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1,163 results for “demonstration”
Figure 11 in Dromaeosaurid crania demonstrate the progressive loss of facial pneumaticity in coelurosaurian dinosaurs
Figure 11. Ancestral state reconstruction of the presence of the jugal foramen in coelurosaurs. Likelihood estimates for nodes represented by pie charts (blue = present, yellow = absent).
PNP D-2 Crossbow - final demonstration
<p>These results are estimated values of preliminary net positions for two days ahead based on the historical data of production, consumption and net positions. The proposed algorithm takes into account recent system’s behavior in similar conditions, using modified moving average with the sliding window, limited to three days. Estimation of net positions is derived out of consumption forecast and generation forecast.</p>
Pulmonary Capillary Perpetual Switching Demonstration
<p>Videos demonstrating pulmonary capillary flow switching.</p>
Transcriptomic analysis demonstrates the expression, origin and function of lncRNAs in multiple skin diseases
<p>Transcriptomic analysis demonstrates the expression, origin and function of lncRNAs in multiple skin diseases</p>
RDF models and SPARQL queries for decoupled analytics demonstration
<p>This directory contains the following:</p> <p>- Two sets of RDF triples using different ontologies, modeling the same "MZVAV-2" air handling unit from the data inventory by Granderson et al. [1].</p> <p>- SPARQL queries for retrieving inputs to APAR [2] rules.</p> <p>- SPARQL queries for discovering "data links" from the models, connecting data points to time series providers.</p> <p>The models were created by manually writing the triples. For details, see included README.md</p> <p> </p> <p>[1] J. Granderson, G. Lin, A. Harding, P. Im, Y. Chen, Building fault detection data to aid diagnostic algorithm creation and performance testing, Scientific Data. 7 (2020) 65. https://doi.org/10.1038/s41597-020-0398-6.</p> <p>[2] J.M. House, H. Vaezi-Nejad, J.M. Whitcomb, An expert rule set for fault detection in air-handling units, ASHRAE Transactions. 107 (2001) 858–871.</p>
Object 6-1: Video Demonstration of Low C to Low C quarter tone sharp
<p>A brief video demonstration showing the challenges of performing a low C to low C quarter tone sharp on the bass clarinet</p>
D2.4 - Demonstration of the ROMI rover
<p>A demonstation of the ROMI Rover at the ACRE challenge.</p>
Video Demonstration of a tool supporting iStar modeling process
<p>A video demonstrating a prototype tool for iStar modeling, additional material for the paper Understanding and satisfying the requirements for facilitating iStar modeling: a tool-supported modeling process.</p>
Figure 2 in Endemic lineages of spiny frogs demonstrate the biogeographic importance and conservational needs of the Hindu Kush-Himalaya region
Figure 2. Minimum-spanning haplotype networks of Allopaa hazarensis generated for 16S and COI sequence data with the number of used sequences, detected haplotypes, and the level of nucleotide variability. Symbol sizes reflect haplotype frequencies, and a small black line between two haplotypes corresponds to one mutation step. Sequence-IDs are indicated for each haplotype (h1–h8). Map shows the localities from where the respective haplotypes originate.
Figure 3 in Endemic lineages of spiny frogs demonstrate the biogeographic importance and conservational needs of the Hindu Kush-Himalaya region
Figure 3. Distribution map for Allopaa hazarensis (A) and Chrysopaa sternosignata (B) derived from species distribution model (SDM) using MAXENT, including known records of the species (red = A. hazarensis, green = C. sternosignata). Photo credit: D. Jablonski.
Figure 1 in Endemic lineages of spiny frogs demonstrate the biogeographic importance and conservational needs of the Hindu Kush-Himalaya region
Figure 1. Bayesian inference (BI; left) and maximum likelihood tree (ML; right) based on concatenated mtDNA and nDNA sequence data (16S + COI + Rag1) of the tribe Paini. Numbers at branch nodes refer to posterior probabilities ≥ 0.9 (BI tree), as well as Felsenstein's bootstrap values ≥ 70% and transfer bootstrap expectation ≥ 0.9 (ML tree). Branches of Allopaa hazarensis are indicated red, while Chrysopaa sternosignata is highlighted green. Species names are followed by voucher number (if available). Coloured shaded boxes indicate subgroups of Nanorana and the new clade (in yellow) with so far unidentified specimens.
