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108
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ShareScore release 0.7.1
Dataset results
108 results for “spatial assessment”
Development of a Non-invasive Assessment of Human Bone Quality Using Spatially Offset Raman Spectroscopy
ClinicalTrials.gov study NCT02814591. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Immune PET and Proteomics for the Assessment of Response to Spatially Fractionated or Palliative Radiotherapy With or Without Immunotherapy
ClinicalTrials.gov study NCT06976021. IPD Sharing: YES. Countries: 0. Publications: 0.
Use of Vegetation Change Tracker, Spatial Analysis, and Random Forest Regression to Assess the Evolution of Plantation Stand Age in Southeast China
<p>It is crucial to determine the spatio-temporal distribution patterns of forest ages across wide regions, as forest management plans and practices, and ecosystem carbon budgeting are highly dependent on these. However, given frequent deforestation events (e.g., harvesting) and rapid recovery of plantation stands in Southern China, field-based forest age measurements over wide regions are time-consuming, labour-intensive, and costly. In the current study, we mapped the spatio-temporal patterns of forest stand ages across three typical plantations in Southern China. This was accomplished by using two new feasible and accurate methods, 1) integrating vegetation change tracker (VCT) algorithm and spatial analysis (VCT-SA) for the pixels that were disturbed at least once from 1987 to 2017, and 2) integrating VCT and random forest (VCT-RF) for the pixels were not disturbed during the study period. The results revealed the spatio-temporal distribution of age structure, which indicated that the plantation stands in our large study area were increasingly aging.</p>
Trend analysis and random forests models assessing spatial and temporal patterns of wildfire probability for the eastern United States
<p>We used historic fire perimeters from Monitoring Trends in Burn Severity to assess trends and drivers of wildfires in the eastern United States. We used a suite of predictor variables relating to weather, vegetation cover, and human infrastructure to parameterize random forests models predicting fire occurrence. Models were used to project annual burned areas using all selected predictors, and to project the marginal response of annual burned areas to the most important weather predictors. This dataset includes Python scripts, raster maps of fire probability, and tables summarizing analysis results. </p>
A spatial transcriptomics based Label-free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis
GEO Series GSE253378. Homo sapiens; Mus musculus. 5 samples. Type: Other.
Smaller is better? Unduly nice accuracy assessments in image classification due to spatial autocorrelation in identification of small sized objects
<p>Deriving the thematic accuracy of models is a fundamental part of image classification analyses. However, due to high spatial autocorrelation in remotely sensed imagery, accuracy assessments can be biased, which leads to relevant overestimation of accuracies. </p>
Supporting Multiscale Maritime Governance through Maritime Spatial Planning Relevant Tools and Assessment Techniques in the Adriatic-Ionian Macroregion
<p>The dataset included in the database were developed according to the mentioned Maritime Spatial Planning relevant governance schemes for the Adriatic-Ionian Macroregion. All spatial data were projected to the UTM33 north. The spatial and non-spatial databases were developed for the specific research entitled "Supporting Multiscale Maritime Governance Through Maritime Spatial Planning Relevant Tools and Assessment Techniques in the Adriatic-Ionian Macroregion".</p>
Dataset related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"
<p>This record contains raw data related to the article "Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment"</p> <p>Abstract</p> <p>Background</p> <p>Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).</p> <p>Methods</p> <p>In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi<sup>42</sup>(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability.</p> <p>Results</p> <p>In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s).</p> <p>Conclusions</p> <p>Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.</p>
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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.