TY - GEN
A1 - Schoonjans, Tom
A1 - Brunetti, A.
A1 - Golosio, B.
A1 - Sanchez del Rio, M.
A1 - Solé, V. A.
A1 - Ferrero, C.
A1 - Vincze, L.
T1 - xraylib 3.1.0
N2 - Quantitative estimate of elemental composition by spectroscopic and imaging techniques using X-ray fluorescence requires the availability of accurate data of X-ray interaction with matter. Although a wide number of computer codes and data sets are reported in literature, none of them is presented in the form of freely available library functions which can be easily included in software applications for X-ray fluorescence. This work presents a compilation of data sets from different published works and an xraylib interface in the form of callable functions. Although the target applications are on X-ray fluorescence, cross sections of interactions like photoionization, coherent scattering and Compton scattering, as well as form factors and anomalous scattering functions, are also available.
xraylib provides access to some of the most respected databases of physical data in the field of x-rays. The core of xraylib is a library, written in ANSI C, containing over 40 functions to be used to retrieve data from these databases. This C library can be directly linked with any program written in C, C++ or Objective-C. Furthermore, the xraylib package contains bindings to several popular programming languages: Fortran 2003, Perl, Python, Java, IDL, Lua, Ruby, PHP and .NET, as well as a command-line utility which can be used as a pocket-calculator. Although not officially supported, xraylib has been reported to be useable from within Matlab and LabView.
The source code is known to compile and run on the following platforms: Linux, Mac OS X, Solaris, FreeBSD and Windows.
Development occurs on Github: http://github.com/tschoonj/xraylib
Downloads are hosted by the X-ray Micro-spectroscopy and Imaging research group of Ghent University: http://lvserver.ugent.be/xraylib
Version 3.1.0 release notes:
- Database of commonly used radionuclides for X-ray sources added (new API: GetRadioNuclideDataByName, GetRadioNuclideDataByIndex, GetRadioNuclideDataList and FreeRadioNuclideData)
- numpy Python bindings added, generated with Cython. Performance basically the same as the core C library. (suggested by Matt Newville)
- docstring support added to Python bindings (suggested by Matt Newville)
- Windows SDKs now have support for Python 3.4.
- Windows 64-bit SDK now comes with IDL bindings
- Confirmed support for LabView (thanks to Dariush Hampai!)
- Universal Intel 32/64 bit Framework built for Mac OS X
- Perl support for Debian/Ubuntu
- Several bugfixes: thanks to those that reported them!
KW - Quantitative estimation
KW - Elemental composition
KW - Spectroscopic techniques
KW - Imaging techniques
PY - 2014
DO - https://doi.org/10.5281/zenodo.12378
PB - Zenodo
CY - Geneva
AN - OPUS4-51924
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Radnik, Jörg
A1 - Chemello, Giovanni
A1 - Knigge, Xenia
A1 - Al-Sabbgh, Dominik
A1 - Hodoroaba, Vasile-Dan
T1 - XPS, HAXPES, XRD and SEM datasets of functionalized graphene nanoplateletes
N2 - The datasets from (Hard Energy) X-ray photoelectron spectroscopy, X-ray diffraction and Scanning Electron Microsopy are related to the publication
G. Chemello, X. Knigge, D. Ciornii, B.P. Reed, A.J. Pollard, C.A. Clifford, T. Howe, N. Vyas, V.-D. Hodoroaba, J. Radnik
"Influence of the morphology on the functionalization of graphene nanoplatelets analyzed by comparative photoelectron spectroscopy with soft and hard X-rays"
Advanced Materials Interfaces (2023), DOI: 10.1002/admi.202300116.
KW - Graphene related 2D materials
KW - X-ray photoelectron spectroscopy
KW - Hard-energy X-ray photoelectron spectroscopy
KW - Scanning electron microscopy
KW - Powder X-ray diffraction
PY - 2023
DO - https://doi.org/10.5281/zenodo.7956497
PB - Zenodo
CY - Geneva
AN - OPUS4-57898
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Schoonjans, Tom
A1 - Vincze, L.
A1 - Solé, V. A.
A1 - Sanchez del Rio, M.
A1 - Brondeel, P.
A1 - Silversmit, G.
A1 - Appel, K.
A1 - Ferrero, C.
T1 - XMI-MSIM 5.0
N2 - XMI-MSIM is an open source tool designed for predicting the spectral response of energy-dispersive X-ray fluorescence spectrometers using Monte-Carlo simulations. It comes with a fully functional graphical user interface in order to make it as user friendly as possible. Considerable effort has been taken to ensure easy installation on all major platforms.
Development of this package was part of my PhD thesis. The algorithms were inspired by the work of my promotor Prof. Laszlo Vincze of Ghent University. Links to his and my own publications can be found in our manual.
A manuscript has been published in Spectrochimica Acta Part B that covers the algorithms that power XMI-MSIM. Please include a reference to this publication in your own work if you decide to use XMI-MSIM for academic purposes.
A second manuscript was published that covers our XMI-MSIM based quantification plug-in for PyMca. Soon information on using this plug-in will be added to the manual.
XMI-MSIM is released under the terms of the GPLv3.
Development occurs at Github: http://github.com/tschoonj/xmimsim
Downloads are hosted by the X-ray Micro-spectroscopy and Imaging research group of Ghent University: http://lvserver.ugent.be/xmi-msim
Version 5.0 release notes:
Changes:
1. Custom detector response function: build a own plug-in containing your own detector response function and load it at run-time to override the builtin routines. Instructions can be found in the manual.
2. Escape peak improvements: new algorithm is used to calculate the escape peak ratios based on a combined brute-force and variance-reduction approach. Ensures high accuracy even at high incoming photon energies and thin detector crystals. Downside: it's slower…
3. Removed maximum convolution energy option. Was a bit confusing anyway.
4. Number of channels: moved from simulation controls into input-file
5. Radionuclide support added: Now you can select one or more commonly used radionuclide sources from the X-ray sources widget.
