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12,393 results for “materiality”
Impact of Freeze-thaw Cycles on Coatings Applied to the Surface of Alkali-activated Materials
<p><span>This paper focuses on the possibility of applying selected types of coatings (synthetic and epoxy) on the surface of alkali-activated materials based on finely ground granulated blast furnace slag with an admixture of cement by-pass dust and silica fly ash. Admixtures represent 30% of the binder component (15% fly ash, 15% cement by-pass dust). The mixture is activated with anhydrous disodium metasilicate. The samples were categorized into two series samples stored in water and samples wrapped in foil. Following this, the surface of these two series was modified by two methods, namely brushing with a steel brush and roughening with a diamond wheel. The properties related to the adhesion of the coating to the surface were mainly investigated before and after 100 freeze-thaw cycles. The cross-cut method and pull-off test for adhesion determined the adhesion. The cross-cut test found that the synthetic coating was less susceptible to surface modification than the epoxy coating. The samples were exposed to 100 freeze-thaw cycles. Then, the cross-cut method was applied to the coated roughened surface and the results of this test were classified into category 3. During the determination of the adhesion of the coatings by the pull-off test on the roughened surface for both methods of sample storage, the character of breakage for the synthetic coating was of the cohesive type, thus it was the tensile strength of the materials, while for the epoxy coating, it was the adhesive breakage, thus it was the adhesion between the surface and the coating.</span></p>
Supplementary materials for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model"
<h2>Overview</h2> <p>This folder contains supplementary materials corresponding to the analysis conducted for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model". The folder is structured into two .zip files. <a href="../api/records/10720907/draft/files/ZENODO_PISCES_MLC.zip/content" target="_blank" rel="noopener noreferrer">ZENODO_PISCES_MLC.zip</a> contains the analysis presented in the paper. BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <h2>ZENODO_PISCES_MLC Folder Structure</h2> <h3>BDM</h3> <ul> <li><strong>MAREDAT_TUNED_SDM.csv</strong>: This file contains the BDM mesozooplankton biomass monthly climatology from MAREDAT data.</li> </ul> <h3>CODE</h3> <p>This directory contains Jupyter Notebook files (<code>.ipynb</code>) and related Python scripts used for data analysis and visualization. Below is a list of the files:</p> <ul> <li><strong>Code_Fig3_FigA8_FigA17.ipynb</strong>: Jupyter Notebook for generating figures 3, A8, and A17.</li> <li><strong>Code_Fig4.ipynb</strong>: Jupyter Notebook for generating figure 4.</li> <li><strong>Code_Fig5_FigA12_FigA13.ipynb</strong>: Jupyter Notebook for generating figures 5, A12, and A13.</li> <li><strong>Code_Fig6.ipynb</strong>: Jupyter Notebook for generating figure 6.</li> <li><strong>Code_Fig7.ipynb</strong>: Jupyter Notebook for generating figure 7.</li> <li><strong>Code_FigA10.ipynb</strong>: Jupyter Notebook for generating figure A10.</li> <li><strong>Code_FigA11.ipynb</strong>: Jupyter Notebook for generating figure A11.</li> <li><strong>Code_FigA14.ipynb</strong>: Jupyter Notebook for generating figure A14.</li> <li><strong>Code_FigA15.ipynb</strong>: Jupyter Notebook for generating figure A15.</li> <li><strong>Code_FigA16.ipynb</strong>: Jupyter Notebook for generating figure A16.</li> <li><strong>Code_FigA1.ipynb</strong>: Jupyter Notebook for generating figure A1.</li> <li><strong>Code_FigA2.ipynb</strong>: Jupyter Notebook for generating figure A2.</li> <li><strong>Code_FigA6_FigA7.ipynb</strong>: Jupyter Notebook for generating figures A6 and A7.</li> <li><strong>Code_FigA9.ipynb</strong>: Jupyter Notebook for generating figure A9.</li> <li><strong>Code_POC_metrics_not_in_the_paper.ipynb</strong>: Jupyter Notebook containing metrics related to particulate organic carbon (POC) not included in the paper.</li> <li><strong>Code_Table3.ipynb</strong>: Jupyter Notebook for generating table 3.</li> <li><strong>Code_Table4.ipynb</strong>: Jupyter Notebook for generating table 4.</li> <li><strong>Code_Table5.ipynb</strong>: Jupyter Notebook for generating table 5.</li> <li><strong>GlobalEstimatesAbstract.ipynb</strong>: Jupyter Notebook containing global estimates abstract.</li> <li><strong>mlctools</strong>: Python package containing utility functions for the analysis.</li> </ul> <h3>OBS</h3> <p>This directory contains observed data used in the analysis:</p> <ul> <li><strong>BATS_zooplankton.csv</strong>: Zooplankton data from the Bermuda Atlantic Time-series Study (BATS).</li> <li><strong>CHL2.nc</strong>: Chlorophyll data in NetCDF format.</li> <li><strong>climatology_n_0_5.nc</strong>: Climatological data in NetCDF format.