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12,393 results for “Materiality”
Experimental data for Understanding frictional behavior in fascia tissues through tribological modeling and material substitution
<p>Dataset (experimental data) for publication titled: Understanding frictional behavior in fascia tissues through tribological modeling and material substitution. The article deals with the development of a tribological model of fascia. As in the paper, the dataset is divided into the three phenomena studied: Effect of geometry, Models, Hyaluronic acid - model E. </p>
Supplementary Material - Uncovering the Effects of Quantum Computing on Software Engineering: A Systematic Mapping
<p>This is the supplementary material regarding: “On the Influence of Quantum Computing on Software Engineering: A Systematic Mapping”, the idea being to make it possible to reproduce the methodology applied in the Systematic Mapping (SM) used in the study. It consists of:</p> <ul> <li>Spreadsheet with the data of the papers selected in each step of the filtering phase of the SM development</li> <li>Fluxogram detailing the Methodology of the SM step-by-step<strong></strong></li> </ul> <h3><strong>Objectives</strong></h3> <p>The main objective of the SM was to provide a detailed analysis on how quantum computing has affected four software engineering topics: Software Testing and Quality; Software Reengineering/Modernization; Software Modeling and Software Processes and Development Platforms</p> <p>Based on the results of such Mapping, four research questions were defined realted to the quantum software engineering topic:</p> <ul> <li> <p>RQ1: <span>How has QC impacted software testing and qual</span><span>ity? This RQ aims to identify proposed methods for </span><span>testing quantum software and topics related to quantum </span><span>metrics and bugs.</span></p> </li> <li> <p>RQ2: How has QC influenced software reengineering and modernization practices? This study seeks to analyze the treatment of reverse engineering and refactorings within this context.</p> </li> <li> <p>RQ3: How is quantum software modeled? This research aims to investigate the impact of QC on modeling languages and the granularity level used.</p> </li> <li> <p>RQ4: How have development processes and platforms been shaped by QC? The goal is to assess the current maturity of processes and platforms for QC.</p> </li> </ul> <h3><strong>Preparation</strong></h3> <p>In this part we outline the resources, digital libraries, the search string and inclusion and exclusion criteria used in this SM on the influence of quantum computing on software engineering.</p> <p>The platform used to manage the articles and define the search string and inclusion and exclusion criteria was Parsifal. In this process articles were sourced from : ACM Digital; IEEE Digital Library; Science Direct and Scopus.</p> <p>A search string was employed in each digital library, adapted to the syntax of each of them, while encompassing the time frame from 01/2018 to 06/2023, with the Base Search String being:</p> <p><strong>(“Software Model” OR “Software Engineering” OR “Software Development” OR “Software Lifecycle” OR “Software Development Methodologies” OR “Software Project Management” OR “Testing” OR “Design Pattern” OR “Reengineering” OR “Reverse Engineering” OR “Metrics” OR “Service-Oriented”) AND (“Quantum Software” OR “Quantum Programming” OR “Quantum Computing” OR “Quantum Software Development” OR “Quantum Subroutine” OR “Quantum Program”)</strong></p> <p>Where the upper part includes terms related to software engineering and the lower part covers the quantum terms.</p> <p>Another important task was to define inclusion and exclusion criteria for filtering the studies, being them:</p> <p><strong>Inclusion Criteria</strong></p> <ul> <li> <p>The study is published in English.</p> </li> <li> <p>The content of the study is related to the topic being analyzed.</p> </li> </ul> <p><strong><strong>Exclusion Criteria</strong></strong></p> <ul> <li> <p>The study is not available</p> </li> <li> <p>The study is duplicated</p> </li> <li> <p>The study is not primary study (surveys, systematic reviews/mappings, talks, proceedings, etc)</p> </li> <li> <p>The content of the study is not related to the theme or it is too superficial.</p> </li> <li> <p>The content of the study was updated in a next more-complete version. When the authors published a new and more complete version of the same content, we remove the previous paper.</p> </li> </ul> <h3><strong>Conduction</strong></h3> <p>During the conduction of the papers filtering process there were four stages to which the set of papers were applied to until reaching the final set:</p> <ul> <li> <p>First stage: Removal of every paper that was duplicated, leaving only one instance of each paper.</p> </li> <li> <p>Second stage: Removal of papers that are not a primary study.</p> </li> <li> <p>Third stage: Removal of papers which keywords, title and abstract are not related to the topics of the Systematic Mapping;</p> </li> <li> <p>Fourth stage: Removal of papers whose content is not related to the topics of the Systematic Mapping.</p> </li> </ul>
Supplementary Materials include results of simulation experiments to investigate the impact of phylogenetic regression with model violations.
