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ShareScore release 0.9.0
Dataset results
128 results for “shadows”
Borrowing Size vs. Agglomeration Shadow
<p>This dataset is used to repeat the analysis in article.</p>
CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2
<p><strong>Description</strong></p> <p>CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups:</p> <p>- clear (0%)</p> <p>- low-cloudy (1% - 25%) </p> <p>- almost clear (25% - 45%)</p> <p>- mid-cloudy (45% - 65%)</p> <p>- cloudy (65% >)</p> <p>An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group:</p> <ul> <li> <p>2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).</p> </li> <li> <p>2000 ROIs with scribble level annotation, where the annotation time is 15 minutes (scribble group).</p> </li> <li> <p>5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).</p> </li> </ul> <p>For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, a system that combines human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no annotation dataset is generated automatically, with manual annotation only in the clear image patch. The dataset is already available here: <strong><a href="https://shorturl.at/cgjtz">https://shorturl.at/cgjtz</a></strong>. Check out our website <strong><a href="https://cloudsen12.github.io/">https://cloudsen12.github.io/</a></strong> for examples of how to download the dataset via STAC.</p>
In situ synthesis of peptide microarrays - shadow mask design [2]
GEO Series GSE50044. synthetic construct; Mus musculus. 8 samples. Type: Protein profiling by protein array.
In situ synthesis of peptide microarrays - shadow mask design
GEO Series GSE50045. synthetic construct; Mus musculus. 113 samples. Type: Protein profiling by protein array.
Reviving Lost Shadows: Investigating the habitat ecology of the rediscovered hispid hare (Caprolagus hispidus) in Nepal
Open the record for dataset details and reuse information.
Analysing crater properties using iSALE to investigate the deposition of volatiles in lunar Permanently Shadowed Regions (PSRs)
<p>This file contains the code (and the figures they produce) that was used in my Honours project.</p>
Shadow mask assisted 193 nm Excimer laser processing of commercial Cyclo-Olefin-Copolymer (COC) substrates for low-cost micro-fluidic biosensing devices.
<p>An Abstract submitted to The 24th International Symposium on Laser Precision Microfabrication (LPM2023)</p>
Citizen science reveals a geographic mosaic of trumpetfish shadowing behaviour in the Caribbean Sea
<p>Citizen science is often used to assess the distribution and abundance of species, but it can be extended to identify the occurrence of different animal behaviours, generating hypotheses for the fields of behavioural ecology, conservation, and evolutionary biology. Here we use a targeted citizen science approach to map the prevalence and spatial spread of a fish's unusual hunting strategy across the Caribbean Sea. Using a standardised questionnaire targeting regular scuba divers, we quantified the occurrence of a behaviour termed 'shadowing', where West Atlantic trumpetfish (<i>Aulostomatus maculeatus</i>) swim behind or next to another 'host' species when hunting prey. While the vast majority of participants observed trumpetfish on nearly every dive, using spatial analyses, we found the frequency of shadowing behaviour was geographically clustered within the Caribbean, particularly to within ~100 km ranges. Our survey also highlighted that shadowing behaviour was more likely to occur in particular habitat types, and with particular host species, suggesting potential mechanisms for such clustering. Our results not only identify a mosaic of different hunting behaviours across thousands of square kilometres, but more generally, demonstrates the power of using citizen science to investigate behaviours over spatial scales that would otherwise be unfeasible.</p>
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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.