# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Dresden University of Applied Sciences.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Dresden University of Applied Sciences

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
TU Bergakademie Freiberg Data Publications

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Data publications from research of Freiberg University of Mining and Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Data publications from research of Freiberg University of Mining and Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
TU Bergakademie Freiberg Data Publications

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Hochschule für Technik und Wirtschaft Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Dresden University of Applied Sciences.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Dresden University of Applied Sciences

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Hochschule für Technik und Wirtschaft Dresden

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Freiberg University of Mining and Technology.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Freiberg University of Mining and Technology

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description[].value
Research data repository of Leipzig University.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Research data repository of Leipzig University

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
A tsv file with associated metadata is stored for each of the individual combinations of displacement step (1-14 mm), gage pitch (0.65 mm, 1.30 mm, 2.60 mm, 5.20 mm) and DFOS type (ACR, ES, ORM, V9). This makes a total of 14 x 4 x 4 = 224 files. The files were processed using Python.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Based on the principle of coherent optical frequency domain reflectometery (c-OFDR), cracks in concrete structures can be precisely localized and their widths determined due to the high spatial resolution in the sub-millimeter range. However, with longer distributed fiber optic sensors (DFOS) lengths, as is common in structural health monitoring, the spatial resolution is reduced. Experimental studies were carried out to investigate the influence of the spatial resolution on the measurement quality in the area of large strain gradients. Four different DFOS, two robust and two filigree DFOS types, were subsequently installed on a 4 m long reinforced concrete beam. In a displacement controlled 4-point bending test, crack widths of up to 0.5 mm were measured with four different gage pitch (gp).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Strain curves measured with different DFOS types and varying spatial resolution by an ODiSI 6108

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The goal of the IDA-KI research project is to develop methods for automated analysis and evaluation of monitoring data. The validation is carried out using real measurement data acquired on a 45 m long research bridge (openLAB). First, the reference condition of the bridge is measured with and without service loads over a period of one year. Subsequently, targeted load tests will be performed up to the severe damage state. At the same time, redundant sensors will be damaged or manipulated to create a real database with specific signal characteristics for metrological and structural anomalies. This will provide an important basis for the development of evaluation methods (also based on machine learning), so that measurement faults in monitoring data can be corrected in the future and differentiated from structural damage.

Further information can be found in: https://onlinelibrary.wiley.com/doi/10.1002/best.202300094

Project title: IDA-KI (Automated assessment of monitoring data for infrastructure constructions using AI and IoT)
Funding: Federal Ministry for Digital and Transport, Germany, within the innovation program mFUND
Funding reference: 19FS2013A

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Ultrafast switching of trions in 2D materials by terahertz photons

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
External control of optical excitations is key for manipulating light–matter coupling and is highly desirable for photonic technologies. Excitons in monolayer semiconductors emerged as a unique nanoscale platform in this context, offering strong light–matter coupling, spin–valley locking and exceptional tunability. Crucially, they allow electrical switching of their optical response due to efficient interactions of excitonic emitters with free charge carriers, forming new quasiparticles known as trions and Fermi polarons. However, there are major limitations to how fast the light emission of these states can be tuned, restricting the majority of applications to an essentially static regime. Here we demonstrate switching of excitonic light emitters in monolayer semiconductors on ultrafast picosecond time scales by applying short pulses in the terahertz spectral range following optical injection. The process is based on a rapid conversion of trions to excitons by absorption of terahertz photons inducing photodetachment. Monitoring time-resolved emission dynamics in optical-pump/terahertz-push experiments, we achieve the required resonance conditions as well as demonstrate tunability of the process with delay time and terahertz pulse power. Our results introduce a versatile experimental tool for fundamental research of light-emitting excitations of composite Bose–Fermi mixtures and open up pathways towards technological developments of new types of nanophotonic device based on atomically thin materials.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Ultrafast switching of trions in 2D materials by terahertz photons

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Supplementary data for: Weighted Shapley values and allocation of portfolio risk: One approach to solve the low-risk puzzle?

