cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35097/1449
title: In Process Porosity Detection Dataset by Means of Structure-Borne Acoustic Emission Measurement Technology in PBF-LB/M by wbk Institute of Production Science and ZEISS - Part 2

description: #In-Process Porosity Detection Dataset by Means of Structure-Borne Acoustic Emission Measurement Technology in PBF-LB/M by wbk Institute of Production Science and ZEISS Please see provided Jupyter Notebook "README" for further information. This dataset is part 2 of 2.
DOI: 10.35097/1152
title: In-situ micro-CT scan of a carrot disc during freeze-drying

description: Über den Ablauf einer Gefriertrocknung auf Mycrometerebene gibt es bislang nur modellhafte Vorstellungen. Über einen speziellen Versuchsaufbau ist es nun möglich, Mycro-CT-Messungen während der Gefriertrocknung durchzuführen. In den gezeigten Daten wurde eine gefrorene Karottenscheibe in einen vakuumfesten, runden Plexiglascontainer gelegt, mittels einer drehbaren Absaugung evakuiert und an der Topo/Tomo-Beamline des IPS mit Synchrotronstrahlung gescannt. Dadurch konnte in einem Abstand von 5 Minuten die Porenstruktur der Karottenscheibe visualisiert werden. Auch die Unterschiede zwischen mit Eis gefüllten Poren und schon leeren, getrockneten Poren konnten aufgelöst werden. Dies macht es möglich, den Verlauf einer Gefriertrocknung zu verfolgen und nachzuvollziehen.
description: The development of a freeze-drying process on a micrometer scale hast not yet been visualized. With a new experimental setup, micro-CT measurements can be performed during a freeze-drying process. For the shown data, a frozen carrot disc was placed inside a round perplex container, which was evacuated through a rotatable hose. The whole container was then positioned in synchrotron radiation in the Topo/Tomo-beamline of the IPS, which enabled a full scan of the carrots pore structure each 5 minutes. Hereby, the differences between ice-filled pores and already dried, empty pores could be visualized. This enabled a visualization of the progress of a freeze-drying process.
description: The data is given as a .tif stack and can be viewed with Fiji/ImageJ. Simply drag and drop the file and you can scroll between the different times during drying. The time of the process is given in the form hh:mm in the top left corner. Bright structures are dense (cell walls, ice), while darker structures are less dense (vacuum, air). Notice that X and Y describe the horizontal dimensions of the carrot disc, while Z describes the height. As a result, the scan in the X-Y-layer shows a horizontal cut through the carrot, while the scan in the X-Z-layer shows a vertical cut.
DOI: 10.35097/1278
title: Industrial Machine Tool Element Surface Defect Dataset

description: Using Machine Learning Techniques in general and Deep Learning techniques in specific needs a certain amount of data often not available in large quantities in some technical domains. The manual inspection of Machine Tool Components, as well as the manual end of line check of products, are labour intensive tasks in industrial applications that often want to be automated by companies. To automate the classification processes and to develop reliable and robust Machine Learning based classification and wear prognostics models there is a need for real-world datasets to train and test models on.
description: The dataset contains 1104 channel 3 images with 394 image-annotations for the surface damage type “pitting”. The annotations made with the annotation tool labelme, are available in JSON format and hence convertible to VOC and COCO format. All images come from two BSD types. The dataset available for download is divided into two folders, data with all images as JPEG, label with all annotations, and saved_model with a baseline model. The authors also provide a python script to divide the data and labels into three different split types – train_test_split, which splits images into the same train and test data-split the authors used for the baseline model, wear_dev_split, which creates all 27 wear developments and type_split, which splits the data into the occurring BSD-types. One of the two mentioned BSD types is represented with 69 images and 55 different image-sizes. All images with this BSD type come either in a clean or soiled condition. The other BSD type is shown on 325 images with two image-sizes. Since all images of this type have been taken with continuous time the degree of soiling is evolving. Also, the dataset contains as above mentioned 27 pitting development sequences with every 69 images. Instruction dataset split The authors of this dataset provide 3 types of different dataset splits. To get the data split you have to run the python script split_dataset.py. Script inputs: split-type (mandatory) output directory (mandatory) Different split-types: train_test_split: splits dataset into train and test data (80%/20%) wear_dev_split: splits dataset into 27 wear-developments type_split: splits dataset into different BSD types Example: C:\Users\Desktop&gt;python split_dataset.py --split_type=train_test_split --output_dir=BSD_split_folder Result: ./BSD_slit_folder/train/ and ./BSD_slit_folder/test/
DOI: 10.35097/1344
title: Influence of refractive index differences on the signal strength for Raman-spectroscopic measurements of double emulsion droplets

