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3,648 results for “induction”
Dataset for paper entitled "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers"
<p>This dataset includes all the experimental and FE results presented in the RoboSoft2019 paper "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers" (DOI: <a href="https://doi.org/10.1109/ROBOSOFT.2019.8722800">10.1109/ROBOSOFT.2019.8722800</a>).</p> <p>https://ieeexplore.ieee.org/abstract/document/8722800</p> <p>List of data:</p> <p>Fig.3-EXP_Coil size.xlsx<br> Fig.4-MRE Characterization.xlsx<br> Fig.6-FE modeling results.xlsx<br> Fig.8-Flat SPA Characterization.xlsx<br> Fig.9-EXP-external load.xlsx<br> Fig.10-Exp-Bending SPA.xlsx</p>
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</p>
Multiple interactive memory representations underlie the induction of false memory
Open the record for dataset details and reuse information.
Dataset of "Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation"
<p>This dataset supports the article "Introduction to Neuromorphic Functions of Memristors: The Inductive Nature of Synapse Potentiation," published in the Journal of Applied Physics.</p> <p>Raw data for the article "Introduction to neuromorphic functions of memristors: The inductive nature of synapse potentiation". For further details see the Readme.txt file.</p>
Data and code for figures: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article: Temperature dependence of microwave losses in lumped-element resonators made from superconducting nanowires with high kinetic inductance, <em>Supercond. Sci. Technol.</em> <strong>37</strong> 075013</p>
Multifunctional Polymer Composites for Automatable Induction Heating with Subsequent Temperature Verification
<p>This data upload contains the metadata and datasets underlying the manuscript: "Multifunctional Polymer Composites for Automatable Induction Heating with Subsequent Temperature Verification".</p> <p>A description of the uploaded data is found in the README.txt.</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
Data set of "Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"
<p>The dataset of all data presented in the article published in the virtual special issue of the Journal of Physical Chemistry Letters:</p> <p>"Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00945">https://doi.org/10.1021/acs.jpclett.4c00945</a></p> <p> </p> <p>The dataset contains the following raw data:</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 2<br>- Fig2a.txt : Representative characteristic _I-V_ response of memristor (5 cycles)<br>- Fig2b.txt : Upper vertex-dependent multilevel/multistate analog resistive switching<br>- Fig2c.txt : Characteristic _I-V_ response of 20 distinct devices<br>- Fig2d.txt : Endurance measurements for 1000 cycles of the LRS (ON state) and HRS (OFF state)</p> <p>### Figure 3<br>- Fig3a.txt : Characteristic _I-V_ response with an upper vertex of 0.25 V<br>- Fig3b.txt : Characteristic _I-V_ response with an upper vertex of 0.75 V<br>- Fig3c.txt : Characteristic _I-V_ response with an upper vertex of 1.25 V</p> <p>### Figure 4<br>- Fig4a.txt : IS spectrum under dark conditions at 0 V<br>- Fig4b.txt : IS spectrum under dark conditions at 0.2 V<br>- Fig4c.txt : IS spectrum under dark conditions at 0.3 V<br>- Fig4d.txt : IS spectrum under dark conditions at 0.4 V<br>- Fig4e.txt : IS spectrum under dark conditions at 0.6 V<br>- Fig4f.txt : IS spectrum under dark conditions at 1.0 V</p> <p>### Figure 5<br>- Fig5a.txt : Voltage-dependent transient current response of the perovskite memristor<br>- Fig5b.txt : Magnified view of the transient current response of a single voltage pulse at representative applied voltages<br>- Fig5c.txt : Pulse width-dependent transient current response<br>- Fig5d.txt : Corresponding magnified view of the first and last transient responses<br>- Fig5e.txt : Synaptic potentiation and depression characteristic response of the memristor</p> <p>### Figure 6<br>- Fig6a.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.4 V<br>- Fig6b.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.4 V<br>- Fig6c.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.8 V<br>- Fig6d.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.8 V<br>- Fig6e.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 1.2 V<br>- Fig6f.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 1.2 V</p>
Data and analysis code for a toxin induction study on two species of Dinophysis dinoflagellates
