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1,506 results for “objects”
Object A-1
<p>Object A-1. A table of metadata collected for <em>P. syringae </em>strains. Kiersten Fullem, Dissertation, University of Florida 2024.</p>
Individual colors of the NEOROCKS objects observed between 2022 and 2023.
Open the record for dataset details and reuse information.
Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software
<p>Dataset of Empirical Evidence of Large-Scale Diversity in API Usage of Object-Oriented Software, SCAM'13.</p> <p>https://www.monperrus.net/martin/companion-diversity-api-usages</p>
"The Mermaid's Tears" object-based audio recording
<p>This file is an object-based audio recording of "The Mermaid's Tears" - an interactive and immersive radio drama produced by the BBC as part of the ORPHEUS project. In the drama, you can choose to follow one of three characters (Lesley, Dee or Bill), each of which have a different audio mix. This is a BW64 file consisting of 15 audio tracks and a chunk of descriptive ADM metadata. The metadata describes 43 audio objects that make up the drama, and describes three different ways to mix those objects - one for each of the characters. You can listen to the drama through <a href="http://mermaidstears.ch.bbc.co.uk/">this website</a>.</p>
The Toybox Dataset of Visual Object Transformations-Part 2: Households
<p>We introduce a new video dataset called Toybox for computer vision research. Videos in Toybox come from first-person, wearable camera recordings of common household objects and toys being manually manipulated to undergo structured transformations like rotations and translations. This is part two of the three-part dataset. Part two contains videos of household objects: ball, cup, mug, and spoon. Part one and three include toy animals and vehicles, respectively.</p>
The Toybox Dataset of Visual Object Transformations-Part 1: Animals
<p>We introduce a new video dataset called Toybox for computer vision research. Videos in Toybox come from first-person, wearable camera recordings of common household objects and toys being manually manipulated to undergo structured transformations like rotations and translations. This is part one of the three-part dataset. Part one contains videos of toy animals: cat, duck, giraffe, and horse. Part two and three include households and vehicles, respectively.</p>
KGloVe DBpedia predicate object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse predicate object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse object split embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse object split weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
KGloVe DBpedia inverse object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a inverse object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
RDF2Vec DBpedia object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse predicate object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a inverse predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
RDF2Vec DBpedia inverse object frequency embeddings
<p>This dataset contains the vectors from computing rdf2vec embeddings from a inverse object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
KGloVe DBpedia object frequency embeddings
<p>This dataset contains the vectors from computing KGloVe embeddings from a object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Global RDF Vector Space Embeddings. In The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I.</p>
RDF2Vec DBpedia predicate object frequency embeddings
<p>This dataset contains the vectors from computing RDF2vec embeddings from a predicate object frequency weighted DBpedia 2016-04 graph.</p> <p>For each entity in the graph, the text file in the zip archive contains a line with the entity name and the embedded vector.</p> <p>The parameter settings for the embedding are as specified in the paper:</p> <p>Michael Cochez, Petar Ristoski, Simone Paolo Ponzetto, and Heiko Paulheim. 2017. Biased graph walks for RDF graph embeddings. In Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (WIMS '17). ACM, New York, NY, USA, Article 21, 12 pages. DOI: https://doi.org/10.1145/3102254.3102279</p>
Raw Experimental Data for work presented in 'Precise Localization of Multiple Noncooperative Objects in a Disordered Cavity by Wave Front Shaping'
<p>This is the raw experimental data for the work presented in 'Precise Localization of Multiple Noncooperative Objects in a Disordered Cavity by Wave Front Shaping', to be published in Physical Review Letters.</p> <p> </p> <p>https://journals.aps.org/prl/accepted/b307fY31A5a13c5579fc4252303f8b2e5e024fc00</p> <p> </p> <p>See the README file for an explanation of the data.</p> <p> </p> <p> </p>
Creating Object-Based Stimuli to Explore Media Device Orchestration Reproduction Techniques
<p>Dataset containing Object-based versions and rendered out MDO loudspeaker feeds of two programme items, adapted from existing material to explore Media Device Orchestration reproduction techniques. </p> <p>This forms part of the PhD research of Craig Cieciura. This was experiment-based research to determine how to render object-based audio in the domestic environment using ad-hoc, audio-capable devices.</p> <p><strong>References</strong></p> <p>Cieciura, C., Mason, R., Coleman, P. and Paradis, M. 2018. Creating Object-Based Stimuli to Explore Media Device Orchestration Reproduction Techniques, Audio Engineering Society Preprint, 145th Convention, Engineering Brief 463.</p> <p> </p>
Support Material for: S.I. Mimilakis et al., ``Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation''
<p>The audio corpus used for conducting the listening tests reported in S.I. Mimilakis et al., ``Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation''.</p>
Audio dataset produced by striking different objects at different locations
<p>The provided audio files originate from recordings produced by striking three different objects, a CD case made from recycled paper (paperbox), a bottle of beer made from glass (bottle) and a plastic bucket (bucket). Each object is stroke at different locations and as a result, a slightly different sound is produced. In the dataset we also provide a pdf file that provides more information about the dataset.</p>
Multi-objective control in human walking: insight gained through simultaneous degradation of energetic and motor regulation systems
<p>See ReadMe.txt</p>
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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.