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673
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Dataset results
673 results for “OCT”
AMPERE GRD & IRD Data (2015-Oct)
<p>2015-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2014-Oct)
<p>2014-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2013-Oct)
<p>2013-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2023-Oct)
<p>2023-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2022-Oct)
<p>2022-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2021-Oct)
<p>2021-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
AMPERE GRD & IRD Data (2020-Oct)
<p>2020-Oct AMPERE GRD and IRD data.</p><p>AMPERE IRD files contain hemispheric delta-B observations in global coordinates (geographic and AACGM). Data are blocked into 10-minute 'chunks' at 2-minute resolution for use in the fitting algorithm. Measurements flagged 'bad' in the earlier processing are removed prior to entry in IRD.</p><p>AMPERE GRD files contain hemispheric delta-B and Birkeland current fits at 2-minute resolution.</p>
Dataset: FAIR AMD OCT Datasets Paper
<p>This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this <a href="https://github.com/fairdataihub/FAIR-AMD-OCT-paper-inventory">inventory</a> for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset. </p>
SuperDARN data in netCDF format (1993-Oct)
<p>1993-Oct SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
OCT porcine kidney dataset for percutaneous nephrostomy guidance
<h2><strong>Code</strong> [<a href="https://github.com/thepanlab/FOCT_kidney" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1364/BOE.421299" target="_blank" rel="noopener">Biomedical Optics Express'21</a>]</h2> <h3>Abstract</h3> <p>Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.</p> <h3>Description</h3> <p>The dataset contains OCT images of 10 porcine kidneys from three tissues: cortex, medulla, and pelvis calyx. There is 1000 images per tissues/per kidney. More information about the dataset can be found in the paper: <a href="https://doi.org/10.1364/BOE.421299">https://doi.org/10.1364/BOE.421299</a></p> <p>The repository that processed this dataset can be found at <a href="https://github.com/thepanlab/FOCT_kidney">https://github.com/thepanlab/FOCT_kidney</a></p> <p> </p>
Wallops SuperDARN data in netCDF format (2023-Oct)
<p>2023-Oct Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
database of Chemicals associated with Plastic Packaging (CPPdb), updated Oct 9, 2018
<p>CPPdb: database of Chemicals associated with Plastic Packaging. Includes chemicals associated with plastic packaging both in terms of use during manufacturing as well as presence in the final plastic packaging articles.</p> <p>CPPdb_ListA: chemicals LIKELY associated with plastic packaging</p> <p>CPPdb_ListB: chemicals POSSIBLY associated with plastic packaging</p>
Festival of Frequency Measurement dataset - WV5L 34.10 -84.46 5 MHz Oct 1 2019
<p>Festival of Frequency Measurement Submission</p> <p>event: WWV Centenial</p> <p>UTC Date: 01 Oct 2019 (starting 30 Sep 2300z, ending 2 Oct 0100z)</p> <p>beacon frequency: 5 MHz</p> <p>station name: WV5L, operated by Vance Loen</p> <p>lat long: 34.10 -84.46</p> <p>city: Woodstock</p> <p>state: Georgia US</p> <p>difficulties: poor propagation Oct 01 from about 1600 to 2000 UTC between Ft. Collins and Woodstock</p> <p>equipment: Icom IC-7610 with Leo Bodnar GPSDO, antenna 96-foot dipole with ladder line (ZS6BKW design)</p> <p>tuning technique: receiver was tuned to 4999.00 KHz, upper sideband, 2.1KHz bandwidth, preamp off, AGC mid, 1 KHz detected audio captured from internal sound card, calibrated and logged by FLDIGI 4.1.08.</p> <p>propagation path: sky wave, about 1215 miles/1955 Km</p>
Wallops SuperDARN data in netCDF format (2022-Oct)
<p>2022-Oct Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2013-Oct)
<p>2013-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2014-Oct)
<p>2014-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2015-Oct)
<p>2015-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (1995-Oct)
<p>1995-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (1994-Oct)
<p>1994-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
SuperDARN Grid data in netCDF format (2000-Oct)
<p>2000-Oct SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</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.
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DANDI Archive for NWB datasets
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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.