description: Double emulsions show great potential for encapsulating active substances and protecting them against external influences. However, due to their complex structure, double emulsions tend to become unstable during storage. Research on double emulsions therefore focuses on maintaining their microstructure during their shelf life. Optical measurement methods such as Raman spectroscopy have hardly been used to date to analyze the microstructure of double emulsions mainly due to multiple scattering effects. This study concentrates on reducing scattering effects by matching the refractive indices of the individual emulsion phases. Double emulsions with adapted refractive indices are investigated using Raman spectroscopy. The refractive indices of the inner and outer water phases are varied, while the refractive index of the oil phase is kept constant. In order to evaluate the signal of the inner water phase the same amount of tracer is present in all inner phases. For individual phase boundaries of single droplets, the refractive index matching plays a minor role. However, if there are many droplets with correspondingly numerous phase boundaries, which leads to multiple scattering during the measurement, the matching has a significant influence on the signal strength of the inner phase. When measuring double emulsions, the phases should always be matched if possible, as this results in higher signals. This in turn improves the sensitivity of the measurement.
description: There are seven different files: 1-5: "Auswertung_16%AN" ... "Auswertung_61%AN" Those files contain all spectroscopic raw data from the experiments and the baseline correction for the ammonium nitrate peak for each measurement Tab "Rohdatenpython": A phython programm imports the spectroscopic data from txt.files to excel (simple copy&paste) Tab "Rohdaten": Data are copied from Rohdatenphyton to this tab. Negative wavenumbers (-88 till -1) are deleted Tab "Auswertung 1.2": Integral of each ammonium nitrate peak is caluclated Tab "Auswertung 2": Summary of all peaks including x-y-diagramm, which shows the linearity between the measurements 6: "Gesamtauswertung": Tab "Diagramm_W1": Diagramm of W1-Matching Tab "Diagramm_W2": Diagramm of W2-Matching Tab "Gesamt": Summary of the measured data (Tab 16%AN ... 61%AN), refractive indizes, linearity of glycerol. It is mentioned, which data is used for which figure/table Tab "Diagramm_Residuen_W2": Residuen as function of W2-Matching Tab "Diagramm_Residuen_W1": Residuen as function of W1-Matching Tab "Multiple lineare Regression": Calculation of the mlr and residues Tab "16%AN" ... "61%AN": Copy of the tabs "Auswertung2" 7. "Spectra_Fig1": All spectroscopic raw data and diagrams regarding the substance system
DOI: 10.35097/1396
title: Infraschalldaten Swabian MOSES 2021

description: Im Rahmen der Swabian MOSES Kampagne 2021 aufgezeichnete Infraschalldaten
description: Infrasound data collected by Swabian MOSES 2021
description: Further details on the infrasound records are provided in "Swabian MOSES 2021: An interdisciplinary field campaign for investigating convective storms and their event chains" by M. Kunz et al., Front. Earth Sci. Sec. Atmospheric Science, 2022, DOI: 10.3389/feart.2022.999593
DOI: 10.35097/1124
title: Inovesa run 5864

description: Simulation results tagged f05864 (see appendix A) generated using Inovesa (10.5281/zenodo.1243264). The data is usable utilizeing e.g. the scripts provided at https://github.com/Inovesa/examples.
DOI: 10.35097/1121
title: Inovesa run f04623

description: Simulation results tagged f04623 (see appendix A) generated using Inovesa (10.5281/zenodo.1243264). The data is usable utilizeing e.g. the scripts provided at https://github.com/Inovesa/examples.
DOI: 10.35097/1122
title: Inovesa run f05135