Data supporting the manuscript: "Demonstrating the value of beaches for adaptation to future coastal flood risk"
<p>* TWL scenarios used to force the flooding model in a .mat structure</p> <p>* Flooded areas obtained for the different TWL scenarios in a .mat structure considering the topobathymetry at the maximum TWL instant and just after the storm</p> <p>* Flooded damages obtained for the different TWL scenarios in a .mat structure at the maximum TWL instant and just after the storm</p> <p> </p>
Dataset for "SUPER Scheme in Action: Experimental Demonstration of Red-Detuned Excitation of a Quantum Emitter"
<p>Dataset for <strong>"SUPER Scheme in Action: Experimental Demonstration of Red-Detuned Excitation of a Quantum Emitter"</strong>.</p> <p>This dataset contains data for <a href="https://pubs.acs.org/doi/full/10.1021/acs.nanolett.2c01783">https://pubs.acs.org/doi/full/10.1021/acs.nanolett.2c01783</a></p> <p>The data is in either <strong>.txt or .csv f</strong>ormat. The zip file <strong>"SUPERDataset"</strong> includes the following folders and data:</p> <ul> <li>Dataset for Figure 3(a) <ul> <li>Folder <strong>.\Figure3\Experiment</strong> <ul> <li><strong>"2D_counts.txt"</strong> as Z matrix of 2D map</li> <li><strong> "delta2_vec.txt"</strong> as X axis of 2D map</li> <li><strong>"trans2_vec.txt"</strong> as Y axis of 2D map</li> </ul> </li> </ul> </li> <li>Dataset for Figure 3(b) <ul> <li> folder <strong>.\Figure3\Theory</strong> <ul> <li><strong>"2D_theory.txt"</strong> as Z matrix of Theory 2D Map</li> <li><strong>"delta2_vec.txt"</strong> as X axis of Theory 2D Map</li> <li><strong>"trans2_vec.txt"</strong> as Y axis of Theory 2D Map</li> </ul> </li> </ul> </li> <li>Dateset for Figure 3(c) <ul> <li>folder <strong>.\Figure3\Experiment</strong> <ul> <li><strong>"Line_cut_10p5meV.txt"</strong> <strong>, "Line_cut_10p6meV.txt" , "Line_cut_10p9meV.txt"</strong> are linecuts of 2D map</li> </ul> </li> </ul> </li> <li>Dataset for Figure 4(a) <ul> <li>folder <strong>.\SUPER_vs_TPE</strong> <ul> <li><strong>"SUPER.txt"</strong> includes 2 columns. The first column corresponds to the power, and the second column corresponds to the counts(a.u.).</li> <li><strong>"TPE.txt" </strong>includes 2 columns. The first column corresponds to the power, and the second column corresponds to the counts(a.u.).</li> </ul> </li> </ul> </li> <li>Dataset for Figure 4(b) <ul> <li>Folder <strong>.\</strong> <ul> <li><strong>"Spectra_SUPER_emission.txt" </strong>includes 2 columns. The first column corresponds to the wavelenght(nm), and the second column corresponds to the counts(a.u.).</li> </ul> </li> </ul> </li> <li>Dataset for Figure (4c) <ul> <li>Folder <strong>.\</strong> <ul> <li><strong>"g2_100ps.txt" </strong>includes 2 columns. The first column corresponds to the Time(ps), and the second column corresponds to the coincidences.</li> </ul> </li> </ul> </li> <li>Dataset for Supporting Information <ul> <li><strong>"Blinking-200ps-NotNormalized.txt" </strong>shows the blinking data. It includes 2 columns. The first column corresponds to the Time(ps), and the second column corresponds to the coincidences.</li> <li><strong>"lifetime-1ps.txt"</strong> shows the blinking data. It includes 2 columns. The first column corresponds to the Time(ps), and the second column corresponds to the coincidences.</li> </ul> </li> </ul> <p> </p> <p>How to extract data:</p> <p><strong>Windows:</strong></p> <ol> <li> <p>Locate the .zip file on your computer. In this case, the zip file is named "SUPERDataset".</p> </li> <li> <p>Right-click on the .zip file and select "Extract All" from the context menu. This will open the extraction wizard.</p> </li> </ol> <p><strong>macOS:</strong></p> <ol> <li> <p>Locate the .zip file on your computer. In this case, the zip file is named "SUPERDataset".</p> </li> <li> <p>Double-click on the .zip file. macOS will automatically extract the contents of the .zip file to the same location.</p> </li> </ol> <p><strong>Linux:</strong></p> <ol> <li> <p>Open a terminal window.</p> </li> <li> <p>Navigate to the directory where the .zip file is located using the <code>cd</code> command.</p> </li> <li> <p>Run the following command to unzip the file:<br> </p> <pre><code class="language-bash">unzip SUPERDataset.zip</code></pre> <p> </p> </li> </ol>
Demonstration data sets for iSEEindex
<p>Source code available at https://github.com/kevinrue/iSEEdatasets</p>
Affective Conditional Modifiers in Adaptive Video Game Music - Demonstration Video
<p>Demonstration video for the Audio Mostly 2023 submission "Affective Conditional Modifiers in Adaptive Video Game Music".<br> <br> The video displays the search and chase tasks with each music state.</p>
Video demonstration of the Turning in place behaviour element in the Interactive group (behaviour element ID code: ILTP) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Turning in place in the interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>ILTP</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <strong><em>How do you do the things that you do? - Ethological approach to the description of robot behaviour</em></strong> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Turning in place behaviour element in the Minimally interactive group (behaviour element ID code: MLTP) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Turning in place in the minimally interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>MLTP</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Leaving table behaviour element in the Interactive group (behaviour element ID code: ILLT) for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour element: Leaving table in the interactive experimental group, as exhibited by a social robot (behaviour element ID code: <strong>ILLT</strong>). The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Moving to checkpoint (behaviour element ID code: MLMC); and Head orientation during locomotion (behaviour element ID code: MHOL) behaviour elements in the Minimally interactive group for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour elements: Moving to checkpoint (behaviour element ID code: <strong>MLMC</strong>); and Head orientation during locomotion (behaviour element ID code: <strong>MHOL</strong>) in the minimally interactive experimental group, as exhibited by a social robot. The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</p>
Video demonstration of the Looking at human face (behaviour element ID code: IILH); and Greeting sound (behaviour element ID code: IVGR) behaviour elements in the Interactive group for Korcsok and Korondi (2023), Biologia Futura
<p>The video demonstrates the behaviour elements: Looking at human face (behaviour element ID code: <strong>IILH</strong>); and Greeting sound (behaviour element ID code: <strong>IVGR</strong>) in the interactive experimental group, as exhibited by a social robot. The video is part of an ethogram cataloguing the behaviour elements of the robot, described in the publication: <em><strong>How do you do the things that you do? - Ethological approach to the description of robot behaviour</strong></em> submitted to Biologia Futura (2023) by Korcsok, B. and Korondi, P.</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.