clone git@gitlab.hzdr.de:fwcc/data-management/user-project-documentation/laserviztool.git.

LaserViz QT-based GUI

Install

Qt5 System libraries (on CentOS):

qt5-qtbase-devel.x86_64 0:5.9.2-3.el7

Simple install with pipenv:

pipenv install -e .

(you can point pipenv to a specific version of python by doing pipenv --python 3.7.3 install -e .)

and start the app with:

pipenv run python laserVisualization/vizGUI.py

Manual

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.

Command line execution with predefined layout:

python laserVisualization/vizGUI.py --config config.json

Command line parameters:

➜ python laserVisualization/vizGUI.py -h
usage: cl_vizGUI.py <command> [<args>]

                    Laser vizGUI commands for initial configuration

optional arguments:
  -h, --help       show this help message and exit
  --config CONFIG  location of the config.json (default: config.json)

The GUI requires a config file as provided here:

{
  "datestring": "2019_03_28",
  "extensionpattern": "original.png",
  "columns": 4,
  "basedir": "/Users/knodel/Desktop/laserdata/",
  "openbis": false,
  "user": "knodel39",
  "space": "FWKT_ION",
  "project": "2019",
  "type": "DEFAULT_EXPERIMENT",
  "sample_type": "EXPERIMENTAL_STEP_FWKT_ION",
  "dataset_type": "FWKT_ION_SHOT_IMAGE",
  "cameras": [
    ["AKO_Bulli", 1],
    ["FF_after_DD", 0],
    ["TransmittedLight_2w", 0],
    ["Keramik_Bulli", 0],
    ["ReflectedLight_1w", 0],
    ["ReflectedLight_2w", 0],
    ["TPS_0deg", 0],
    ["Szintillator_45_deg", 0],
    ["tiefendosis", 1],
    ["Lanex", 0]
  ]
}

# data[].attributes.title
Learning path: Metadata catalogue services including PaN ontologies

# data[].attributes.description
Three different workshops were held linking metadata catalogues, OAI-PMH, the PaNET ontology, the pan ontologies API.

# data[].attributes.workflow-content.nodes[].data.name
Workshop: Hands on session on the PaN ontologies API (Feb 17)

# data[].attributes.workflow-content.nodes[].data.description
In this hands on session, the pan-ontologies-api service was presented.

# data[].attributes.workflow-content.nodes[].data.html-description
In this hands on session, the pan-ontologies-api service was presented.

# data[].attributes.workflow-content.nodes[].data.name
Workshop on Metadata Catalogues (Apr 22)

# data[].attributes.workflow-content.nodes[].data.description
Presentations on ICAT, SciCAT and Rodare.

# data[].attributes.workflow-content.nodes[].data.html-description
Presentations on ICAT, SciCAT and Rodare.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
D3.3: Demonstrate ICAT and SciCat released with APIs compatible with ExPaNDS federated EOSC services

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Workshop on Metadata Catalogues (Slides)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Workshop on Metadata Catalogues (Recording)

# data[].attributes.workflow-content.nodes[].data.name
Workshop: Delivering data services to EOSC (Apr 21)

# data[].attributes.workflow-content.nodes[].data.description
### Ontology
The Photon and Neutron Experimental Technique (PaNET) ontology provides a taxonomy of experimental techniques relevant for the Photon and Neutron (PaN) community. This ontology is being developed as part of the Work Package 3 on EOSC Data Catalogue Services for PaN national Research Infrastructures of the ExPaNDS project.

The ontology allows to add semantic tags to datasets, and other related research objects, to indicate the experimental technique(s) that was (or were) used to create them.

### OAI-PMH
The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) is a low barrier mechanism for repository interoperability.

1. OAI-PMH: is a set of six verbs or services that are invoked within HTTP
2. Data Providers: are repositories that expose structured metadata via OAI-PMH. A network accessible server that can process the six OAI-PMH requests. (our facilities which implemented the OAI-PMH)
3. Service Providers: then make OAI-PMH service requests to harvest that metadata. (DataCite, B2FIND, OpenAIRE…)

It enables to expose the metadata you have in your data catalogue in a standard way, and represents an easy way to integrate in B2FIND and OpenAIRE. It uses a predefined set of urls that can be invoked. Link to the OAI-PMH protocol specification.

# data[].attributes.workflow-content.nodes[].data.html-description
Ontology

The Photon and Neutron Experimental Technique (PaNET) ontology provides a taxonomy of experimental techniques relevant for the Photon and Neutron (PaN) community. This ontology is being developed as part of the Work Package 3 on EOSC Data Catalogue Services for PaN national Research Infrastructures of the ExPaNDS project.

The ontology allows to add semantic tags to datasets, and other related research objects, to indicate the experimental technique(s) that was (or were) used to create them.

OAI-PMH

The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) is a low barrier mechanism for repository interoperability.

OAI-PMH: is a set of six verbs or services that are invoked within HTTP

Data Providers: are repositories that expose structured metadata via OAI-PMH. A network accessible server that can process the six OAI-PMH requests. (our facilities which implemented the OAI-PMH)

Service Providers: then make OAI-PMH service requests to harvest that metadata. (DataCite, B2FIND, OpenAIRE…)

It enables to expose the metadata you have in your data catalogue in a standard way, and represents an easy way to integrate in B2FIND and OpenAIRE. It uses a predefined set of urls that can be invoked. Link to the OAI-PMH protocol specification.

# data[].attributes.workflow-content.nodes[].data.description
Service Providers: make OAI-PMH service requests to harvest that metadata.

B2FIND, OpenAIRE, Datacite ... are metadata harvesters.

# data[].attributes.workflow-content.nodes[].data.html-description
Service Providers: make OAI-PMH service requests to harvest that metadata.

B2FIND, OpenAIRE, Datacite ... are metadata harvesters.

# data[].attributes.workflow-content.nodes[].data.description
ICAT and SciCat are metadata catalogues.

# data[].attributes.workflow-content.nodes[].data.html-description
ICAT and SciCat are metadata catalogues.

# data[].attributes.workflow-content.nodes[].data.description
Data Providers: are repositories that expose structured metadata via OAI-PMH. A network accessible server that can process the six OAI-PMH requests. (our facilities which implemented the OAI-PMH)

# data[].attributes.workflow-content.nodes[].data.html-description
Data Providers: are repositories that expose structured metadata via OAI-PMH. A network accessible server that can process the six OAI-PMH requests. (our facilities which implemented the OAI-PMH)

# data[].attributes.workflow-content.nodes[].data.description
### Step by step deployment of the SciCat OAI-PMH

1. Clone the SciCat OAI-PMH repository `git clone https://github.com/SciCatProject/oai-provider-service.git`.

2. Customise the mongo connection parameters here (see "mongo connection parameters " link below) to point to the mongoDB instance to use.

3. Add the key value pair: `oaiProviderRoute: '$url:$port/Scicat/oai/Publication'` to the module.exports map in the config.local.js file of your catamel service deployment, using the url and port from (2).

4. Run `npm install+node` or build the Dockerfile image and run it.

5. Restart the catamel service.

# data[].attributes.workflow-content.nodes[].data.html-description
Step by step deployment of the SciCat OAI-PMH

Clone the SciCat OAI-PMH repository git clone https://github.com/SciCatProject/oai-provider-service.git.

Customise the mongo connection parameters here (see "mongo connection parameters " link below) to point to the mongoDB instance to use.

Add the key value pair: oaiProviderRoute: '$url:$port/Scicat/oai/Publication' to the module.exports map in the config.local.js file of your catamel service deployment, using the url and port from (2).

Run npm install+node or build the Dockerfile image and run it.

Restart the catamel service.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
module.exports map in the config.local.js file

# data[].attributes.workflow-content.nodes[].data.description
### The ICAT component for OAI-PMH

The latest release version of the OAI-PMH component `icat.oaipmh` can be found here (see "OAI-PMH component" link below), with detailed instructions to install and download the component.