description: Simulation results tagged f05135 (see Appendix A in 10.5445/IR/1000084466) generated using Inovesa (10.5281/zenodo.1243264). The data is usable utilizeing e.g. the scripts provided at https://github.com/Inovesa/examples.
DOI: 10.35097/1123
title: Inovesa run f05493

description: Simulation results tagged f05493 (see appendix A) generated using Inovesa (10.5281/zenodo.1243264). The data is usable utilizeing e.g. the scripts provided at https://github.com/Inovesa/examples.
DOI: 10.35097/1451
title: Input Dataset for RICHARD 1.0 - Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data

description: Here, we provide the input dataset, used for the introduction of version 1.0 of the RICHARD algorithm, a Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data, e.g. in satellite measurement datasets (level-2 and above) or atmospheric chemistry model output. The overall goal of the algorithm is to identify "hotspot" areas in which local enhancements of an atmospheric constituent can only occur due to strong local emissions. To detect hotspot areas, we use a mathematical method that combines spatiotemporal proxy data for calibration purposes and a selection algorithm in a novel way.
description: The data set consists of a csv file containing the coordinates of the pattern selected by the user and the ICON-ART simulation used in the corresponding paper, consisting of 730 individual model time steps. With the model simulation and csv file, the user can follow the instructions for the command line operations in the paper to run the RICHARD algorithm.
DOI: 10.35097/1316
title: Integrated shimming enables independently parallel NMR detection

description: Data sets used for plotting graphs in the figures and COMSOL files for the manuscript of "Integrated shimming enables independently parallel NMR detection"
DOI: 10.35097/1314
title: Kinetics, SOA yields and chemical composition of secondary organic aerosol from β-caryophyllene ozonolysis with and without nitrogen oxides between 213 and 313 K

description: The data in the Excel file is related to the publication Gao et al., ACP, 2022. Each sheet of the Excel file corresponds to one plot in the publication. The data originates mainly from FIGAERO-CIMS and HR-ToF-AMS (both Aerodyne Inc.) measurements. “Kinetics, SOA yields and chemical composition of secondary organic aerosol from β-caryophyllene ozonolysis with and without nitrogen oxides between 213 and 313 K” Linyu Gao1,2; Junwei Song1,2; Claudia Mohr3; Wei Huang4; Magdalena Vallon1; Feng Jiang1,2; Thomas Leisner1,5; and Harald Saathoff1 1 Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany 2 Institute of Geography and Geoecology, Working Group for Environmental Mineralogy and Environmental System Analysis, Karlsruhe Institute of Technology, Karlsruhe, Germany 3 Department of Environmental Science, Stockholm University, Stockholm, Sweden 4 Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Helsinki, Finland 5 Institute of Environmental Physics, Heidelberg University, Heidelberg, Germany Correspondence to: Linyu Gao (linyu.gao@kit.edu) and Dr. Harald Saathoff (harald.saathoff@kit.edu) Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1067, in review, 2022. (accepted on April 4th, 2022)
description: The data in the Excel file is related to the publication Gao et al., ACP, 2022. Each sheet of the Excel file corresponds to one plot in the publication. The data originates mainly from FIGAERO-CIMS and HR-ToF-AMS (both Aerodyne Inc.) measurements (see corresponding readme.pdf file).
DOI: 10.35097/1687
title: Knowledge-driven design of Solid-Electrolyte Interphases on lithium metal via multiscale modelling