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.relatedItem.IsSupplementTo[].value
Weighted Shapley values and allocation of portfolio risk: One approach to solve the low-risk puzzle?

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
Simulation study, We analyze three-asset scenario with low-risk puzzle on the set of assets. We simulate 100,000 times. For different level structures, we calculate the weighted Shapley value (Kalai / Samet), the Haeringer weighted value and the proportional Shapley value. We identify cases with rank order corrections with respect to asset variance. 
See also "Simulation Settings"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The data set contains data for the article “Weighted Shapley values and allocation of portfolio risk: One approach to solve the low-risk puzzle?”. This includes a description of the simulation settings, the Excel macros for calculating the results in the case of equal shares and random asset shares, and the 100,000 simulation results for both cases in Excel.

Abstract of the paper: In this article, we extend the application of cooperative game theory to solve the so-called low-risk puzzle. Specifically, we apply concepts that consider asset weights in the allocation of portfolio risk. These weights can take into account various asset characteristics (e.g., image, popularity of an asset, corporate social responsibility, innovative strength) that have not been previously considered in portfolio risk allocation using cooperative game theory. We demonstrate the application through a simulation study. Our results show that considering assets weights could deliver added value when solving the low-risk puzzle.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Supplementary data for: Weighted Shapley values and allocation of portfolio risk: One approach to solve the low-risk puzzle?

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The dataset belongs to the article “Weighted Shapley values and allocation of portfolio risk: One approach to solve the low-risk puzzle?”. It is part of a research project investigating whether cooperative game theory can solve the so-called low-risk puzzle. In the data set, asset weights are included as a structural feature in the calculation of asset payoffs.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Weighted Shapley values and allocation of portfolio risk: One approach to solve the low-risk puzzle?

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Correlation Velocimetry inside a model of the human airways

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TechnicalInfo[].value
The time lag between both signals can be derived using cross-correlation between both channels. In combination with the physical distance between the regions observed by each photodiode, the average velocity can be calculated using: v = Δs/Δt with Δs distance between detectors (272 micrometer) and Δt time lag from cross-correlation. A detailed evaluation is presented in the corresponding publication: Kluwe et al. 2025 "Deterministic periodic structures in a model of the human airways" DOI: 10.1007/s00348-025-03971-9

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
The data contains stereo audio (.wav) files where each channel corresponds to the electrical signal produced by a photodiode used for Correlation Velocimetry. The setup is described in detail in the corresponding publication: Kluwe et al. 2025 "Deterministic periodic structures in a model of the human airways" DOI:10.1007/s00348-025-03971-9. The file name convention follows the pattern:
f_<breathing frequency in Hz>_<tidal-volume>ml_position_<position-identifier>.wav

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The dataset corresponds to the publication "Deterministic periodic structures in a model of the human airways" by Kluwe et al. 2025. It contains raw data obtained from an optical setup used for point-wise velocity measurements using Correlation Velocimetry in a model of human airways. The recorded data consists of stereo audio files sampling the photo-electric response of two photodiodes to the passage of tracer particles. The time lag between both signals corresponds to the mean time of flight of ensembles of particles between both detectors. Using the known distance between the detectors (272 micrometers), the mean velocity of the tracer particles can be calculated. This dataset includes the raw data evaluated and discussed in the publication.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Correlation Velocimetry inside a model of the human airways

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
A novel measurement technique called "Correlation Velocimetry" (CV) was developed and employed in this project. CV enables point-wise velocity measurements in fluids by measuring the time of flight of tracer particles between two sensors with a known distance apart. The concept and setup are described in detail in the corresponding publication (DOI: 10.1007/s00348-025-03971-9).