6. Advanced Compton scattering simulation: a new alternative implementation of the Compton scattering has been implemented based on the work of Fernandez and Scot (http://dx.doi.org/10.1016/j.nimb.2007.04.203), which takes into account unpopulated atomic orbitals. Provides an improved simulation of the Compton profile, as well as fluorescence contributions due to Compton effect (extremely low!), but slows the code down considerably. Advanced users only. Default: OFF
7. Plot spectra before convolution in results
8. Windows: new Inno Setup installers. Contains the headers and import libraries
9. Windows: compilers changed to GCC 4.8.1 (TDM-GCC)
10. Windows: rand_s used to generate seeds on 64-bit version (requires Vista or later)
11. Windows: new gtk runtime for the 64-bit version (see also https://github.com/tschoonj/GTK-for-Windows-Runtime-Environment-Installer)
12. Mac OS X: compilers changed to clang 5.1 (Xcode) and gfortran 4.9.1 (MacPorts)
13. Original input-files from our 2012 publication (http://dx.doi.org/10.1016/j.sab.2012.03.011) added to examples
14. Updater performs checksum verification after download
15. X-ray sources last used values stored in preferences.ini
16. xmimsimdata.h5 modified: even bigger now...
Bugfixes:
1. Windows: support for usernames with unicode characters. Fixed using customized builds of HDF5. Thanks to Takashi Omori of Techno-X for the report!
2. Spectrum import from file fixes. Was never properly tested apparently
Note:
For those that compiled XMI-MSIM from source: you will need to regenerate the xmimsimdata.h5 file with xmimsim-db. Old versions of this file will not work with XMI-MSIM 5.0.
KW - Spectral response
KW - Energy-dispersive X-ray fluorescence spectrometers
KW - Monte-Carlo simulations
PY - 2014
DO - https://doi.org/10.5281/zenodo.12381
PB - Zenodo
CY - Geneva
AN - OPUS4-51925
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Schumacher, David
A1 - Waske, Anja
T1 - XCT data of metallic feedstock powder with pore size analysis
N2 - X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)2(P,Si) alloy. The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction. The powder material is an (Mn,Fe)2(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].
KW - Additive Manufacturing
KW - Feedstock powder
KW - Powder Characterization
KW - X-Ray Computed Tomography
PY - 2022
DO - https://doi.org/10.5281/zenodo.5796487
PB - Zenodo
CY - Geneva
AN - OPUS4-55556
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
A1 - Smales, Glen Jacob
T1 - X-ray scattering Datasets of gold and silver nanoparticle composites, relating to the publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup"
N2 - Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology).
KW - X-ray scattering
KW - MOUSE
KW - Saxs
KW - Waxs
KW - Analyses
KW - Datasets
PY - 2022
DO - https://doi.org/10.5281/zenodo.7193859
PB - Zenodo
CY - Geneva
AN - OPUS4-55979
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
A1 - Kolmangadi, Mohamed Aejaz
T1 - X-ray scattering datasets associated with the publication "Side chain length dependent dynamics and conductivity in self assembled ion channels"
N2 - X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).
KW - X-ray scattering
KW - SAXS
KW - MOUSE
KW - Columnar ionic liquid crystals
KW - Liquid crystals
PY - 2023
DO - https://doi.org/10.5281/zenodo.7621358
PB - Zenodo
CY - Geneva
AN - OPUS4-56973
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
A1 - Kolmangadi, Mohamed Aejaz
T1 - X-ray scattering datasets associated with the publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism"
N2 - X-ray scattering datasets for samples described in the 2022 publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism". This dataset includes both raw and processed X-ray scattering data for samples APTCN and MPTCN, alongside background measurements files (BKG).
KW - X-ray scattering
KW - SAXS
KW - MOUSE
KW - Membrane polymers
KW - Microporous polymers
PY - 2023
DO - https://doi.org/10.5281/zenodo.7621204
PB - Zenodo
CY - Geneva
AN - OPUS4-56972
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
A1 - Kolmangadi, Mohamed Aejaz
T1 - X-ray scattering datasets associated with the publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation"
N2 - X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG). This data was collected using the MOUSE project (instrument and methodology).
KW - X-ray scattering
KW - SAXS
KW - MOUSE
KW - Alkyls
KW - Polymers
PY - 2023
DO - https://doi.org/10.5281/zenodo.7614835
PB - Zenodo
CY - Geneva
AN - OPUS4-56971
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Hahn, Marc Benjamin
A1 - Hallier, Dorothea C.
A1 - Seitz, H.
T1 - X-ray scattering datasets and simulations associated with the publication "Bio-SAXS of single-stranded DNA-binding proteins: Radiation protection by the compatible solute ectoine"
N2 - This dataset contains the processed and analysed small-angle X-ray scattering data associated with all samples from the publications "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine" (https://doi.org/10.1039/D2CP05053F).
Files associated with McSAS3 analyses are included, alongside the relevant SAXS data, with datasets labelled in accordance to the protein (G5P), its concentration (1, 2 or 4 mg/mL), and if Ectoine is present (Ect) or absent (Pure). PEPSIsaxs simulations of the GVP monomer (PDB structure: 1GV5 ) and dimer are also included.
TOPAS-bioSAXS-dosimetry extension for TOPAS-nBio based particle scattering simulations can be obtained from https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry which is further described in https://doi.org/10.26272/opus4-55751.
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number 442240902 (HA 8528/2-1 and SE 2999/2-1). We acknowledge Diamond Light Source for time on Beamline B21 under Proposal SM29806. This work has been supported by iNEXT-Discovery, grant number 871037, funded by the Horizon 2020 program of the European Commission.
KW - SAXS
KW - Radiation protection
KW - Microdosimetry
KW - G5P
KW - Ectoine
KW - DNA-Binding protein
PY - 2023
DO - https://doi.org/10.5281/zenodo.7515394
PB - Zenodo
CY - Geneva
AN - OPUS4-56811
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Fabry, Çağtay
A1 - Hirthammer, Volker
A1 - Scherer, Martin K.
T1 - weldx-widgets: advanced visualisation and jupyter widgets for weldx
N2 - This package provides advanced visualisation and interactive widgets for the weldx core package.
KW - Weldx
KW - Welding
KW - Research data
KW - Visualisation
PY - 2025
DO - https://doi.org/10.5281/zenodo.17790485
PB - Zenodo
CY - Geneva
AN - OPUS4-64973
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Fabry, Çağtay
A1 - Hirthammer, Volker
A1 - Scherer, Martin K.
T1 - weldx - welding data exchange format
N2 - Scientific welding data covers a wide range of physical domains and timescales and are measured using various different sensors. Complex and highly specialized experimental setups at different welding institutes complicate the exchange of welding research data further. The WelDX research project aims to foster the exchange of scientific data inside the welding community by developing and establishing a new open source file format suitable for the documentation of experimental welding data and upholding associated quality standards. In addition to fostering scientific collaboration inside the national and international welding community an associated advisory committee will be established to oversee the future development of the file format. The proposed file format will be developed with regard to current needs of the community regarding interoperability, data quality and performance and will be published under an appropriate open source license. By using the file format objectivity, comparability and reproducibility across different experimental setups can be improved.
