Point cloud file (e57) of the main campus of Hamburg University of Technology containing intensity and color information for 125 separate scans.

Nanoporous metals with their complex microstructure represent an ideal candidate for method developments that combine physics, data and machine learning. They allow to tune the solid fraction, ligament size and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical properties. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure-property relationships. Efficient finite element beam modeling techniques are used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A data base is provided that uses consistent settings of structural and mechanical properties on the microscale for which the elastic-plastic macroscopic compression behavior and the hardness is predicted. Ligament geometries of two different initial solid fractions are chosen, for which the structural randomization, the connectivity density, the yield stress and the work hardening rate are randomly varied in large ranges. This data base allows deriving the microstructure-properties relationships of nanoporous metals by means of dimensionality reduction, data mining and machine learning.

Laser powder bed fusion (PBF-LB/M) of metals belongs to the advanced additive manufacturing processes on the brink of industrialization. Successful manufacturing often requires the utilization of support structures to support overhangs, dissipate heat, and prevent distortion due to residual stresses. Since the support structures result in increased costs, research, as well as industry, aim at optimizing the application of those or the support structures themselves. New approaches are validated with individual use cases, though, preventing an objective comparison of optimization strategies. This paper contributes to the advance of support structure optimization by providing a benchmark strategy including part geometries, which enables to evaluate technical as well as economical aspects of support structures or support strategies. The benchmark process is demonstrated with the help of the currently most used block and pin support structures.

The available data include the raw data and calculations on chemical analyses of various urban and rural green wastes. The wastes were selected in the context of a biorefinery concept and characterised within the FLEXIBI project. The data can be used to evaluate potentials for different recovery pathways. One pathway included in the evaluation is anaerobic digestion. The description of the methods, calculations and results is included in the PDF file.

Simulated and approximated values for the article "Analysis of semi-open queueing networks using lost customers approximation with an application to robotic mobile fulfilment systems".

This dataset was used to characterize devolatilized wood pellets for fluidized bed applications. The results and the details about the methodology are presented in "Jarolin, K.; Wang, S.; Dymala, T.; Song, T.; Heinrich, S.; Shen, L.; Dosta, M. (2021): Characterizing devolatilized wood pellets for fluidized bed applications. In: Biomass Conversion and Biorefinery." In this work, three types of wood pellets, called Type A (white sawdust pellet from spruce wood), Type B (brown sawdust pellet from foliage and coniferous wood), and Type C (brown sawdust pellet from mixed, unspecified wood types) were characterized after devolatilization. Three different methods for devolatilization were applied: 1. Muffle Furnace (MF) at 900°C 2. Fluidized Bed Reactor (FBR) at 900°C 3. empty fluidized bed reactor with high gas flow rate (HFR) at 900°C 4. empty fluidized bed reactor with low gas flow rate (LFR) at 900°C The raw data from the three main methods for characterization are provided in this repository: 1. compressionTest: Formatted force-displacement measurements of quasi-steady, uniaxial compression tests in the radial direction. The data was used to study breakage behavior. 2. impactTest: Mass loss of the pellets due to impact on a steel plate at different velocities. The mass loss is given after 10 consecutive impacts. In case of breakage, the masses of the fragments are given. The data was used to study resistance to impact and the continuous wear of the pellets. 3. uCT: Micro-computed tomography images from the same pellets before and after devolatilization as well as images of devolatilized pellet for reference. One of the pellets' end-faces was ground into an angle to allow the identification of the orientation. The data was used to study the change in the porosity of the pellets. Please refer to the linked publication for further details.