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Terminus area change of 42 key glaciers of the Antarctic Peninsula from 2013 to 2023 derived from remote sensing and deep learning

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Glacier terminus area changes are derived using the rectilinear box method applied to time series of glacier calving front locations. Terminus changes are provided in text file format. The following glaciers are included in this data record: Akaga-Sinion Glacier system, Avsyuk Glacier, Birley Glacier, Bleriot Glacier, Boydell Glacier, Cadman Glacier, Carlson Glacier, Cayley Glacier, Clarke Glacier, Crane Glacier, Darvari-Boryana-Desudava Glacier system, Dinsmore-Bombardier-Edgeworth Glacier system, Drygalski Glacier, Drummond Glacier, Erskine Glacier, Fleming Glacier, Funk Glacier, Hariot Glacier, Heim Glacier, Hektoria-Green-Evans Glacier system, Hopkins Glacier, Hugi Glacier, Jorum Glacier, Keith Glacier, Malorad Glacier, McCance Glacier, Murphy Wilkinson Glacier, Otlet Glacier, Peter Glacier, Prospect Glacier, Punchbowl Glacier, Pyke Glacier, Remus-Snowshoe Glacier, Renard-Krebs Glacier, Sjogren Glacier, Stringfellow Glacier, Swithinbank Glacier, Temple Glacier, Trooz Glacier, Valot Glacier, Widdowson Glacier and Zaychar Glacier.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Terminus area change of 42 key glaciers of the Antarctic Peninsula from 2013 to 2023 derived from remote sensing and deep learning

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Supplementary STM data on Thianthrene-based Molecules on AU(111)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Supplementary raw STM data to the publication "Tuning the Planarity of an Aromatic Thianthrene-Based Molecule on Au(111)" https://arxiv.org/abs/2409.05489v1

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Supplementary STM data on Thianthrene-based Molecules on AU(111)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
European Pathfinder Open project Energy Storage in Molecules (ESiM)

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Monitoring Data of the openLAB Research Bridge – Load Test on PE 2.1

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Between May 5 and 7, 2025, extensive experiments were carried out on the openLAB research bridge. The test program was structured as follows: 
 
• Day 1: Static and dynamic measurements in the reference state 
• Day 2: Loading of the bridge using hydraulic jacks up to the ultimate limit state (yielding of prestressing steel) 
• Day 3: Local damage of prestressing tendons (cutting wires at several locations) 
 
The present dataset contains force and displacement measurements from the load test conducted on Day 2. The load was applied locally to precast element (PE) 2.1 (Field 2, PE axis 1) using two hydraulic jacks and increased up to 400 kN. Displacements were measured at five different positions along PE axis 1. 
 
Details on the measurement setup can be found in the attached file readme.txt. Additional information on the project, the bridge and reference-phase measurements is available under the following references: 
 
https://doi.org/10.1016/j.dib.2025.111624 
 
https://onlinelibrary.wiley.com/doi/10.1002/best.202300094 
 
Herbers, M., Richter, B., Jan-Hauke, B., Al-Zuriqat, T., Smarsly, K., & Marx, S. (2024, November). openLAB – A large-scale demonstrator for advancing digital twin developments of bridges. In 2024 fib Symposium (pp. 2057–2067). (see attachment) 
 
Value of the data: 
 
• Validation and calibration of finite element models (FEM) 
• Comparison with geodetic measurement methods (e.g., total station, drone survey) 
• Basis for interpreting data from additional monitoring systems (e.g., distributed fiber optic sensing, acceleration measurements, acoustic emission monitoring, etc.) 
 
The provided Python script enables data import, post-processing, and visualization of the measurement data (see example figures).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Monitoring Data of the openLAB Research Bridge – Load Test on PE 2.1