KW - Welding
KW - Research data management
KW - Open science
KW - Open Data
KW - WelDX
PY - 2021
DO - https://doi.org/10.5281/zenodo.6563282
PB - Zenodo
CY - Geneva
AN - OPUS4-55226
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Völker, Christoph
T1 - WEBSLAMD
N2 - The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future.
1. Summary
Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact.
Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution.
Workflow
Digital Lab
Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources
Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations.
Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization.
AI-Optimization
Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements.
KW - Materials informatics
KW - Scientific software
KW - Sequential learning
PY - 2022
UR - https://github.com/BAMresearch/WEBSLAMD
DO - https://doi.org/10.26272/opus4-56640
PB - GitHub
CY - San Francisco, CA, USA
AN - OPUS4-56640
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Beygi Nasrabadi, Hossein
A1 - Skrotzki, Birgit
T1 - Vickers test ontology (VTO)
N2 - Vickers Test Ontology (VTO) has developed for representing the Vickers testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 6507-1 standard.
Versions info:
V2 developed using BFO+CCO top-level ontologies.
Repositories:
GitLab: https://gitlab.com/kupferdigital/process-graphs/vickers-hardness-test
GitHub: https://github.com/HosseinBeygiNasrabadi/Vickers-Test-Ontology-VTO-
MatPortal: https://matportal.org/ontologies/VTO
IndustryPortal: https://industryportal.enit.fr/ontologies/VTO
KW - Ontology
KW - Knowledge graph
KW - Data mapping
KW - Vickers hardness
KW - FAIR data.
PY - 2024
UR - https://gitlab.com/kupferdigital/process-graphs/vickers-hardness-test
UR - https://github.com/HosseinBeygiNasrabadi/Vickers-Test-Ontology-VTO-
UR - https://matportal.org/ontologies/VTO
UR - https://industryportal.enit.fr/ontologies/VTO
PB - GitLab
CY - San Francisco, CA, USA
AN - OPUS4-60544
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
T1 - Trinamic TMCL IOC for exposing Trinamics motor controllers to EPICS CA
N2 - Trinamic TMCL IOC is a Python package designed for controlling stepper motors connected to a Trinamic board using the TMCL language (all boards supported by PyTrinamic should now work, has been tested on the TMCM 6110 and the TMCM 6214). Since it is implementing the TMCL protocol, it should be easy to adapt to other Trinamic motor controller boards. This package assumes the motor controller is connected over a machine network via a network-to-serial converter, but the underlying PyTrinamic package allows for other connections too.
This allows the control of attached motors via the EPICS Channel-Access virtual communications bus. If EPICS is not desired, plain Pythonic control via motion_control should also be possible. An example for this will be provided in the example.ipynb Jupyter notebook.
This package leverages Caproto for EPICS IOCs and a modified PyTrinamic library for the motor board control, and interfaces between the two via an internal set of dataclasses. Configuration for the motors and boards are loaded from YAML files (see tests/testdata/example_config.yaml).
The modifications to PyTrinamic involved extending their library with a socket interface. This was a minor modification that should eventually find its way into the official package (a pull request has been submitted).
KW - Instrumentation
KW - Motor controller
KW - EPICS
KW - Channel access
KW - Instrument control
KW - Laboratory automation
PY - 2024
DO - https://doi.org/10.5281/zenodo.10792593
PB - Zenodo
CY - Geneva
AN - OPUS4-59624
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Kern, Simon
A1 - Liehr, Sascha
A1 - Wander, Lukas
A1 - Bornemann-Pfeiffer, Martin
A1 - Müller, S.
A1 - Maiwald, Michael
A1 - Kowarik, Stefan
T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks
N2 - Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.
KW - NMR spectroscopy
KW - Real-time process monitoring
KW - Artificial neural networks
KW - Online NMR spectroscopy
KW - Automation
KW - Process industry
PY - 2020
DO - https://doi.org/10.5281/zenodo.3677139
PB - Zenodo
CY - Geneva
AN - OPUS4-50456
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Hahn, Marc Benjamin
T1 - TOPAS cell model with nanoparticles
N2 - These files contain cell models for TOPAS/Geant4 and the inclusion of nano particles in particle scattering simulations. A simple spherical cell with nanoparticles can be generated in a fast manner. The user has the option to include the following organelles: nucleus, mitochondria, cell membrane. Additionally nanoparticles can be included in the cytosol and at the surface of the nucleus and/or the mitochondria.
The C++ classes in this repository extend the functionality of the TOPAS (http://www.topasmc.org/) Monte-Carlo program, which is itself a wrapper of the Geant4 MCS Toolkit (http://geant4.org). The sourcecode together with examples and scorers are provided.
"If you use this extension please cite the following literature:
Hahn, M.B., Zutta Villate, J.M. "Combined cell and nanoparticle models for TOPAS to study radiation dose enhancement in cell organelles." Sci Rep 11, 6721 (2021).
https://doi.org/10.1038/s41598-021-85964-2 "
KW - Monte-Carlo simulation
KW - MCS
KW - Geant4
KW - TOPAS
KW - TOPAS-nBio
KW - Dosimetry
KW - Nanoparticles
KW - Nanoparticle
KW - AuNP
KW - Gold
KW - Microdosimetry
KW - Targeted nanoparticle
KW - Simulation
KW - Particle scattering
KW - Cell
KW - Nucleus
KW - Mitochondria
KW - Cancer therapy
KW - Radiation therapy
PY - 2020
UR - https://github.com/BAMresearch/TOPAS-CellModels
UR - https://github.com/MarcBHahn/TOPAS-CellModels
DO - https://doi.org/10.26272/opus4-51150
PB - Bundesanstalt für Materialforschung und -prüfung (BAM)
CY - Berlin
AN - OPUS4-51150
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Lecompagnon, Julien
T1 - Thermographic Reference Dataset: Experimentally simulated Gaussian-shaped internal defects in GFRP
N2 - We introduce a thermographic reference dataset consisting of 100 experimentally recorded temperature fields that replicate defect-like heat signatures in glass fiber reinforced polymer (GFRP) composites. Instead of relying on the fabrication of numerous damaged specimens, the dataset was generated by directly imprinting defect signatures onto an intact laminate using a near-infrared laser projector with spatial light modulation. The projected patterns are derived from parameterized Gaussian distributions, enabling systematic variation in defect size, shape, and orientation. The resulting steady-state thermal responses were captured with infrared thermography, providing high-resolution temperature distribution measurements for each case. This novel approach ensures that the heat transfer physics are preserved experimentally, while no special samples with different defects need to be manufactured to acquire datasets for different defect parameters. The dataset is ultimately designed as a resource for the benchmarking of thermographic non-destructive testing techniques, the validation of numerical heat transfer simulations, and the training of data-driven algorithms for defect detection in composite materials.