description: Due to its high energy density, lithium metal is a promising electrode for future energy storage. However, its practical capacity, cyclability and safety heavily depend on controlling its reactivity in contact with liquid electrolytes, which leads to the formation of a solid electrolyte interphase (SEI). In particular, there is a lack of fundamental mechanistic understanding of how the electrolyte composition impacts the SEI formation and its governing processes. Here, we present an in-depth model-based analysis of the initial SEI formation on lithium metal in a carbonate-based electrolyte. Thereby we reach for significantly larger length and time scales than comparable molecular dynamic studies. Our multiscale kinetic Monte Carlo/continuum model shows a layered, mostly inorganic SEI consisting of LiF on top of Li2CO3 and Li after 1 µs. Its formation is traced back to a complex interplay of various electrolyte and salt decomposition processes. We further reveal that low local Li+ concentrations result in a more mosaic-like, partly organic SEI and that a faster passivation of the lithium metal surface can be achieved by increasing the salt concentration. Based on this we suggest design strategies for SEI on lithium metal and make an important step towards knowledge-driven SEI engineering.
description: The published source data file contains the underlying data of all figures of the manuscript and Supplementary Information of the publication “Knowledge-driven design of Solid-Electrolyte Interphases on lithium metal via multiscale modelling”. The source data of each figure is presented in a single sheet named after the corresponding figure. Legends are given if required. Please note that the source data of the kMC boxes is provided as xyz coordinates and that each species number corresponds to a chemical species (cf. legend). Numerical details: The kMC/continuum model was implemented in MATLAB R2021a and differential equations were solved with the ode15s solver. The calculations were performed on an i7-8000 CPU with 16 GB RAM. For detailed information on the modelling approach see the Method section in the manuscript and the Supplementary Information.
DOI: 10.35097/1203
title: Kristallisation einer Hexadecan-in-Wasser Emulsion unter dem Polarisationsmikroskop

description: Video
DOI: 10.35097/1185
title: Large-eddy simulation during the Wind and Storms Experiment (WASTEX)

description: The dataset contains model output from a large-eddy simulation of windstorm Thomas, which was sampled on 23 February 2017 during the WASTEX field campaign (see Pantillon et al. 2018). The ICOsahedral Nonhydrostatic (ICON) model was run with one-way nesting on four nested domains with 623, 311, 156 and 78 m grid spacing and 150 vertical levels over the Upper Rhine Valley. Only wind from 78 m data is available here. The data are saved in hourly files, e.g. at 06 UTC: LL_078m_wind_3D_10m_DOM04_ML_20170223T060000Z_zoom Files are in netCDF format with the following structure: netcdf LL_078m_wind_3D_10m_DOM04_ML_20170223T060000Z_zoom { dimensions: time = UNLIMITED ; // (6 currently) lon = 450 ; lat = 450 ; height = 40 ; bnds = 2 ; height_2 = 40 ; variables: double time(time) ; time:standard_name = "time" ; time:units = "day as %Y%m%d.%f" ; time:calendar = "proleptic_gregorian" ; time:axis = "T" ; double lon(lon) ; lon:standard_name = "longitude" ; lon:long_name = "longitude" ; lon:units = "degrees_east" ; lon:axis = "X" ; double lat(lat) ; lat:standard_name = "latitude" ; lat:long_name = "latitude" ; lat:units = "degrees_north" ; lat:axis = "Y" ; double height(height) ; height:standard_name = "height" ; height:long_name = "generalized_height" ; height:axis = "Z" ; height:bounds = "height_bnds" ; double height_bnds(height, bnds) ; double height_2(height_2) ; height_2:standard_name = "height" ; height_2:long_name = "generalized_height" ; height_2:axis = "Z" ; float ua(time, height, lat, lon) ; ua:standard_name = "eastward_wind" ; ua:long_name = "Zonal wind" ; ua:units = "m s-1" ; ua:param = "2.2.0" ; float va(time, height, lat, lon) ; va:standard_name = "northward_wind" ; va:long_name = "Meridional wind" ; va:units = "m s-1" ; va:param = "3.2.0" ; float wa(time, height_2, lat, lon) ; wa:standard_name = "upward_air_velocity" ; wa:long_name = "Vertical velocity" ; wa:units = "m s-1" ; wa:param = "9.2.0" ; Three-dimensional wind components are stored in variables "ua", "va" and "wa" as function of coordinates "time", "height/height_2", "lat" and "lon". They are saved for the 40 first vertical levels on a regular latitude/longitude grid on a zoomed 35x35km² domain in the neighborhood of Karlsruhe.
DOI: 10.35097/1249
title: Large characteristic lengths in 3D chiral elastic metamaterials