</li> <li><strong>HOTS_zooplankton.csv</strong>: Zooplankton data from the Hawaii Ocean Time-series (HOTS).</li> </ul> <h3>OUTPUT</h3> <p>This directory contains output files from PISCES simulations (yearly, monthly and 5-day-average outputs). </p> <ul> <li><strong>0class</strong>: Output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>0classregrid</strong>: Regridded output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>10classes</strong>: Output files for the '10classes' classification corresponding to PISCES-MOG.</li> <li><strong>10classesregrid</strong>: Regridded output files from PISCES-MOG.</li> <li><strong>2classes</strong>: Output files from PISCES-MOG-2LS.</li> <li><strong>2classesregrid</strong>: Regridded output files from PISCES-MOG-2LS.</li> <li><strong>NOALLOregrid</strong>: Regridded output files from PISCES-MOG-NA.</li> </ul> <h3>PLOT</h3> <p>This directory contains plots generated during the analysis:</p> <h3>TEMP</h3> <p>This directory contains temporary files used during the analysis, including data files and matrices.</p> <h2>BDM-MAREDAT-ZENODO Folder </h2> <p>BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <p>For any inquiries or data access requests, please contact corentin.clerc -at- usys.ethz.ch</p>
Supplementary Material 1: Phylogenetic tree from Unraveling an unknown diversity of archaeal and bacterial tetraether membrane lipid producers in a euxinic marine system
<p>Phylogenetic tree (Black Sea MAGs)</p>
Process data for development of a digital material shadow for the press hardening route of medium manganese steel
<p>Press-hardened ultra-high strength steel parts are widely used in the automotive sector for their lightweight and safety advantages. Medium-Manganese Steels (MMnS) are being explored as an alternative to boron-manganese steels due to their high strength and ductility after quenching, achieved at lower annealing temperatures thus reducing energy usage and carbon emissions. However, industrial adoption of MMnS is hindered by challenging processing requirements, e.g. in cold-rolling and press hardening. To expedite and improve the process development, data-driven decisions based on process parameters hold promise. Establishing a link between process data and the final produced part necessitates the development of a framework for a Digital Material Shadow (DMS). This paper investigates the development of a DMS framework for the cold rolling and press hardening process chain. In conjunction with conventional data acquisition methods employed for cold rolling, novel data acquisition techniques are introduced specifically tailored for press hardening, ensuring the comprehensive availability of relevant data. Moreover, a data pipeline is implemented to enable automatic processing, visualization, and analysis of process data. To facilitate seamless data linkage across processes in the DMS, an ID-system is introduced. Finally, the developed framework’s validity is demonstrated by creating a DMS for press-hardened MMnS parts, showcasing its potential for practical applications.</p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
Plastic additive qualitative analysis - Plastic reference materials
<p><span> Clusters of tabular data (with metadata including variable labels, code labels and definition of missing value) reporting plastic additive profile in the reference materials with ID P3, P4, P5, P6, P8 and P9. </span><span>Quality assurance measures were in place during analysis including calibration curves, blank measurements and use of validated methods.</span></p>
DHNB2024 Catalogues as data workshop materials
<p>Materials used for the Digital Humanities in the Nordic and Baltic Countries pre-conference workshop entitled "<a href="https://dhnb.eu/conferences/dhnb2024/workshops/catalogues-as-data/">Catalogues as Data in Library Practice and Research: A Use Case with the British Library Pritned Catalogue of Books Published in the 15th Century</a>" delivered by Rossitza Atanassova, Digital Curator, and Harry Lloyd, Research Software Engineer. The dataset includes the participants instructions, presentation slides, the text files with the incunabula descriptions to use for the corpus linguistics analysis, a reference file with descriptions for photographic collections and a keyness analysis results file. The accompanying Jupyter notebook is published on GitHub <a href="https://github.com/britishlibrary/Incunabula-Catalogue-Entry-Detection/tree/v1.0.0">here.</a>.</p>
Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation
<p>This dataset contains the raw data for the publication "Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation" by Fila et al, published in Chemical Engineering Journal. The upload includes raw data of physicochemical characterizations of calcium alginate and its composites, i.e. alginate-biochar and alginate-clinoptilolite, including BET, SEM, TG, XPS and XRD analyses. </p>
New parameter estimates for exogenous organic materials including biochar for the Rothamsted carbon model
<p><span>This parameter set has been established within the EJP Soil project Carboseq and is fully explained in the corresponding report (Leifeld, J., Hardy, B., Budai, A., Elsgaard, L., Keel, S.G., Levavasseur, F., Liang, Z., Mondini, C., Plaza, C., Rodrigues, L. 2024. Soil organic carbon sequestration potential of agricultural soils in Europe. Final report EJP Soil CarboSeq work package 3 – Biochar and other organic amendments).</span></p>
VasTexture: Vast repository of textures and PBR Materials extracted from images using unsupervised approach
<h2><strong>VasTexture: Vast repository of textures and SVBRDF/PBR Materials extracted from images using an unsupervised approach.</strong></h2> <p> </p> <p>This dataset contains hundreds of thousands of textures and PBR/SV-BRDF materials extracted from real-world natural images.</p> <p> </p> <p>The repository is composed of RGB images of textures given as RGB images (each image is one uniform texture) and folders of PBR/SVBRDF materials given as a set of property maps (base color, roughness, metallic, etc).</p> <p>Note that this contain subset of repository more could be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>Visualisation of sampled PBRs and Textures can be seen in: <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a></p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">Link to the main project page</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Link to paper</a></p> <p> </p> <h2>File structure</h2> <p>Texture images are given in the <strong><a href="../records/11391127/files/Extracted_textures_1.zip?download=1" target="_blank" rel="noopener">Extracted_textures_</a>*.zip</strong> files.</p> <p>Each image in this zip file is a single texture, the textures were extracted and cropped from the <a href="https://storage.googleapis.com/openimages/web/index.html" target="_blank" rel="noopener">open images dataset</a>. </p> <p> </p> <p>PBR Materials are available in <strong><a href="../records/11391127/files/PBR_O0_1.zip?download=1" target="_blank" rel="noopener">PBR_*.zip</a></strong> files these PBRs were generated from the texture images in an unsupervised way (with no human intervention). Each subfolder in this file contains the properties map of the PBRs (roughness, metallic, etc, suitable for blender/unreal engine). Visualization of the rendered material appears in the file Material_View.jpg in each PBR folder.</p> <p>PBR materials and textures which are larger then 512x512 pixels contain '<strong>large' </strong>or<strong> 'larger' </strong>in their file name there about 40k in this repository but 100k more can be found in the <a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">main project page</a>.</p> <p>PBR materials and textures who are seamless marked as contain<strong> 'seamless'</strong> in their file name. </p> <p> </p> <p>PBR materials that were generated by mixing other PBR materials are available in files with the names<a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1"> </a><strong><a href="../records/11391127/files/PBR_mix_O2_2.zip?download=1">PBR_mix*.zip</a> </strong></p> <p> </p> <p>Samples for each case can be found in files named: <strong> Sample_*.zip</strong></p> <p> </p> <p>File with the word <strong>Seamless </strong>contain tileable seamless textures this files also contain textures and PBR>512 size that were extracted from the Segment Anything Dataset.</p> <p>Since the textures were extracted fom natural images they were not natively seamless but were turned to seamless using the code at <strong><a href="https://github.com/sagieppel/convert-image-into-seamless-tileable-texture">this url</a></strong></p> <p> </p> <p> </p> <p><strong>Documented code used to extract the textures and generate the PBRs is available at: </strong></p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <h2>Details:</h2> <p>The materials and textures were extracted from real-world images using an unsupervised extraction method (code supplied). As such they are far more diverse and wide in scope compared to existing repositories, at the same time they are much more noisy and contain more outliers compared to existing repositories. This repository is probably more useful for things that demand large-scale and very diverse data, yet can use noisy and lower quality compared to professional repositories with manually made assets like ambientCG. It can be very useful for creating machine learning datasets, or large-scale procedural generation. It is less suitable for areas that demand precise clean and categorized PBR like CGI art and graphic design. For preview It is recommended to look at <a href="../records/11391127/files/PBR_examples.jpg?download=1" target="_blank" rel="noopener">PBR_examples.jpg</a> and <a href="../records/11391127/files/Textures_Examples.jpg?download=1" target="_blank" rel="noopener">Textures_Examples.jpg</a> or download the Sample files and look at the Material_View.jpg files to visualize the quality of the materials.</p> <h3>Scale:</h3> <p>Currently, there are a few hundred of thousands PBR materials and textures but the goal is to make this into over a million in the near future.</p> <h2>Data generation code:</h2> <p>The Python scripts used to extract these assets are supplied at: </p> <p><strong><a href="../records/11391127/files/Texture_And_Material_ExtractionCode_And_Documentation.zip?download=1" target="_blank" rel="noopener">Texture_And_Material_ExtractionCode_And_Documentation.zip</a></strong></p> <p>The code could be run in any folder of random images extract regions with uniform textures and turn these into PBR materials. </p> <h2>Alternative download sources:</h2> <p>Alternative download sources:</p> <p><a href="https://sites.google.com/view/infinitexture/home" target="_blank" rel="noopener">https://sites.google.com/view/infinitexture/home</a></p> <p><a href="https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70" target="_blank" rel="noopener">https://e.pcloud.link/publink/show?code=kZON5TZtxLfdvKrVCzn12NADBFRNuCKHm70</a></p> <p><a href="https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz" target="_blank" rel="noopener">https://icedrive.net/s/jfY1xSDNkVwtYDYD4FN5wha2A8Pz</a></p> <p> </p> <h2>Paper</h2> <p>This work was done as part of the paper "<a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">Learning Zero-Shot Material States Segmentation,</a></p> <p><a href="https://www.arxiv.org/pdf/2403.03309" target="_blank" rel="noopener">by Implanting Natural Image Patterns in Synthetic Data</a>".</p> <p>@article{eppel2024learning,</p> <p> title={Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data},</p> <p> author={Eppel, Sagi and Li, Jolina and Drehwald, Manuel and Aspuru-Guzik, Alan},</p> <p> journal={arXiv preprint arXiv:2403.03309},</p> <p> year={2024}</p> <p>}</p> <p> </p> <h2>License:</h2> <p>All the code and repositories are available on CC0 (free to use) licenses.</p> <p>Textures were extracted from the open images dataset which is an Apache license.</p>
STRIDE I-5 Project Materials
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
Supplementary materials to the article "Practices and attitudes toward replication in empirical translation and interpreting studies"
<p><strong>Abstract of the paper:</strong></p> <p>This article presents the results of three studies on practices in and attitudes toward replication in empirical translation and interpreting studies. The first study reports on a survey in which 52 researchers in translation and interpreting with experience in empirical research answered questions about their practices in and attitudes toward replication. The survey data were complemented by a bibliometric study of publications indexed in the Bibliography of Interpreting and Translation (BITRA) (Franco Aixelá 2001–2019) that explicitly stated in the title or abstract that they were derived from a replication. In a second bibliometric study, a conceptual replication of Yeung’s (2017) study on the acceptance of replications in neuroscience journals was conducted by analyzing 131 translation and interpreting journals. The article aims to provide evidence-based arguments for initiating a debate about the need for replication in empirical translation and interpreting studies and its implications for the development of the discipline.</p> <p><strong>Description of the supplementary materials:</strong></p> <ul> <li>Material 1: questionnaire in English</li> <li>Material 2: questionnaire in Spanish</li> <li>Material 3: results of both questionnaires</li> <li>Material 4: corpus of identified replications in Translation and Interpreting Studies (TIS)</li> <li>Material 5: acceptance of replications in TIS journals</li> </ul>
Supplementary material for "Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic"
<p><span>Dataset presented and discussed in the research article “</span><span>Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic<span><span>” by Zocher et al. The article is published in <em>Geochimica et Cosmochimica Acta</em> and was prepared by the following authors: Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></span></p> <p> </p> <p>(1) School of Science, Constructor University, Bremen, Germany</p> <p>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</p> <p>(3) Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</p> <p>(4) SINTEF Ocean, Trondheim, Norway</p> <p> </p> <p><span><span>Table A1 includes the general information and data for all stations, Table A2 the more detailed salinity data for stations Nidelva A, B and C. </span></span></p> <p> </p> <p><span><span>Reference research article:</span></span></p> <p><span><span> </span></span></p> <p><span><span>Zocher, A.-L., Ciesielski, T. M., Piarulli, S., Farkas, J., & Bau, M. (2024). </span></span><span><span>Rare earth elements and yttrium (REY) in fjord waters: Comparison between seawater in the Trondheimfjord (Norway), its local riverine REY sources and the North Atlantic. Geochimica et Cosmochimica Acta.</span></span></p>
Supporting materials for 'Building quantitative skills with a simplified physical model of coastal storm deposition'
<p>Supporting Materials for Lazarus (2024): "Building quantitative skills with a simplified physical model of coastal storm deposition" (preprint <a href="https://doi.org/10.31223/X56H5B">here</a>).</p> <p>Files include:</p> <ul> <li><strong>DEM_washover_final_demo.tif </strong>– "final" DEM for a bare back-barrier floodplain in a physical laboratory experiment of coastal barrier overwash</li> <li><strong>Lazarus_2024_experimental_washover_exercise_instructions_Zenodo_release.pdf</strong> – step-by-step instructions for a classroom exercise that guides students through using the 'DEM_washover_final_demo' file to digitise, measure, and plot washover deposits with QGIS and Python</li> <li><strong>experiments_plotting_simple.ipynb</strong> – Python notebook for plotting results from classroom exercise</li> <li><strong>Lazarus_2024_washover_exercise_figs.ipynb</strong> – Python notebook for plotting Figs. 3 & 4 in the accompanying manuscript (Lazarus, 2024)</li> <li><strong>GGES2021_S24_data_all_release.csv</strong> – dataset of morphometric measurements presented and discussed in the accompanying manuscript (<a href="https://doi.org/10.31223/X56H5B">Lazarus, 2024</a>)</li> </ul>
Supplementary materials for "Forecasting Soccer Matches With Betting Odds: A Tale of Two Markets"
<p>Replication package for International Journal of Forecasting article "Forecasting Soccer Matches With Betting Odds: A Tale of Two Markets".</p>
Figure 1 in Endemic spring snails Terrestribythinella (Mollusca) as unusual material for larval case of Crunoecia irrorata (Trichoptera: Lepidostomatidae) in Transcarpathian Ukraine
Figure 1. Map showing the locality of Crunoeсia irrorata larva sampling.
Figure 1 in Recent progress in magnetic nanoparticles and mesoporous materials for enzyme immobilization: an update
Figure 1. Mesoporous material-functioned MNPs.
Frisian breaking in adjectives, supplementary material
<p>Additional files for the article Frisian breaking in adjectives re-analyzed. </p> <p>Includes:<br>Qualtrics questionnaire<br>Audio files used for questionnaire<br>CSV-files used for data analysis<br>RMD & HTML file of data analysis</p>
Supplementary Material to "Evaluation of Active Occlusion Effect Cancellation in Earphones by Subjective, Real-Ear and Coupler Measurements"
<p>Supplementary figures for the Publication</p> <p> </p> <p>F. Denk, L. Jürgensen, H. Husstedt (2024) <a href="https://doi.org/10.17743/jaes.2022.0124." target="_blank" rel="noopener"><strong>Evaluation of Active Occlusion Effect Cancellation in Earphones by Subjective, Real-Ear and Coupler Measurements</strong></a>, <em>Journal of the Audio Engineering Society</em> 72(3), pp. 145-160</p> <p> </p> <p>This supplementary material contains 3 types of figures:</p> <ul> <li>Overview_REMall_Levels_T: Overview of 1/6 octave long-term average levels of Real-Ear Measured (REM) sound pressure levels at eardrum for each subject (panel columns), Device (panel rows, rows 1-4 correspond to devices A-D, respectively) and condition (see line color). For better readability, two separate figures 1 and 2 are given for subjects 1-6 and 7-12.</li> <li>Overview_REMall_R: As Overview_REMall_Levels_T, but showing the Occlusion Effect OE, i.e., the difference to the Open-Ear condition.</li> <li>REM_Retest: Test and Retest results for three subjects (individual panels) for which the experiment was repeated. The results for the individual devices are given in separate figures.</li> </ul>
Dataset for FEM modelling of emission reference material (ERM) developed in the EMPIR project 20NRM04 MetrIAQ
<p>This dataset contains the numerical data used in the project 20NRM04 MetrIAQ for the FEM model simulations on the emission reference material (ERM) with the software COMSOL 6.2. The data published here are freely accessible with a text editor.<br>The accompanying document '<em>FEM data and file name explanations.pdf'</em> contains additional information for each data file.<br>A detailed description of the development of the FEM model can be found in the '<em>Guideline with recommendations on the customised design and dimensioning of the ERM, including the uncertainty of the numerical model developed for the prediction of the emission rate. The numerical model will (i) support the customised generation of the ERM (ii) simulate the transport processes inside the ERM and the compound release into test chamber air and (iii) enable the prediction of the emissions for each of the selected target VOC</em>' (<a href="https://doi.org/10.5281/zenodo.12749007" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12749007</a>).</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.