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483 results for “SEMANTICS”
Figures 7-12 from: Mullins P, Kawada R, Balhoff J, Deans A (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1-38. https://doi.org/10.3897/zookeys.223.3572
Figures 7-12 - Brightfield images of Evaniscus rafaeli Kawada sp. n. 7, 8 Lateral habitus 9, 10 Dorsal habitus 11 Anterior oblique 12 Anterior face.
Figures 1-6 from: Mullins P, Kawada R, Balhoff J, Deans A (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1-38. https://doi.org/10.3897/zookeys.223.3572
Figures 1-6 - Brightfield images of Evaniscus lansdownei Mullins sp. n. 1, 2 Lateral habitus 3, 4 Dorsal habitus 5 Anterior oblique 6 Anterior face.
Figures 25-30 from: Mullins P, Kawada R, Balhoff J, Deans A (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1-38. https://doi.org/10.3897/zookeys.223.3572
Figures 25-30 - Brightfield images of Evaniscus tibialis Szépligeti. 25, 26 Lateral habitus 27, 28 Dorsal habitus 29 Anterior oblique 30 Anterior face.
Figures 32-33 from: Mullins P, Kawada R, Balhoff J, Deans A (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1-38. https://doi.org/10.3897/zookeys.223.3572
Figures 32-33 - Brightfield images of Evaniscus rufithorax . 32 Male specimen; arrow points to visible lower tubular sclerite 33 Female specimen; arrow shows where lower tubular sclerite is not visible.
Figures 19-24 from: Mullins P, Kawada R, Balhoff J, Deans A (2012) A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation. ZooKeys 223: 1-38. https://doi.org/10.3897/zookeys.223.3572
Figures 19-24 - Brightfield images of Evaniscus marginatus Cameron. 19, 20 Lateral habitus 21, 22 Dorsal habitus 23 Anterior oblique 24 Anterior face.
Semantic Clones Dataset for TrainTicket Microservices benchmark
<p>It contains Component Call Graphs pairs for the analysis of TrainTicket benchmark (release 0.1.0).</p> <p>It classifies the pairs into semantic clones and non-clones. It contains 27,222 total CCGs pairs.</p> <p>This dataset is published as part of the paper titled as "Detecting Semantic Clones In Microservices Using Components".</p> <p> </p> <p>This version contains correction for 6 pairs are classified as clones. (Highlighted in yellow)</p> <p>This correction is detected using our automatic approach. Check it out in the paper.</p> <p> </p>
Dataset from the article, "False memory facilitation through semantic overlap"
<p>Dataset from the article, "False memory facilitation through semantic overlap".</p>
Artefact for PhD thesis "Dynamic Fault Trees: Semantics, Analysis and Applications"
<p>This artefact contains the example files, tools, and log files for the evaluation in Chapter 7 of the PhD thesis</p> <p><em>Matthias Volk - Dynamic Fault Trees: Semantics, Analysis and Applications</em></p> <p> </p> <p>The tools are licensed according to the license provided with each tool.</p>
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
<p>The dataset for CodeSearchNet. See https://github.com/github/CodeSearchNet and https://arxiv.org/abs/1909.09436 for more.</p>
Dataset from "Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective"
<p>This dataset contains the images and ground truth label masks for semantic segmentation created and described in "Hinniger, C.; Rüter, J. Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective. Aerospace 2023, 10, 604. https://doi.org/10.3390/aerospace10070604".</p>
SEM Data Base Microvilli Semantic Segmentation in Microscopic Images Using a Visual Learning Pipeline
<p>SEM images raw data X1, X1, Y1, Y2, Z1 and Z2</p>
Event Data and Semantic Header for OCED-PG
<p>Data sets and json files (describing the semantic header and dataset description) to build an Event Knowledge Graph (EKG) using OCED-PG as used in [1]. </p> <p>Provides input data for 6 datasets (BPIC14, BPIC15, BPIC16, BPIC17, BPIC19 and a simulated libraray example).</p> <p>EKGs are built using OCED-PG, implemented in <a href="https://pypi.org/project/promg/0.1.25/">PromgG v0.1.25</a>. The source code can be found at <a href="https://github.com/PromG-dev/promg-core">Github</a>.</p> <p>To build EKGs using OCED-PG</p> <ul> <li>for one of the BPIC challenges, fork the query code from Github: <a href="https://github.com/Ava-S/ekg_bpi_challenges">ekg_bpi_challenges</a>.</li> <li>for the simulated library example, fork the query code from Github: <a href="https://github.com/Ava-S/ekg_library_example">ekg_library_example</a>.</li> </ul> <p>[1] Swevels, A., Fahland, D., Montali, M.: Implementing Object-Centric Event Data Models in Event Knowledge Graphs (2023)</p>