KW - Thermography
KW - Temperature
KW - Composites
KW - GFRP
KW - Reference Data
KW - Laser Projector
KW - Nondestructive Testing
PY - 2025
DO - https://doi.org/10.5281/zenodo.17378681
PB - Zenodo
CY - Geneva
AN - OPUS4-64472
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Bustamante, Joana
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
A1 - George, Janine
A1 - Ertural, Christina
T1 - Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study. DFT-part
N2 - This repository contains computational data supporting the manuscript titled *“Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study”* It includes raw data for vibrational properties, elastic properties and Bonding analysis.
KW - DFT
KW - QHA
KW - Lattice thermal conductivity
KW - Grüneisen parameter
PY - 2025
DO - https://doi.org/10.5281/zenodo.17399975
PB - Zenodo
CY - Geneva
AN - OPUS4-64671
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Bustamante, Joana
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
A1 - Ertural, Christina
A1 - George, Janine
T1 - Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study.
N2 - This repository includes raw data for bonding analysis and lattice thermal conductivity using MLIP-MACE-MP03b, supporting the manuscript “Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study”
KW - DFT
KW - LOBSTER
KW - Lattice thermal conductivity
KW - MLIP
PY - 2025
DO - https://doi.org/10.5281/zenodo.17397456
PB - Zenodo
CY - Geneva
AN - OPUS4-64674
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
T1 - The SPONGE v.1.0.0: Modeling scattering of shapes by STL input, with absolute intensities and size distribution.
N2 - Simulates X-ray and Neutron scattering patterns from arbitrary shapes defined by STL files.
Features:
- Uses multithreading to compute a number of independent solutions, then uses the variance of the results to estimate an uncertainty on the output.
- Can be launched from the command line using an excel sheet to define settings, or from a jupyter notebook.
- Outputs scattering patterns in absolute units if the contrast is set.
- A Gaussian size distribution is available, where the relative scaling of objects for each repetion can be varied. Recommended to be used with limited width (max. 10%) to avoid artefacts.
- Writes results with settings to an archival HDF5 file.
Application examples:
This software has been used in several studies to date. For example, it has been used here to simulate a model scattering pattern for a cuboid shape, which was then fed forward into the McSAS3 analysis program for analyzing scattering patterns of polydisperse cuboids. A second use is here, where it was used for the modeling of flattened helices. In this paper, scattering pattern features could be matched with particular morphological changes in the structure. Lastly, this paper has an example where it was used to validate the analytical analysis model, and explore the realistic limits of application of the analytical model.
KW - Software
KW - Scattering pattern analysis
KW - Scattering pattern simulation
KW - X-ray
KW - Neutron
KW - Command-line interface
KW - Scattering pattern
KW - STL file input
KW - Python
KW - Arbitrary shapes
PY - 2023
DO - https://doi.org/10.6084/m9.figshare.21857130.v1
PB - Digital Science
CY - Cambridge, MA, USA
AN - OPUS4-56815
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Fritzsche, Sven
A1 - Pauw, Brian Richard
A1 - Weimann, Christiane
A1 - Sturm, Heinz
T1 - Test artifact for fs-LDW
N2 - Data to generate the given graphs in the publication as well as raw images of the shown images.
KW - stl code
KW - Images
KW - Graphs
KW - Data
PY - 2023
DO - https://doi.org/10.5281/zenodo.7671945
PB - Zenodo
CY - Geneva
AN - OPUS4-58096
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Schilling, Markus
T1 - Tensile Test Ontology (TTO)
N2 - This is the stable version 2.0.1 of the PMD ontology module of the tensile test (Tensile Test Ontology - TTO) as developed on the basis of the 2019 standard ISO 6892-1: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature.
The TTO was developed in the frame of the PMD project. The TTO provides conceptualizations valid for the description of tensile test and corresponding data in accordance with the respective standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of TTO facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of tensile test data is enhanced and data querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated.
The TTO class structure forms a comprehensible and semantic layer for unified storage of data generated in a tensile test including the possibility to record data from analysis, re-evaluation and re-use. Furthermore, extensive metadata allows to assess data quality and reproduce experiments. Following the open world assumption, object properties are deliberately low restrictive and sparse.
KW - Ontology
KW - Tensile Test
KW - Digitalization
KW - Plattform MaterialDigital
KW - Structured Data
PY - 2023
UR - https://github.com/MarkusSchilling/application-ontologies/blob/479311832819af695a2c64fa8eb772f2da398061/tensile_test_ontology_TTO/pmd_tto.ttl
UR - https://github.com/materialdigital/core-ontology/blob/59f5727b0437ceea5e3d9fcb8fcd0ac211e92cc3/pmd_tto.ttl
PB - GitHub
CY - San Francisco, CA, USA
AN - OPUS4-57935
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Beygi Nasrabadi, Hossein
A1 - Skrotzki, Birgit
T1 - Tensile test ontology (TTO)
N2 - Tensile Test Ontology (TTO) has developed for representing the Tensile testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 6892-1 standard.
Versions info:
V2 developed using BFO+CCO top-level ontologies.
V3 developed using PROVO+PMDco top-level ontologies.
Repositories:
GitLab: https://gitlab.com/kupferdigital/process-graphs/tensile-test
GitHub: https://github.com/HosseinBeygiNasrabadi/Tensile-Test-Ontology-TTO-
MatPortal: https://matportal.org/ontologies/TTO
IndustryPortal: https://industryportal.enit.fr/ontologies/TTO
KW - Ontology
KW - Tensile testing
PY - 2024
UR - https://gitlab.com/kupferdigital/process-graphs/tensile-test
UR - https://github.com/HosseinBeygiNasrabadi/Tensile-Test-Ontology-TTO-
UR - https://matportal.org/ontologies/TTO
UR - https://industryportal.enit.fr/ontologies/TTO
PB - GitLab
CY - San Francisco, CA, USA
AN - OPUS4-60545
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Beygi Nasrabadi, Hossein
A1 - Skrotzki, Birgit
T1 - Tensile stress relaxation test ontology (TSRTO)
N2 - Tensile Stress Relaxation Test Ontology (TSRTO) has developed for representing the tensile stress relaxation testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 10319-1 standard.
Versions info:
V1 developed using BFO+CCO top-level ontologies.