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The aim of the IDA-KI (Infrastructure Data Analysis with Artificial Intelligence) research project is to develop methods for the automated analysis and evaluation of monitoring data. The validation is carried out using real measurement data recorded on a 45 m long research bridge (openLAB). First, the reference condition of the bridge is measured with and without service loads over a period of one year. Subsequently, load tests are carried out in a targeted manner up to the severely damaged state and significant damage to the structure is actively induced. At the same time, redundant sensors will be damaged or manipulated so that a real database with specific signal characteristics for metrological and structural anomalies can be created. This will create an important basis for the development of evaluation methods (also based on machine learning) so that measurement errors in monitoring data can be corrected in the future and differentiated from structural damage.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Raw data of the paper "Brillouin spectroscopic investigation of corneal hydration and the impact of cross-linking therapy on water retention"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Raw Brillouin, Raman and PS-OCT data acquired within the project and paper "Brillouin spectroscopic investigation of corneal hydration and the impact of cross-linking therapy on water retention"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Raw data of the paper "Brillouin spectroscopic investigation of corneal hydration and the impact of cross-linking therapy on water retention"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Recently, Brillouin spectroscopy has been proposed as a promising non-invasive tool to evaluate corneal biomechanics, e.g. during corneal cross-linking (CXL) treatment. However, the impact of corneal hydration on the Brillouin shift hampers straightforward interpretation of the measurements, especially when judging on the success of the CXL procedure. Therefore, in this work, we first quantify the effect of corneal (de)hydration on the Brillouin shift revealing that reliable measurements are only possible under constant hydration conditions, which was subsequently achieved by immersing porcine eyes in solution and waiting until an equilibrium state was reached. Investigations showed that Brillouin spectroscopy evaluates the CXL effect mainly indirectly via reduced water uptake, while polarization-sensitive optical coherence tomography evaluates the CXL effect directly via changes in collagen fiber alignment and is therefore insensitive to corneal hydration. Raman spectroscopy is not indicating any alterations in the molecular structure revealing that new cross-links are not created due to the CXL procedure. Compared to large water retention in balanced salt solution, the missing water uptake in dextran-based (16%) solution hampers the evaluation of the CXL effect by Brillouin spectroscopy.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Brillouin spectroscopic investigation of corneal hydration and the impact of cross-linking therapy on water retention

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Supplementary materials for the publication "Assessing perceived landscape change from opportunistic spatio-temporal occurrence data"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
The repository contains:
- Input Data: Observed and Expected Frequencies abstracted using probabilistic HyperLogLog data
- Code: *.py
- Jupyter Notebooks: *.ipynb (Code)
- Jupyter Notebooks: *.md (code, comments)
- Jupyter Notebooks: *.html (code, comments, output, for archive purposes)
- Output: png (figures and graphics)
- Output: svg (selected figures and graphics)
- Chore: Other files necessary to reproduce figures and output

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Supporting Information for the publication "Assessing perceived landscape change from opportunistic spatio-temporal occurrence data" (Release v1.0.1).
Updates from the peer review process may not be reflected in this submission.
The original git repository with the latest versions can be found at https://gitlab.hrz.tu-chemnitz.de/ad/temporal_landscapes

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Supplementary materials for the publication "Assessing perceived landscape change from opportunistic spatio-temporal occurrence data"

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.descriptionInstrument[].value
Python, API, JupyterLab, CartoLab Docker

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Assessing perceived landscape change from opportunistic spatio-temporal occurrence data

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Statistical Evaluation of Strain Reading Anomalies in Distributed Fiber Optic Strain Sensing Measurements

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.relatedItem.IsCitedBy[].value
Richter, B., Ulbrich, L., Herbers, M., Marx, S.: Advances in data pre-processing methods for distributed fiber optic strain sensing. Sensors 24(23), 7454 (2024). https://doi.org/10.3390/s24237454

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
There are four .xlsx files available: "cracked.xlsx", "prestressed.xlsx", "unloaded.xlsx" and "Evaluation.xlsx".

The files "cracked.xlsx", "prestressed.xlsx" and "unloaded.xlsx" include the following datasheets:
- "Measurement_orginal_tsv" - the original .tsv-dataset of ODiSi6100 imported into excel
- "Measurement_reorientated" - the inverted dataset used for manual strain reading anomalie detection
- "Measurement_marked" - the measurement with marked strain reading anomalies (marked as "SRA")

The file "Evaluation.xlsx" includes datasheets for each load step for "SRA" and "NaN" (dropouts), which include information about each registrated SRA and dropout. Additionally, "Evaluation.xlsx" includes a datasheet called "Categorie" in order to clarify the assignment of the SRA to a category regarding their neighbouring datatype (accurate strain value, SRA, dopout).

