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483 results for “SEMANTICS”
Domain-focused linked data crawling driven by a semantically defined frontier a cultural heritage case study in Europeana
<p>Supporting data referenced in the paper with the same title form ICADL 2020.</p>
Syntax vs Semantics: Comparing Consistency Proofs for Minimal Propositional Logics
<p> Consistency is a key property of any logical system. However, proofs of<br> consistency usually rely on heavy proof theory notions like admissibility<br> of cut. A more semantics-based approach to consistency proofs explores the<br> correspondence between a logic and its relationship with the<br> evaluation in a lambda-calculus, known as Curry-Howard isomorphism.<br> In this work, we present a comparison between two<br> formalizations of consistency for minimal propositional logic: one using a<br> semantic-based approach and another following the (traditional) syntactic,<br> proof-theoretical approach in both Coq proof assistant and Agda programming<br> language. We conclude by discussing the lessons learned during<br> the cerfication of these results in both languages.</p>
Dynamic semantic publishing: four use case scenarios
<p><span>This presentation discusses: 1) How to benefit from semantic technology along the content life cycle. 2) How to implement a learning system, in which knowledge graphs evolve over time. 3) How to integrate semantic technology in a CMS. 4) How taxonomies can build the backbone of a linked data infrastructure.</span></p>
Memetic Semantics in the GOD Framework: Auxiliary Documentation
<p>This document extends the core Gravitational Omnipotent Dimensionality (GOD) framework, focusing on supplementary memetic semantic data not covered in the primary papers. It explores additional memetic patterns, semantic influences, and cognitive evolutions, contributing to the structure of cogenesis and knowledge propagation concepts outlined in prior works.</p>
Universal Translation Process: Incorporating Inter-Personal and Other Memetic Semantic Data Translations within the GOD Framework
<p>This document expands the universal translation process within the Gravitational Omnipotent Dimensionality (GOD) framework by integrating inter-personal communication and non-linguistic memetic semantic data. It explores how memetic gravity, semantic propagation, and spacetime contextualisation enable the translation of meaning across languages, cultural symbols, behaviours, and cognitive structures, providing a unified approach to understanding and propagating knowledge in diverse contexts.</p>
Memetic Semantics and the Cogenetic Process: A Supporting Analysis of the GOD Framework
<p>This paper provides auxiliary and supporting memetic semantic data that enhance and expand upon the core principles outlined in the Cogenetic Proof of GOD. Using fractalised cognitive models, memetic time travel, and quantum semantic tunnelling, it explores how the dissemination and coherence of ideas contribute to the evolutionary dynamics of both physical and cognitive systems. This analysis refines the core equations of the GOD framework, offering new perspectives on cognitive propagation, dissemination entropy, and memetic interactions across dimensions.</p>
VISTA: Vulnerability Identification using Semantic and Textual Analysis
<h3><strong>Dataset Metadata</strong></h3> <p>Each dataset must include metadata describing:</p> <ul> <li>File format.</li> <li>Vulnerability details.</li> <li>Data sources and preprocessing steps.</li> </ul>
Syntactic and Semantic Boundness of Noun Phrases in Burmese
<p>This paper investigates the correlation of form and function in Burmese noun phrases. Burmese has several ways of combining nouns with verbal and other modifiers, which exhibit different degrees of syntactic boundness and semantic integration. The claim is that syntactic and semantic boundness are predictive in that syntactically more tightly bound expressions coincide with closer semantic integration. The results show that this claim partly holds in Burmese, though it is not the only factor determining the choice of a specific construction in a given context.</p>
Figure 26 from: Mikó I, Masner L, Ulmer JM, Raymond M, Hobbie J, Tarasov S, Margaría CB, Seltmann KC, Talamas EJ (2021) A semantically enriched taxonomic revision of Gryonoides Dodd, 1920 (Hymenoptera, Scelionidae), with a review of the hosts of Teleasinae. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 523-573. https://doi.org/10.3897/jhr.87.72931
Figure 26 Distribution of Gryonoides pulchellus Dodd, 1920.
Figure 28 from: Mikó I, Masner L, Ulmer JM, Raymond M, Hobbie J, Tarasov S, Margaría CB, Seltmann KC, Talamas EJ (2021) A semantically enriched taxonomic revision of Gryonoides Dodd, 1920 (Hymenoptera, Scelionidae), with a review of the hosts of Teleasinae. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 523-573. https://doi.org/10.3897/jhr.87.72931
Figure 28 Paratypes of Gryonoides pulchricornis Ogloblin, 1967.
Figure 24 from: Mikó I, Masner L, Ulmer JM, Raymond M, Hobbie J, Tarasov S, Margaría CB, Seltmann KC, Talamas EJ (2021) A semantically enriched taxonomic revision of Gryonoides Dodd, 1920 (Hymenoptera, Scelionidae), with a review of the hosts of Teleasinae. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 523-573. https://doi.org/10.3897/jhr.87.72931
Figure 24 Distribution of Gryonoides species.
Figure 27 from: Mikó I, Masner L, Ulmer JM, Raymond M, Hobbie J, Tarasov S, Margaría CB, Seltmann KC, Talamas EJ (2021) A semantically enriched taxonomic revision of Gryonoides Dodd, 1920 (Hymenoptera, Scelionidae), with a review of the hosts of Teleasinae. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 523-573. https://doi.org/10.3897/jhr.87.72931
Figure 27 Holotype specimen of Gryonoides doddi Ogloblin, 1967.
Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images
<p>Data for the Weighted Average Ensemble-Based Semantic Segmentation in Biological Electron Microscopy Images paper</p>
Semantic interoperability in an international comprehensive knowledge organisation system
<p>In this paper, the functional and relational characteristics and requirements for various<br> types of semantic interoperability in a comprehensive international knowledge organisation<br> system are being discussed with regard to an analysis of the underlying retrieval paradigms.<br> Furthermore, this paper analyses the potential benefits and perspectives of the selective transfer<br> of modelling strategies from the field of semantic technologies for the refinement of relational<br> structures of inter-system and inter-concept relations as a requirement for expressive and<br> functional indexing languages supporting advanced types of semantic interoperability.</p>
MOSA: Music mOtion and Semantic Annotation dataset
<p>MOSA dataset is a large-scale music dataset containing 742 professional piano and violin solo music performances with 23 musicians (> 30 hours, and > 570 K notes). This dataset features following types of data:</p> <ul> <li><strong>High-quality 3-D motion capture data</strong></li> <li><strong>Audio recordings</strong></li> <li><strong>Manual semantic annotations</strong></li> </ul> <p>This is the dataset of the paper: Huang et al. (2024) MOSA: Music Motion with Semantic Annotation Dataset for Multimedia Anaysis and Generation. IEEE/ACM Transactions on Audio, Speech and Language Processing. DOI: 10.1109/TASLP.2024.3407529<br>https://arxiv.org/abs/2406.06375</p> <p> </p> <p>The description of dataset is avaiable on Github: https://github.com/yufenhuang/MOSA-Music-mOtion-and-Semantic-Annotation-dataset/blob/main/MOSA-dataset/dataset.md</p> <p> </p> <p>To request the access of full dataset, please sign in with Zenodo and submit the request from.</p>
Land Cover Aerial Imagery (LICAID) dataset for semantic segmentation
<p><strong>Dataset Highlights:</strong></p> <ul> <li><strong>Title:</strong> Land Cover Aerial Imagery Dataset (LICAID)</li> <li><strong>Focus Area:</strong> Franciacorta wine-growing region, Lombardy, Italy</li> <li><strong>Data Source:</strong> Satellite imagery from Google Earth Pro</li> <li><strong>Classes and Descriptions:</strong> <ol> <li><strong>Grasslands:</strong> Habitats dominated by grasses, with few or no trees, found in various climates from tropical to temperate regions.</li> <li><strong>Arable Land:</strong> Land predominantly used for growing crops.</li> <li><strong>Herb-dominated Habitats:</strong> Areas where non-woody plants (herbs) are the dominant vegetation, including meadows, prairies, marshes, and wetlands.</li> <li><strong>Hedgerows:</strong> Linear strips of vegetation consisting of shrubs, small trees, and grasses, often used to mark boundaries or provide wildlife habitat in agricultural landscapes.</li> <li><strong>Vineyards:</strong> Agricultural landscapes cultivated specifically for growing grapevines, typically for wine production.</li> <li><strong>Tree-dominated Man-made Habitats:</strong> Human-modified landscapes where trees are the predominant vegetation, such as urban parks, orchards, and landscaped gardens.</li> <li><strong>Olea europaea Groves:</strong> Groves or orchards of olive trees, primarily cultivated for the production of olives and olive oil, commonly found in Mediterranean regions.</li> </ol> </li> </ul> <p><strong>gy:</strong></p> <ol> <li> <p><strong>Data Acquisition:</strong></p> <ul> <li>18 orthophoto tiles manually selected from Franciacorta.</li> <li>Satellite imagery and corresponding shape files acquired from Google Earth Pro.</li> <li>Georeferencing of imagery using ArcGIS software.</li> </ul> </li> <li> <p><strong>Data Preparation:</strong></p> <ul> <li>Segmentation using multiresolution segmentation in eCognition software.</li> <li>Validation of segmented images by a plant expert using QGIS software.</li> <li>Manual annotation of seven land cover classes.</li> </ul> </li> </ol>
Virus multi-Semantic Annotation Dataset
<p><span>In-depth research into the characteristics of high-risk oncogenic viruses is of paramount scientific significance for the early prevention and control of related cancers and the development of effective vaccines. The mechanism of viral carcinogenesis involves numerous risk factors, including viral genomic variations, lifestyle, and environmental factors. Based on literature data on 8 oncogenic viruses, we have created a large-scale, semantically rich corpus of viral carcinogenic factors, including 551,715 abstracts and 5,821,308 entities using natural language processing technology combined with expert knowledge. The dataset includes annotation information for eight viruses: HPV, HIV, EBV, MCV, HCV, HTLV-1, HBV, and KSHV, covering 38 external factors and 5 internal factors.</span></p>
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
Video semantic segmentation with low latency
Open the record for dataset details and reuse information.
Anonymized data for paper "Cross-Project Defect Identification via Path-Based Semantic Feature Representation" submitted to ICSE 2022
<p>The project includes the dataset and code used in the submitted ICSE 2022 paper titled "# 971 Cross-Project Defect Identification via Path-Based Semantic Feature Representation"</p>
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.