This is the readme file for all data used within the publication "Exploring key ionic interactions for magnesium degradation in simulated body fluid - a data-driven approach" Created: 25th January 2020 by Dr. Berit Zeller-Plumhoff Contact: berit.zeller-plumhoff@hzg.de The publication is based on a number of experimental and computational data sets. These are: - microCT imaging data (raw and processed) before and after degradation - Fiji/ImageJ scripts for automated processing and read-out of the imaging data - SEM+EDX imaging data (raw and processed) - Matlab scripts for processing of the EDX data - Hydra/Medusa output files for precipitation prediction - Jupyter Notebooks for the final analysis and plotting of the processed data Due to the large amount of imaging data (~70GB per microCT dataset, raw+processed), we are publishing only the processed data of two exemplary data sets - one prior to degradation and one after degradation of the sample. All other data is stored and will be provided upon request. Where a differentiation of datasets into 'C' and 'D' is denoted, this corresponds to the ph-adjusted and non-adjusted cases, respectively, as described in the publication. Please find below a short description of all datasets that are made available. For better handling the data was divided into nine .zip files. The folder "microCT_sol1_sample1_degraded" contains the exemplary microCT data for the first sample degraded in the pH-adjusted solution 1, following data processing after reconstruction. All image files are in .tif format, with a 1.6 micrometer isotropic voxel size. The folder "cropped" contains the manually cropped filtered images of the 0.7 mm ROI (from the sample centre) after "script filtering.ijm". Using the trainable WEKA segmentation with the "classifier.model" file images were segmented and saved in "seg_weka" by utilising the script "Segmenting_with_weka.ijm". Some files had to be manually corrected - these were saved in "seg_weka_edited". Finally, the images were aligned along their longitudinal axis using the script "BW_Conversion_Alignment.ijm" and saved in the folder "aligned". Folders "microCT_sol1_sample1_degraded_raw" and "microCT_sol1_sample1_degraded_reco" are the raw projection data and tomogaphic reconstruction of this dataset. The folder structure for the initial scan of sample 1 (sample_C1_initial) is simimilar; however, it contains only a general segmented folder "segmented_all" with the filtered, segmented and aligned images and their respective outline (subfolder "outline") for the calculation of the initial sample surface area. The image processing was performed using the "filtering.ijm" script. Again, folders "sample_C1_initial_raw" and "sample_C1_initial_reco" are the raw projection data and tomogaphic reconstruction of this dataset. The Fiji scripts are saved in the folder "Fiji_Scripts", divided into those used for before and after degradation scans. Note that all file paths require changing for the scripts to run.The scripts "Data_Gathering_Initialscan_BZP_C_D.ijm" and was used to gather outline data from the entire sample, outline data from the ROI, and volume data from the ROI. The file "edx_analysis.m" is the Matlab file that was used to analyse all EDX data for further processing. The variable f was saved as "EDX_results.mat". The paths are given relative to the .m file and need to be adjusted if this is moved. The file accesses the "EDX_data" folder. This folder contains the "SEMEDX" folder in which the elemental wt% maps from EDX measurements for each sample are stored as .txt files. The folder "Segmented" then contains the respective segmented residual magnesium images and the segmented degradation layer images that the Matlab file requires for its computation. The folder "Data_Mg_Ion contains" all processed data that is then read and further processed by the respective Jupyter notebooks. The subfolders "Initial_data" and "Final_data" contain the histograms as calculated by Fiji/ImageJ for the volume (V) and area (A) assessment of the initial and degraded wires. The data structure is: # Final V data: /Final_data/histo_1.1C # Initial V data: /Initial_data/initial_histo_1.1C # Initial A data (whole sample, not 0.7mm ROI): /Initial_data/initial_outline_1.1C # Initial A data (0.7mm ROI): /Initial_data/initial_outline_ROI_1.1C The subfolder "Hydra_Medusa" contains the Mg and Ca precipitates as computed by Hydra Medusa for each solution in .csv format. The files "EperimentalData.csv" and "ph_per_sample.csv" contain the pH and temperature measurements for each sample/solution in averaged or full format. The file "EDX_results.mat" contains the mean elemental wt% for each element and its distribution along the degradation layer as computed in Matlab. The file "ion_concentrations.txt" contains the theoretical initial concentration for all ions. The precipitation-dependent corrections are given in "corr_conc.pkl" (which is calculated in the file "precipitation_graphs_publication.ipynb"). "statistics_lincorr_publication.ipynb" - this Jupyter notebook contains the results for the Student's t-test and the linear correlation of parameters "precipitation_graphs_publication.ipynb" - this Jupyter notebook creates the precipitation graphs used in the publication "graphs_publication.ipynb" - this Jupyter notebook creates all other graphs "tree_regression_publication.ipynb" - this Jupyter notebook contains the tree regression analysis using different input parameter sets and different regression models All figures generated by the Jupyter notebooks are saved in the "figures" subfolder. Please note that for all Jupyter notebooks to work you may need to install missing modules.