V3 developed using PROV+PMDco top-level ontologies.
Repositories:
GitLab: https://gitlab.com/kupferdigital/process-graphs/relaxation-test
GitHub: https://github.com/HosseinBeygiNasrabadi/Tensile-Stress-Relaxation-Test-Ontology-TSRTO
MatPortal: https://matportal.org/ontologies/TSRTO
IndustryPortal: https://industryportal.enit.fr/ontologies/TSRTO
KW - Ontology
KW - Tensile stress relaxation testing
KW - FAIR data
PY - 2024
UR - https://gitlab.com/kupferdigital/process-graphs/relaxation-test
UR - https://github.com/HosseinBeygiNasrabadi/Tensile-Stress-Relaxation-Test-Ontology-TSRTO
UR - https://matportal.org/ontologies/TSRTO
UR - https://industryportal.enit.fr/ontologies/TSRTO
PB - GitLab
CY - San Francisco, CA, USA
AN - OPUS4-60546
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Hahn, Marc Benjamin
T1 - Temperature effects in the Object Oriented Micromagnetic Framework (OOMMF) - OOMMF input parameter files for Tc determination
N2 - To simulate the movement of the macroscopic magnetic moment in ferromagnetic systems under the influence of elevated temperatures, the stochastic version of the Landau-Lifshitz (LL) or the Landau-Lifshitz-Gilbert equation with a spin density of one per unit cell has to be used.
To apply the stochastic LL to micromagnetic simulations, where the spin density per unit cell is generally higher, a conversion has to be performed. OOMMF sample files MIF) are provided which can be used to determine the Curie temperature for the classical bulk magnets, iron, nickel and cobalt.
KW - OOMMF
KW - Temperature
KW - Micromagnetism
KW - Thetaevolve
KW - Ferromagnetism
KW - Exchange interaction
KW - LLG
KW - Landau Lifshitz equation
KW - Magnetic moment
KW - Magnetic nanoparticles
KW - Object oriented micromagnetic framework
KW - Stochastic Landau Lifshitz Gilbert equation
KW - Temperature scaling
PY - 2020
DO - https://doi.org/10.26272/opus4-51169
PB - Bundesanstalt für Materialforschung und -prüfung (BAM)
CY - Berlin
AN - OPUS4-51169
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
T1 - Technical Drawings for The MOUSE Modular Sample Holder: Solid Sample Rack/Plate
N2 - OpenSCAD, STL and technical drawings for the solid sample rack designed primarily for use with The MOUSE instruments.
This solid sample rack can be used in conjunction with:
- Laser-cut sample holder (10.5281/zenodo.7499437)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
KW - X-ray scattering
KW - SAXS
KW - Sample holder
KW - MOUSE
PY - 2023
DO - https://doi.org/10.5281/zenodo.7499424
PB - Zenodo
CY - Geneva
AN - OPUS4-56778
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
T1 - Technical Drawings for The MOUSE Modular Sample Holder: Laser-cut Sample Holder
N2 - PDF file for the laser-cut sample holder designed primarily for use with The MOUSE instruments.
This sample holder can be used in conjunction with:
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
KW - X-ray scattering
KW - SAXS
KW - Sample holder
KW - MOUSE
PY - 2023
DO - https://doi.org/10.5281/zenodo.7499437
PB - Zenodo
CY - Geneva
AN - OPUS4-56779
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
T1 - Technical Drawings for The MOUSE Modular Sample Holder: Capillary Flow-through Cell
N2 - OpenSCAD, STL and technical drawings for the capillary flow-through cell designed primarily for use with The MOUSE instruments.
This flow-through cell can be used in conjunction with:
- Modular sample holder (10.5281/zenodo.7499416)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
KW - X-ray scattering
KW - SAXS
KW - Sample holder
KW - MOUSE
PY - 2023
DO - https://doi.org/10.5281/zenodo.7499421
PB - Zenodo
CY - Geneva
AN - OPUS4-56776
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Smales, Glen Jacob
A1 - Pauw, Brian Richard
T1 - Technical Drawings for The MOUSE Modular Sample Holder
N2 - OpenSCAD, STL and technical drawings for the Modular sample holder designed primarily for use with The MOUSE instruments.
This modular sample holder can be used in conjunction with the following:
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
KW - X-ray scattering
KW - SAXS
KW - Sample holder
KW - MOUSE
PY - 2023
DO - https://doi.org/10.5281/zenodo.7499416
PB - Zenodo
CY - Geneva
AN - OPUS4-56775
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Chaurasia, Prashant Kumar
A1 - Fabry, Çağtay
A1 - Pittner, Andreas
A1 - Rethmeier, Michael
T1 - Tandem-GMAW: advanced pulsed and short cicuiting process mode experimental data
N2 - This dataset consists of raw recordings for 5 Tandem gas metal arc welding experiments (Tandem-GMAW / T-GMAW).
KW - T-GMAW
KW - High power welding
KW - Deposition efficiency
KW - Life Cycle Assessment
PY - 2025
DO - https://doi.org/10.5281/zenodo.17951724
PB - Zenodo
CY - Geneva
AN - OPUS4-65206
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Klewe, Tim
T1 - Synthetic Dataset for Sequential Learning-Based Optimisation of Bio-Ash Binder Formulations under Seasonal Availability Constraints
N2 - This dataset accompanies the study on sequential learning–based optimisation of bio-ash–cement binder formulations under seasonally varying material availability. It provides a fully synthetic but chemically inspired benchmark design space for evaluating data-driven optimisation strategies in cementitious materials research.
The dataset comprises 5,006 unique binder formulations, each defined by the mass fractions of cement and five bio-based ash components (A1–A5). Ash components represent generic bio-ash types derived from agricultural residues (e.g. rice husk ash, cassava peel ash), and their internal proportions are systematically varied under mass-balance constraints. Cement content ranges from 0 to 100 wt% in discrete steps.
To reflect dynamic supply conditions, the dataset includes season-specific ash usage metrics for four seasons (S1–S4), expressing the fraction of available ash resources consumed by each formulation. A synthetic compressive strength value is assigned to every formulation using a nonlinear scoring function based on chemically inspired descriptors, with added noise to generate a structured yet non-trivial optimisation landscape. These strength values do not represent calibrated physical predictions and are intended solely as a hidden objective function for benchmarking sequential learning algorithms.
The dataset is designed for in silico benchmarking, reproducibility studies, and methodological comparisons of optimisation and active learning strategies. It enables systematic evaluation of algorithmic performance without the need for physical experiments.