It is designed as a new separate component and connects to the `icat.server` as a normal client via the standard API. 3 steps for the flexible mapping:

1. In a configuration file, select the entity classes that shall be exposed, along with relevant attributes and related objects
2. Out of that, `icat.oaipmh` generates an internal XML representation of this data using a one-to-one mapping from the ICAT schema
3. Use XSLT (extended schema language translation) provided as a separate configuration file to generate the output from that internal XML representation, transforming it into whatever we expose, mapping to any metadata standard you want

Example XSLT files for the most relevant cases (mapping generic ICAT investigation onto Dublin Core and DataCite e.g.) are provided as a starting point to write facility specific customised versions. Which means that ICAT-using facilities would only need to tweak the example XSLT files according to how they represent their data and would already be set, at least for Dublin Core and DataCite schemas.

A practical example of a run.properties configuration file:
```
# The metadata formats to be supported
metadataPrefixes   = oai_dc oai_datacite

oai_dc.xslt        = /path/to/oai_dc.xslt
oai_datacite.xslt  = /path/to/oai_datacite.xslt

# Identifiers for metadata configuration
data.configurations          = inv stud
data.inv.metadataPrefixes    = oai_dc oai_datacite
data.stud.metadataPrefixes   = oai_dc

# Relevant data objects and properties
data.inv.mainObject = Investigation
data.inv.stringProperties = summary doi title
data.inv.subPropertyLists = investigationUsers
data.inv.investigationUsers.stringProperties = role
data.inv.investigationUsers.subPropertyLists = user
data.inv.investigationUsers.user.stringProperties = fullName givenName familyName orcidId affiliation
```

From this list, the internal XML representation will be created (step 2 above) and the right XSLT file will be used to generate the external XML file for that specific harvester (step 3 above).

# data[].attributes.workflow-content.nodes[].data.html-description
The ICAT component for OAI-PMH

The latest release version of the OAI-PMH component icat.oaipmh can be found here (see "OAI-PMH component" link below), with detailed instructions to install and download the component.

It is designed as a new separate component and connects to the icat.server as a normal client via the standard API. 3 steps for the flexible mapping:

In a configuration file, select the entity classes that shall be exposed, along with relevant attributes and related objects

Out of that, icat.oaipmh generates an internal XML representation of this data using a one-to-one mapping from the ICAT schema

Use XSLT (extended schema language translation) provided as a separate configuration file to generate the output from that internal XML representation, transforming it into whatever we expose, mapping to any metadata standard you want

Example XSLT files for the most relevant cases (mapping generic ICAT investigation onto Dublin Core and DataCite e.g.) are provided as a starting point to write facility specific customised versions. Which means that ICAT-using facilities would only need to tweak the example XSLT files according to how they represent their data and would already be set, at least for Dublin Core and DataCite schemas.

A practical example of a run.properties configuration file:

# The metadata formats to be supported
metadataPrefixes   = oai_dc oai_datacite

oai_dc.xslt        = /path/to/oai_dc.xslt
oai_datacite.xslt  = /path/to/oai_datacite.xslt

# Identifiers for metadata configuration
data.configurations          = inv stud
data.inv.metadataPrefixes    = oai_dc oai_datacite
data.stud.metadataPrefixes   = oai_dc

# Relevant data objects and properties
data.inv.mainObject = Investigation
data.inv.stringProperties = summary doi title
data.inv.subPropertyLists = investigationUsers
data.inv.investigationUsers.stringProperties = role
data.inv.investigationUsers.subPropertyLists = user
data.inv.investigationUsers.user.stringProperties = fullName givenName familyName orcidId affiliation

From this list, the internal XML representation will be created (step 2 above) and the right XSLT file will be used to generate the external XML file for that specific harvester (step 3 above).

# data[].attributes.workflow-content.nodes[].data.description
B2FIND is an interdisciplinary discovery tool for research data, running since 2014.

It harvests communities, which in our case is a facility data catalogue, preferably via OAI-PMH because it offers good functionalities for harvesting but they also support REST APIs.

Since recently, all metadata in B2FIND is now also automatically in OpenAIRE.

# data[].attributes.workflow-content.nodes[].data.html-description
B2FIND is an interdisciplinary discovery tool for research data, running since 2014.

It harvests communities, which in our case is a facility data catalogue, preferably via OAI-PMH because it offers good functionalities for harvesting but they also support REST APIs.

Since recently, all metadata in B2FIND is now also automatically in OpenAIRE.

# data[].attributes.workflow-content.nodes[].data.description
OpenAIRE aggregates published metadata from all over the world, from more than 12,000 data sources, mainly on OAI-PMH (see link below). There is "direct" harvesting from the data source itself or "indirect" harvest from aggregators like Microsoft Academic Graph, DataCite (without selection), CrossRef, also from aggregators like La Referencia and national aggregators. All this metadata is processed to produce the OpenAIRE research graph.

# data[].attributes.workflow-content.nodes[].data.html-description
OpenAIRE aggregates published metadata from all over the world, from more than 12,000 data sources, mainly on OAI-PMH (see link below). There is "direct" harvesting from the data source itself or "indirect" harvest from aggregators like Microsoft Academic Graph, DataCite (without selection), CrossRef, also from aggregators like La Referencia and national aggregators. All this metadata is processed to produce the OpenAIRE research graph.

# data[].attributes.workflow-content.nodes[].data.description
The Photon and Neutron Experimental Technique (PaNET) ontology provides a taxonomy of experimental techniques relevant for the Photon and Neutron (PaN) community. This ontology is being developed as part of the Work Package 3 on EOSC Data Catalogue Services for PaN national Research Infrastructures of the ExPaNDS project.

The ontology allows to add semantic tags to datasets, and other related research objects, to indicate the experimental technique(s) that was (or were) used to create them.

# data[].attributes.workflow-content.nodes[].data.html-description
The Photon and Neutron Experimental Technique (PaNET) ontology provides a taxonomy of experimental techniques relevant for the Photon and Neutron (PaN) community. This ontology is being developed as part of the Work Package 3 on EOSC Data Catalogue Services for PaN national Research Infrastructures of the ExPaNDS project.

The ontology allows to add semantic tags to datasets, and other related research objects, to indicate the experimental technique(s) that was (or were) used to create them.

# data[].attributes.workflow-content.nodes[].data.description
The application provides a data catalogue independent implementation retrieval and processing of ontologies from an external source. Currently, only the PaNET ontology is implemented.

# data[].attributes.workflow-content.nodes[].data.html-description
The application provides a data catalogue independent implementation retrieval and processing of ontologies from an external source. Currently, only the PaNET ontology is implemented.

# data[].attributes.workflow-content.nodes[].data.description
Explorer and visualizer of the PaNET ontology.

# data[].attributes.workflow-content.nodes[].data.html-description
Explorer and visualizer of the PaNET ontology.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
PaNET ontology on BioPortal (visualisation)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
D3.3: Demonstrate ICAT and SciCat released with APIs compatible with ExPaNDS federated EOSC services

# data[].attributes.workflow-content.nodes[].data.description
Presentations on ICAT, SciCAT and Rodare.

# data[].attributes.workflow-content.nodes[].data.html-description
Presentations on ICAT, SciCAT and Rodare.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Workshop on Metadata Catalogues (Slides)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Workshop on Metadata Catalogues (Recording)

# data[].attributes.workflow-content.nodes[].data.description
The PaN search API provides a uniform way via a web portal or computer program to interrogate all data catalogues with specific searches in order to find data of interest to an individual

# data[].attributes.workflow-content.nodes[].data.html-description
The PaN search API provides a uniform way via a web portal or computer program to interrogate all data catalogues with specific searches in order to find data of interest to an individual

# data[].attributes.description
Ptychography and ptychographic X-ray computed tomography (PXCT) are synchrotron-based imaging techniques that are taking advantage of the increased coherence of the new generation diffraction-limited storage rings. PXCT allows reconstruction of the sample electron-density in space and imaging 3D samples with resolution down to nanometers.

NanoMAX and SoftiMAX beamlines at the MAX IV Laboratory provide cutting edge coherent nano-beams very suitable for PXCT experiments in hard and soft X-ray regimes. PXCT experiments produce large data volumes and are demanding on computing resources.