The data is organized as a ZIP Archive split in 5GB parts. To use the Data, all files have to be downloaded and extracted together. The Archive contains 4 folders. 1. Dynamic-Test-Data 2. Modulation_measurements 3. Pictures 4. Tensile test 1. Dynamic-Test-Data This folder contains the vibrational data from the hydraulic testing machine. It is not shown in the publication but was used to validate the correctness of the measurements. ### 2. Modulation_measurements This folder contains the vibroacoustic measurements for all samples. There are subfolders in which the data files of the different combinations between the piezoceramics have been saved as '.mat'. Each File contains four arrays. + freq ... Frequencies used for the high-frequency excitation 201000 Hz to 220000 Hz in steps of 500 Hz (Low frequency is 5Hz due to the limitations of the pulsing machine.) + VAM ... Measurement signal, from which the modulation is calculated. It contains 2s with a sampling rate of 2e6 MSa for each high frequency. + Chirp_mod ... Contains the measurement of a linear chirp ranging from 1Hz to 300kHz in 5 seconds. Here the Frequency-Response-Spectrum can be evaluated. The sampling rate was set to 1e6 to save data. + pulser .... 2 seconds of measurement with a sampling rate of 1e6 MSa where just the hydraulic testing machine is running. To evaluate if there are any differences in the machine. This data was not. used for the publication. ### 3. Pictures This folder contains pictures of all samples in different stages. + C-Scans of the bonds + Fracture Surfaces after the tensile test + Trough - light photos of the bonded plates + Through-light images of each cut-out specimen ### 4. Tensile Test This folder contains the tensile-test data for every sample measured at a Zwick Z100. Exemplary pictures of the testing are in the pictures folder. Questions can be directed to benjamin.boll@tuhh.de or robert.meissner@tuhh.de

The data set contains stress strain measurements and 3D computed tomography (CT)-data measured in in situ uniaxial compression experiments with parallel CT-imaging on unsaturated granular (soil) specimens. The CT-data has been measured with the laboratory X-ray tomograph at Laboratoire 3SR, Université Grenoble Alpes during uniaxial compression of the specimens applied by a miniaturized uniaxial compression apparatus developed at TUHH.

This data set contains research data related to the article "In situ X-ray CT imaging of transient water retention experiments with cyclic drainage and imbibition" to be published in the Journal Open Geomechanics. The research data include 3D CT images acquired during cyclic drainage and imbibition of a sand specimen in a transient in situ water retention experiment that was run in the X-ray tomograph at Laboratoire 3SR at Univ. Grenoble Alpes. Besides the CT images, also data from the multiphase image analysis as well as macroscopic water retention data, measured in parallel to the CT scans, are published.

Results and complementary data for the paper: Study on the Cohesive Edge Crack in a Square Plate with the Cohesive Element Method. A numerical CEM-based model was built to compute the fracture process zone size for an edge crack in a finite square plate of different lengths. The fracture process zone size is given for different plate sizes, physical crack lengths and softening (cohesive) laws.

In this paper, we present a gecko-inspired soft robot that is able to climb inclined, flat surfaces. By changing the design of the previous version, the energy consumption of the robot could be reduced, and at the same time, its ability to climb and its speed of movement could be increased. As a result, the new prototype consumes only about a third of the energy of the previous version and manages to climb slopes of up to 84deg. In the horizontal plane, its velocity could be increased from 2 to 6 cm/s. We also provide a detailed analysis of the robot's straight gait. This dataset includes all measurement data and evaluation scripts.

