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ShareScore release 0.9.0
Dataset results
6 results for “Deep Net”
A deep learning-based dataset of WFA-positive perineuronal nets and parvalbumin neurons localizations in the adult mouse brain
<p><strong>Quality-controlled predictions of deep learning models for cell counting</strong></p> <p>This dataset contains high-resolution images for the visualization of perineuronal nets (PNNs) and parvalbumin-expressing (PV) cells analyzed in the paper:</p> <p><em>A Comprehensive Atlas of Perineuronal Net Distribution and Colocalization with Parvalbumin in the Adult Mouse Brain.</em></p> <p>The dataset integrates the raw data published on a <a href="https://zenodo.org/record/7419282">previous upload</a> on Zenodo.</p> <p>Cell locations were obtained using two deep-learning models for cell counting (publicly available on <a href="http://github.com/ciampluca/counting_perineuronal_nets">GitHub</a>, details in the paper by <a href="https://www.sciencedirect.com/science/article/pii/S1361841522001475">Ciampi et al., 2022</a>). The output of the deep-learning pipeline was filtered based on the <em>score</em> assigned to each cell prediction, by removing all the PNNs with a score lower than 0.4 and all the PV cells with a score lower than 0.55. Cases of artefactual cell detection were finally removed manually by visual inspection of the images. </p> <p><strong>Content</strong></p> <p>The dataset contains microscopy images of coronal brain slices from 7 adult mice. The objects highlighted in these images represent the final set of PNNs/PV cells that were used in all the analysis of the paper.</p> <p><strong>Folder Structure and file naming conventions</strong></p> <p>There are separate folders for each mouse. Each folder is named with the ID of that mouse. Within each folder, images are assigned a code specifying the channel (C1 for PNNs, C2 for PV cells).</p> <p> </p>
Dataset for "Surf-Net: A deep-learning-based method for extracting surface-wave dispersion curves"
<p>dataset for the article "Surf-Net: A deep-learning-based method for extracting surface-wave dispersion curves"<br> corrLSynV8.h5: the generated synthetic waveform<br> dispersion.tar : the dispersion curves set for the generated synthetic waveform; the dispersion curves extracted in Northeast China; the dispersion curves extracted in Southeast China</p>
Konza Prairie site, station Konza Prairie LTER watershed 001d, annually burned, on deep Tully soils, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Konza Prairie (KNZ) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Konza Prairie site, station Konza Prairie LTER watershed 004b, burned every four years, on deep Tully soils, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Konza Prairie (KNZ) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Konza Prairie site, station Konza Prairie LTER watershed 020b, burned every 20 years, on deep Tully soils, study of aboveground net primary productivity in units of gramsPerMeterSquaredPerYear on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Konza Prairie (KNZ) contains aboveground net primary productivity measurements in gramsPerMeterSquaredPerYear units and were aggregated to a yearly timescale.
Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm - deep net Matlab
<p>The file is the trained convolutional neural network (CNN) developed in "De Logu, Francesco, et al. "Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm." <em>Frontiers in Oncology</em> 10 (2020)". The CNN is based on a pretrained Inception-ResNet-v2 to automatically recognizes cutaneous melanoma from histopathological digitalized slides. The file is in a Matlab format (.mat).</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.