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483 results for “SEMANTICS”
Figure 1 from: Deans A, Mullins P, Kawada R, Balhoff J (2013) Corrigenda: Mullins PL, Kawada R, Balhoff JP, Deans AR (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1–38, doi: 10.3897/zookeys.223.3572. ZooKeys 278: 105-113. https://doi.org/10.3897/zookeys.278.5108
Figure 1 - Evaniscus sulcigenis Roman, 1917. Lateral habitus (whole body) of holotype.
Figure 2 from: Deans A, Mullins P, Kawada R, Balhoff J (2013) Corrigenda: Mullins PL, Kawada R, Balhoff JP, Deans AR (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1–38, doi: 10.3897/zookeys.223.3572. ZooKeys 278: 105-113. https://doi.org/10.3897/zookeys.278.5108
Figure 2 - Evaniscus sulcigenis Roman, 1917. Lateral habitus (mesosma and head) of holotype.
Semantic Census RDF data
<p>Semantic Census RDF data files</p>
SMWCloud: A Corpus of Domain-Specific Knowledge Graphs from Semantic MediaWikis
<p>Semantic wikis have become an increasingly popular means of collaboratively managing Knowledge Graphs. They are powered by platforms such as Semantic MediaWiki and Wikibase, both of which enable MediaWiki to store and publish structured data. While there are many semantic wikis currently in use, there has been little effort to collect and analyse their structured data, nor to make it available for the research community. This resource seeks to address this gap by systematically collecting structured data from an extensive corpus of Semantic-MediaWiki-powered portals and providing an in-depth analysis of the ontological diversity (and re-use) amongst these wikis using a variety of ontological metrics. Our resource aims to demonstrate that semantic wikis are a valuable and extensive part of Linked Open Data (LOD), and in fact may be considered an own active ``sub-cloud'' within the LOD ecosystem, which can provide useful insights into the evolution of small and medium-sized domain-specific Knowledge Graphs.</p>
SS-GoldenDOT: Semantic Segmentation for Mold Development
<p>A totally semantic segmentated dataset with 144 high resolution videos of 20 frames (summing a total of 2,880 images) showing the degradation due to the effects of fungi of halved Golden Delicious apples over 10 days.</p>
M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
The Effect of Semantic Support on Word Learning
ClinicalTrials.gov study NCT05921214. IPD Sharing: NO. Countries: 1. Publications: 0.
Semantic Recognition Task (SRT) in Alzheimer Disease
ClinicalTrials.gov study NCT05711888. IPD Sharing: NO. Countries: 1. Publications: 0.
Semantic Rehabilitation for Patients With Primary Progressive Semantic Aphasia
ClinicalTrials.gov study NCT04957537. IPD Sharing: NO. Countries: 1. Publications: 0.
Semantic Networks in Alcohol Use Disorder Patients: Exploratory Study
ClinicalTrials.gov study NCT05636033. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CAMPUS Expanded "Classification and Assessment of Mental Health Performance Using Semantics Expanded"
ClinicalTrials.gov study NCT04493736. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Core Semantic Systems TMS
ClinicalTrials.gov study NCT06870552. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Semantic and Syntactic Computerized Analysis of Free Speech
ClinicalTrials.gov study NCT03525054. IPD Sharing: NO. Countries: 1. Publications: 0.
Metacognition in Semantic Dementia: Comparison With Alzheimer's Disease
ClinicalTrials.gov study NCT04597827. IPD Sharing: Not stated. Countries: 2. Publications: 0.
A Lexico-semantic Program on Tactile Tablet for Patients With Alzheimer's Disease
ClinicalTrials.gov study NCT03047694. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Language Acquisition in the Brain and Algorithms: Towards Systematic Monitoring of the Evolution of Semantic Representations in Biological and Artificial Neural Networks
ClinicalTrials.gov study NCT05217043. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Metamemory Using a Semantic Construction Strategy in Patients With Schizophrenia
ClinicalTrials.gov study NCT02932059. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The Yin and the Yang of Prediction: an fMRI study of semantic predictive processing
Open the record for dataset details and reuse information.
Dataset used in COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations
<p>This dataset consists of two hdf5 files that contain pre-computed log-mel spectrograms that have been used to to train audio embedding models. The dataset is split into a training set and a validation set containing respectively 170793 and 19103 spectrogram patches with their accompanying multi-hot encoded tags from a vocabulary of 1000 tags provided by <a href="https://freesound.org/">Freesound</a> users.</p> <p>More details can be found in "COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations" by X. Favory, <a href="https://kdrossos.net">K. Drossos</a>, <a href="https://tutcris.tut.fi/portal/en/persons/tuomas-virtanen(210e58bb-c224-40a9-bf6c-5b786297e841).html">T. Virtanen</a>, and X. Serra. The code is available at this <a href="https://github.com/xavierfav/coala">GitHub repository</a>.</p> <p> </p> <p>License:</p> <p>This dataset is derived from content from the Freesound collection. All sounds are released under Creative Commons (CC) licenses from either <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a>, <a href="https://creativecommons.org/licenses/by/3.0/">CC-BY,</a> <a href="https://creativecommons.org/licenses/sampling+/1.0/">CC-S+</a>, or <a href="https://creativecommons.org/licenses/by-nc/3.0/">CC-BY-NC</a>. We attribute authors of all the sounds used in the dataset and provide their corresponding licenses in the attributions.txt file.</p> <p> </p>
Dataset and models for Adversarial Semantic Collisions (EMNLP20)
<p>Dataset and models for Adversarial Semantic Collisions (EMNLP20),</p>
ScienceDex guides
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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.