The description of the data format as well as the analysis processes for data have been summarized in the file "Description_of_data.pdf" (the last file in this data list). The datasets in this work are files containing atom position coordinates of volume elements approximating nanoporous gold made by dealloying and annealing. The material is represented in an as-prepared state and in various stages of coarsening, as described in Phys. Rev. Mater, 3 (2019) 076001. Realistic initial structures of different solid fractions have been constructed by the leveled-wave algorithm, approximating mixtures at the end of early-stage spinodal decomposition. The microstructural evolution during coarsening by surface diffusion was approximated by on-lattice kinetic Monte-Carlo simulation. The data sets refer to solid fractions from 0.22 to 0.50, providing for different initial connectivity of the bicontinuous structures. Coarsening at two temperatures, 900 K and 1800 K, explores two different degrees of surface energy anisotropy – more faceted at 900 K and more rough at 1800 K. Each structure takes the form of a face-centred cubic lattice with approximately 32 million sites. A site can be occupied by either void or atom. 3D periodic boundary conditions are satisfied. Tables list each structure’s properties, and specifically the specific surface area, two different measures for the ligament size, the net topologic genus as well as the scaled genus. The atom coordinate files may serve as the basis for geometry analysis and for atomistic as well as finite element simulation studies of nanoporous as well as spinodally decomposed materials.

The supplementary material data sheet provide the measured data for the developed illustrations for the publication https://doi.org/10.3139/9783446471733.006. The varied parameters are: support distance, support diameter, and overhang angle. The target parameter is the component thickness which has to be 1 mm as designed. More information are avaible in the ReadMe file.

The dataset represents the static mechanical properties for DMLM of IN718 under as-built condition, as well as under hot-siostatic pressed condition.

The dataset presents the parameters used for printing of DMLM specimens and parts production in the alloy IN718 optimized for density and productivity. The dataset was developed in the funded project 'MOnACO - Manufacturing of a large-scale AM component' with an increased layer thickness of 60µm to enable faster printing of large scale parts.

The dataset presents the measured mean roughness Rz during DMLM in the alloy IN718, using the parameter set published at 10.15480/336.4351. Both as-built and sandblasted conditions are evaluated for downskin (oriented towards the build platform) and upskin surfaces (oriented away from the build platform), and the values are presented in dependence of the overhang angle.

These are data from Deutschen Wetterdienst and their COSMO-DE model. We've got data of the assimilation analysis run. The assimilation analysis is a re-run of the COSMO-xx model with measuremtns of DWD weather stations as a boundary condition. It is as close as possible to the actually then prevelant weather. All wind and temperature data are directly taken from DWD and from the COSMO-DE model. Creator of the data is the DWD. This is simply a prepared, gathered and cut of the DWD data to have a handy and easily accessible data set for energy system simulation. Missing data: 02. November 12 am (UTC). Replaced by values from one step before. File format: hdf5 - Version 1 (created with Matlab) Every file has the following values: Latitudes in °, /latitude, 2D-grid, single-precision (4-Byte floating point), (X Y) Longitudes in °, /longitude, 2D-grid, single-precision (4-Byte floating point), (X Y) The recorded value (see below), /, 3D-grid, single-precision (4-Byte floating point), (X Y time) Every file has the following attributes: /creation_date /author /datasource /datatype /datatype_description /unit /timeframe /steptime /license /comment Wind data files have this additional attribute /level Dataset includes: Temperature at 2m elevation (shorthand TMP) in °C zonal wind speed (shorthand WZU) in m/s at levels 45-50 meridional wind speed (shorthand WMV) in m/s at levels 45-50 Descritpion COSMO-DE model (german): https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/cosmo_de/cosmo_de_dbbeschr_version_2_3_201406.pdf?__blob=publicationFile%26v=5 DWD-Pamore-Program (german): https://www.dwd.de/DE/leistungen/pamore/pamore.html Description of the assimilation analysis (german): https://www.dwd.de/DE/forschung/wettervorhersage/num_modellierung/01_num_vorhersagemodelle/regionalmodell_cosmo_de.html?nn=512942 Documentation of the data set: Scritps for using this data set: https://collaborating.tuhh.de/ietge/dataevaluationdwd License: GeoNutzV