<h3>General description</h3> <p>This repository contains the datasets, analysis code and output generated and used in the manuscript "Effects of copepod chemical cues on intra- and extracellular toxins in two species of <em>Dinophysis</em>", which has been published as a research article in Harmful Algae (https://doi.org/10.1016/j.hal.2024.102793).</p> <h3>Files</h3> <p><strong>HRMS_data_Dinophysis_induction_experiment.zip </strong>contains the source high-resolution mass-spectroscopy endometabolomics data in open file formats.</p> <p><strong>Put_annot_sign_affect_feat_metabol_data.xlsx</strong> (corresponds to <strong>Supplementary spreadsheet 1</strong> in the main manuscript) contains putative annotations of significantly affected features from the metabolomics data, for each <em>Dinophysis </em>species (<em>D.</em> <em>sacculus </em>and <em>D. acuminata</em>) and each mode of ionization. Notably, results obtained from GNPS (Global Natural Products Social Molecular Networking, <a href="https://gnps.ucsd.edu/" target="_blank" rel="noopener noreferrer">https://gnps.ucsd.edu/</a>), and SIRIUS (<a href="https://bio.informatik.uni-jena.de/sirius/" target="_blank" rel="noopener noreferrer">https://bio.informatik.uni-jena.de/sirius/</a>) were mentionned. When available, MS/MS spectra were also provided. </p> <p><strong>Tabl_sign_affect_feat.xlsx</strong> (corresponds to <strong>Supplementary spreadsheet 3</strong> in the main manuscript) contains tables of significantly affected features (ANOVA, Tukey’s post hoc test, adjusted p-value cut-offs of 0.001 or 0.01) from the metabolomics data, for each <em>Dinophysis </em>species (<em>sacculus </em>and <em>acuminata</em>) and each mode of ionization. A visual representation (heatmaps) of these significant fetures are available as Figs S3-S6 in the supplementary information of the main mauscript. </p> <p><strong>Toxin_analysis_code_output.Rmd</strong> is the R-markdown file that produces the interactive analysis output output (<strong>Toxin_analysis_code_output.html</strong>, corresponds to <strong>Supplementary code & output </strong>in the main manuscript) of the toxin analysis, and uses the datasets <strong>Toxin_analysis_data.csv</strong>,<strong> pca_score_sacculus.csv</strong>,<strong> </strong>and<strong> pca_score_acuminata.csv</strong> source datasets to perform the statistical analyses and generate figures (details for each dataset below).</p> <p><strong>Toxin_analysis_data.csv</strong> (corresponds to <strong>Supplementary spreadsheet 2</strong> in the main manuscript) contains the main data used to statistically analyse the toxin and growth dynamics of both <em>Dinophysis</em> species in response to different grazer treatments, and to produce the majority of the figures in the main manuscript (Figs. 2-6) and supplementary information (Figs. S1-S2). </p> <p><strong>pca_score_sacculus.csv</strong> & <strong>pca_score_acuminata.csv</strong> contain the scores of the first two principal components of the PCA performed on LC-HRMS derived metabolomic profiles of <em>D. sacculus </em>and <em>D. acuminata</em> respectively, in both positive and negative ion mode. These are used to produce PCA score plots (Fig. 6 in the main manuscript and Fig. S2 in the supplementary information).</p> <p><strong>custom.css</strong> is a custom html style sheet file that formats the <strong>Toxin_analysis_code_output.html</strong> to display scrollable tables correctly. It is used by <strong>Toxin_analysis_code_output.Rmd </strong>and is necessary for true reproduction of the output (<strong>.html</strong>) file.</p>
Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
<p>This dataset supplements the article “<a href="https://doi.org/10.1162/COLI_a_00354">Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</a>” published in the Computational Linguistics journal:</p> <ul> <li> <p><code>watset-coli-lcc-performance.tsv</code>: runtime analysis</p> </li> <li> <p><code>watset-coli-synsets.zip</code>: synset induction experiment (note that <code>pairwise-{en-babelnet,ru-rwn}.pkl</code> files are excluded due to the licensing issues)</p> </li> <li> <p><code>watset-coli-triframes.zip</code>: semantic frame induction experiment</p> </li> <li> <p><code>watset-coli-classes.zip</code>: semantic class induction experiment</p> </li> </ul>
Data from "Predicting Global Ground Geoelectric Field With Coupled Geospace and Three‐Dimensional Geomagnetic Induction Models"
<p>Data presented in http://dx.doi.org/10.1029/2018SW001859 excluding the first and last hours which were determined to contain partially unphysical results and probably should not be used.</p> <p>Each file contains the external ground magnetic field components calculated on 5x5 degree geographic grid and the results of induction modeling using 1d and 3d ground conductivity models: total ground magnetic field components, horizontal ground electric field components. Times are given in UTC.</p> <p>To reproduce Figure 8 in above reference use:</p> <p> </p> <p>import numpy<br> import matplotlib.pyplot<br> data = numpy.load('2006-12-14T22:58:00.npz') # or 2006-12-14T22_58_00.npz<br> matplotlib.pyplot.colorbar(<br> matplotlib.pyplot.imshow(<br> data['B_3D_north'],<br> cmap = matplotlib.pyplot.get_cmap('bwr'),<br> vmin = -800, vmax = 800,<br> extent = (-180, 180, -90, 90)<br> ),<br> format = '%.0f', fraction = 0.02, pad = 0.03<br> )<br> matplotlib.pyplot.show()</p> <p> </p> <p>and substitute B_3D_east, E_3D_north, etc. for the different panels.</p>
Customizable induction heating profiles: from tailored colloidally stable nanoparticles towards multi-stage heatable supraparticles
<p>This data publication is based on the metadata and datasets underlying the manuscript: "Inductively heatable nano- and supraparticles: from colloidally stable hot nanoparticles to supraparticles with customizable multi-stage heating profiles"</p> <p>Magnetic nanoparticles (NPs) are efficient heat mediators in induction heating. Originally explored for hyperthermia, their applications have broadened to industrial processes where temperature control is crucial. By adjusting the NP composition or morphology, magnetic characteristics such as Curie temperatures can be tailored, allowing control over maximum heating thresholds. These NPs are, however, usually designed for maximum heating rates at specific magnetic fields. In this work, the synthesis is presented for colloidally stable Co and ZnCo ferrite NPs with customizable maximum heating temperatures, and their combination within micron-scaled supraparticles (SPs). Maximum induction heating temperatures of ZnCo ferrite NPs are tuned between 150 and 220 °C, while customization of Co ferrite species yields temperatures between 200 and 350 °C. These distinct magnetic properties are exploited in the selective multi-stage heating of SPs consisting of both species. Here, ZnCo ferrite components heat up to a first temperature plateau at low alternating magnetic fields (AMF), while Co ferrite NPs reach higher temperatures at increased AMF. The precise control of induction heating thresholds through the adaptability of NPs offers a high degree of customizability which makes induction heating particularly attractive for applications requiring sequential or spatial heating, such as catalysis or debonding on demand.</p>
Data: An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction
<p><strong>Dataset supporting the manuscript "</strong>An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction<strong>" by the authors of this dataset.</strong></p> <p><strong>Where to start</strong></p> <p>This Zenodo repository contains both raw data and runnable code for the manuscript "An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction". The runnable code is best executed directly at CodeOcean (https://doi.org/10.24433/CO.6934377.v1). Alternatively, CodeOcean capsules are Docker images and can be run locally after download and unzipping. The full CodeOcean capsule is stored here as "CodeOceanCapsule_Injectable_meta_biomaterial.zip", it contains all the information and data to full reproduce the evaluation underpinning the manuscript " An Injectable Meta-biomaterial: From Design and Simulation to In-vivo Shaping and Tissue induction".</p> <p>Quantitative raw data, in the form of text files, Excel files and R-data files useful for the data evaluation are included in "CodeOceanCapsule_Injectable_meta_biomaterial.zip". As especially the numerical simulation files are rather voluminous (100GB), we also provide a copy of the capsule without this large part, which however otherwise remains runnable for most evaluations ("CodeOceanCapsule_Injectable_meta_biomaterial_no_raw_simulation.zip"), and, for lightweight documentation of the code section only "CodeOceanCapsule_Injectable_meta_biomaterial_code_only.zip". The results of a capsule run are also provided, as "CodeOceanCapsule_Injectable_meta_biomaterial_results_run_4899036.zip".</p> <p>Besides archival of the CodeOcean evaluation capsule, this repository contains additional imaging data from which some of the quantitative data treated in the CodeOcean capsule was extracted, and additionally raw files for the illustrative figures in the manuscript. This data is contained in the files "Raw_images_For_Figure_1.zip", "Raw_images_For_Figure_3.zip", "Raw_images_For_Figure_4.zip"; "Raw_images_For_Figure_5.zip", "Raw_images_For_SFigure_S6.zip", "Raw_images_For_SFigure_S8.zip", "Raw_images_For_SFigure_S9.zip", "Raw_images_For_SFigure_S19.zip".</p> <p><strong>External dependencies</strong></p> <p>To facilitate centralized software development and installation, custom R and Python libraries used by the CodeOcean capsule "CodeOceanCapsule_Injectable_meta_biomaterial.zip" are hosted on Github, with releases archived in separate Zenodo repositories. These libraries are included automatically during the build phase of the CodeOcean capsule.</p> <p>This concerns the Python discrete particle simulation particleShear (DOI: <a href="https://doi.org/10.5281/zenodo.4589212">10.5281/zenodo.4589212</a>), and the R packages textureAnalyzerGels (for analysis of mechanical compression curves, DOI: <a href="https://doi.org/10.5281/zenodo.4589276">10.5281/zenodo.4589276</a>), rheologyEvaluation (for analysis of oscillatory sweep rheology, DOI: <a href="https://doi.org/10.5281/zenodo.4594353">10.5281/zenodo.4594353</a>), particleShearEvaluation (evaluation of the output of the Python simulations, DOI: <a href="https://doi.org/10.5281/zenodo.4594649">10.5281/zenodo.4594649</a>), plot.counts (convenience functions for scientific plotting, DOI: <a href="https://doi.org/10.5281/zenodo.4589498">10.5281/zenodo.4589498</a>) and reproducibleCalculationTools (numerical comparision of subsequent evaluations to validate reproducibility, DOI: <a href="https://doi.org/10.5281/zenodo.4594515">10.5281/zenodo.4594515</a>).</p> <p>For automated evaluation of ImageJ macros from Excel files, we also developed an Excel macro runner plugin in ImageJ, termed PoreSizeExcel (DOI: <a href="https://doi.org/10.5281/zenodo.4589546">10.5281/zenodo.4589546</a>). While the R and Python libraries listed above are actively loaded and used by the CodeOcean capsule, we used the PoreSizeExcel ImageJ plugin manually to streamline our quantitative image treatment, but not in a fully automated fashin.</p> <p>The Zenodo archives cited above reproducibly provide the state of the libraries as used for evaluation of this dataset, we continue to develop the libraries and continuously make them available at Github ( at <a href="https://github.com/tbgitoo">https://github.com/tbgitoo</a> ).</p> <p><strong>Version history</strong></p> <p>This is the third version of this Zenodo repository.</p> <p>We undertook major efforts from version v1.0 to the present version v2.0 to increase reprodubility of evaluation (via the use of the CodeOcean platform) and via separation of generic libraries (listed above, and installable on their own independently of this particular project) from specific project-associated data and evaluation (here). For this reason, while the data is maintained and in part completed due to new experiments having been carried out in the mean time, the structure of the repository has undergone major changes from v1.0 to the present version v2.0.</p> <p>With this version v3.0 we added raw data on cell transplantation, and completed the CodeOcean capsule, including adaptation to peer review changes to the manuscript.</p>
Dataset related to article "New in silico models to predict in vitro micronucleus induction as marker of genotoxicity"
<p>The .txt file contains the dataset of the in silico model for genotoxicity as induction of micronuclei.</p> <p>The .doc file contains the descriptors of the models and the structural alerts.</p>
Ionome analysis of Salmonella mutants by Inductively coupled plasma mass spectrometry (ICP-MS)
<p>In many Gram-negative bacteria, the stress sigma factor of RNA polymerase, σS/RpoS, remodels global gene expression to reshape the physiology of quiescent cells and ensure their survival under non-optimal growth conditions. In the foodborne pathogen <i>Salmonella enterica</i> serovar Typhimurium, σS is also required for biofilm formation and virulence.</p><p>We have previously shown that a Δ<i>rpoS</i> mutation affects the <i>Salmonella</i> ionome. Indeed, inductively coupled plasma mass spectrometry analyses have unraveled a significant effect of the Δ<i>rpoS </i>mutation on the cellular concentration of manganese, magnesium, cobalt and potassium, suggesting that σS controls fluxes of ions that might be important for the fitness of quiescent cells (Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Study: These findings prompted us to evaluate the impact on the<i> Salmonella</i> ionome of deletions of genes encoding the <i>Salmonella</i> Mn2+ transporters (<i>sitABCD</i> and <i>mntH</i>), the Co2+ transporter (<i>cbiMNQO</i> operon) and small proteins of unkown function (<i>yqaE</i> and <i>yqjDEK</i>) that accumulate in quiescent <i>Salmonella</i> under the tight control of σS (Levi-Meyrueis et al. PloS one. 2014; 9(5):e96918, Lago et al. Scientific reports. 2017; 7(1):2127 and Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Material and Methods: Cell-associated contents of several elements were measured by inductively coupled plasma mass spectrometry (ICP-MS) as previously described in Metaane <i>et al </i>2022 PLoS ONE 17(3): e0265511.Dried cell pellets were prepared by V. Monteil and F. Norel (Institut Pasteur, Université de Paris, CNRS UMR3528, Biochimie des Interactions Macromoléculaires, F-75015, Paris, France). Cell-associated contents of several elements were measured by S. Ayrault and L. Bordier (ICP-MS platform, Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRSUVSQ,Université Paris-Saclay, 91191, Gif-sur-Yvette, France)</p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p><p><strong>Linked studies:</strong></p><ul><li>NOREL Francoise, MONTEIL Veronique, DOUCHE Thibaut, & MATONDO Mariette. (2023). Global effects of deletions of the sitABCD, mntH, cbiMNQO and corA genes, encoding transporters for manganese, cobalt and magnesium on protein abundance in Salmonella enterica serovar Typhimurium grown to stationary phase in LB. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8279780</li><li>Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of <i>Salmonella, </i>is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PloS one. 2023;18(9):e0291736.</li></ul>
Convolutional neural network for automated surface crack detection using inductive thermography
<p>Two phase images of the samples AIT_01 and AIT_08, analysed in the publication "Convolutional neural network for automated surface crack detection using inductive thermography", submitted to the Journal of Electronic Imaging.</p>
A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories
<p>Dataset presented in Figures 3-7, S1 and S3 in the recently submitted AGU paper "A New Method for Accurate and Efficient Modeling of the Local Ocean Induction Effects. Application to Long-Period Responses from Island Geomagnetic Observatories".</p>
Granulosa cell transcriptome data from four different time points before and up to 48 hours after PMSG induction to GTH-depend phase in mice
<p>GTH-dependent follicle development begins with small antrum follicles and ends with preovulatory follicles. GTH-dependent follicle development is mainly controlled by gonadotropins, and the development time is 48h. The purpose of this study is to monitor the changes in gene expression of granulosa cells at four different time points during the GTH-dependent phase to increase our understanding of human GTH-dependent follicle development.Granulosa cell mRNA profiles of GTH-depend phase in mice.There are 12 samples represented four time points 0h (n=3), 12h (n=3), 24h (n=3) and 48h (n=3)</p>
Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"
<p>Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>.</p> <p>The R code was written for R version 3.6.3.</p> <p>References<br> Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]</p>
Dataset for paper entitled "Development of Fully Shielded Soft Inductive Tactile Sensors"
<p>This dataset includes all the experimental and FE results presented in the IEEE ICECS 2019 paper "Development of Fully Shielded Soft Inductive Tactile Sensors" (DOI: 10.1109/ICECS46596.2019.8964922).<br> URL of IEEE Xplore:<br> https://ieeexplore.ieee.org/abstract/document/8964922</p> <p>List of data in this dataset:<br> Fig-2-FE modeling-FS-SITS.xlsx<br> Fig-4-Exp_characterization-FS-SITS.xlsx<br> Fig-5-Exp_Demo-FS-SITS.xlsx</p> <p>All the data included in this dataset were collected by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
ScienceDex guides
Understand access before you commit
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.