To find more ressources on Tomography please check out the data portal

# data[].attributes.workflow-content.nodes[].data.description
#### Ptychography

Ptychography and ptychographic X-ray computed tomography (PXCT) are synchrotron-based imaging techniques that are taking advantage of the increased coherence of the new generation diffraction-limited storage rings. PXCT allows reconstruction of the sample electron-density in space and imaging 3D samples with resolution down to nanometers. 

![electron-density of an artificial nickel-based inverse opal material](https://journals.iucr.org/j/issues/2020/06/00/dy5004/dy5004fig1mag.jpg)

Figure : A three-dimensional reconstructed electron-density of an artificial nickel-based inverse opal material. Image, experiment and data processing by M. Kahnt et al., DOI: 10.1107/S160057672001211X

NanoMAX and SoftiMAX beamlines at the MAX IV Laboratory provide cutting edge coherent nano-beams very suitable for PXCT experiments in hard and soft X-ray regimes. PXCT experiments produce large data volumes and are demanding on computing resources.

To find more ressources on Tomography please check out the open data portal. 

# data[].attributes.workflow-content.nodes[].data.html-description
Ptychography

Ptychography and ptychographic X-ray computed tomography (PXCT) are synchrotron-based imaging techniques that are taking advantage of the increased coherence of the new generation diffraction-limited storage rings. PXCT allows reconstruction of the sample electron-density in space and imaging 3D samples with resolution down to nanometers.

Figure : A three-dimensional reconstructed electron-density of an artificial nickel-based inverse opal material. Image, experiment and data processing by M. Kahnt et al., DOI: 10.1107/S160057672001211X

NanoMAX and SoftiMAX beamlines at the MAX IV Laboratory provide cutting edge coherent nano-beams very suitable for PXCT experiments in hard and soft X-ray regimes. PXCT experiments produce large data volumes and are demanding on computing resources.

To find more ressources on Tomography please check out the open data portal.

# data[].attributes.workflow-content.nodes[].data.description
A single spatial point of the PXCT measurement is a 2D scattering image recorded by an X-ray detector. The sample is 2D scanned with an X-ray nano-probe. Such a set of 2D spatial points is forming a single projection measurement. In the next step, the sample is rotated and 2D scanned again. Similar to conventional tomography, the full dataset consists of several hundreds of angular projections.
The 3D electron density in the sample is then reconstructed in the following three steps.
A 2D ptychographic-like reconstruction is obtained from each projection sub-dataset. In the next step, the resulting projections are aligned and, in the final step, a tomography-like reconstruction is applied to the aligned projections.

# data[].attributes.workflow-content.nodes[].data.html-description
A single spatial point of the PXCT measurement is a 2D scattering image recorded by an X-ray detector. The sample is 2D scanned with an X-ray nano-probe. Such a set of 2D spatial points is forming a single projection measurement. In the next step, the sample is rotated and 2D scanned again. Similar to conventional tomography, the full dataset consists of several hundreds of angular projections. The 3D electron density in the sample is then reconstructed in the following three steps. A 2D ptychographic-like reconstruction is obtained from each projection sub-dataset. In the next step, the resulting projections are aligned and, in the final step, a tomography-like reconstruction is applied to the aligned projections.

# data[].attributes.workflow-content.nodes[].data.description
A ptypy software [6] is used.

The ptypy software is published under the GNU General Public Licence, version 2.12.
However, there is a company (Phase Focus Ltd, UK) that holds a portfolio of patents related to ptychography. Phase Focus claims that, by using the ptypy software, a scientist may be using one of their patented inventions, for which the scientist would require a license. The company offers a free license for non-commercial, academic research.

Such situations are solved at the HPC centres and synchrotron facilities in two ways:

● by allowing the scientist to use the software only after they agreed to follow the Code of Conduct and the specific licence agreements from the software authors and vendors  
● by providing an environment in which the scientist may upload the required software. In this way, the user takes full legal responsibility for the software they use.

Given the availability of a free license for academic use, we consider this procedure is only a minor inconvenience, particularly as the issue of whether patents apply to software is not completely resolved within the EU.

# data[].attributes.workflow-content.nodes[].data.html-description
A ptypy software [6] is used.

The ptypy software is published under the GNU General Public Licence, version 2.12. However, there is a company (Phase Focus Ltd, UK) that holds a portfolio of patents related to ptychography. Phase Focus claims that, by using the ptypy software, a scientist may be using one of their patented inventions, for which the scientist would require a license. The company offers a free license for non-commercial, academic research.

Such situations are solved at the HPC centres and synchrotron facilities in two ways:

● by allowing the scientist to use the software only after they agreed to follow the Code of Conduct and the specific licence agreements from the software authors and vendors
● by providing an environment in which the scientist may upload the required software. In this way, the user takes full legal responsibility for the software they use.

Given the availability of a free license for academic use, we consider this procedure is only a minor inconvenience, particularly as the issue of whether patents apply to software is not completely resolved within the EU.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
A computational framework for ptychographic reconstructions

# data[].attributes.workflow-content.nodes[].data.description
The researcher has to provide (from a template) a reconstruction script describing the procedure and relevant parameters for the first ptychographic reconstruction step.

# data[].attributes.workflow-content.nodes[].data.html-description
The researcher has to provide (from a template) a reconstruction script describing the procedure and relevant parameters for the first ptychographic reconstruction step.

# data[].attributes.workflow-content.nodes[].data.description
The reconstruction script for a series of projection datasets is submitted to the HPC cluster using Slurm.

This results in text logs, reconstructed data in the HDF5 format, and standardised images illustrating the quality of the reconstruction procedure and other relevant information.

# data[].attributes.workflow-content.nodes[].data.html-description
The reconstruction script for a series of projection datasets is submitted to the HPC cluster using Slurm.

This results in text logs, reconstructed data in the HDF5 format, and standardised images illustrating the quality of the reconstruction procedure and other relevant information.

# data[].attributes.workflow-content.nodes[].data.description
The recorded detector data and the relevant experimental metadata are stored in shared
network storage and available for processing on an HPC cluster.

# data[].attributes.workflow-content.nodes[].data.html-description
The recorded detector data and the relevant experimental metadata are stored in shared network storage and available for processing on an HPC cluster.

# data[].attributes.workflow-content.nodes[].data.description
Ptychographic X-ray computed tomography is a quantitative three-dimensional imaging technique offered to users of multiple synchrotron radiation sources. Its dependence on the coherent fraction of the available X-ray beam makes it perfectly suited to diffraction-limited storage rings. Although MAX IV is the first, and so far only, operating fourth-generation synchrotron light source, none of its experimental stations is currently set up to offer this technique to its users. The first ptychographic X-ray computed tomography experiment has therefore been performed on the NanoMAX beamline. From the results, information was gained about the current limitations of the experimental setup and where attention should be focused for improvement. The extracted parameters in terms of scanning speed, size of the imaged volume and achieved resolutions should provide a baseline for future users designing nano-tomography experiments on the NanoMAX beamline.

# data[].attributes.workflow-content.nodes[].data.html-description
Ptychographic X-ray computed tomography is a quantitative three-dimensional imaging technique offered to users of multiple synchrotron radiation sources. Its dependence on the coherent fraction of the available X-ray beam makes it perfectly suited to diffraction-limited storage rings. Although MAX IV is the first, and so far only, operating fourth-generation synchrotron light source, none of its experimental stations is currently set up to offer this technique to its users. The first ptychographic X-ray computed tomography experiment has therefore been performed on the NanoMAX beamline. From the results, information was gained about the current limitations of the experimental setup and where attention should be focused for improvement. The extracted parameters in terms of scanning speed, size of the imaged volume and achieved resolutions should provide a baseline for future users designing nano-tomography experiments on the NanoMAX beamline.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
First ptychographic X-ray computed tomography experiment on the NanoMAX beamline

# data[].attributes.workflow-content.nodes[].data.description
Raw data used in "First ptychographic X-ray computed tomography experiment at the NanoMAX beamline" by M. Kahnt, S. Sala, U. Johansson, Z. Jiang, S. Kalbfleisch, F. Lenrick, J. H. Pikul and K. Thånell, submitted to the journal of applied crystallography.