KW - Sequential learning
KW - Bio-based ashes
KW - Low-clinker cement
KW - Mixture optimisation
KW - Supplementary cementitious materials
PY - 2026
DO - https://doi.org/10.5281/zenodo.18389681
PB - Zenodo
CY - Geneva
AN - OPUS4-65420
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Miliūtė, Aistė
T1 - Synthesis and phase purity of the negative thermal expansion material ZrV2O7
N2 - This repository contains computational and experimental data for the manuscript titled “Synthesis and phase purity of the negative thermal expansion material ZrV2O7”.
KW - NTE
KW - Sol-gel
KW - Solid-state
KW - Ab-initio
KW - XRD
KW - Raman
PY - 2024
DO - https://doi.org/10.5281/zenodo.12688634
PB - Zenodo
CY - Geneva
AN - OPUS4-61360
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Stawski, Tomasz
A1 - Smales, Glen Jacob
A1 - Scoppola, E.
A1 - Jha, D.
A1 - Morales, L. F. G.
A1 - Moya, A.
A1 - Wirth, R.
A1 - Pauw, Brian Richard
A1 - Emmerling, Franziska
A1 - Van Driessche, A. E. S.
T1 - Supplementary data set for "Seeds of imperfection rule the mesocrystalline disorder in natural anhydrite single crystals"
N2 - In recent years, we have come to appreciate the astounding intricacy of the formation process of minerals from ions in aqueous solutions. In this context, a number of studies have revealed that nucleation in the calcium sulfate system is non-classical, involving the aggregation and reorganization of nanosized prenucleation particles. In a recent work we have shown that this particle-mediated nucleation pathway is actually imprinted in the resultant single micron-sized CaSO4 crystals. This property of CaSO4 minerals provides us with an unique opportunity to search for evidence of non-classical nucleation pathways in geological environments. In particular, we focused on the quintessential single crystals of anhydrite extracted from the Naica mine in Mexico. We elucidated the growth history from this mineral sample by mapping growth defects at different length scales. Based on these data we argue that the nano-scale misalignment of the structural sub-units observed in the initial calcium sulfate crystal seed propagate through different length-scales both in morphological, as well as strictly crystallographic aspects, eventually causing the formation of large mesostructured single crystals of anhydrite. Hence, the nanoparticle mediated nucleation mechanism introduces a 'seed of imperfection', which leads to a macroscopic single crystal, in which its fragments do not fit together at different length-scales in a self-similar manner. Consequently, anisotropic voids of various sizes are formed with very well-defined walls/edges. But, at the same time the material retains its essential single crystal nature. These findings shed new light on the longstanding concept of crystal structure.
KW - Calcium sulfate
KW - Mesocrystal
KW - Anhydrite
PY - 2021
DO - https://doi.org/10.5281/zenodo.4943234
PB - Zenodo
CY - Geneva
AN - OPUS4-53765
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Stawski, Tomasz
A1 - Miliute, Aiste
T1 - Structural Refinement of ZrV₂O₇ with Negative Thermal Expansion Using Pair Distribution Function Analysis
N2 - This repository contains Python scripts specifically developed for structural refinement of Zirconium Vanadate (ZrV₂O₇), a material known for its negative thermal expansion (NTE). The scripts implement Pair Distribution Function (PDF) analysis to refine crystal structures directly from experimental X-ray diffraction (XRD) data. The refinement workflow is built around the DiffPy-CMI library, enhanced with custom functionalities tailored for ZrV₂O₇ and similar oxide materials.
KW - Zirconium vandate
KW - Total scattering
KW - Pair distribution function
KW - Structure refinement
PY - 2025
DO - https://doi.org/10.5281/zenodo.15395752
PB - Zenodo
CY - Geneva
AN - OPUS4-64750
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Jentzsch, Stefan
A1 - Stock, Daniel
A1 - Häcker, Ralf
A1 - Skrotzki, Birgit
A1 - Darvishi Kamachali, Reza
A1 - Klingbeil, Dietmar
A1 - Kindrachuk, Vitaliy
T1 - Split Hopkinson Pressure Bar (SHPB) investigations of steel S355 specimens and complementary characterization methods
N2 - The workbook SHPB_S355.xlsx contains in the main spreadsheet S355_TestOverview an overview on the S355 specimens, which were tested in Split Hopkinson Pressure Bar (SHPB) and complementary quasi-static (QS) tests. The tests were conducted with notched hat-shaped specimens without a notch offset (S355_TestOverview/column geometry: “no offset”) and a small notch offset of x=0.35 mm (S355_TestOverview/column geometry: “offset”), cf. Fig. 1 [1]. Furthermore, for selected specimens, links are provided to DIC and bar strain measurement files as well as to evaluations from further characterization methods (microhardness, EBSD). For the boundary conditions at the SHPB projectile impact, the pressures of the compressor p, driving the projectile, and the associated projectile impact velocities are provided in SHPB_S355.xlsx.
The DIC displacement measurements are provided in FurtherMeasurements/DIC with frame output times in the file labels, which are associated with an imaginary trigger at the left end of the shortened incident bar (length 300 mm), which is considered within the SHPB simulation setup, see [1]. Furthermore, the DIC reference coordinate systems are provided as COS.jpg files in the respective DIC folders.
Starting from the strain signals at the bars, captured by strain gauges at the incident (file name BC_Inc) and transmission bar (BC_Trans), displacement boundary conditions (which are provided for the tests in FurtherMeasurements/BarDisplBCs) are calculated by eq. (12) in [1], incorporating the acoustic velocity equal to 4639 m/s at the bars and a correction factor. Fig. 1 shows the shear specimen geometry (lengths in mm) with the offset of the notches x.
For the quasi-static tests force(displacements)-values are directly provided in FurtherMeasurements/Fu_curves, which are considered from the relative displacements of the specimens, evaluated by DIC.
The Vickers microhardness (HV 0.01) distribution across the shear localization zone was assessed by QNESS 60A+ EVO (DIN EN ISO 6507-1) for quasi-statically and dynamically tested specimens, applying the small notch offset, such that the specimens did not fail in the localization region. Therefore, raw data is provided in FurtherMeasurements/Microhardness. For fitting the microhardness distribution perpendicularly to the shear bands (as i.e. provided for the SHPB specimen in [1]), representative microhardness profiles were considered. Similarly for specimens with the small notch offset, EBSD data on dynamic and quasi-static tests is provided in FurtherMeasurements/EBSD. The grain size distributions for positions at the notch and the undeformed region are included in the subdirectory ./GrainLists_Shortened and EBSD images in the PowerPoint Presentations. Further information as the sizes and misorientations of the single grains, is incorporated in the subfolders ./GrainLists_FurtherInf.
