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1,079 results for “source data”
Source data for RC work
Open the record for dataset details and reuse information.
Source data: High respiration rates induce net CO2 emissions in an urban allotment garden in Finland
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Scaled and Translated Image Recognition (STIR) Source Data
<p>While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size <span class="math-tex">\(s \in [17,64]\)</span>, each randomly placed in a <span class="math-tex">\(64 \times 64\)</span> pixel image.</p> <p><strong>Original Source Data</strong></p> <ul> <li><code>dota/</code> (from <a href="https://captain-whu.github.io/DOTA/dataset.html">DOTA v1.5 Google Drive</a> website) <ul> <li><code>train/</code> <ul> <li><code>DOTA-v1.5_train.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> <li><code>part2.zip</code> <strong>not</strong> unzipped</li> <li><code>part3.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>val/</code> <ul> <li><code>DOTA-v1.5_val.zip</code> <strong>not</strong> unzipped</li> <li><code>part1.zip</code> <strong>not</strong> unzipped</li> </ul> </li> </ul> </li> <li><code>fontawesome/</code> (from <a href="https://fontawesome.com/v5/download">Font Awesome</a> 5.15.3 "Free for Desktop") <ul> <li><code>svgs/</code> unzipped from archive</li> </ul> </li> <li><code>mapillary/</code> (from <a href="https://www.mapillary.com/dataset/trafficsign">Mapillary Traffic Sign Dataset</a>) <ul> <li><code>mtsd_v2_fully_annotated</code> unzipped from archive</li> <li><code>train.0.zip</code> <strong>not</strong> unzipped</li> <li><code>train.1.zip</code> <strong>not</strong> unzipped</li> <li><code>train.2.zip</code> <strong>not</strong> unzipped</li> <li><code>val.zip</code> <strong>not</strong> unzipped</li> </ul> </li> <li><code>mnist/</code> (from <a href="http://yann.lecun.com/exdb/mnist/">Yann LeCun</a> website) <ul> <li><code>t10k-images-idx3-ubyte.gz</code></li> <li><code>t10k-labels-idx1-ubyte.gz</code></li> <li><code>train-images-idx3-ubyte.gz</code></li> <li><code>train-labels-idx1-ubyte.gz</code></li> </ul> </li> </ul> <p><strong>License and Attribution</strong></p> <p>When using the original source data for your own research, please respect the individual licenses. For attribution in papers, we recommend the following citations which introduce the respective datasets.</p> <ol> <li>D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022.</li> <li>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998.</li> <li> C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020.</li> <li>G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.</li> </ol>
Source Data
<p>The source data of "The KLF7/PFKL/ACADL axis modulates cardiac metabolic remodelling during cardiac hypertrophy".</p>
Raw data for "Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm": unpublished figure and source data.
<p>Classification Performances for each stroke participant according to the GUIDER and the MANUAL procedure. </p>
Raw data for "Corticomuscular and Intermuscular Coupling in Simple Hand Movements to Enable a Hybrid Brain-Computer Interface": unpublished figures and source data.
<p>Figures 1-4: Selected features (EEG-EMG and EMG-EMG couples) for movement vs rest classification (Ext R and L, Gra R and L). </p> <p>Figure 5: Distribution of Gra vs Ext classification among bands. </p> <p>Source data. </p>
Source data for neurite outgrowth assay for the: NrCAM is a marker for substrate-selective activation of ADAM10 in Alzheimer's disease
<p><strong>Neurite outgrowth assay:</strong> 1x10<sup>5</sup> neurons were plated into XONA-microfluidic chambers (standard neuron device, SND450) that had been placed on PDL coated coverslips, according to the manufacturers’ instructions. After 4h the plated cells were infected with the respective viruses (scr. shRNA-EGFP control, or NrCAM shRNA-EGFP, 1:1000). Cells were kept until DIV3 at 37°C and 5% CO<sub>2</sub>, and then the first photomicrographs of the fluorescent neurons were taken with a Leica DM6000 inverted microscope. The images covered the whole channel area in the middle of the respective chambers. Afterwards, the neurons were treated with GI254023x (5 µM), or vehicle and kept at 37°C and 5% CO<sub>2</sub> for 24h. At DIV4, a second set of photomicrographs of the same areas were taken and analyzed for length differences of single neurites (length in mm at 24h-0h) with Leica LASX software. Only neurites that had already entered and not yet left the channels on the other side at the timepoint 0h were used for the calculation. When neurites were separating after leaving the main channel the longest process was quantified.</p> <p> </p>
Data contained in "22-Kyr-Long Record Of Surface Faulting Along The Source Of The 30 October 2016 Earthquake (Central Apennines, Italy), From Integrated Paleoseismic Datasets" - Journal of Geophysical Research - Solid Earth - DOI: 10.1029/2019JB017757
<p>Data contained in “22-Kyr-Long Record Of Surface Faulting Along The Source Of The 30 October 2016 Earthquake (Central Apennines, Italy), From Integrated Paleoseismic Datasets” - Journal of Geophysical Research - Solid Earth - DOI: 10.1029/2019JB017757 by Cinti F.R.*, De Martini P.M.*, Pantosti D.*, Baize S.°, Smedile A.*, Villani F.*, Civico R.*, Pucci S.*, Lombardi A.M.*, Sapia V.*, Pizzimenti L.*, Caciagli M.*, Brunori C.A.*<br> * Istituto Nazionale di Geofisica e Vulcanologia, Italy<br> ° Institut de Radioprotection et de Sûreté Nucléaire, France</p>
Source data for the article of AFM-based multifaceted mechanoproperty characterisation of cancer cells with a fluid-containing porous medium model
<p>Source data for the article of AFM-based multifaceted mechanoproperty characterisation of cancer cells with a fluid-containing porous medium model</p>
Source Data of Supplementary Information
<p>Supplementary Information Source Data of Photoluminescence Mechanism of Carbon Dots: Triggering High-colour-purity Red Fluorescence Emission through Edge Amino Protonation</p>
Supplementary Source Data
<p>Supplementary Main Data.</p>
Source Data for Hurricane Reconstruction in the Central and Eastern Caribbean Sea (CECR) region
<p>The accompanying source data includes excerpts taken from sources used to document<br> hurricane intensity and landfall and/or impacts on land. This data is extracted from a<br> database of tropical cyclone data for the entire North Atlantic. Data includes storm<br> number for the given year, the data extracted from original sources, the digraph<br> indicating location and intensity of hurricane impact. In some instances, in lines marked<br> with ***** there is additional information to amplify the source data. For the years that<br> overlap with the official hurricane record from NOAA (HURDAT) for 1851-1930, the<br> storm numbers indicated are given for both my database and then for HURDAT. If there<br> is no HURDAT entry, then the storm does not exist in HURDAT or assessed to be<br> incorrect.</p>
Aerosol 3-DVAR source code and Lidar data
<p>The code of this system can be obtained on request from the corresponding author</p>
Source Data
<p>Source Data</p>
Source data - Langerhans islets induce CCL27-driven anti-tumor immunity at the expense of glycemic control and predict chemotherapy response in pancreatic cancer
Open the record for dataset details and reuse information.
Source code and data of CodeReviser in ASE2022
<p>The source code and datasets of CodeReviser in ASE2022.</p>
Source data
<p>Once the paper is considered for acceptance, the data can be accessed.</p>
Supplementary Source Data
<p>Supplementary Main Data.</p>
Supplementary Source Data Files
<p>Supplementary Main Data.</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.