# data[].attributes.workflow-content.nodes[].data.html-description
Raw data used in "First ptychographic X-ray computed tomography experiment at the NanoMAX beamline" by M. Kahnt, S. Sala, U. Johansson, Z. Jiang, S. Kalbfleisch, F. Lenrick, J. H. Pikul and K. Thånell, submitted to the journal of applied crystallography.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Raw data for "First ptychographic X-ray computed tomography experiment at the NanoMAX beamline"

# data[].attributes.description
This training workflow shows the combination of the demonstrations from the ExPaNDS mid-term review. 

The workflow consist of three steps:

* Basic material to introduce the problem,
* The associated dataset with the related scientific publication and
* Source code and reference to the Jupyter notebooks.

# data[].attributes.workflow-content.nodes[].data.name
Dataset for Synchrotron Imaging Experiment

# data[].attributes.workflow-content.nodes[].data.description
Synchrotron Imaging of Complex Vascular Lesions in Human Pulmonary Hypertension: Pathology Distribution in 3D Space

# data[].attributes.workflow-content.nodes[].data.html-description
Synchrotron Imaging of Complex Vascular Lesions in Human Pulmonary Hypertension: Pathology Distribution in 3D Space

# data[].attributes.workflow-content.nodes[].data.description
Reference notebooks for tomography reconstructions, image quantification and phase retrieval methods.

# tomography-notebooks

Reference notebooks for tomography reconstructions, image quantification and phase retrieval.

```
# setup local repo (once)
cd /mxn/home/$USER/jupyter_notebooks # (optional, do this when you want to use it in jupyterhub.maxiv.lu.se)
git clone https://github.com/tomograms/tomography-notebooks.git

# update from gitlab (from time to time when there is something new on the server)
git pull

# edit your notebooks
# ....
# ....

# add recent changes in the notebook(s) or add a new notebook
git add my-updated-notebook1.ipynb
git add my-new-notebook2.ipynb

# commit the changes
git commit -m "Added new method in reconstructions methods."

# synchronise changes to server
#   you need to setup proper ssh-keys
#   or you will be prompted for a password with https 
git push

```
Note: You can do all above directly in JupyterHub terminal. Left panel-> plus icon -> lunch terminal

# data[].attributes.workflow-content.nodes[].data.html-description
Reference notebooks for tomography reconstructions, image quantification and phase retrieval methods.

tomography-notebooks

Reference notebooks for tomography reconstructions, image quantification and phase retrieval.

# setup local repo (once)
cd /mxn/home/$USER/jupyter_notebooks # (optional, do this when you want to use it in jupyterhub.maxiv.lu.se)
git clone https://github.com/tomograms/tomography-notebooks.git

# update from gitlab (from time to time when there is something new on the server)
git pull

# edit your notebooks
# ....
# ....

# add recent changes in the notebook(s) or add a new notebook
git add my-updated-notebook1.ipynb
git add my-new-notebook2.ipynb

# commit the changes
git commit -m "Added new method in reconstructions methods."

# synchronise changes to server
#   you need to setup proper ssh-keys
#   or you will be prompted for a password with https
git push

Note: You can do all above directly in JupyterHub terminal. Left panel-> plus icon -> lunch terminal

# data[].attributes.workflow-content.nodes[].data.description
The aim of this proposal is to image the complex vascular lesions seen in human pulmonary hypertension (PH). High resolution 3D image data can by itself, or when combined with subsequent sectioning and immunohistochemical analyses of the same specimen, create a detailed map of the distribution of pathology in 3D space. This project has the potential to advance our understanding of PH pathophysiology and increase our chances of finding relevant targets for drug development.

# data[].attributes.workflow-content.nodes[].data.html-description
The aim of this proposal is to image the complex vascular lesions seen in human pulmonary hypertension (PH). High resolution 3D image data can by itself, or when combined with subsequent sectioning and immunohistochemical analyses of the same specimen, create a detailed map of the distribution of pathology in 3D space. This project has the potential to advance our understanding of PH pathophysiology and increase our chances of finding relevant targets for drug development.

# data[].attributes.workflow-content.nodes[].data.html-description
Will be added in 2022

# data[].attributes.description
This use case employs the data reduction software Mantid ,the most used data reduction software at ISIS and adopted by a number of other facilities. Furthermore, it demonstrates a typical workflow to turn raw data into reduced data, which can then be used by subsequent analysis.

The main purpose of this use case is to highlight a bread-and-butter analysis workflow for analysing neutron data using the Mantid software. At a high level this workflow is similar for analysing data from a whole range of other neutron experimental techniques and is the most common analysis workflow at the ISIS Neutron and Muon Source.

# data[].attributes.workflow-content.nodes[].data.description
This use case employs the data reduction software Mantid ,the most used data reduction software at ISIS and adopted by a number of other facilities. Furthermore, it demonstrates a typical workflow to turn raw data into reduced data, which can then be used by subsequent analysis.

The main purpose of this use case is to highlight a bread-and-butter analysis workflow for analysing neutron data using the Mantid software. At a high level this workflow is similar for analysing data from a whole range of other neutron experimental techniques and is the most common analysis workflow at the ISIS Neutron and Muon Source.

This data set was part of an investigation into creating a new protein standard for protein small angle scattering (SAS), which is published in Biomedical Spectroscopy and Imaging 6 (2017) 123–134 (https://doi.org/10.3233/BSI-170167). 

#### SANS
  
Small-Angle Neutron Scattering, SANS is a special form of elastic neutron scattering, providing information on the size and shape of particles on length scales from ~1 nm to ~1000 nm. The probe, at which the neutron beam is directed, can be an aqueous solution, a solid, a powder or a crystal. For more information, please find a detailed description and discussion in our PaN teaching portal.

# data[].attributes.workflow-content.nodes[].data.html-description
This use case employs the data reduction software Mantid ,the most used data reduction software at ISIS and adopted by a number of other facilities. Furthermore, it demonstrates a typical workflow to turn raw data into reduced data, which can then be used by subsequent analysis.

The main purpose of this use case is to highlight a bread-and-butter analysis workflow for analysing neutron data using the Mantid software. At a high level this workflow is similar for analysing data from a whole range of other neutron experimental techniques and is the most common analysis workflow at the ISIS Neutron and Muon Source.

This data set was part of an investigation into creating a new protein standard for protein small angle scattering (SAS), which is published in Biomedical Spectroscopy and Imaging 6 (2017) 123–134 (https://doi.org/10.3233/BSI-170167).

SANS

Small-Angle Neutron Scattering, SANS is a special form of elastic neutron scattering, providing information on the size and shape of particles on length scales from ~1 nm to ~1000 nm. The probe, at which the neutron beam is directed, can be an aqueous solution, a solid, a powder or a crystal. For more information, please find a detailed description and discussion in our PaN teaching portal.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Biomedical Spectroscopy and Imaging 6 (2017) 123–134

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
European Photon and Neutron Open Data Search Portal (SANS elastic search results)

# data[].attributes.workflow-content.nodes[].data.description
All the files associated with this use case are published on GitHub: 
[https://github.com/DAaaS-reference-data/SANS](https://github.com/DAaaS-reference-data/SANS)

This repository contains a Small Angle Neutron Scattering (SANS) data set taken on the  LOQ time-of-flight (TOF) instrument at the ISIS Neutron and Muon Source (UK). This data set was part of an investigation into creating a new protein standard for protein small angle scattering (SAS), which is published in Biomedical Spectroscopy and Imaging 6 (2017) 123–134. The purpose of this data set is to act as a reference data set to test and validate Data Analysis as a Service (DAaaS) platforms, for example as part of the EU ExPaNDS project.

# data[].attributes.workflow-content.nodes[].data.html-description
All the files associated with this use case are published on GitHub: https://github.com/DAaaS-reference-data/SANS

This repository contains a Small Angle Neutron Scattering (SANS) data set taken on the LOQ time-of-flight (TOF) instrument at the ISIS Neutron and Muon Source (UK). This data set was part of an investigation into creating a new protein standard for protein small angle scattering (SAS), which is published in Biomedical Spectroscopy and Imaging 6 (2017) 123–134. The purpose of this data set is to act as a reference data set to test and validate Data Analysis as a Service (DAaaS) platforms, for example as part of the EU ExPaNDS project.

