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ShareScore release 0.9.0
Dataset results
31 results for “Embedded System”
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
Jacdac: Service-based Prototyping of Embedded Systems (PLDI 2024 Artifact Evaluation)
<p>This artifact allows others to reproduce and explore the results seen in "Jacdac: Service-based Prototyping of Embedded Systems". The artifact contains a prebuilt docker image and the Dockerfile source used to produce the prebuilt docker image. Evaluators should follow the README contained in this artifact for complete instruction.</p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 3. Sentiment analysis on extracted themes
<p>Figure 3 is presenting the sentiment analysis results from the point of view of the themes extracted from the corpus. The same preoccupation for the cost of the course is revealed, but this time the fact that MOOCs are free is appreciated. Students perceive that an integration of MOOCs into blended courses leads to a rapid information of the topics, such a feature receiving a high positive score of +3.46. The detailed explanations in this blended approach received a positive impact from the students with a total score of +2.70, but also the gained knowledge is among the most highly rated corpus themes. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 2. Twitter sentiment analysis results
<p> In order to validate our results, the next step was to extract the sentiment analysis from Tweeter’s tweets (Figure 2) which are based on blending embedded systems-related courses. The obtained polarity is positive, so this results shows not only that students appreciated this in a positive manner, but also that the proposed technique for integrating MOOCs into embedded systems courses is a viable one. </p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 1. Semantria result
<p>In Figure 1 the Semantria output is presented, having a positive polarity, with a score of 0.218. What is interesting to note here are the keywords extracted from students’ feedback. They noticed the integration of MOOCs in the Embedded Systems course as positive due to the fact that the new information is perceived as easier and the gained knowledge seems to be valuable. Students are affected by too many concepts and also by the idea of paying for the course.</p>
MakeCode and CODAL: Intuitive and Efficient Embedded Systems Programming for Education (Artifact Evaluation)
<p>This artifact allows others to reproduce the results seen in this paper for MakeCode and CODAL, using the BBC micro:bit. The artifact contains an offline build environment for CODAL and MakeCode, allowing evaluators to test and build programs locally. In addition, we also provide espruino and micropython virtual machines to further increase repeatability of our results. Evaluators should download the virtual machine containing all pre-requisite tools, and use an oscilloscope to observe wave forms (used for timing) generated by the micro:bit, and a serial terminal to observe results reported from the micro:bit over serial.</p> <p>Full documentation is available at: https://lancaster-university.github.io/lctes-artefact-evaluation/</p>
Input data for performing a model evaluation of the sectional aerosol module SALSA embedded to PALM model system 6.0
<p>This dataset includes the input information applied to perform a model evaluation study of the PALM model system together with the sectional aerosol module SALSA. </p> <p>The content:</p> <ul> <li>PIDS_STATIC: building height and leaf area density data</li> <li>PIDS_AERO_<simulation time>_<number of aerosol size bins>: aerosol emission data as size bin specific surface emissions (level of detail 2) and aerosol background concentrations</li> <li>PIDS_CHEM_<simulation time>: emission data and background concentrations of gaseous compounds</li> </ul> <p>PIDS_STATIC contains static data and is therefore the same for all simulations.</p> <p>See the model documentation https://palm.muk.uni-hannover.de/trac/wiki/doc for further details.</p>
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
Model systems with GPCRs embedded in multicomponent membranes
<p>Files required to run coarse-grained simulations on various lipid membranes with embedded adenosine A_2A and dopamine D_2 receptors. These simulations were performed to study the effect of the presence of polyunsaturated fatty acid on GPCR oligomerization. The detailed description of the aims, methodologies and results of this study are explained in the research paper [1]. The composition and purpose of each simulated system will also be explained in this paper [1].</p> <p>The Martini force field [2,3] was employed in the study, and the simulations were performed with version 4.5.x of the GROMACS package [4].</p> <p>For each system the following (in GROMACS compatible format) is provided:</p> <p>1) Initial structure (*Start.gro)</p> <p>2) Topology file (*.top)</p> <p>3) Index file (*.ndx)</p> <p>In addition, for systems other than those containing only one protein, the final structure is given (*End.gro).</p> <p>Other files required to run the simulations, which are common for all the systems, are also provided:</p> <p>4) Simulation parameter file (.mdp)</p> <p>5) Force field parameters (.itp)</p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>[1] Guixà-González et al., Membrane omega-3 fatty acids modulate the oligomerisation kinetics of adenosine A2A and dopamine D2 receptors. <em>Scientific Reports</em> <strong>6</strong>, Article number: 19839 (2016) <strong>DOI:</strong>10.1038/srep19839</p> <p>[2] Marrink et al., The MARTINI Force Field:  Coarse Grained Model for Biomolecular Simulations.<em> Journal of Physical Chemistry B </em><strong>111</strong>, 7812–7824 (2007), <strong>DOI:</strong>10.1021/jp071097f</p> <p>[3] Monticelli et al., The MARTINI Coarse-Grained Force Field: Extension to Proteins. <em>Journal of Chemical Theory and Computation </em><strong>4</strong>, 819–834 (2008), <strong>DOI:</strong>10.1021/ct700324x</p> <p>[4] Pronk et al., GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. <em>Bioinformatics </em><strong>29</strong> 845-854 (2013), <strong>DOI:</strong>10.1093/bioinformatics/btt055</p>