These are data from Deutschen Wetterdienst and their COSMO-DE model. We've got data of the assimilation analysis run. The assimilation analysis is a re-run of the COSMO-xx model with measuremtns of DWD weather stations as a boundary condition. It is as close as possible to the actually then prevelant weather. All wind and temperature data are directly taken from DWD and from the COSMO-DE model. Creator of the data is the DWD. This is simply a prepared, gathered and cut of the DWD data to have a handy and easily accessible data set for energy system simulation. Missing data: 06. January 6 am und 08. December 6 am(UTC). Replaced by values from one step before. File format: hdf5 - Version 1 (created with Matlab) Every file has the following values: Latitudes in °, /latitude, 2D-grid, single-precision (4-Byte floating point), (X Y) Longitudes in °, /longitude, 2D-grid, single-precision (4-Byte floating point), (X Y) The recorded value (see below), /, 3D-grid, single-precision (4-Byte floating point), (X Y time) Every file has the following attributes: /creation_date /author /datasource /datatype /datatype_description /unit /timeframe /steptime /license /comment Wind data files have this additional attribute /level Dataset includes: Temperature at 2m elevation (shorthand TMP) in °C zonal wind speed (shorthand WZU) in m/s at levels 45-50 meridional wind speed (shorthand WMV) in m/s at levels 45-50 Descritpion COSMO-DE model (german): https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/cosmo_de/cosmo_de_dbbeschr_version_2_3_201406.pdf?__blob=publicationFile%26v=5 DWD-Pamore-Program (german): https://www.dwd.de/DE/leistungen/pamore/pamore.html Description of the assimilation analysis (german): https://www.dwd.de/DE/forschung/wettervorhersage/num_modellierung/01_num_vorhersagemodelle/regionalmodell_cosmo_de.html?nn=512942 Documentation of the data set: Scritps for using this data set: https://collaborating.tuhh.de/ietge/dataevaluationdwd License: GeoNutzV

These are data from Deutschen Wetterdienst and their COSMO-DE model. We've got data of the assimilation analysis run. The assimilation analysis is a re-run of the COSMO-xx model with measuremtns of DWD weather stations as a boundary condition. It is as close as possible to the actually then prevelant weather. All wind and temperature data are directly taken from DWD and from the COSMO-DE model. Creator of the data is the DWD. This is simply a prepared, gathered and cut of the DWD data to have a handy and easily accessible data set for energy system simulation. File format: hdf5 - Version 1 (created with Matlab) Every file has the following values: Latitudes in °, /latitude, 2D-grid, single-precision (4-Byte floating point), (X Y) Longitudes in °, /longitude, 2D-grid, single-precision (4-Byte floating point), (X Y) The recorded value (see below), /, 3D-grid, single-precision (4-Byte floating point), (X Y time) Every file has the following attributes: /creation_date /author /datasource /datatype /datatype_description /unit /timeframe /steptime /license /comment Wind data files have this additional attribute /level Dataset includes: Temperature at 2m elevation (shorthand TMP) in °C zonal wind speed (shorthand WZU) in m/s at levels 44-50 meridional wind speed (shorthand WMV) in m/s at levels 44-50 Descritpion COSMO-DE model (german): https://www.dwd.de/SharedDocs/downloads/DE/modelldokumentationen/nwv/cosmo_de/cosmo_de_dbbeschr_version_2_3_201406.pdf?__blob=publicationFile%26v=5 DWD-Pamore-Program (german): https://www.dwd.de/DE/leistungen/pamore/pamore.html Description of the assimilation analysis (german): https://www.dwd.de/DE/forschung/wettervorhersage/num_modellierung/01_num_vorhersagemodelle/regionalmodell_cosmo_de.html?nn=512942 Documentation of the data set: Scritps for using this data set: https://collaborating.tuhh.de/ietge/dataevaluationdwd License: GeoNutzV