# data[].attributes.workflow-content.nodes[].data.description
The data reduction software Mantid is used to turn raw data into reduced data, which can then be used by subsequent analysis. It is specifically aimed at neutron and muon data. Mantid can either be run through its graphical user interface (GUI) or via its Python API. In this case we leverage the Python API to run Mantid within a Jupyter notebook using the "Mantid files" provided.

Further information about Mantid can be found at https://www.mantidproject.org/.

# data[].attributes.workflow-content.nodes[].data.html-description
The data reduction software Mantid is used to turn raw data into reduced data, which can then be used by subsequent analysis. It is specifically aimed at neutron and muon data. Mantid can either be run through its graphical user interface (GUI) or via its Python API. In this case we leverage the Python API to run Mantid within a Jupyter notebook using the "Mantid files" provided.

Further information about Mantid can be found at https://www.mantidproject.org/.

# data[].attributes.workflow-content.nodes[].data.description
The reduced data is the output produced when running the data reduction that is performed in the Jupyter notebook. It is for reference purposes, so that users can check that their data reduction workflow was successful.

# data[].attributes.workflow-content.nodes[].data.html-description
The reduced data is the output produced when running the data reduction that is performed in the Jupyter notebook. It is for reference purposes, so that users can check that their data reduction workflow was successful.

# data[].attributes.workflow-content.nodes[].data.description
#### LOQ beamline that produces the data 

The specific example here is of ‘protein standard’ data required for calibration purposes on the ISIS Neutron and Muon Source LOQ beamline and it has been published (https://doi.org/10.3233/BSI-170167). 

![LOQ beamline](https://www.isis.stfc.ac.uk/Gallery/loq.jpg)

The LOQ beamline (pictured above) is used to investigate the shape and size of large molecules, small particles or porous materials with dimensions in the range of 1–100nm. Length scales of up to 400 nm can be probed in highly anisotropic systems. The experimental technique leveraged for this is called small-angle neutron scattering, whereby neutrons are scattered elastically (without energy loss) by interacting with the nuclei of the atoms and with the magnetic moment due to unpaired electrons.

#### Metadata

The experimental setup file details the setup of the experiment, while the experimental log file summarises some basic information about the individual experimental runs.

# data[].attributes.workflow-content.nodes[].data.html-description
LOQ beamline that produces the data

The specific example here is of ‘protein standard’ data required for calibration purposes on the ISIS Neutron and Muon Source LOQ beamline and it has been published (https://doi.org/10.3233/BSI-170167).

The LOQ beamline (pictured above) is used to investigate the shape and size of large molecules, small particles or porous materials with dimensions in the range of 1–100nm. Length scales of up to 400 nm can be probed in highly anisotropic systems. The experimental technique leveraged for this is called small-angle neutron scattering, whereby neutrons are scattered elastically (without energy loss) by interacting with the nuclei of the atoms and with the magnetic moment due to unpaired electrons.

Metadata

The experimental setup file details the setup of the experiment, while the experimental log file summarises some basic information about the individual experimental runs.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Biomedical Spectroscopy and Imaging 6 (2017) 123–134

# data[].attributes.workflow-content.nodes[].data.html-description
A jupyter notebook is provided as an example of how these files can be utilised to reduce the raw data and reproduce the published data (as shown below).

Running the workflow

This use case can be run out-of-the-box on workspaces within the ISIS Data Analysis as a Service. In theory in can be run on any workspace, but in practise it is most straightforward to use a workspace of the "Reflectometry" kind as it has Jupyter pre-configured. On a "Reflectometry" workspace the notebook can be run out-of-the-box using one of the Mantid kernels (click "kernel" then choose one with Mantid in the name) if the files and directories mirror that of the GitHub repository (e.g. by downloading the files through a "git clone" command). If used at a different facility then the following are required:

● Mantid (GPLv3)
● Jupyter (3-clause BSD)
● internet access

and the availability of ‘git’ is optional.

Once these requirements are fulfilled, the path to the Mantid installation needs to be updated in the Jupyter notebook, together with the directory paths to be used

# data[].attributes.workflow-content.nodes[].data.description
The following is the abstract from publication that is based on this data set.

#### Abstract

Protein small angle scattering (SAS) has become increasing important in structural biochemistry, due to the increased performance and specification of new instruments and advances in the software and hardware used to analyse the data. Whilst all of this is encouraging, there is a lack of standardised experimental methodology within the community. Although a number of protein standards are currently used in SAS experiments to allow accurate molecular weight determination, each has specific advantages and disadvantages. We therefore propose the use of a mutated monomeric enhanced green fluorescent protein, as a protein standard, abbreviated to m-eGFP. It has a number of advantages over the currently used protein standards, for example it is cheap and easy to produce. It can be expressed in large amounts (>40mg/L) in both hydrogenated and deuterated form. The mutation means it is highly monodisperse and GFP being a beta-barrel structure is thermodynamically stable over a number of days, giving highly reproducible results. We therefore believe m-eGFP is a good protein standard for small angle scattering (SAS).

# data[].attributes.workflow-content.nodes[].data.html-description
The following is the abstract from publication that is based on this data set.

Abstract

Protein small angle scattering (SAS) has become increasing important in structural biochemistry, due to the increased performance and specification of new instruments and advances in the software and hardware used to analyse the data. Whilst all of this is encouraging, there is a lack of standardised experimental methodology within the community. Although a number of protein standards are currently used in SAS experiments to allow accurate molecular weight determination, each has specific advantages and disadvantages. We therefore propose the use of a mutated monomeric enhanced green fluorescent protein, as a protein standard, abbreviated to m-eGFP. It has a number of advantages over the currently used protein standards, for example it is cheap and easy to produce. It can be expressed in large amounts (>40mg/L) in both hydrogenated and deuterated form. The mutation means it is highly monodisperse and GFP being a beta-barrel structure is thermodynamically stable over a number of days, giving highly reproducible results. We therefore believe m-eGFP is a good protein standard for small angle scattering (SAS).

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Biomedical Spectroscopy and Imaging 6 (2017) 123–134

# data[].attributes.title
SOLEIL DISCO (Dichroism, Imaging, mass Spectrometry for Chemistry and biOlogy) beamline from sample preparation to data analysis.

# data[].attributes.description
DISCO is a VUV to visible beamline dedicated to biochemistry, chemistry and cell biology. The spectral region is optimised between 60 and 700 nm with conservation of the natural polarization of the light.  
DISCO offers two experimental end stations to the scientific communities: SRCD (circular dichroism using the synchrotron light) and VUV imaging.  
DISCO is also a part of HelioBio.
The HelioBio section at SOLEIL, being strongly inter-disciplinary and also at the junction where scientific measurement and project meet, 
is a natural focal point for the development of innovative methods in order to help advance the most difficult « external user » research. 


# data[].attributes.workflow-content.nodes[].data.description
![NACDDB](https://www.synchrotron-soleil.fr/sites/default/files/logo_0.png)  

DISCO is a VUV to visible beamline dedicated to biochemistry, chemistry and cell biology. The spectral region is optimised between 60 and 700 nm with conservation of the natural polarization of the light.  

DISCO offers two experimental end stations to the scientific communities: SRCD (circular dichroism using the synchrotron light) and VUV imaging.  

DISCO is also a part of HelioBio.  
The HelioBio section at SOLEIL, being strongly inter-disciplinary and also at the junction where scientific measurement and project meet, 
is a natural focal point for the development of innovative methods in order to help advance the most difficult « external user » research. 

# data[].attributes.workflow-content.nodes[].data.html-description
DISCO is a VUV to visible beamline dedicated to biochemistry, chemistry and cell biology. The spectral region is optimised between 60 and 700 nm with conservation of the natural polarization of the light.

DISCO offers two experimental end stations to the scientific communities: SRCD (circular dichroism using the synchrotron light) and VUV imaging.

DISCO is also a part of HelioBio.
The HelioBio section at SOLEIL, being strongly inter-disciplinary and also at the junction where scientific measurement and project meet, is a natural focal point for the development of innovative methods in order to help advance the most difficult « external user » research.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
DISCO: a low-energy multipurpose beamline at synchrotron SOLEIL

# data[].attributes.workflow-content.nodes[].data.description
This video shows you how to prepare thin slices of solid or liquid samples for UV imaging on the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.html-description
This video shows you how to prepare thin slices of solid or liquid samples for UV imaging on the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.name
Data acquisition (for full field microscopy)

# data[].attributes.workflow-content.nodes[].data.description
An imaging endstation for biological (living cells) and material applications with new possibilities of excitation and detection, also allowing biomedical investigation of normal and tumoral tissues moreover, new paradigms in autofluorescence diagnosis appear and "abandonned" molecules that fluoresce only in the UV can be used as probes.  

Numerous molecules present a specific fluorescence when excited in UV (190 to 400 nm), here are some examples that we did encounter: aromatic amino acids of course, 
but also NADH, DHE (that may be sustitued to cholesterol for studying membranes), ferulic acids, lignin, G quadruplexes, polyenes, metal oxydes ...

# data[].attributes.workflow-content.nodes[].data.html-description
An imaging endstation for biological (living cells) and material applications with new possibilities of excitation and detection, also allowing biomedical investigation of normal and tumoral tissues moreover, new paradigms in autofluorescence diagnosis appear and "abandonned" molecules that fluoresce only in the UV can be used as probes.

Numerous molecules present a specific fluorescence when excited in UV (190 to 400 nm), here are some examples that we did encounter: aromatic amino acids of course, but also NADH, DHE (that may be sustitued to cholesterol for studying membranes), ferulic acids, lignin, G quadruplexes, polyenes, metal oxydes ...

# data[].attributes.workflow-content.nodes[].data.description
A circular dichroism endstation in the VUV range which opens new fields in the investigation of biological molecules, with, in particular, the possibility to follow rapid kinetics such as proteins folding and unfolding in real time.  
SRCD allows to record circular dichroism spectra in solution down to 165 nm even with very aborbing buffers and films down to 120 nm.
 Very fast  acquisition of datas about folding and stability of biomacromolecules.

# data[].attributes.workflow-content.nodes[].data.html-description
A circular dichroism endstation in the VUV range which opens new fields in the investigation of biological molecules, with, in particular, the possibility to follow rapid kinetics such as proteins folding and unfolding in real time.
SRCD allows to record circular dichroism spectra in solution down to 165 nm even with very aborbing buffers and films down to 120 nm. Very fast acquisition of datas about folding and stability of biomacromolecules.

# data[].attributes.workflow-content.nodes[].data.description
In this video is shown the charging of CaF2 cells, transparent in UV light up to 170nm, on the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.html-description
In this video is shown the charging of CaF2 cells, transparent in UV light up to 170nm, on the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.html-description
Some tools used on the SRCD branch.

# data[].attributes.workflow-content.nodes[].data.description
![NACDDB](https://genesilico.pl/nacddb/static/img/graphical_abstract_wien_JMB.png)  

The Nucleic Acid Circular Dichroism Database (NACDDB) is the result of a collaborative efforts between researchers at the SOLEIL synchrotron, Université de Paris, Université Paris Saclay, and the International Institute of Molecular and Cell Biology in Warsaw. We provide the research community, including academia and industry, with a resource containing an extensive repertoire of CD experiments performed at the SOLEIL synchrotron on various nucleic acid molecules, including DNA, RNA, DNA/RNA hybrids, and various nucleic acid derivatives, as well as data on CD experiments on nucleic acids from the literature. NACDDB provides a systematic collection of CD data sets for many different types of nucleic acid molecules, with detailed descriptions of experimental conditions, along with models of 3D structure, associated data, and literature references. The database is continuously updated as new CD datasets for nucleic acids become available. We encourage authors and users to submit their data and contact us by filling out the form under the Contact Form tab. The database is free for all users, requires no password and is mobile-friendly.

# data[].attributes.workflow-content.nodes[].data.html-description
The Nucleic Acid Circular Dichroism Database (NACDDB) is the result of a collaborative efforts between researchers at the SOLEIL synchrotron, Université de Paris, Université Paris Saclay, and the International Institute of Molecular and Cell Biology in Warsaw. We provide the research community, including academia and industry, with a resource containing an extensive repertoire of CD experiments performed at the SOLEIL synchrotron on various nucleic acid molecules, including DNA, RNA, DNA/RNA hybrids, and various nucleic acid derivatives, as well as data on CD experiments on nucleic acids from the literature. NACDDB provides a systematic collection of CD data sets for many different types of nucleic acid molecules, with detailed descriptions of experimental conditions, along with models of 3D structure, associated data, and literature references. The database is continuously updated as new CD datasets for nucleic acids become available. We encourage authors and users to submit their data and contact us by filling out the form under the Contact Form tab. The database is free for all users, requires no password and is mobile-friendly.

# data[].attributes.workflow-content.nodes[].data.description
BeStSel (Beta Structure Selection) is a novel method for the secondary structure determination and fold recognition from protein circular dichroism spectra.

# data[].attributes.workflow-content.nodes[].data.html-description
BeStSel (Beta Structure Selection) is a novel method for the secondary structure determination and fold recognition from protein circular dichroism spectra.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

# data[].attributes.workflow-content.nodes[].data.description
The Protein Circular Dichroism Data Bank (PCDDB) is a public repository that archives and freely distributes circular dichroism (CD) and synchrotron radiation CD (SRCD) spectral data and their associated experimental metadata.  

All entries undergo validation and curation procedures to ensure completeness, consistency and quality of the data included. A web-based interface enables users to browse and query sample types, sample conditions, experimental parameters and provides spectra in both graphical display format and as downloadable text files.  

The entries are linked, when appropriate, to primary sequence (UniProt) and structural (PDB) databases, as well as to secondary databases such as the Enzyme Commission functional classification database and the CATH fold classification database, as well as to literature citations.

# data[].attributes.workflow-content.nodes[].data.html-description
The Protein Circular Dichroism Data Bank (PCDDB) is a public repository that archives and freely distributes circular dichroism (CD) and synchrotron radiation CD (SRCD) spectral data and their associated experimental metadata.

All entries undergo validation and curation procedures to ensure completeness, consistency and quality of the data included. A web-based interface enables users to browse and query sample types, sample conditions, experimental parameters and provides spectra in both graphical display format and as downloadable text files.

The entries are linked, when appropriate, to primary sequence (UniProt) and structural (PDB) databases, as well as to secondary databases such as the Enzyme Commission functional classification database and the CATH fold classification database, as well as to literature citations.

# data[].attributes.workflow-content.nodes[].data.description
Micro-Manager is an application to control microscope hardware, such as cameras, xy-stages, filter wheels, etc. It includes a hardware abstraction layer written in C++ and a user interface written in Java (Swing).

# data[].attributes.workflow-content.nodes[].data.html-description
Micro-Manager is an application to control microscope hardware, such as cameras, xy-stages, filter wheels, etc. It includes a hardware abstraction layer written in C++ and a user interface written in Java (Swing).

# data[].attributes.workflow-content.nodes[].data.description
HORIBA Scientific’s LabSpec 6 Spectroscopy Suite provides an intuitive, powerful software platform for imaging and spectroscopy by Raman, photoluminescence (PL), cathodoluminescence (CL) and AFM-Raman. It offers complete and versatile functionality for acquisition, processing, analysis, and display of data, in addition to flexible automation solutions.

# data[].attributes.workflow-content.nodes[].data.html-description
HORIBA Scientific’s LabSpec 6 Spectroscopy Suite provides an intuitive, powerful software platform for imaging and spectroscopy by Raman, photoluminescence (PL), cathodoluminescence (CL) and AFM-Raman. It offers complete and versatile functionality for acquisition, processing, analysis, and display of data, in addition to flexible automation solutions.

