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ShareScore release 0.7.1
Dataset results
9 results for “TACoS”
TACoS Speech Caption Dataset
<ul> <li><strong>Dataset Introduction: </strong>This dataset is an extension of Charades-STA dataset, where audio is read from text using machine simulation method "microsoft/speecht5_tts"</li> <li><strong>Associated Code: <a href="https://github.com/xian-sh/UniSDNet">https://github.com/xian-sh/UniSDNet</a></strong></li> <li><strong>Associated Paper: <a href="https://arxiv.org/abs/2403.14174">https://arxiv.org/abs/2403.14174</a><br></strong></li> <li><strong>Disclaimer: </strong>This dataset is for academic research only, non-commercial use, if you use this dataset please cite the <a href="https://arxiv.org/abs/2403.14174">associated paper</a></li> <li><strong>Cite:</strong></li> </ul> <p>@article{hu2024unified,<br> title={Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding},<br> author={Jingjing Hu and Dan Guo and Kun Li and Zhan Si and Xun Yang and Xiaojun Chang and Meng Wang},<br> year={2024},<br> Journal={CoRR},<br> volume={abs/2403.14174},<br>}</p>
TACO: a benchmark for connectivity-invariance in shape correspondence
<div> <h1>TACO: a benchmark for connectivity-invariance in shape correspondence</h1> </div> <p>In real-world scenarios, a major limitation for shape-matching datasets is represented by having all the meshes of the same subject share their connectivity across different poses. Specifically, similar connectivities could provide a significant bias for shape-matching algorithms, simplifying the matching process and potentially leading to correspondences based on recurring triangle patterns rather than geometric correspondences between mesh parts. As a consequence, the resulting correspondence may be meaningless, and the evaluation of the algorithm may be misled.<br>To overcome this limitation, we introduce TACO, a new dataset where meshes representing the same subject in different poses do not share the same connectivity, and we compute new ground truth correspondences between shapes. We extensively evaluate our dataset to ensure that ground truth isometries are properly preserved. We also use our dataset to validate state-of-the-art shape-matching algorithms, verifying a degradation in performance when the connectivity gets altered.</p> <p> </p> <h2><strong>Dataset structure</strong></h2> <ul> <li>offs: a directory containing all the triangular meshes in the dataset in <a href="https://en.wikipedia.org/wiki/OFF_(file_format)#:~:text=OFF%20(Object%20File%20Format)%20is,higher%2Ddimensional%20objects%20as%20well." target="_blank" rel="noopener">OFF file format</a></li> <li>pairs.txt: a list of all the 420 possible pairs of shapes in the dataset</li> <li>gt_matches: a directory containing all the ground truth correspondences listed in `pairs.txt` and stored in <a href="https://it.mathworks.com/help/matlab/apiref/matfileapi.html">MAT file format</a></li> </ul>
TACO: Twitter Arguments from COnversations
<p>This marks the open-source release of TACO.</p> <p>For access to the original data, kindly get in touch with the authors.</p>
La Llorona statue at Taco Boy
Scanned with 3D Live Scanner and the Samsung Galaxy S21 Ultra. I'm 99% sure this is supposed to be La Llorona. From Wikipedia: "Early colonial texts provide evidence that the lore is pre-Hispanic, originating in the central highlands, however La Llorona is most commonly associated with the colonial era and the dynamic between Spanish conquistadores and indigenous women. The most common lore about La Llorona includes her initially being an Indigenous woman who murdered her own children, which she bore from a wealthy Spaniard, after he abandoned her. The villainous qualities of La Llorona, including infanticide and the murdering of one's own blood is assumed to be connected to the narrative surrounding Doña Marina, also known as La Malinche, or Maltinzin in her original nomenclature. Today, the lore of La Llorona is well known in Mexico and the Southwestern United States." https://en.wikipedia.org/wiki/La_Llorona Source: Objaverse 1.0 / Sketchfab
TACO Crossover TRIAL
ClinicalTrials.gov study NCT03135457. IPD Sharing: NO. Countries: 1. Publications: 2.
TACO dataset YOLO Split
<p>TACO dataset converted to YOLO txt with different splits</p>
Cone Beam and CT Scan for the Diagnosis of Otosclerosis (TACOS)
ClinicalTrials.gov study NCT02435446. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Atezolizumab and Cabozantinib for the Treatment of Adolescents and Young Adults With Recurrent or Metastatic Osteosarcoma, TACOS Study
ClinicalTrials.gov study NCT05019703. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Tobacco, Alcohol and Cancerization of the Oral Mucosa (TACO)
ClinicalTrials.gov study NCT06425146. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.