<p>Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution—the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging–yet biologically important–settings.</p>
Supplementary Material for A Metrics Suite for Quantifying Legal Compliance of Smart Contracts
<p>This repository contains the supplementary material for the paper titled "A Metrics Suite for Quantifying Legal Compliance of Smart Contracts". It includes natural-language legal contracts, their smart contract implementations, Petri net models of said contracts, and their reachability graphs. The Petri net models are presented as graphics, as well as .cpn files that can be opened with either <a href="https://cpntools.org/" target="_blank" rel="noopener">CPN Tools</a> or <a href="https://cpnide.org/" target="_blank" rel="noopener">CPN IDE</a>.</p>
Supplementary Material for "The Anion Photoelectron Spectrum and Diabatization of Tetrazolyl"
<p>This is the supplementary material for "The Anion Photoelectron Spectrum and Diabatization of Tetrazolyl."</p> <p>The file "README - table of contents.pdf" lays out what information and data is present in each file.</p>
Probabilistic Fault Displacement Hazard Assessment materials
<p>The models, data, and information provided here were created as part of the Fault Displacement Hazard Initiative. We provide the Electronic Supplement for Chiou et al., 2023, CDF Fortran subroutines; the ArcGIS least-cost path (LCP) model and implementation guide, LCP MATLAB and Python scripts, and the LCP for 75 events in a shapefile and KMZ format for Thomas et al., 2023; and the Fortran code for Chiou et al. in review for Earthquake Spectra. </p>
Dataset of paper "Photoelectrochemical disinfection efficiency of WO3-based photoanodes: Development of multifunctional photoelectrocatalytic materials"
<p>Dataset of paper "Photoelectrochemical disinfection efficiency of WO3-based photoanodes: Development of multifunctional photoelectrocatalytic materials"</p> <ul> <li><span><span>Linear Sweep Voltammetry data for five photoanodes, a) nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, b) nanostructured WO<sub>3</sub>, c) flat WO<sub>3</sub>/BiVO<sub>4</sub>, d) flat WO<sub>3</sub>, and e) flat BiVO<sub>4</sub></span></span></li> <li><span><span>Kinetic constant for the partial oxidation of methanol to formaldehyde data for five photoanodes, nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, nanostructured WO<sub>3</sub>, flat WO<sub>3</sub>/BiVO<sub>4</sub>, flat WO<sub>3</sub>, and flat BiVO<sub>4</sub></span></span></li> <li><span><span>Kinetic constant for the inactivation of MS2 data for five photoanodes, nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, nanostructured WO<sub>3</sub>, flat WO<sub>3</sub>/BiVO<sub>4</sub>, flat WO<sub>3</sub>, and flat BiVO<sub>4</sub></span></span></li> <li><span><span>Kinetic constant for the inactivation of <em>E. coli</em> data for five photoanodes, nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, nanostructured WO<sub>3</sub>, flat WO<sub>3</sub>/BiVO<sub>4</sub>, flat WO<sub>3</sub>, and flat BiVO<sub>4</sub></span></span></li> </ul>
Bromination of 2D materials [doi: 10.1088/1361-6528/ad1201]
<p>Primary data, meta data, and corresponding lists of figures & tables are included. [doi: 10.1088/1361-6528/ad1201]</p>
Dataset related to "High-frequency optimally windowed chirp rheometry for rapidly evolving viscoelastic materials: Application to a crosslinking thermoset"
<p>Knowledge of the evolution in the mechanical properties of a curing polymer matrix is of great importance in composite parts or structure<br>fabrication. Conventional rheometry, based on small amplitude oscillatory shear, is limited by long interrogation times. In rapidly evolving<br>materials, time sweeps can provide a meaningful measurement albeit at a single frequency. To overcome this constraint, we utilize a combined<br>frequency- and amplitude-modulated chirped strain waveform in conjunction with a homemade sliding plate piezo-operated rheometer (PZR)<br>and a dual-head commercial rotational rheometer (Anton Paar MCR 702) to probe the linear viscoelasticity of these time-evolving materials.