# data[].attributes.workflow-content.nodes[].data.html-description
LabSpec and MATLAB are used.

# data[].attributes.workflow-content.nodes[].data.description
For 2D images ImageJ is used.  
For image deconvolution and 3D image reconstruction Huygens is used.

# data[].attributes.workflow-content.nodes[].data.html-description
For 2D images ImageJ is used.
For image deconvolution and 3D image reconstruction Huygens is used.

# data[].attributes.workflow-content.nodes[].data.description
A microspectrofluorimeter (POLYPHEME) for recording fluorescence spectral images of molecules absorbing between 190 and 400 nm. Allows femtoliter spectral recording.  

This video describes the basic steps to install your samples on the multispectral microscope (called POLYPHEME) of the DISCO beamline.  

# data[].attributes.workflow-content.nodes[].data.html-description
A microspectrofluorimeter (POLYPHEME) for recording fluorescence spectral images of molecules absorbing between 190 and 400 nm. Allows femtoliter spectral recording.

This video describes the basic steps to install your samples on the multispectral microscope (called POLYPHEME) of the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Synchrotron UV Fluorescence Microscopy Uncovers New Probes in Cells and Tissues

# data[].attributes.workflow-content.nodes[].data.description
A full field, fully automatized inverted microscope (TELEMOS), for fast imaging of live cells or any other specimen that do present fluorescence in UV. Often used in Z-Stacks configurations.  

This video describes the basic steps to install your sample on the full field UV microscope (called TELEMOS) of the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.html-description
A full field, fully automatized inverted microscope (TELEMOS), for fast imaging of live cells or any other specimen that do present fluorescence in UV. Often used in Z-Stacks configurations.

This video describes the basic steps to install your sample on the full field UV microscope (called TELEMOS) of the DISCO beamline.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Synchrotron UV Fluorescence Microscopy Uncovers New Probes in Cells and Tissues

# data[].attributes.workflow-content.nodes[].data.html-description
Resources to come soon.

# data[].attributes.description
The radiation source ELBE (**E**lectron **L**inac for beams with high **B**rilliance and low **E**mittance) at the Helmholtz Centre Dresden Rossendorf (HZDR) can produce several kinds of secondary radiations. THz radiation is one of them and can be used with a typical pulse frequency of 100 kHz as a stimulation source for elementary low-energy degrees of freedom in matter. It was used in the \"Phase-resolved Higgs response in superconducting cuprates\" publication (DOI: [10.1038/s41467-020-15613-1](https://doi.org/10.1038/s41467-020-15613-1)). The raw data for this publication can be accessed on RODARE (DOI: [10.14278/rodare.277](https://doi.org/10.14278/rodare.277)) and will be used to reproduce the figures from the publication.

![](https://www.hzdr.de/db/PicUser?pOid=49797&pWidth=1280)

# data[].attributes.workflow-content.nodes[].data.description
Load the juypter notebook in a provided Jupyter Hub instance or on a lokal instance. 

# data[].attributes.workflow-content.nodes[].data.html-description
Load the juypter notebook in a provided Jupyter Hub instance or on a lokal instance.

# data[].attributes.workflow-content.nodes[].data.description
Open the record in our data repository RODARE, select one measurement and copy the URL into the Jupyter notebook in section 1.3.

![](https://rodare.hzdr.de/record/2/files/rodare-lg.png)

# data[].attributes.workflow-content.nodes[].data.html-description
Open the record in our data repository RODARE, select one measurement and copy the URL into the Jupyter notebook in section 1.3.

# data[].attributes.workflow-content.nodes[].data.name
Scientific publication on "Phase-resolved Higgs response"

# data[].attributes.workflow-content.nodes[].data.description
The original publication on "**Phase-resolved Higgs response in superconducting cuprates**" gives insights in high energy physics, the Higgs field couples to gauge bosons and fermions and gives mass to their elementary excitations. The data used for the publication is available in the **Ro**ssendorf **Da**ta **Re**pository [RODARE](https://doi.org/10.14278/rodare.1289) and [B2Find](https://b2find.eudat.eu/dataset/74e37edc-e26e-5439-a0c1-5fa369f6f5c6). The scientific metadata describing the detailed experimental datasets can be found in the [HZDR metadata catalogue SciCat](https://scicat.hzdr.de).

The resources listed here are explained in more detail  and will be brought together in this training workflow.

![](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41467-020-15613-1/MediaObjects/41467_2020_15613_Fig1_HTML.png?as=webp)

# data[].attributes.workflow-content.nodes[].data.html-description
The original publication on "Phase-resolved Higgs response in superconducting cuprates" gives insights in high energy physics, the Higgs field couples to gauge bosons and fermions and gives mass to their elementary excitations. The data used for the publication is available in the Rossendorf Data Repository RODARE and B2Find. The scientific metadata describing the detailed experimental datasets can be found in the HZDR metadata catalogue SciCat.

The resources listed here are explained in more detail and will be brought together in this training workflow.

# data[].attributes.workflow-content.nodes[].data.description
Nonlinear THz Spectroscopy has developed into a prosperous field of basic research in physics, chemistry and material sciences. In high energy physics, the Higgs field couples to gauge bosons and fermions and gives mass to their elementary excitations.

![](https://www.hzdr.de/db/Pic?pOid=38497)

# data[].attributes.workflow-content.nodes[].data.html-description
Nonlinear THz Spectroscopy has developed into a prosperous field of basic research in physics, chemistry and material sciences. In high energy physics, the Higgs field couples to gauge bosons and fermions and gives mass to their elementary excitations.

# data[].attributes.workflow-content.nodes[].data.name
TELBE raw data evaluation and analysis workflow

# data[].attributes.workflow-content.nodes[].data.description
Fo the analysis workflow it is possible to use an preconfigured Jupyter notebook in a binder environment (proceed to next step) or to perform the analysis on a local machine.

![](https://github.githubassets.com/images/modules/logos_page/Octocat.png)

# data[].attributes.workflow-content.nodes[].data.html-description
Fo the analysis workflow it is possible to use an preconfigured Jupyter notebook in a binder environment (proceed to next step) or to perform the analysis on a local machine.

# data[].attributes.workflow-content.nodes[].data.description
The primary analysis steps, used to process the raw data generated by the experimental setup, are available as jupyter notebooks. The notebooks can be executed using any Jupyter notebook instance or the services provided below.

![](https://pan-training.eu/system/content_providers/images/000/000/014/original/Unknown.jpg)

* For the EGI Binder service, multiple academic/social accounts can be used for login.
* The HIFIS Jupyter service requires Helmholtz AAI.

# data[].attributes.workflow-content.nodes[].data.html-description
The primary analysis steps, used to process the raw data generated by the experimental setup, are available as jupyter notebooks. The notebooks can be executed using any Jupyter notebook instance or the services provided below.

For the EGI Binder service, multiple academic/social accounts can be used for login.

The HIFIS Jupyter service requires Helmholtz AAI.

# data[].attributes.workflow-content.nodes[].data.name
ELBE Center for High-Power Radiation Sources

# data[].attributes.workflow-content.nodes[].data.description
In the ELBE Center for High-Power Radiation Sources, the superconducting linear electron accelerator ELBE, serving  two free electron lasers, sources for intense coherent THz radiation, mono-energetic positrons, electrons, γ-rays, a neutron time-of-flight system as well as two synchronized ultra-short pulsed Petawatt laser systems are collocated. The characteristics of these beams make the ELBE center a unique research instrument for a variety of external users in fields ranging from material science over nuclear physics to cancer research, as well as scientists of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR).

![](https://www.hzdr.de/db/PicUser?pOid=50136&pWidth=800)

# data[].attributes.workflow-content.nodes[].data.html-description
In the ELBE Center for High-Power Radiation Sources, the superconducting linear electron accelerator ELBE, serving two free electron lasers, sources for intense coherent THz radiation, mono-energetic positrons, electrons, γ-rays, a neutron time-of-flight system as well as two synchronized ultra-short pulsed Petawatt laser systems are collocated. The characteristics of these beams make the ELBE center a unique research instrument for a variety of external users in fields ranging from material science over nuclear physics to cancer research, as well as scientists of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR).

# data[].attributes.workflow-content.nodes[].data.description
The TELBE user experiment is part of the ELBE Center for High-Power Radiation Sources, the superconducting linear electron accelerator ELBE, serving two free electron lasers, sources for intense coherent THz radiation, mono-energetic positrons, electrons, γ-rays, a neutron time-of-flight system as well as two synchronized ultra-short pulsed Petawatt laser systems are collocated. The characteristics of these beams make the ELBE center a unique research instrument for a variety of external users in fields ranging from material science over nuclear physics to cancer research, as well as scientists of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR).

![](https://www.hzdr.de/db/PicOri?pOid=64938)

# data[].attributes.workflow-content.nodes[].data.html-description
The TELBE user experiment is part of the ELBE Center for High-Power Radiation Sources, the superconducting linear electron accelerator ELBE, serving two free electron lasers, sources for intense coherent THz radiation, mono-energetic positrons, electrons, γ-rays, a neutron time-of-flight system as well as two synchronized ultra-short pulsed Petawatt laser systems are collocated. The characteristics of these beams make the ELBE center a unique research instrument for a variety of external users in fields ranging from material science over nuclear physics to cancer research, as well as scientists of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR).

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
TELBE: High-Field High-Repetition-Rate Terahertz facility @ ELBE

# data[].attributes.workflow-content.nodes[].data.html-description
The Jupyter notebook is available on Github:

Clone the Git repository:

git clone https://github.com/hzdr/TELBE-raw-data-evaluation.git

Start a Jupyter notebook server:

jupyter notebook sorting_binning.ipynb

# data[].attributes.workflow-content.nodes[].data.description
In this workflow we will focus on Jupyter notebooks. Additional Python basics and an introduction to Jupyter notebooks can be found in the blended learning course [Illumidesk: Python Course](https://pan-learning.org/moodle/course/view.php?id=81) provided by our integrated e-learning platform PAN-learning.

![](https://pan-training.eu/system/content_providers/images/000/000/027/original/PaNelearning.png)

# data[].attributes.workflow-content.nodes[].data.html-description
In this workflow we will focus on Jupyter notebooks. Additional Python basics and an introduction to Jupyter notebooks can be found in the blended learning course Illumidesk: Python Course provided by our integrated e-learning platform PAN-learning.

# data[].attributes.workflow-content.nodes[].data.name
B2Find Search for "Phase-resolved Higgs response"

# data[].attributes.workflow-content.nodes[].data.description
A web or more specific a B2Find search on *Phase-resolved Higgs response* can lead to our dataset or analysis workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
A web or more specific a B2Find search on Phase-resolved Higgs response can lead to our dataset or analysis workflow.

# data[].attributes.workflow-content.nodes[].data.description
The metadata for every dataset is stored in our internal eLabbook and published afterwards together with the reference to the open available dataset in our metadata catalogue SciCat.

![](https://scicatproject.github.io/documentation/SciCatLogo.png)

# data[].attributes.workflow-content.nodes[].data.html-description
The metadata for every dataset is stored in our internal eLabbook and published afterwards together with the reference to the open available dataset in our metadata catalogue SciCat.

# data[].attributes.title
Use Case 3: X-ray absorption spectroscopy

# data[].attributes.description
Here you can find the last oficial slide of UC3.


[Slide of UC 3 ](https://s3.desy.de/hackmd/uploads/46fef5ba-f05e-4cbe-b6cf-c2a03a4f0cb9.png)

# data[].attributes.workflow-content.nodes[].data.html-description
Reference data

# data[].attributes.workflow-content.nodes[].data.description
Using a template, the user enters all the required metadata for the sample and experiment.

# data[].attributes.workflow-content.nodes[].data.html-description
Using a template, the user enters all the required metadata for the sample and experiment.

# data[].attributes.workflow-content.nodes[].data.name
Measurement files in standardized format  

# data[].attributes.workflow-content.nodes[].data.description
Due to Minimum Viable Product (MVP) the measurements are available in different formats, such as .SPEC, .XDI, .ASCII or .CIF. 

# data[].attributes.workflow-content.nodes[].data.html-description
Due to Minimum Viable Product (MVP) the measurements are available in different formats, such as .SPEC, .XDI, .ASCII or .CIF.

# data[].attributes.workflow-content.nodes[].data.description
Once the data is on RefXAS it can be accessed also via SciCat. 

# data[].attributes.workflow-content.nodes[].data.html-description
Once the data is on RefXAS it can be accessed also via SciCat.

# data[].attributes.workflow-content.nodes[].data.description
The review process consists different steps. There are automated data checks in different depth (MVP has simple checks only) and also manual data curation by dadicated experts as reviewer.

# data[].attributes.workflow-content.nodes[].data.html-description
The review process consists different steps. There are automated data checks in different depth (MVP has simple checks only) and also manual data curation by dadicated experts as reviewer.

# data[].attributes.workflow-content.nodes[].data.description
Here are involved scripts as well as AI models based on expert's know-how. 

# data[].attributes.workflow-content.nodes[].data.html-description
Here are involved scripts as well as AI models based on expert's know-how.

# data[].attributes.workflow-content.nodes[].data.html-description
Incl. traceable comments

# data[].attributes.workflow-content.nodes[].data.description
Processed data are also stored on RefXAS DB

# data[].attributes.workflow-content.nodes[].data.html-description
Processed data are also stored on RefXAS DB

# data[].attributes.description
The workflow describes how content can be added to our ExPaNDS/PaNOSC Training Portal Ecosyste

# data[].attributes.workflow-content.nodes[].data.name
Click on "Register training material " or "Register event"

# data[].attributes.workflow-content.nodes[].data.description
You can add a reference to external training material by adding the URL of the content (website, video, e-learning, repository, ...). It is also possible to show dependencies with a training workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
You can add a reference to external training material by adding the URL of the content (website, video, e-learning, repository, ...). It is also possible to show dependencies with a training workflow.

# data[].attributes.workflow-content.nodes[].data.description
Only Title, Link and short description are necessary, but with more fields a search on the keywords is possible...

# data[].attributes.workflow-content.nodes[].data.html-description
Only Title, Link and short description are necessary, but with more fields a search on the keywords is possible...

# data[].attributes.workflow-content.nodes[].data.description
The final step is the publication of the entry by registering the material (or event)!

# data[].attributes.workflow-content.nodes[].data.html-description
The final step is the publication of the entry by registering the material (or event)!

# data[].attributes.workflow-content.nodes[].data.description
We have three required fields for training materials:

* Title
* URL
* Short description

# data[].attributes.workflow-content.nodes[].data.html-description
We have three required fields for training materials:

Title

URL

Short description

# data[].attributes.workflow-content.nodes[].data.description
All other metadata fields are optional, but we recommend using them to make the subsequent search easier and more efficient. 

# data[].attributes.workflow-content.nodes[].data.html-description
All other metadata fields are optional, but we recommend using them to make the subsequent search easier and more efficient.

# data[].attributes.workflow-content.nodes[].data.description
The first step is a registration at our catalogue. Registration is possible with an email address from one of our content providers. If you are not member of one of our content providers please send us a [mail](mailto:pan-training@hzdr.de). In the future we provide a login with Umbrella.

# data[].attributes.workflow-content.nodes[].data.html-description
The first step is a registration at our catalogue. Registration is possible with an email address from one of our content providers. If you are not member of one of our content providers please send us a mail. In the future we provide a login with Umbrella.

# data[].attributes.workflow-content.nodes[].data.name
Is the material available or is a new course necessary?

# data[].attributes.workflow-content.nodes[].data.name
Reference external available content in PaN-Training

# data[].attributes.workflow-content.nodes[].data.description
To create an own course on on PaN e-learning it is necessary to write an email to admin@pan-learning.org.

# data[].attributes.workflow-content.nodes[].data.html-description
To create an own course on on PaN e-learning it is necessary to write an email to admin@pan-learning.org.

# data[].attributes.workflow-content.edges[].data.name
Event, material or collection of materials

# data[].attributes.workflow-content.edges[].data.name
Collection of materials with dependencies 

# meta.available-facets.licence[].value
Open Data Commons Attribution License 1.0