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Three different strain measurements with distributed fibre optic sensors using the ODiSI 6100 in different load conditions have been analyzed regarding the occurence of strain reading anomalis. Therefore, strain reading anomalies have been marked manually. 

The load states include a cracked state ( prestressed concrete specimen in 4-point-bending with increasing load), a prestressed state (concrete specimen after prestressing, small cracking) and an unloaded state (prestressed concrete specimen). The used EpsilonSensors (3 mm, ripped) of NerveSensors have been embedded in the concrete specimens. All measurements have been carried out using a gage pitch of 1.3 mm and frequencies between 1 and 6 Hz. The key has been created before concreting of the specimens.

The datasets can be used f.e. for the evaluation of pre-processing algorithms or the creation of benchmark datasets.

The datasets have been choosen due to their bad measurement quality and are no measure for the overall quality of distributed fibre optic strain sensing measurements. The quality issues might be due to dirty connections, scratched connections or sharp strain gradients.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Statistical Evaluation of Strain Reading Anomalies in Distributed Fiber Optic Strain Sensing Measurements

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
2D Imaging Simulation from 3D Particle Data: Extended Python Notebooks, Simulation Results

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains two distinct parts: Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. This dataset extends a previous one (https://doi.org/10.25532/OPARA-479) by an additional method of particle surface area determination and a larger dataset of randomly oriented projections – ten instead of the previous three. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
2D Imaging Simulation from 3D Particle Data: Extended Python Notebooks, Simulation Results

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
Correlation Clustering of Organoid Images: Data

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.contributor.Producer[].value
Technology Development Studio of Max Planck Institute of Molecular Cell Biology and Genetics

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.contributor.RightsHolder[].value
Anne Grapin-Botton (Max Planck Institute of Molecular Cell Biology and Genetics)

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.description.TechnicalInfo[].value
Images of organoids and masks are in 16 bit TIFF format. 

Images in Train-100 and Test-100 are named as "single_organoid_<organoid_class><image_number>.tif" while masks are named as "single_organoid_<organoid_class><image_number>_mask.tif". The first one to two digits define the organoid class, 1 through 10 (organoid_class). The last two digits enumerate the images within one organoid class, 00 through 09 (image_number).
Images in Test-30 are named as "single_organoid_<organoid_class><image_number>.tif", while masks are named as "single_organoid_<organoid_class><image_number>_mask.tif". The first two digits define the organoid class, 11 through 13 (organoid_class). The last two digits enumerate the images within one organoid class, 00 through 09 (image_number).
Images in Test-Unlabeled are named as "single_organoid_<image_number>.tif" while masks are named as "single_organoid_<image_number>_mask.tif". The image number is consecutive, i.e., from 1 through 999.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.relatedItem.IsSupplementTo[].value
J. Presberger, R. Keshara, D. Stein, Y. H. Kim, A. Grapin-Botton and B. Andres. Correlation Clustering of Organoid Images. In: GCPR 2024.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.datacite.resourceType[].value
This collection contains the following four datasets.
Train-100 contains 100 images and masks of individual organoids. It contains images of ten different organoid classes, and ten distinct images for each organoid class.
Test-100 contains 100 images and masks of individual organoids. It contains images of ten different organoid classes, and ten distinct images for each organoid class. The organoid classes in this dataset are the same as in Train-100. 
Test-30 contains 30 images and masks of individual organoids. It contains images of three different organoid classes, and ten distinct images for each organoid class. The organoid classes are distinct from those in Train-100 and Test-100.
Test-Unlabeled contains 1000 images and masks of individual organoids.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
The data considered in: J. Presberger, R. Keshara, D. Stein, Y. H. Kim, A. Grapin-Botton and B. Andres. Correlation Clustering of Organoid Images. In: GCPR 2024.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Correlation Clustering of Organoid Images: Data