KW - Adiabatic shear bands
KW - Split Hopkinson pressure bar
KW - Digital image correlation
PY - 2025
DO - https://doi.org/10.5281/zenodo.17591439
PB - Zenodo
CY - Geneva
AN - OPUS4-64842
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Kuchenbecker, Petra
A1 - Lindemann, Franziska
T1 - SOP and reference data for determination of the Volume-specific Surface Area (VSSA) of a commercially available CeO2 nano powder
N2 - Detailed SOP and reference data for the determination of the VSSA of a commercially available CeO2 nano powder:
specific (BET-) Surface Area by gas adsorption (Ar and N2)
skeletal (true solid state) density by gas pycnometry.
Estimation of the particle size by VSSA screening method.
KW - Nano powder
KW - VSSA
KW - Volume specific surface area
KW - Screening method
KW - Ceria
KW - CeO2
PY - 2023
DO - https://doi.org/10.5281/zenodo.10061235
PB - Zenodo
CY - Geneva
AN - OPUS4-58786
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Ueltzen, Katharina
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Benner, Philipp
A1 - George, Janine
T1 - Software and data repository: Can simple exchange heuristics guide us in predicting magnetic properties of solids?
N2 - Software and data for the publication "Can simple exchange heuristics guide us in predicting magnetic properties of solids?" Release that corresponds to the first preprint version of the article. Full Changelog: https://github.com/DigiMatChem/paper-exchange-heuristics-in-magnetic-materials/commits/v1.0.0
KW - Magnetism
KW - Machine Learning
KW - Materials Design
KW - Chemically Complex Materials
KW - Sustainable Materials Design
PY - 2025
DO - https://doi.org/10.5281/zenodo.16811104
PB - Zenodo
CY - Geneva
AN - OPUS4-64672
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
A1 - Smales, Glen Jacob
T1 - Small-angle scattering data analysis round robin dataset - Original for participants
N2 - These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
In this repository, there are:
1) a PDF document with more details for the study,
2) the datasets for people to try and fit,
3) an Excel spreadsheet to document the results.
Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834
Datasets 3 and 4 were collected in-house on the MOUSE instrument.
KW - Round robin
KW - SAXS
KW - Small angle scattering
KW - SANS
KW - X-ray
KW - Neutron
KW - Human factor
KW - Data analysis
KW - Data fitting
KW - Human influence
PY - 2023
DO - https://doi.org/10.5281/zenodo.7506365
PB - Zenodo
CY - Geneva
AN - OPUS4-56799
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
A1 - Smales, Glen Jacob
A1 - Rosalie, Julian
T1 - Small-angle scattering data analysis round robin - Anonymized results, figures and Jupyter notebook
N2 - The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included.
KW - Round robin
KW - Small-angle scattering
KW - Data analysis
PY - 2023
DO - https://doi.org/10.5281/zenodo.7509710
PB - Zenodo
CY - Geneva
AN - OPUS4-56803
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Völker, Christoph
A1 - Moreno Torres, Benjami
T1 - SLAMD-FIB-Case-Study
N2 - With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders part of the energy-intensive clinker production process can be dispensed with. However, because numerous chemicals are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced cement formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of cement formulations can significantly accelerate research. The „Sequential Learning App for Materials Discovery“ (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings. Our material database already includes more than 120,000 data points of alternative cements and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials. Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
KW - Alkali activated concrete
KW - Concrete
PY - 2022
UR - https://github.com/BAMcvoelker/SLAMD-FIB-Case-Study#slamd-fib-case-study
PB - GitHub
CY - San Francisco, CA, USA
AN - OPUS4-56637
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Friedrich, Alexander
T1 - Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification
N2 - This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver.
KW - Non-destructive testing
KW - Eddy current testing
KW - Eddy current simulstion
PY - 2024
DO - https://doi.org/10.5281/zenodo.13970374
PB - Zenodo
CY - Geneva
AN - OPUS4-61470
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Wolf, Jako
A1 - Stawski, Tomasz
A1 - Smales, Glen Jacob
A1 - Thünemann, Andreas
A1 - Emmerling, Franziska
T1 - SI Files for "Towards automation of the polyol process for the synthesis of silver nanoparticles"
N2 - The graphml file: reaction_graph_AgNP.graphml is included. It contains topological information (Fig. 1 in the main text) about the reaction setup and metadata with reaction condtions. It used by the Python API used to control the Chemputer.
SAXS reports. The complete report sheets generated by McSAS are included. They contain extended information characterising the size distributions and the fitting parameters.
NP3_I: saxs_report_NP3_I.pdf
NP3_II: saxs_report_NP3_II.pdf
NP3_III: saxs_report_NP3_III.pdf
NP3_IV: saxs_report_NP3_IV.pdf
NP5_I: saxs_report_NP5_I.pdf
NP5_II: saxs_report_NP5_II.pdf
NP5_III: saxs_report_NP5_III.pdf
KW - Automated synthesis
KW - Silver
KW - Nanoparticles
PY - 2022
DO - https://doi.org/10.5281/zenodo.5910614
PB - Zenodo
CY - Geneva
AN - OPUS4-55197
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
T1 - Set of technical drawings, associated with manuscript entitled: "Extending Synchrotron SAXS instrument ranges through addition of a portable, inexpensive USAXS module with vertical rotation axes"
N2 - This is a set of drawings accompanying the submitted paper entitled "Extending Synchrotron SAXS instrument ranges through addition of a portable, inexpensive USAXS module with vertical rotation axes". The parts described herein will combine with commercial off-the-shelf components to build a high precision pair of rotation stages for accurate measurement of scattering angles with a sub-microradian precision.
KW - X-ray scattering
KW - USAXS
KW - ultra-small-angle X-ray scattering
KW - technical drawings
KW - module
KW - extension
PY - 2021
DO - https://doi.org/10.5281/zenodo.4604703
PB - Zenodo
CY - Geneva
AN - OPUS4-52547
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Döring, Sarah
T1 - Sequence of Mus Musculus monoclonal IgG2c anti-CCMV Immunoglobulin
N2 - We report the full-length nucleotide and amino acid sequences of the light (κ) and heavy chain of the IgG2c anti-CCMV monoclonal antibody (hybridoma clone BAM-CCMV-29-81), generated against Cowpea Chlorotic Mottle Virus (CCMV). Sequencing of hybridoma clone was performed using a cost-effective Sanger-based workflow that includes DNA-level subclass determination and peptide mass fingerprint confirmation. The κ-chain sequence was assigned to IGKV4-6101 / IGKJ101, while the heavy chain was assigned to IGHV2-601 / IGHD1-101 / IGHJ3*01 with IgG2c constant regions from the NOD strain. Both chains were validated against RNA Illumina sequencing, confirming 100% identity. The sequences are available in GenBank under accession numbers PX123807 (κ-chain) and PX123808 (heavy chain).