Classical art semantics information extraction
<p>Abstract. The paper discusses the application of Natural Language Processing (NLP) techniques in the context of semantic annotation of classical art text via rule-based Information Extraction (IE) techniques combined with ontological and domain vocabulary input. The CASIE (Classical Art Semantics Information Extraction) was a pilot collaborative project between the Hypermedia Research<br> Unit (University of South Wales) and the Beazley Archive (Oxford University), which aims to automatically extract information about cultural objects from classical art scholarly texts and represent this information in terms of the ISO metadata standard for cultural heritage, the International Council of Museum’s CIDOC Conceptual Reference Model (CRM). In total 12 documents (fascicules<br> – high quality catalogues) were processed, originating from the Corpus Vasorum Antiquorum (CVA) collection containing over 350 high quality catalogues of mostly ancient Greek painted pottery, illustrating more than 100,000 vases. The extracted information was expressed in interoperable RDF graphs consistent with the CLAROS project format. The role of CIDOC-CRM is central for enabling semantic interoperability across the range of datasets that contribute to CLAROS. The CASIE pilot enabled a complementary exploitation of terminological and ontological resources via rule-based information extraction techniques, delivering semantic annotation with respect to the CRM in the broader field of digital humanities.</p>
Ketamine's Effect Changes the Cortical Electrophysiological Activity Related to Semantic Affective Dimension of Pain
ClinicalTrials.gov study NCT03915938. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Semantic Learning Deficits in School Age Children With Developmental Language Disorder
ClinicalTrials.gov study NCT04508699. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation
Open the record for dataset details and reuse information.
Data from: The influence of evaluative right/wrong feedback on phonological and semantic processes in word learning
Open the record for dataset details and reuse information.
Data from: Evaluating active learning methods for annotating semantic predications
Open the record for dataset details and reuse information.
Sundqvist et al. - The white matter module-hub network of semantics revealed by semantic dementia - Supplementary Figure
<p><strong>Supplementary Figure</strong> The correlation circle: correlations between the different MRI measures and semantic scores and the two first principal components via coordinates. The two first components sum up 68% of the total variance.</p> <p>Semantic composite (verbal, non-verbal) scores are measured in percentage. L indicates left hemisphere, R indicates right hemisphere. Mean Diffusivity (MD) for white matter tracts are: UNC-L, UNC-R, ILF-L, ILF-R, ATL-WVFA-L, ATL-FFA-R, ATL-LA-L, and ATL-LA-R. Mean cortical thickness of the regions of interests are: ATL-L, ATL-R, WVFA-L, FFA-R, LA-L, and LA-R.</p> <p>Link to publication: https://doi.org/10.1162/jocn_a_01549</p> <p> </p>
Modeling Factual Claims with Semantic Frames
<p>We introduce an extension of the Berkeley FrameNet for the structured and semantic modeling of factual claims. Modeling is a robust tool that can be leveraged in many different tasks such as matching claims to existing fact-checks and translating claims to structured queries. Our work introduces 11 new manually crafted frames along with 9 existing FrameNet frames, all of which have been selected with fact-checking in mind. Along with these frames, we are also providing 2,540 fully annotated sentences, which can be used to understand how these frames are intended to work and to train machine learning models. </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.