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
In biological and medical research, scientists now routinely acquire microscopy images of hundreds of morphologically heterogeneous organoids and are then faced with the task of finding patterns in the image collection, i.e., subsets of organoids that appear similar and potentially represent the same morphological class. We implement models and algorithms for correlating organoid images, i.e., for quantifying the similarity in appearance and geometry of the organoids they depict, and for clustering organoid images by consolidating conflicting correlations. For correlating organoid images, we implement and compare two alternatives, a partial quadratic assignment problem and a twin network. For clustering organoid images, we employ the correlation clustering problem. Empirically, we learn the parameters of these models, infer a clustering of organoid images, and quantify the accuracy of the inferred clusters, with respect to a training set and a test set we contribute of state-of-the-art light microscopy images of organoids clustered manually by biologists.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.title[].value
Correlation Clustering of Organoid Images

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.name
GRK 2802: Vaporization of Vanadium Pentoxide from Iron and Steel-Making Slags

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Vanadium is present in increased amounts in slag because of the production of certain steel grades and manufacturing technologies. In terms of circular economy, vanadium should be recovered from the slag, to use it again as an alloying element. Only a few studies on the extraction of vanadium have been carried out with synthetic slags. The core of this study is the characterisation of the extraction behaviour of vanadium by evaporation of its oxides from real iron and steel-making slags. The effect of various influencing factors on the evaporation behaviour of the slags should be demonstrated. The evaluation of the results should also take crucible wear into account. In addition, the phases formed in the slag are to be analysed. For this purpose, several melting tests are carried out with different blast furnace slags. To determine the vaporised components, the slags are analysed by an X-ray fluorescence spectrometer.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
GRK 2802: Vaporization of Vanadium Pentoxide from Iron and Steel-Making Slags

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
The focus of this doctoral project is to investigate the interaction of electrical steels (transformer steels), with high-silicate purity ladle slags free of running slag, with MgO-C products based on recyclates and environmentally friendly binders. The formation of ladle glaze can be studied by immersing a MgO-C product in a tracer-labelled slag during an immersion test. Investigations of the thermophysical properties of the liquid, killed/unkilled electrical steels and liquid slag as a function of temperature and SiO2 content, combined with thermodynamic calculations using FactSage software, make it possible to determine their influence on the interaction with MgO-C and ladle glaze formation. The influence of ladle glaze on the oxidic inclusion population, on the pick-up effect (undesirable increase of alloying elements) and reoxidation of the alloying elements is investigated by ladle glaze dissolution in the electric steel melt accompanied by chemical analysis and optical microscopy in combination with AFA (Automatic Feature Analysis) in the P-SEM. Research into the effect of the oxidic, non-metallic inclusions that form from ladle glaze on clogging in the spout nozzle based on recycled Al2O3-C is another focus.

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The dataset was generated within the framework of the Research Training Group GRK 2802 (project ID: 461482547) funded by the German Research Foundation (DFG).

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GRK2802_P4_Interaction of electrical steels with MgO-C products based on recyclates and environmentally friendly binders

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PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)

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Pictures
ROOT files 
Scripts for analysis

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Proton therapy requires range verification in order to exploit its full potential. One of the most promising approaches is to monitor prompt gamma-rays produced by nuclear interactions of the therapeutic particles in the patient tissues. In our paper, we test PETsys electronics with a detector with a wide energy range from 100 keV to 15 MeV. We tested what time resolution we could achieve as a high time resolution is required to achieve millimetric precision in the proton range. PETsys should survive high count rates and the fraction of pile-up events should be low or separatable. We are investigating a full acceptance approach with increased granularity in order to reduce the size of the scintillators and consequently the count rate per channel. Ideally, we want to stack the scintillators in matrices that require suitable multi-channel photo-multipliers and a fitting acquisition system. Here, we present two geometries of CeBr3 crystals 5 × 5 × 20 mm3 and 10 × 10 × 30 mm3, together with modern silicon photo-multipliers (SiPM) adapted to work with the PETsys TOFPET2 ASIC. The TOFPET2 ASIC was developed for Time-of-Flight Positron Emission Tomography (TOF-PET) applications. Here are our data measured for the publication for time resolution and coincidence time resolution, energy resolution with AmBe source, and dead time studies.