description: Overview of the folder structure: --------------------------------- The folder here correspond to the section in the Communications Materials paper "Large characteristic lengths in 3D chiral elastic metamaterials" by Tobias Frenzel, Vincent Hahn, Patrick Ziemke, et. al. - 3. Microstructure Calculations Contains both beam theory and finite volume element calculations of the microstructure. - Results Beam Thoery contians the tabulated results obtaint by the abaques files as .dat and .txt files as well as the abaqus files used to calculate the data in the paper, the data stored here is displayed in Fig. 3, Fig. 5, Fig. S1, Fig. S3. - Results Volume Elements contians the tabulated results obtaint by the comsol files as .dat and .txt files as well as the comsol files used to calaculate the data. in the paper, the data stored here is displayed in Fig. 5, Fig. S1, Fig. S3. - 4. Micropolar Continuum Elasticity Contains the Comsol files with the micropolar continuum implementation - Relsuts Micropolar Elasticity contians the tabulated results obtaint by the comsol files as .dat and .txt files as well as the corresponding comsol file - 5. Experiments Contains all the picture obtained during measurement for the different samples - Multifoucs setup in the subfolder, the data for varius scaling Factors N are included. Every subfolder also contains the text data "rotation_angle.txt" with the extracted rotation angles - Nanoscribe setup same es in the previous folder, but for different samples - Fabrication contains all files nessecary to print the structures using the direct laser writing setups Treatment of the experimental data ---------------------------------- - a grid was created with the regions of intrest that should be tracked for evaluation using "grid_generator.m" - a filelist including all picture names was created using "filelist_generator.m" - the image cross-correlation was performed using "automate_image2" - using the matlab scriped "evaluation_roation.m" or "evaluation_rotation_Graphics.m" that is included in every folder "N_1", "N_c", etc. the rotation angle was evaluated and written to a text file "rotation_angle.txt" in the same folder.
DOI: 10.35097/1300
title: Light spectra measured inside the AIDA aerosol and cloud simulation chamber

description: Spektren der Lichtquelle in der AIDA Aerosol und Wolkenkammer
description: ADDITIONAL INFORMATION ON THE SPECTRAL LIGHT MEASUREMENTS IN THE AIDA AEROSOL AND CLOUD SIMULATION CHAMBER TO CHARACTERIZE AN LED BASED SOLAR SIMULATOR
description: The spectra were measured on January 9th, 2020 at the heights above the chamber bottom of 1 m, 2.1 m, 2.9 m and 4.0 m with an uncertainty of ±10 cm in the center of the chamber with an Opsytec SR Pro UV-Vis Spectrometer connected to a UV-diffusor in W m-2 (spectral resolution: 2.3 nm (FHWM)) . The Opsytec SR Pro UV-Vis Spectrometer had been calibrated on December 11th, 2019 by Opsytec Dr. Gröbel GmbH showing spectral differences of less than 0.3 nm and an accuracy of ±9% for the radiometric results. The AIDA light source was operated at the highest output possible at room temperature for this characterization. Drastic outliers were considered as measurement artefacts and replaced by the median. The median was calculated with 15 points and every point exceeding the median by a threshold of 0.04 was replaced with the median. This resulted in the replacement of at most 21 out of the 2048 data points and does not change the general spectral information. The data measured at different heights were saved as a space separated text file: SRPro-central-measurements-20200109.txt. Please note that these data are related to the following publication in the AMT Special Issue: Simulation chambers as tools in atmospheric research: Vallon, M., Gao, L., Jiang, F., Krumm, B., Nadolny, J., Song, J., Leisner, T., and Saathoff, H.: LED based solar simulator to study photochemistry over a wide temperature range in the large simulation chamber AIDA, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-362, in review, 2021.
DOI: 10.35097/1392
title: Light-Sheet 3D Microprinting via Two-Colour Two-Step Absorption

description: Research data corresponding to the publication "Light-Sheet 3D Microprinting via Two-Colour Two-Step Absorption". Please refer to the readme files for information on the folders' contents.