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ShareScore release 0.9.0
Dataset results
483 results for “SEMANTICS”
Figure 1 in Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects
Figure 1. Example specimen. Example physical herbarium object and its stable HTTP URI identifier.
STRUCTURAL-SEMANTIC CHARACTERISTICS OF PHRASEOLOGISMS IN DIFFERENT SYSTEMIC LANGUAGES
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Data for The "Effect of Semantic Knowledge Graph Richness on Embedding Based Recommender Systems"
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Boston & Corniolo Datasets - road segmentation - described in "An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images""
<p>In Corniolo.rar the masks (values {0,1}) are saved in the png files</p>
SEMANTIC STUDY OF ETHNOGRAPHIC LEXICON
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Computed Semantic clues scores for Grafematik (Magistry & Goudin, 2022)
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SEMANTIC-STYLISTIC CHARACTERISTICS OF EXPRESSIONS IN THE WORKS OF ERKIN A'ZAM
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COMPARATIVE HISTORICAL LINGUISTICS AND DIMINUTIVE FUNCTIONAL-SEMANTIC FIELD
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Facilitation of allocentric coding by virtue of object-semantics
<p>Results of both experiments.</p>
Dataset for semantic-enhanced indirect call solver
<p>datasets among 31 projects for SEA</p>
Test Dataset for 3D semantic image segmentation of the Liver and Tumor
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SEMANTIC GROUPS OF ANTONYMS IN ENGLISH
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Tracing vs. Semantics: On the Different Levels of Understanding Boolean Expressions
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Data from: Evaluating active learning methods for annotating semantic predications
Objectives: This study evaluated and compared a variety of active learning strategies, including a novel strategy we proposed, as applied to the task of filtering incorrect SemRep semantic predications. Materials and Methods: We evaluated three types of active learning strategies – uncertainty, representative, and combined– on two datasets of semantic predications from SemMedDB covering the domains of substance interactions and clinical medicine, respectively. We also designed a novel combined strategy with dynamic β without hand-tuned hyperparameters. Each strategy was assessed by the Area under the Learning Curve (ALC) and the number of training examples required to achieve a target Area Under the ROC curve (AUC). We also visualized and compared the query patterns of the query strategies. Results: Combined strategies outperformed all other methods in terms of ALC, outperforming the baseline by over 0.05 ALC for both datasets and reducing 58% annotation efforts in the best case. While representative strategies performed well, their performance was matched or outperformed by the combined methods. All the uncertainty sampling methods beat the baseline but they were the worst performing methods overall. Our proposed AL method with dynamic β shows promising ability to achieve near-optimal performance across two datasets. Discussion: Our visual analysis of query patterns indicates that strategies which efficiently obtain a representative subsample perform better on this task. Conclusion: Active learning is shown to be effective at reducing annotation costs for filtering incorrect semantic predications from SemRep. Our proposed AL method demonstrated promising performance.
Figure 3 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 3 - Flowchart of an integrated, XML-based editorial, publishing and dissemination process applied in ZooKeys through the Pensoft Mark Up Tool (PMT).
Figure 2 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 2 - Four stages of an XML-based editorial, publication and dissemination workflow applied in ZooKeys (stages 1, 2, 4) and/or Plazi (stages 3, 4). Forms in blue are either implemented or prototyped, forms in red are in a process of development.
Figure 4 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 4 - Pensoft Taxon Profile created dynamically by PMT and available through a link to any taxon name mentioned within a paper. In this case, this is the oak species Quercus suber L., cited in a zoological paper (Stoev et al. 2010). The red arrow indicates the "Create your own taxon profile" option, that may be used by the reader to create profiles of any taxon name or to improve search results for taxonomic names cited in the paper.
Figure 1 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 1 - Conventional layout of a standard taxonomic publication in PDF format (A) and the same portion of text in XML-tagged format (B). Explanations: The sign "<" incidates the start tag and the symbol " denotes the start of the treatment and the tag (not visible here) marks up the end of the treatment within the text of the paper. The tags and denote the start and end of a particular section of the treatment, in this case the type material data (labelled as Type material.)
Figure 31 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
Figure 31 - Most parsimonious tree from exhaustive search in PAUP*. Numbers above nodes show bootstrap support and numbers below nodes show jackknife support from the maximum parsimony analysis.
Figures 13-18 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 13-18 - Brightfield images of Evaniscus rufithorax Enderlein. 13, 14 Lateral habitus 15, 16 Dorsal habitus 17 Anterior oblique 18 Anterior face.
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.