Composite Embedding Systems Based on DNN-HMM and Attention End-To-End for ZeroSpeech2017 track1 (1)
<p>Deep neural networks (DNNs) were trained for posterior and bottleneck features using Japanese and other language speech data. We explore various DNN types, their combinations, and dimension reduction by principal component analysis (PCA).</p> <p>This version (version 1) extracts DNN bottleneck features obtained from GMM based SAT features. The DNN and GMM were trained by speech data from the corpus of spontaneous Japanese (CSJ).</p>
Composite Embedding Systems Based on DNN-HMM and Attention End-To-End for ZeroSpeech2017 track1 (2)
<p>Deep neural networks (DNNs) were trained for posterior and bottleneck features using Japanese and other language speech data. We explore various DNN types, their combinations, and dimension reduction by principal component analysis (PCA).</p> <p>This version (version 2 ) concatenates CSJ feature vector and PCA compressed feature vector made from attention end-to-end feature.</p> <p>X:CSJ feature (60 dim bottleneck, (version 1 feature))</p> <p>S:Attention end-to-end feature (320 dim)</p> <p>T:PCA(S) (60 dim)</p> <p>Z=concat(X,T)</p>
Jacdac: Service-based Prototyping of Embedded Systems (Artifact Evaluation)
<p>This artifact allows others to reproduce and explore the results seen in "Jacdac: Service-based Prototyping of Embedded Systems". The artifact contains a prebuilt docker image and the Dockerfile source used to produce the prebuilt docker image. Evaluators should follow the README contained in this artifact for complete instruction.</p>
PLayer: A Plug-and-Play Embedded Neural System to Boost Neural Organoid 3D Reconstruction
<p>This dataset supports the study titled "PLayer: A Plug-and-Play Embedded Neural System to Boost Neural Organoid 3D Reconstruction." It comprises a total of 539 high-resolution images, each with dimensions of 2048x2048 pixels. The image collection took place roughly over one month. We cultured the neural organoids ourselves based on the STEMdiff™ Cerebral Organoid Kit (STEMCELL Technologies Catalog #08570), aged 19, 34, 71, and 112 days, and the STEMdiff™ Dorsal Forebrain Organoid Differentiation Kit (STEMCELL Technologies Catalog #08620), aged 82 days. All organoids were collected on the same day, rinsed twice with PBS to remove any residual medium, and subsequently fixed with 4.0% (w/v) PFA at 4°C overnight before processing.</p>
Network embedding for understanding the National Park System through the lenses of news media, scientific communication and biogeography
<p>The United States national parks encompass a variety of biophysical and historical resources important for national cultural heritage. Yet how these resources are socially constructed often depends upon the beholder. Parks tend to be conceptualized according to their (fixed) geographic context, so our understanding of this system of systems is dominated by this geographic lens. To expose the systemic structure that exists beyond their geographic embedding, we analyze three representations of the national park system using park-park similarity networks according to their co-occurrence in: (a) ~423,000 news media articles; (b) ~11,000 research publications; and (c) ~60,000 species inhabiting parks. We quantify structural variation between network representations by leveraging similarity measures at different scales: park-level (park-park correlations) and system-level (network communities' consistency). Because parks are governed and experienced at multiple scales, cross-network comparison informs how management should account for the varying objectives and constraints that dominate at each scale. Our results identify an interesting paradox: whereas park-level correlations depend strongly on the representative lens, the network communities are remarkably robust and consistent with the underlying geographic embedding. Our data-driven methodology is generalizable to other geographically embedded socio-environmental systems and supports the holistic analysis of systems-level structure that may elude other approaches.</p>
Network embedding for understanding the National Park System through the lenses of news media, scientific communication and biogeography
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Dataset from "A Hybrid 3D Printed Hand Prosthesis Prototype Based on sEMG and a Fully Embedded Computer Vision System"
<p>Open access dataset containing objects images to be used in training computer vision systems for hand gestures recognition. There are 4 zip files with 6900 images for tripod pinch, 8345 images for palmar grasp with neutral wrist position, 8280 images for palmar grasp with pronated wrist, and 2188 images for key grasp pattern. These are images from the Newcastle Grasp Library (NGL) and the Amsterdam Object Image Library (ALOI). There are other 3 zip files with musical and computer keyboards and tablets images.</p> <p> </p>
Exploring the CI/CD Pipeline in FLOSS Repositories of IoT Embedded Systems
<p>Spreadsheets, scripts, and graphs.</p> <p>Data for responses from the first round of review.</p>
EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems
<p>Dataset of firmwares used for the experiments of the paper <em>EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems</em></p>
Guo's Aortic Arch Reconstruction :The Prospective ,Multiple Center Study About the Safety and Efficacy of WeFlow-Arch Modeler Embedded Branch Stent Graft System (GIANT Study)
ClinicalTrials.gov study NCT04765592. IPD Sharing: YES. Countries: 1. Publications: 1.
Point of Care RandOmisation Systems for Performing Embedded Comparative Effectiveness Trials Of Routine Treatments
ClinicalTrials.gov study NCT05149820. IPD Sharing: NO. Countries: 1. Publications: 2.
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.