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251
datasets available to search
ShareScore release 0.7.1
Dataset results
251 results for “deep learning models”
Bioinformation and deep-learning based model reveals norepinephrine inhibiting PRDX1 aggravates atherosclerosis
GEO Series GSE282003. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Positional Interpretation of Cis-Regulatory Code and Nucleosome Organization with Deep Learning Models
GEO Series GSE313524. Saccharomyces cerevisiae. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Malware dataset from VirusShare used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model"
<p>This repository contains all malicious samples used in article "DeepDetectNet vs RLAttackNet: An Adversarial Method to Improve Deep Learning-based Static Malware Detection Model". It is safe to download these samples <strong>without </strong>running them. Please note that when downloading these samples, <strong>close all </strong>anti-virus software, including Windows Defender.</p>
Pre-trained word2vec models for ``Easy over Hard: A Case Study on Deep Learning''
<p>Since the whole stack overflow dump is so big, we can't easily handle well. Here, we provide 10 pre trained word2vec models with different seeds.</p> <p> </p> <p>More details about how to use it, please see paper </p>
Model Results for 'Deep Learning Forecast and Physical Interpretation of Semidiurnal Internal Tides from the Mariana Arc'
<p>please contact</p>
Supplementary Data and Models of Melt-based Thermo-barometer for paper "'No Free Lunch' in Tabular Geochemical Data: An example of Shallow versus Deep Machine Learning Algorithms for Geothermobarometry"
Open the record for dataset details and reuse information.
Soil Moisture Forecasting integrating Physical-based model and Deep Learning
<p>Dataset used in "Soil Moisture Forecasting integrating Physical-based model and Deep Learning".</p> <p>(1) <strong>1-24.tar</strong> is training/test data (after preprocessing) over 24 sub-regions in China.</p> <p>(2) <strong>GFS*</strong> is 3-day forecast of Global Forecast System (GFS) over 2015-2017 and 2018 years.</p> <p>(3) <strong>auxiliary.json</strong> is utility data (e.g., land mask for sub-task).</p> <p>(4) <strong>valid_data.tar</strong> contains 2018 year of SoMo.ml, ERA5-Land, SMOS L3, LPRM-AMSR2, which were used to triple collocation analysis in our study. The CMA in-situ datasets only could be available from us after certain permission in CMA.</p> <p> </p>
National-Scale Spatial Flood Modeling with an Optimized Deep Learning Approach (case study: Sweden)
<p>National-Scale Spatial Flood Modeling with an Optimized Deep Learning Approach (case study: Sweden)</p>
Dataset related to article "Deep learning and atlas-based models to streamline the segmentation workflow of Total Marrow and Lymphoid Irradiation"
<p>This record contains raw data related to article “Deep learning and atlas-based models to streamline the segmentation workflow of Total Marrow and Lymphoid Irradiation"</p> <p>Abstract:</p> <p><strong>Purpose: </strong>To improve the workflow of Total Marrow and Lymphoid Irradiation (TMLI) by enhancing the delineation of organs-at-risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.</p> <p><strong>Materials and Methods:</strong> Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients and a semi-automatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR.</p> <p><strong>Results: </strong>The two DL models achieved a median Dice Similarity Coefficient (DSC) of 0.84 [0.73;0.92] and 0.84 [0.77;0.93] across the OARs. The absolute median dose (Dmedian) difference between manual and the two DL models was 2% [1%;5%] and 1% [0.2%;1%]. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmedian differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 hours the time required to complete the entire segmentation process.</p> <p><strong>Conclusion: </strong>DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.</p> <p> </p> <p><strong>Statements & Declarations</strong></p> <p><strong>Funding:</strong> This work was funded by the Italian Ministry of Health, grant AuToMI (GR-2019-12370739).</p> <p><strong>Competing Interests:</strong> The authors have no conflict of interests to disclose.</p> <p><strong>Author Contributions:</strong> All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by D.D., N.L., L.C., R.C.B., D.L., and P.M. The first draft of the manuscript was written by D.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.</p> <p><strong>Ethics approval:</strong> The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of IRCCS Humanitas Research Hospital (ID 2928, 26 January 2021). ClinicalTrials.gov identifier: NCT04976205.</p> <p><strong>Consent to participate: </strong>Informed consent was obtained from all individual participants included in the study.</p>
Pre-trained word2vec model for ``Easy over Hard: A Case Study on Deep Learning''
<p>Since the whole stack overflow dump is so big, we can't easily handle well. Here, we provide 10 pre trained word2vec models with different seeds using skip-gram algorithms</p> <p> </p> <p>More details about how to use it, please see paper </p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes daily basin average forcing data (consisting of precipitation, temperature, potential evapotranpiration, air pressure, relative humidity, and wind speed), as well as simulated daily runoff (mm/d) of five models during 1960‒2019 at the three subbasins in the source region of the Yellow River. For more details please see the publication.</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.