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78
datasets available to search
ShareScore release 0.9.0
Dataset results
78 results for “SOURCE IMAGING”
Data from: Electromagnetic source imaging in presurgical workup of patients with epilepsy: a prospective study
Open the record for dataset details and reuse information.
Arecaceae. Sources of seed images in the web
<p>A list of web pages containing seed images of the Arecaceae.</p>
Data from "SynBot: An open-source image analysis software for automated quantification of synapses"
<p>Primary image datasets and associated tables from the paper "SynBot: An open-source image analysis software for automated quantification of synapses". </p>
Ictal source imaging in epilepsy patients - Supplementary Data
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Simultaneous optogenetic activation and MEG source imaging show non-human primate brain circuits
<p>MEG and MRI data used to generate Figures 2-5 of manuscript titled, "Simultaneous optogenetic activation and MEG source imaging show non-human primate brain circuits". </p>
TMS With Real-time E-field and EEG Source Imaging
ClinicalTrials.gov study NCT06645613. IPD Sharing: NO. Countries: 1. Publications: 0.
Swept Source OCT Imaging With the DREAM VG-OCT
ClinicalTrials.gov study NCT05876689. IPD Sharing: NO. Countries: 1. Publications: 0.
Electrophysiological Source Imaging Guided Transcranial Focused Ultrasound
ClinicalTrials.gov study NCT03192436. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Adenosine-induced Stress Dynamic Myocardial Perfusion Imaging With Dual-source CT
ClinicalTrials.gov study NCT01680081. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prognostic Value of High-resolution Electrical Source Imaging on the Success of Pediatric Focal Epilepsy Surgery
ClinicalTrials.gov study NCT06271785. IPD Sharing: NO. Countries: 1. Publications: 0.
Swept-Source Optical Coherence Tomography for Noninvasive Retinal Vascular Imaging
ClinicalTrials.gov study NCT01834196. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prediction Model of Treatment Efficacy for Age-related Macular Degeneration Based on Multi-source Imaging Modalities
ClinicalTrials.gov study NCT06583109. IPD Sharing: NO. Countries: 0. Publications: 0.
Swept Source Enhanced Depth Imaging Optical Coherence Tomography
ClinicalTrials.gov study NCT02443129. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Solar Bolometric Imager for Investigating the Sources of Solar Irradiance Variability Project
<p> N/A</p>
Spitzer Enhanced Imaging Products Source List
The Spitzer Science Center and IRSA have released a set of Enhanced Imaging Products (SEIP) from the Spitzer Heritage Archive. These include Super Mosaics (combining data from multiple programs where appropriate) and a Source List of photometry for compact sources. The primary requirement on the Source List is very high reliability -- with areal coverage, completeness, and limiting depth being secondary considerations. The SEIP include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. The full set of products for the Spitzer cryogenic mission includes around 42 million sources.
Multi-source optical remote sensing image of ISW
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Dataset related to article "Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction"
<p><strong>SCORING SEGMENTATIONS</strong></p> <ul> <li>Qualitative segmentation scores by two raters (rater1; rater2).</li> <li>Scale: excellent (4); good (3); doubtful (2) and failed (1).</li> </ul> <p> </p> <p><strong>DATABASES</strong></p> <ul> <li>IXI database, Imperial College of London (<a href="https://brain-development.org/ixi-dataset/">https://brain-development.org/ixi-dataset/</a>)</li> <li>Autism Brain Imaging Data Exchange (ABIDE) database (<a href="http://fcon_1000.projects.nitrc.org">http://fcon_1000.projects.nitrc.org</a>)</li> <li>SchizConnect database (<a href="http://schizconnect.org">http://schizconnect.org</a>)</li> </ul> <p> </p> <p><strong>SEGMENTATION METHODS</strong></p> <ul> <li>MR-TIM (Taberna et al., 2021), green rows</li> <li>WTS (Liu et al., 2017), red rows</li> </ul> <p> </p> <p><strong>TABLES</strong></p> <p><strong>IXI_young </strong></p> <ul> <li>20 MRI from the IXI database, participants 20–35 years old;</li> <li>MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)</li> </ul> <p><strong>IXI_older</strong></p> <ul> <li>20 MRI from the IXI database, participants 60–75 years old;</li> <li>MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G)</li> </ul> <p><strong>ABIDE</strong></p> <ul> <li>10 MRI from the ABIDE database, participants 18-25 years old;</li> <li>MR scanner: Philips Achieva 3.0T</li> </ul> <p><strong>SchizConnect</strong></p> <ul> <li>10 MRI from the SchizConnect database, participants 19-66 years old;</li> <li>MR scanner: Siemens Trio Tim 3.0T</li> </ul> <p> </p> <p><strong>REFERENCES</strong></p> <p>Liu, Q., Farahibozorg, S., Porcaro, C., Wenderoth, N., & Mantini, D. (2017). Detecting large-scale networks in the human brain using high-density electroencephalography. Hum Brain Mapp, 38(9), 4631-4643. doi:10.1002/hbm.23688</p> <p>Taberna, G. A., Samogin, J., & Mantini, D. (2021). Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinformatics. doi:10.1007/s12021-020-09504-5</p>
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>
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.