KW - Antibody Sequencing
KW - BAM-CCMV-29-81
KW - anti-CCMV-antibody
PY - 2025
UR - https://www.ncbi.nlm.nih.gov/nuccore/PX123807
UR - https://www.ncbi.nlm.nih.gov/nuccore/PX123808
PB - National Library of Medicine
CY - Bethesda
AN - OPUS4-63968
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Liepold, Philipp
A1 - Kromm, Arne
A1 - Kannengießer, Thomas
T1 - Semantic Segmentation for Hot Cracking
N2 - The images in this dataset are light microscope images of the surfaces of modified Varestraint-Transvarestraint test samples. The training images show the surface of a tungsten inert gas weld with possible hot cracks. The dataset is intended for use in training crack segmentation networks.
This database consists of images in .png format. The training images were taken in 24-bit, 3-color channels with an optical light microscope (Keyence). The corresponding mask images are 8-bit grayscale images, where 255-valued pixels (white) denote crack areas and 0-valued pixels (black) denote non-cracked areas. This dataset was created manually with the help of a self-written segmentation program based on OpenCV. I used it to train networks based on ViT, ConvNeXt, U-Net, and EfficientNet architectures. The images were taken at magnifications of 50x and 100x.
The dataset is structured as follows:
Wire → Magnification (50, 100) → Data (train, mask = label) → Numbered images (1, 2, 3, …)
(There may be missing numbers. The reasons are:
1) I did not take images of all surfaces if they were not cracked, and
2) the 100x magnification images were taken at a later point, by which time some samples had already been prepared for further evaluation, so no images could be taken.)
For every image number in train, there is an equivalent number in mask with the same image size (but a different channel size, since train is 3-channel color and mask is single-channel).
There is no fixed image size (h × w). If you want to train a network, I recommend either rescaling or taking sub-images for both the training and mask sets, or using architectures that can handle varying input sizes.
The research project " Evaluation of design-induced solidification cracks during welding of high-strength fine-grained structural steels" is funded by the Federal Ministry of Economics and Climate Protection as part of the "Industrial Collective Research" programme on the basis of a resolution of the German Bundestag. This project IGF 01IF21837N FE 1 / P 1516 from the Research Association for steel Application (FOSTA), Düsseldorf, is carried out at Bundesanstalt für Materialforschung und -prüfung.
KW - CNN
KW - Segmentation
KW - Hot cracking
KW - AI
PY - 2025
DO - https://doi.org/10.5281/zenodo.16919144
PB - Zenodo
CY - Geneva
AN - OPUS4-64031
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Han, Ying
T1 - Segmented primary phases of Al-alloy EN AW-2618A in the T61 state using synchrotron computed tomography
N2 - The video file contains data from a synchrotron computed tomography measurement of the primary phases of the Al-alloy EN AW-2618A in the T61 state. The volume fraction of the primary phases was determined.
KW - Aluminum alloy
KW - EN AW-2618A
KW - Computed tomography
KW - Primary phases
PY - 2024
DO - https://doi.org/10.5281/zenodo.12730717
PB - Zenodo
CY - Geneva
AN - OPUS4-60657
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - George, Janine
A1 - Naik, Aakash
A1 - Jackson, A. J.
A1 - Baird, S.
T1 - Scripts to reproduce "Automated bonding analysis with crystal orbital Hamilton populations"
N2 - This repo allows to recreate our publication: https://doi.org/10.1002/cplu.202200123 In contrast to 0.2.2, we fixed an issue with absolute path.
KW - Automation
KW - High-throughput computations
KW - Bonding analysis
PY - 2022
UR - https://github.com/JaGeo/LobsterAutomation
UR - https://doi.org/10.5281/zenodo.6421928
UR - https://doi.org/10.5281/zenodo.6595062
UR - https://doi.org/10.5281/zenodo.6599556
UR - https://doi.org/10.5281/zenodo.6674670
UR - https://doi.org/10.5281/zenodo.6704163
DO - https://doi.org/10.5281/zenodo.6421927
PB - Zenodo
CY - Geneva
AN - OPUS4-55177
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Deumer, J.
A1 - Andresen, Elina
T1 - SAXS data of bipyramidal LiYF4 nanoparticles
N2 - SAXS data of bipyramidal LiYF4 nanoparticles synthesized by the Federal Institute for Materials Research and Testing (BAM) and measured by the Physikalisch-Technische Bundesanstalt (PTB) at the synchrotron facility Bessy II, Berlin, Germany.
KW - Reference data set
KW - Simulation
KW - Reference material
KW - Upconversion nanoparticles
KW - Particle number concentration
KW - SAXS
PY - 2024
DO - https://doi.org/10.5281/zenodo.13691409
PB - Zenodo
CY - Geneva
AN - OPUS4-62134
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Kohlbrecher, J.
A1 - Breßler, Ingo
T1 - SASfit 0.94.12
N2 - Small-angle scattering is an increasingly common method for characterizing particle ensembles in a wide variety of sample types and for diverse areas of application. SASfit has been one of the most comprehensive and flexible curve-fitting programs for decades, with many specialized tools for various fields.
KW - Small-angle scattering
KW - SAXS
KW - SANS
KW - Scattering pattern analysis
PY - 2023
UR - https://doi.org/10.5281/zenodo.7530357
DO - https://doi.org/10.5281/zenodo.7530356
PB - Zenodo
CY - Geneva
AN - OPUS4-57913
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - von Hartrott, Philipp
A1 - Skrotzki, Birgit
T1 - Room temperature and elevated temperature tensile test and elastic properties data of Al-alloy EN AW-2618A after different aging times and temperatures
N2 - The dataset contains two types of data: elastic properties (Young's and shear modulus, Poisson's ratio) between room temperature and 250 °C and a set of tensile tests at different aging times, aging temperatures, and test temperatures.
KW - Aluminium alloy
KW - Young's modulus
KW - Shear modulus
KW - Aging
KW - Tensile test
KW - Strength
PY - 2023
UR - https://doi.org/10.5281/zenodo.10377164
DO - https://doi.org/10.5281/zenodo.10377163
PB - Zenodo
CY - Geneva
AN - OPUS4-59161
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -