# 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
The university group at RWTH Aachen specializes in neutron TOF powder diffraction method development, primarily driven by the new concepts of the neutron time-of-flight diffractometer POWTEX, developed in collaboration with Forschungszentrum Jülich at FRM-II/MLZ in Garching. Unfortunately, no free neutrons were available in 2024. Within the DAPHNE project, we are therefore spreading our methods to other neutron TOF diffractometers while generally aiming to allow a broader, more sustainable applicability of the new developments. This overall goal splits into the following tasks, addressing the different steps in the workflow from raw data to scientific result: 1. multidimensional data reduction using Mantid, 2. derive and include fundamental instrument description in NeXuS data files, 3. multidimensional Rietveld test-cases, 4. AIXtal, a web and cross-platform Rietveld platform for (not only) first-time users, 5. AI tools for structure solution and profile refinement of powder diffraction data.

Utilizing our Mantid routine PowderReduceP2D for high-pressure data collected from SNAP@SNS, ORNL, USA combined with the derived and iterated fundamental instrumental parameters of SNAP allowed us to test-case the multidimensional Rietveld refinement on high-pressure neutron TOF powder diffraction data of a PbNCN sample. While the details were recently published in [1], it is important to note three things: At first, it is very tedious to collect instrumental parameters from various sources while they should actually be part of the data file. At second, a detector coverage of only 1.3 sr (SNAP) vs. ≈ 9 sr (POWTEX) already allows to do multidimensional Rietveld, which is remarkable and underlines the general applicability. While the data reduction steps, with the exception of the one- or multi-dimensional binning, were as similar as possible, the scientific result of the multi-dimensional refinement does differ significantly from the conventional, one-dimensional Rietveld refinement.

Based on these results, we created a sample nexus file containing data fields for the fundamental instrumental parameters. This was presented and discussed at a workshop with the SNS diffraction department which also allowed us to collect the view from the facility perspective. Shaping the future of the Rietveld diffraction software was also recognized as a common interest, especially since the existing software has been mostly around for a long time and the future of the method needs to be clarified.

AIXtal v1 was developed as prototypic, modern web-platform, which allows first-time users to utilize the Rietveld method while partly hiding the complexity of the existing tools GSAS-II and FullProf for the case of conventional X-ray diffraction. The web interface now runs on WebAssembly, which promises performance gains, and moreover now runs not only in the WebBrowser but also natively on Windows, Linux to allow a local installation as well. The higher-performance GUI shall allow to process multidimensional neutron data in the future. While multidimensional Rietveld (GSAS-II 2D) is available as worker already, we only recently started working on GUI and plotting features for this case. The integration of AI methods into AIXtal, e.g. to predict the space group symbol or to support the refinement process, shall ease the structure solution and refinement from powder dat, not only for the unexperienced user.

[1] Y. Meinerzhagen, K. Eickmeier, P.C. Müller, J. Hempelmann, A. Houben, R. Dronskowski, J. Appl. Crystallogr. 2024, 57, 1436–1445, doi:10.1107/s1600576724007635.

# data[].attributes.workflow-content.nodes[].data.description
Write a proposal to the Facility/Instrument of your choice. It will be reviewed by experts and if approved measuring time will be allocated. 

For the multidimensional Rietveld method, we recommend the instruments POWGEN and SNAP located at the Spallation Neutron Source of Oak Ridge National Laboratory (USA). In the future the method will also be used at the POWTEX instrument located at the FRMII in Garching (Germany).

POWGEN: https://neutrons.ornl.gov/powgen

SNAP: https://neutrons.ornl.gov/snap

POWTEX: https://mlz-garching.de/powtex/de

# data[].attributes.workflow-content.nodes[].data.html-description
Write a proposal to the Facility/Instrument of your choice. It will be reviewed by experts and if approved measuring time will be allocated.

For the multidimensional Rietveld method, we recommend the instruments POWGEN and SNAP located at the Spallation Neutron Source of Oak Ridge National Laboratory (USA). In the future the method will also be used at the POWTEX instrument located at the FRMII in Garching (Germany).

POWGEN: https://neutrons.ornl.gov/powgen

SNAP: https://neutrons.ornl.gov/snap

POWTEX: https://mlz-garching.de/powtex/de

# data[].attributes.workflow-content.nodes[].data.html-description
This is you!

# data[].attributes.workflow-content.nodes[].data.description
Provide all the information about your sample thats available. This might include crystal structure information, special handling necessary and safety procedures when handling the sample.

# data[].attributes.workflow-content.nodes[].data.html-description
Provide all the information about your sample thats available. This might include crystal structure information, special handling necessary and safety procedures when handling the sample.

# data[].attributes.workflow-content.nodes[].data.description
Add team members related to the experiment.

# data[].attributes.workflow-content.nodes[].data.html-description
Add team members related to the experiment.

# data[].attributes.workflow-content.nodes[].data.description
Describe your planned experiment and the reason for it.

# data[].attributes.workflow-content.nodes[].data.html-description
Describe your planned experiment and the reason for it.

# data[].attributes.workflow-content.nodes[].data.description
At the instrument you will perform the experiment you proposed. During the experiment not only the measuring data but a lot of metadata will be collected as well.

# data[].attributes.workflow-content.nodes[].data.html-description
At the instrument you will perform the experiment you proposed. During the experiment not only the measuring data but a lot of metadata will be collected as well.

# data[].attributes.workflow-content.nodes[].data.description
Raw measuring data gets saved in a Nexus file as single events.

# data[].attributes.workflow-content.nodes[].data.html-description
Raw measuring data gets saved in a Nexus file as single events.

# data[].attributes.workflow-content.nodes[].data.description
Raw data gets processed in the Mantid software. Either conventional 1D data or new 2D data can be produced depending on the users wishes. 

Mantid: https://archive.mantidproject.org/Main_Page

![Sample](https://archive.mantidproject.org/skins/Strapping/images/Mantid_Logo_Transparent_wiki.png)

# data[].attributes.workflow-content.nodes[].data.html-description
Raw data gets processed in the Mantid software. Either conventional 1D data or new 2D data can be produced depending on the users wishes.

Mantid: https://archive.mantidproject.org/Main_Page

# data[].attributes.workflow-content.nodes[].data.description
Multidimensional Data is saved to a .p2d file containing information on 2theta, lambda, d, d-perpendicular and intensity.

Conventional data can be saved in different formats depending on the refinement software the user prefers, for example .dat, .gsa, .xy.

# data[].attributes.workflow-content.nodes[].data.html-description
Multidimensional Data is saved to a .p2d file containing information on 2theta, lambda, d, d-perpendicular and intensity.

Conventional data can be saved in different formats depending on the refinement software the user prefers, for example .dat, .gsa, .xy.

# data[].attributes.workflow-content.nodes[].data.description
Proposed model that will be refined against the measuring data. 

# data[].attributes.workflow-content.nodes[].data.html-description
Proposed model that will be refined against the measuring data.

# data[].attributes.workflow-content.nodes[].data.description
Modified in-house version of GSASII to refine multidimensional data. Can be used outside of AIXtal as well. Code not publicly available yet.

# data[].attributes.workflow-content.nodes[].data.html-description
Modified in-house version of GSASII to refine multidimensional data. Can be used outside of AIXtal as well. Code not publicly available yet.

# data[].attributes.workflow-content.nodes[].data.description
AIXtal is an accessible refinement platform designed specifically for first-time users. It includes a simplified user interface with reduced complexity, tutorials, tooltips and AI helpers for assistance. Currently, AIXtal supports GSAS-II and Fullprof (undergoing a refactoring) refinement of powder X-ray diffraction data.
The platform provides visualization of structural, experimental and refinement data, including graphical background selection. The user is supported by on-demand assistance on refinement parameters, either through short tooltips or an AI guide that semi-quantitatively assesses the last refinement result/parameter and recommends next refinement steps. For more in-depth investigation, the user can also access a compact view (web or PDF) of the refinement parameters, including esd's. If unfavorable refinement steps are indicated, the user can rely on the history function to jump back to any point in time. AIXtal also allows the user to export the refinement results in GPX format for further analysis with GSAS-II, if required. The platform AIXtal v1 with this functionality is in alpha state and was tested with students of chemistry lab course in April/May 2024.
AIXtal v2 is currently being developed based on the experience from AIXtal v1 and in order to broaden the use case scenarios. With multiple modes of operation, adding an expert mode with full refinement control to the simplified interface, it opens up for future developments of a more universal GUI for Rietveld programs also addressing other common, related crystallographic issues, e.g., a space group prediction. Furthermore, the web interface now runs on WebAssembly, which promises performance gains, and moreover now runs not only in the WebBrowser but also natively on Windows and Linux to allow a local installation as well. The GSAS-II implementation has also been extended to support refinement and visualization of multi-dimensional (angular and wavelength dispersive) data sets.
Current status, AI-helpers and features of AIXtal as well as its technical design principles are shown.

https://events.hifis.net/event/1906/contributions/15679/

# data[].attributes.workflow-content.nodes[].data.html-description
AIXtal is an accessible refinement platform designed specifically for first-time users. It includes a simplified user interface with reduced complexity, tutorials, tooltips and AI helpers for assistance. Currently, AIXtal supports GSAS-II and Fullprof (undergoing a refactoring) refinement of powder X-ray diffraction data. The platform provides visualization of structural, experimental and refinement data, including graphical background selection. The user is supported by on-demand assistance on refinement parameters, either through short tooltips or an AI guide that semi-quantitatively assesses the last refinement result/parameter and recommends next refinement steps. For more in-depth investigation, the user can also access a compact view (web or PDF) of the refinement parameters, including esd's. If unfavorable refinement steps are indicated, the user can rely on the history function to jump back to any point in time. AIXtal also allows the user to export the refinement results in GPX format for further analysis with GSAS-II, if required. The platform AIXtal v1 with this functionality is in alpha state and was tested with students of chemistry lab course in April/May 2024. AIXtal v2 is currently being developed based on the experience from AIXtal v1 and in order to broaden the use case scenarios. With multiple modes of operation, adding an expert mode with full refinement control to the simplified interface, it opens up for future developments of a more universal GUI for Rietveld programs also addressing other common, related crystallographic issues, e.g., a space group prediction. Furthermore, the web interface now runs on WebAssembly, which promises performance gains, and moreover now runs not only in the WebBrowser but also natively on Windows and Linux to allow a local installation as well. The GSAS-II implementation has also been extended to support refinement and visualization of multi-dimensional (angular and wavelength dispersive) data sets. Current status, AI-helpers and features of AIXtal as well as its technical design principles are shown.

https://events.hifis.net/event/1906/contributions/15679/

# data[].attributes.workflow-content.nodes[].data.description
Description of scientific results based on measuring data

# data[].attributes.workflow-content.nodes[].data.html-description
Description of scientific results based on measuring data

# data[].attributes.workflow-content.nodes[].data.description
Information on the Crystal structure resulting from the analysis of the measured data.

# data[].attributes.workflow-content.nodes[].data.html-description
Information on the Crystal structure resulting from the analysis of the measured data.

# data[].attributes.workflow-content.nodes[].data.description
The structure determination pipeline of a polycrystalline sample from its powder diffraction measurement has several adjunct steps, which are as follows: background removal, peak finding, indexing, database matching, extinction and space group(s) determination, atom type and position determination, and finally Rietveld refinement. Experienced users employ various well-established algorithms—available in software tools such as EXPO2014, Match!, GSASII, and FullProf—at different stages of the pipeline. At each step, they must apply their expertise to interpret results, evaluate candidate solutions, and make informed decisions to advance the structure determination process effectively. Our research focuses on (1) leveraging data-driven models as alternatives to various steps in the aforementioned pipeline and (2) developing a broader universal AI framework to directly propose candidate solutions. In the first direction, we have achieved state-of-the-art results in symmetry prediction tasks, with our model outperforming well-known indexing algorithms, such as those in EXPO2014, when used independently. We analyze our models in terms of robustness to noise and experimental effects, generalizability, and scalability, with a focus on their applicability to our broader goal of developing an automated AI for structure solution. From a user-focused software standpoint, we aim to integrate our most robust models into our AIXtal platform, as AI helpers.

# data[].attributes.workflow-content.nodes[].data.html-description
The structure determination pipeline of a polycrystalline sample from its powder diffraction measurement has several adjunct steps, which are as follows: background removal, peak finding, indexing, database matching, extinction and space group(s) determination, atom type and position determination, and finally Rietveld refinement. Experienced users employ various well-established algorithms—available in software tools such as EXPO2014, Match!, GSASII, and FullProf—at different stages of the pipeline. At each step, they must apply their expertise to interpret results, evaluate candidate solutions, and make informed decisions to advance the structure determination process effectively. Our research focuses on (1) leveraging data-driven models as alternatives to various steps in the aforementioned pipeline and (2) developing a broader universal AI framework to directly propose candidate solutions. In the first direction, we have achieved state-of-the-art results in symmetry prediction tasks, with our model outperforming well-known indexing algorithms, such as those in EXPO2014, when used independently. We analyze our models in terms of robustness to noise and experimental effects, generalizability, and scalability, with a focus on their applicability to our broader goal of developing an automated AI for structure solution. From a user-focused software standpoint, we aim to integrate our most robust models into our AIXtal platform, as AI helpers.

# data[].attributes.workflow-content.nodes[].data.description
# ICSD - the world's largest database for completely identified inorganic crystal structures
FIZ Karlsruhe provides the scientific and the industrial community with the world's largest database for completely identified inorganic crystal structures, ICSD. The ICSD data are of excellent quality and its first records date back to 1913. Only data which have passed thorough quality checks are included.

As the world’s leading provider of scientific information on inorganic crystal structures, we take full responsibility for database production, maintenance and quality control, and we ensure that the ICSD database and our software solutions meet the highest possible quality standards.

Around 12,000 new structures are added every year. Through our continuous quality assurance, existing content is modified, supplemented or duplicates removed. As a result, and by filling gaps from previous years, even the older content is not static.

https://icsd.products.fiz-karlsruhe.de/

# data[].attributes.workflow-content.nodes[].data.html-description
ICSD - the world's largest database for completely identified inorganic crystal structures

FIZ Karlsruhe provides the scientific and the industrial community with the world's largest database for completely identified inorganic crystal structures, ICSD. The ICSD data are of excellent quality and its first records date back to 1913. Only data which have passed thorough quality checks are included.

As the world’s leading provider of scientific information on inorganic crystal structures, we take full responsibility for database production, maintenance and quality control, and we ensure that the ICSD database and our software solutions meet the highest possible quality standards.

Around 12,000 new structures are added every year. Through our continuous quality assurance, existing content is modified, supplemented or duplicates removed. As a result, and by filling gaps from previous years, even the older content is not static.

https://icsd.products.fiz-karlsruhe.de/

# data[].attributes.description
To illustrate the initiative, we focus on the three axes spectrometer (TAS) as our demonstration use case. At its core, this instrument serves users by producing data that can easily be organized in an ASCII file. Yet, the flexibility of a TAS and its wide range of applications still yield a complex set of metadata. The challenge intensifies when comparing data files collected from various TAS instruments at different facilities. Here, we have proposed a comprehensive metadata set applicable to most TAS experiments and are in discussions with large scale facilities to implement it.

# data[].attributes.workflow-content.nodes[].data.description
Before you can submit a proposal you need to register at the User Office online system.

Please note: At the moment, we are working with two User Office online Systems because we are introducing our new system called GhOST gradually. In order to submit a proposal you will use this new system! Due to this, you also need to register at this new systenm even if you have already an account at our current system.

# data[].attributes.workflow-content.nodes[].data.html-description
Before you can submit a proposal you need to register at the User Office online system.

Please note: At the moment, we are working with two User Office online Systems because we are introducing our new system called GhOST gradually. In order to submit a proposal you will use this new system! Due to this, you also need to register at this new systenm even if you have already an account at our current system.

# data[].attributes.workflow-content.nodes[].data.description
Das Heinz Maier-Leibnitz Zentrum (MLZ) repräsentiert die Zusammenarbeit der Technischen Universität München und zweier Forschungszentren der Helmholtz Gemeinschaft, nämlich das Forschungszentrum Jülich und das Helmholtz-Zentrum Hereon (früher HZG) zur wissenschaftlichen Nutzung der Forschungs-Neutronenquelle Heinz Maier-Leibnitz in Garching bei München. Diese Kooperation wird vom Bundesministerium für Bildung und Forschung (BMBF) und dem Bayerischen Staatsministerium für Wissenschaft und Kunst finanziell unterstützt. Durch diese Zusammenarbeit entsteht ein weltweit führendes Zentrum für Forschung mit Neutronen und Positronen.

Die Partner der Kooperation bauen und betreiben ein breites Spektrum erstklassiger Streuinstrumente um wissenschaftlichen Fragestellungen beispielsweise aus der Physik, Chemie, Biologie, Geo-, Ingenieur- oder Materialwissenschaften nachzugehen. Das Heinz Maier-Leibnitz Zentrum unterstützt maßgeblich nationale und internationale Wissenschaftler bei der Lösung grundlegender aktueller Fragestellungen unserer Gesellschaft.

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Das Heinz Maier-Leibnitz Zentrum (MLZ) repräsentiert die Zusammenarbeit der Technischen Universität München und zweier Forschungszentren der Helmholtz Gemeinschaft, nämlich das Forschungszentrum Jülich und das Helmholtz-Zentrum Hereon (früher HZG) zur wissenschaftlichen Nutzung der Forschungs-Neutronenquelle Heinz Maier-Leibnitz in Garching bei München. Diese Kooperation wird vom Bundesministerium für Bildung und Forschung (BMBF) und dem Bayerischen Staatsministerium für Wissenschaft und Kunst finanziell unterstützt. Durch diese Zusammenarbeit entsteht ein weltweit führendes Zentrum für Forschung mit Neutronen und Positronen.

Die Partner der Kooperation bauen und betreiben ein breites Spektrum erstklassiger Streuinstrumente um wissenschaftlichen Fragestellungen beispielsweise aus der Physik, Chemie, Biologie, Geo-, Ingenieur- oder Materialwissenschaften nachzugehen. Das Heinz Maier-Leibnitz Zentrum unterstützt maßgeblich nationale und internationale Wissenschaftler bei der Lösung grundlegender aktueller Fragestellungen unserer Gesellschaft.

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PUMA is characterised by a very high neutron flux as a result of the efficient use of focussing techniques. An innovative option of the spectrometer is the multianalyser/ detector system. It allows a unique and flexible type of multiplexing. Using this option, a scattering angle range of 16° can be measured simultaneously, and flexible Q-ω paths can be realised without repositioning the instrument. Mapping of excitations is equally well possible as kinetic single-shot experiments on time scales that have not been accessible so far.

A unique feature of the instrument is the possibility to perform stroboscopic, time resolved measurements of both elastic and inelastic signals on time scales down to the microsecond regime. Using this technique, the sample is periodically perturbed by an external variable such as temperature, electric field, etc. The signal is then recorded not only as a function of momentum and energy transfer but also given a time stamp relative to the periodic perturbation.

# data[].attributes.workflow-content.nodes[].data.html-description
PUMA is characterised by a very high neutron flux as a result of the efficient use of focussing techniques. An innovative option of the spectrometer is the multianalyser/ detector system. It allows a unique and flexible type of multiplexing. Using this option, a scattering angle range of 16° can be measured simultaneously, and flexible Q-ω paths can be realised without repositioning the instrument. Mapping of excitations is equally well possible as kinetic single-shot experiments on time scales that have not been accessible so far.

A unique feature of the instrument is the possibility to perform stroboscopic, time resolved measurements of both elastic and inelastic signals on time scales down to the microsecond regime. Using this technique, the sample is periodically perturbed by an external variable such as temperature, electric field, etc. The signal is then recorded not only as a function of momentum and energy transfer but also given a time stamp relative to the periodic perturbation.

# data[].attributes.workflow-content.nodes[].data.description
The experiment scientist is part of the experimental team and serves as a local contact for a user.

# data[].attributes.workflow-content.nodes[].data.html-description
The experiment scientist is part of the experimental team and serves as a local contact for a user.

# data[].attributes.workflow-content.nodes[].data.description
A research activity whose purpose is to assign a number to an attribute of an object or event or to classify the object or event according to that attribute.

# data[].attributes.workflow-content.nodes[].data.html-description
A research activity whose purpose is to assign a number to an attribute of an object or event or to classify the object or event according to that attribute.

# data[].attributes.workflow-content.nodes[].data.description
The User Office organises the scientific use of the neutron research source Heinz Maier-Leibnitz (FRM II) at Garching. It also takes care of all needs of the scientists visiting the MLZ in the scope of their scientific work.

# data[].attributes.workflow-content.nodes[].data.html-description
The User Office organises the scientific use of the neutron research source Heinz Maier-Leibnitz (FRM II) at Garching. It also takes care of all needs of the scientists visiting the MLZ in the scope of their scientific work.

# data[].attributes.workflow-content.nodes[].data.description
The user developes the experimental plan, applies for beamtime, and provides the sample that is investigated in the measurement.

# data[].attributes.workflow-content.nodes[].data.html-description
The user developes the experimental plan, applies for beamtime, and provides the sample that is investigated in the measurement.

# data[].attributes.workflow-content.nodes[].data.description
The experimental team consists of user(s) and instrument scientist(s).

# data[].attributes.workflow-content.nodes[].data.html-description
The experimental team consists of user(s) and instrument scientist(s).

# data[].attributes.workflow-content.nodes[].data.description
A planned process with the objective to establish the relationship between data produced by a measurement device and physical qualities. This is done by using the measurement device under defined conditions, and either tuning it to adjust the measured output, or record the output and use it as a reference in future measurements.

# data[].attributes.workflow-content.nodes[].data.html-description
A planned process with the objective to establish the relationship between data produced by a measurement device and physical qualities. This is done by using the measurement device under defined conditions, and either tuning it to adjust the measured output, or record the output and use it as a reference in future measurements.

# data[].attributes.workflow-content.nodes[].data.description
 NICOS is a network-based control system written for neutron scattering instruments, originally at the MLZ. Its main goals are flexibility, ease of use and maintenance, and reusability.

NICOS allows remote control of scientific instruments via both interactive interfaces (GUI, command line) and a scripting system. The NICOS user scripts are written in Python with some minor restrictions. This choice allows for very flexible scripts for complex problems and easy interactive use at the same time.

# data[].attributes.workflow-content.nodes[].data.html-description
NICOS is a network-based control system written for neutron scattering instruments, originally at the MLZ. Its main goals are flexibility, ease of use and maintenance, and reusability.

NICOS allows remote control of scientific instruments via both interactive interfaces (GUI, command line) and a scripting system. The NICOS user scripts are written in Python with some minor restrictions. This choice allows for very flexible scripts for complex problems and easy interactive use at the same time.

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The original information, collected from the primary source.

# data[].attributes.workflow-content.nodes[].data.html-description
The original information, collected from the primary source.

# data[].attributes.workflow-content.nodes[].data.description
SciCat is a data catalogue web application to provide a searchable interface for datasets, publish DOIs, as well as the option to carry out actions (i.e. archiving and publishing) and acts as a place to reference datasets used in publications.

# data[].attributes.workflow-content.nodes[].data.html-description
SciCat is a data catalogue web application to provide a searchable interface for datasets, publish DOIs, as well as the option to carry out actions (i.e. archiving and publishing) and acts as a place to reference datasets used in publications.

# data[].attributes.workflow-content.nodes[].data.description
The data which is modified and processed for analysis or other experiments.

# data[].attributes.workflow-content.nodes[].data.html-description
The data which is modified and processed for analysis or other experiments.

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A rigid magnetic disk mounted permanently in a drive unit.

# data[].attributes.workflow-content.nodes[].data.html-description
A rigid magnetic disk mounted permanently in a drive unit.

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A report that is published in a journal.

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A report that is published in a journal.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Example: Magnon spectrum of the helimagnetic insulator Cu2OSeO3

# data[].attributes.workflow-content.edges[].data.name
provides input (e.g. sample can dimensions)

# 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

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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.

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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.

1. Download dataset

2. Unzip dataset

3. 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.description
This shall be the top-level, very generic workflow for LPA

# data[].attributes.workflow-content.nodes[].data.description
Overarching placeholder for all parameters set for an experiments

# data[].attributes.workflow-content.nodes[].data.html-description
Overarching placeholder for all parameters set for an experiments

# data[].attributes.workflow-content.nodes[].data.description
Parameters set to or requested from the drive laser system

# data[].attributes.workflow-content.nodes[].data.html-description
Parameters set to or requested from the drive laser system

# data[].attributes.workflow-content.nodes[].data.description
Parameters describing the target, generally before undergoing any laser interaction.

# data[].attributes.workflow-content.nodes[].data.html-description
Parameters describing the target, generally before undergoing any laser interaction.

# data[].attributes.workflow-content.nodes[].data.description
Placeholder for all diagnostics fielded in the experiment

# data[].attributes.workflow-content.nodes[].data.html-description
Placeholder for all diagnostics fielded in the experiment

# data[].attributes.workflow-content.nodes[].data.html-description
Measured parameters of the laser system

# data[].attributes.workflow-content.nodes[].data.html-description
Geometry, timings etc.

# 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.

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.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

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.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:

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.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

# meta.available-facets.licence[].value
Creative Commons Attribution 4.0 International

# meta.available-facets.licence[].value
Creative Commons Attribution Non Commercial 4.0 International