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PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)

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PETsys Electronics Evaluation Kit, PMT, CAEN ADC v1730

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.opara.project.description[].value
Proton therapy requires range verification in order to exploit its full potential. One of the most promising approaches is to monitor prompt gamma-rays produced by nuclear interactions of the therapeutic particles in the patient tissues. In our paper, we test PETsys electronics with a detector with a wide energy range from 100 keV to 15 MeV. We tested what time resolution we could achieve as a high time resolution is required to achieve millimetric precision in the proton range. PETsys should survive high count rates and the fraction of pile-up events should be low or separatable. We are investigating a full acceptance approach with increased granularity in order to reduce the size of the scintillators and consequently the count rate per channel. Ideally, we want to stack the scintillators in matrices that require suitable multi-channel photo-multipliers and a fitting acquisition system. Here, we present two geometries of CeBr3 crystals 5 × 5 × 20 mm3 and 10 × 10 × 30 mm3, together with modern silicon photo-multipliers (SiPM) adapted to work with the PETsys TOFPET2 ASIC. The TOFPET2 ASIC was developed for Time-of-Flight Positron Emission Tomography (TOF-PET) applications. Here are our data measured for the publication for time resolution and coincidence time resolution, energy resolution with AmBe source, and dead time studies.

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PGT Prompt Gamma Timing PETsys data for publication at IKTP (PGT)

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Predictive maintenance demonstrator dataset with individual load histories

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A thorough documentation of this dataset, including experimental setup, creation of the signals, postprocessing and data access is provided in the textfile dataset_description.txt in the dataset.

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The research field of predictive maintenance aims to develop methods that are capable of predicting component failure before it occurs. This way, maintenance can be conducted with the component is still intact, while at the same time increasing resource efficiency by exploiting the fatigue strength of the component. In order to enable predictive maintenance, sensor data of the component is required, from which the predictions can be generated. Furthermore, the individual loads acting on each component of a system depend on the operating conditions of the systems and are therefore different between different systems. Failure therefore occurs as a result of individual load histories.

Virtual sensing methods aim to predict unmeasured physical quantities from available measurement data. These methods offer significant benefits to predictive maintenance, since virtual sensors can be used to estimate quantities that are difficult to measure. In fleet situations, where many identical systems with different usage histories are monitored, virtual sensors can also be deployed as a replacement for some sensors of the system. This can be done in a data-driven manner by installing sensors for all quantities of interest at one reference system, whose measurement data is used to parameterize the virtual sensor model, so that it can then be deployed for the remaining standard systems.

In many real applications, the time to failure is in the range of years, complicating the development and validation of predictive maintenance and virtual sensing approaches. The aim of this dataset is therefore to provide a demonstrator example where failure occurs based on individual load histories. Its sensor setup is designed to provide a virtual sensor use case with independent training and testing data, so that the dataset can be used for algorithm development and benchmarking purposes.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.abstract[].value
Predictive maintenance aims to develop methods that are capable of predicting component failure before it occurs. Virtual sensing methods predict unmeasured physical quantities from available measurement data. These methods offer significant benefits to predictive maintenance, since virtual sensors can be used to estimate quantities that are difficult to measure. In many real applications, the time to failure is in the range of years, complicating the development and validation of predictive maintenance and virtual sensing approaches. This dataset provides a demonstrator example where failure occurs based on individual load histories. The sensor setup consists of simple notched steel specimens, which are clamped between two servo-hydraulic cylinders of a fatigue test bench. It is designed to provide a virtual sensor use case with independent training and testing data, so that the dataset can be used for algorithm development and benchmarking purposes.

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Predictive maintenance demonstrator dataset with individual load histories

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The aim of the project is to increase the availability of a tramcar fleet by evaluating standard sensors that are available in every tramcar. Insights into the condition of tramcar and track enable target-oriented fleet usage and reduce follow-up maintenance costs. The increase in rail reliability increases the acceptance of public transport among the population. Financial advantages arise for operators and users.

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LRVTwin - A digital twin for light rail vehicles