<br>The direct controllability of the PZR, resulting from the absence of any kind of firmware and the microsecond actuator-sensor response<br>renders this device ideal for exploring the advantages of this technique. The high frequency capability allows us to extend the upper limits of<br>the accessible linear viscoelastic spectrum and, most importantly, to shorten the length of the interrogating strain signal (OWCh-PZR) to subsecond<br>scales, while retaining a high time-bandwidth product. This short duration ensures that the mutation number (NMu) is kept sufficiently<br>low, even in fast-curing resins. The method is validated via calibration tests in both instruments, and the corresponding limitations are discussed.<br>As a proof of concept, the technique is applied to a curing vinylester resin. The linear viscoelastic (LVE) spectrum is assessed every<br>20 s to monitor the rapid evolution in the time and frequency dependence of the complex modulus. Comparison of the chirp implementation,<br>based on parameters such as duration of the experiment, sampling frequency, and frequency range, in a commercial rotational rheometer with<br>the PZR provides further information on the applicability of this technique and its limitations. Finally, FTIR spectroscopy is utilized to gain<br>insights into the evolution of the chemical network, and the gap dependence of the evolving material properties in these heterogeneous<br>systems is also investigated</p>
Supplementary Material of the research study "Towards Gender Parity: Analyzing the Women Participation in STEM"
<p><span>The files present data considered in the analysis of students graduated in Brazil, from 2010 to 2019, with the purpose of contrasting STEM and Not STEM courses. </span></p> <p><span>The data was derived from Higher Education Census [Inep,2021].</span></p> <p><span>For the classification of courses as STEM courses, it was used the definition given by the SAGA methodology [UNESCO, 2016, 2017b], which refers to the International Standard Classification of Education (ISCED). </span></p> <p><span>ISCED was then related to the International Standard Classification of Education for Graduate and Specialized Courses in Brazil (Cine Brazil) [Inep, 2021], which classifies Brazilian undergraduate courses and is referenced by the official classification of courses in the Higher Education Census [Inep,2021].</span></p> <p> </p> <p><strong><span>References</span></strong></p> <p><span>Inep (2021). </span><em><span>Censo da Educação Superior</span></em><span>. </span><span>Bras</span><span>í</span><span>lia, DF. Available at https://www.gov.br/inep/pt-br/areasde-atuacao/pesquisas-estatisticas-e-indicadores/censo-daeducacao-superior/censo-da-educacao-superior.</span></p> <p><span> </span><span>Inep (2021). </span><em><span>Cine Brasil</span></em><span>. Bras</span><span>í</span><span>lia, DF. </span><span>Available at https://www.gov.br/inep/pt-br/areas-eatuacao/</span></p> <p><span>pesquisas-estatisticas-e-indicadores/cinebrasil/cine-brasil.</span></p> <p><span> </span><span>UNESCO (2016). Measuring Gender Equality in Science and Engineering: the SAGA Science, Technology and Innovation Gender Objective List (STI GOL). Technical Report SAGA Working Paper 1, UNESCO, Paris.</span></p> <p><span> </span><span>UNESCO (2017b). Measuring Gender Equality in Science and Engineering: the SAGA Toolkit. Technical Report SAGA Working Paper 2, UNESCO, Paris.</span></p>
Supplementary Material for Requirements documentation containing natural language: A Systematic Tertiary Literature Review
<p>Context: Requirements documentation in natural language has diverse artifacts, but few studies address their suitability to types<br>of requirements or ease of communication.</p> <p>Methods: We conducted a systematic tertiary literature review (STLR) and identified 22 relevant review papers that address natural language artifacts used by practitioners to document software requirements. We also investigated which types of requirements are addressed by artifacts and if there are guidelines for each.</p> <p>Results: A variety of artifacts used for this purpose were identified, of which the most referenced in the literature were diagrams,<br>use cases, conceptual models, user stories, and prototypes. The analysis highlighted that artifacts are applied differently to functional and non-functional requirements. In general, diagrams, use cases, scenarios, and prototypes can be used for both types of requirements, depending on the content (usability, security, etc.). However, user stories and derived artifacts are more recommended for functional requirements and have limitations for non-functional requirements.</p> <p>Conclusion: Furthermore, the study explored different guidelines, structures, and formats used in documentation artifacts, reflecting the diversity in requirements documentation practices in software projects.</p>
Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material)
<p>These are the nested sampling inference products used to obtain the results for <span><a href="https://arxiv.org/abs/2404.16428">arXiv:2404.16428</a>. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given. </span></p> <p><span>Folders:</span></p> <p><span>outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms. </span></p> <p><span>outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100. </span></p> <p><span>outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run.</span></p>
Supplementary material related to the thesis "Perception of airborne sounds and vibrations in crocodiles"
<p>This repository contains all datasets, statistical codes and videos examples for the 4 different studies conducted during my thesis. </p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </p>
Supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale
<p>This file contains the supporting material for von Fromm et al (2024) Moisture and soil depth govern relationships between soil organic carbon and oxalate-extractable metals at the global scale (<em>submitted</em>). </p> <p>For more details see the corresponding manuscript (once it is published) and the github repository (https://github.com/SophievF/Global_Mox_analysis/tree/main). </p>
MaTableGPT: GPT-based Table Data Extractor from Materials Science Literature
<p>Using the MaTableGPT tool, which extracts table data from material literature based on GPT, performance data of catalysts were extracted from 11,077 documents on OER catalysts. GPT-3.5 fine-tuning was conducted using 126 tables, achieving an accuracy of 96.8%. The data consists of DOI, catalyst, performance, and property(reaction_type, value, current_density, electrolyte, overpotential, potential, substrate, versus, condition). For more details, please refer to the DOI.</p>
Data, code and supplementary material - Gone but not forgotten: dynamics of sperm storage and potential ejaculate digestion in the black soldier fly Hermetia illucens
Open the record for dataset details and reuse information.
Supplementary Material - Systematic Mapping Study - Information and Software Technology
<p>Supplementary material - Article title: "Constructing the graphical structure of expert-based Bayesian networks in the context of software engineering: A systematic mapping study" - Information and Software Technology. This file contains the detailed procedure employed in the systematic mapping.</p>
Towards Immersive Process Simulation for Declarative Models - Accompanying Material
<div>This dataset contains the research protocol, transcripts and codings for the paper “Towards Immersive Process Simulation for Declarative Models.” Accepted for publication at the International Conference on Business Process Management - Forum. 2024.</div> <div> </div> <div>The documents are ordered by number. We describe their contents below:</div> <div> </div> <div>1. Appendix A - Consent Form used in research and validation interviews in section 4 and section 6</div> <div>2. Appendix B - DCR Training Material used to familiarize the research participants with DCR in section 4</div> <div>3. Appendix C - Elicitation Interview Script & Validation Session Interview Protocol used in sections 4 and 6</div> <div>4. Appendix D - First round of coding of Elicitation interviews</div> <div>5. Appendix E - Second round of coding of Elicitation interviews</div> <div>6. Appendix F - Third round of coding of Elicitation interviews</div> <div>7. Appendix G - First round of coding of Validation interviews</div> <div>8. Appendix H - Second round of coding of Validation interviews</div> <div>9. Appendix I1 - Third round of coding of Validation interviews, part 1</div> <div>10. Appendix I2 - Third round of coding of Validation interviews, part 2</div> <div>11. Appendix J - Validation participant DCR graph representation preferences: questionnaire and answers</div> <p>Moreover, we included all the transcripts of the interviews in raw form. </p> <div>The file transcript_ideation.pdf contains the transcripts of the requirement elicitation interviews conducted during the research phase.</div> <div>The file transcript_validation.pdf contains the the transcripts of the validation interviews.</div> <p> </p> <p>The github repository https://github.com/GloriousHypnotoad/DCR-Graph-artifact contains the source code of the software artifact used in the paper.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.