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73
datasets available to search
ShareScore release 0.9.0
Dataset results
73 results for “SWAN”
Swan-Ganz CCOmbo V for Obtaining an Optimal Depth of Right Internal Jugular Venous Catheter
ClinicalTrials.gov study NCT03544151. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Mute Swan
Drawing uploaded to scidraw.io on: 14 August 2019
Mute Swan
Drawing uploaded to scidraw.io on: 14 August 2019
Swan Feeding Frenzy
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677408533.8739.jpg">https://4dcity.org/imgupload/1677408533.8739.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/52643774924_7ae6912bd7_m.jpg">https://live.staticflickr.com/65535/52643774924_7ae6912bd7_m.jpg</a>
Mounted Swan Skeleton
This specimen was documented with a Faro Arm scanner and processed in Geomagic, including the reconstruction of the inner areas. The original scan is 51 million triangles. It was reduced for web viewing using Instant Meshes and ZBrush. Documentation was done as part of a collaborative project between the Muséum national d'histoire naturelle, Paris, France and Global Digital Heritage, St. Petersburg, Florida, USA. Swan A11496 Edit5unordered Sampled40+80CP Source: Objaverse 1.0 / Sketchfab
SWAN: Study on the Tolerance and Efficacy of a New Anti-regurgitation Formula (PC-2016-01)
ClinicalTrials.gov study NCT02710955. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison Between TOF and SWAN Sequences in the Detection of Arterial Occlusion in Acute Ischemic Stroke Using MRI
ClinicalTrials.gov study NCT03645902. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Comparative Analysis of the Transcriptome of Hypoxia and Inflammation in Swan 71 Human Trophophoblast Cells: Its Implications in Preeclampsia
GEO Series GSE31679. Homo sapiens. 39 samples. Type: Expression profiling by array.
Differential expression of miRNAs in HGF treated SWAN-71 versus untreated control by next generation sequencing
GEO Series GSE125790. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Differential expression of miRNAs in IFN-gamma treated SWAN-71 versus untreated control by next generation sequencing
GEO Series GSE125789. Homo sapiens. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Differential expression of miRNAs inEGF treated SWAN-71 versus untreated control by next generation sequencing
GEO Series GSE125763. Homo sapiens. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
SWAN-Idiap
<p>SWAN-Idiap dataset is a multimodal biometric dataset (face, voice, and periocular) acquired using a smartphone. The SWAN-Idiap dataset comprises 150 subjects that are captured in six different sessions reflecting real-life scenarios of smartphone assisted authentication. One of the unique features of this dataset is that it is collected in four different geographic locations representing a diverse population and ethnicity. Additionally, it also contains a multimodal Presentation Attack (PA) or spoofing dataset using low-cost Presentation Attack Instruments (PAI) such as print and electronic display attacks. The novel acquisition protocols and the diversity of the data subjects collected from different geographic locations will allow developing a novel algorithm for either unimodal or multimodal biometrics.</p>
SWAN-DF database of audio-video deepfakes
<p><strong>Description</strong></p> <p>SWAN-DF: the first high fidelity publicly available dataset of realistic audio-visual deepfakes, where both faces and voices appear and sound like the target person. The SWAN-DF dataset is based on the public <a href="https://www.idiap.ch/dataset/swan">SWAN database</a> of real videos recorded in HD on iPhone and iPad Pro (in year 2019). For 30 pairs of manually selected people from SWAN, we swapped faces and voices using several autoencoder-based face swapping models and using several blending techniques from the well-known open source repo <a href="https://github.com/iperov/DeepFaceLab">DeepFaceLab</a> and voice conversion (or voice cloning) methods, including zero-shot <a href="https://github.com/Edresson/YourTTS">YourTTS</a>, <a href="https://github.com/huawei-noah/Speech-Backbones/tree/main/DiffVC">DiffVC</a>, <a href="https://github.com/tinkoff-ai/hifi_vc">HiFiVC</a>, and several models from <a href="https://github.com/OlaWod/FreeVC">FreeVC</a>.</p> <p>For each model and each blending technique, there are 960 video deepfakes. We used three types of models of the following resolutions: 160x160, 256x256, and 320x320 pixels. We took one pre-trained model corresponding for each resolution, and tuned it for each of the 30 pairs (both ways) of subjects for 50K iterations. Then, when generating deepfake videos for each pair of subjects, we used one of the tuned models and a way to blend the generated image back into the original frame, which we call blending technique. SWAN-DF dataset contains 25 different combinations of models and blending, which means the total number of deepfake videos is 960*25=24000.</p> <p>We generated speech deepfakes using four voice conversion methods: YourTTS, HiFiVC, DiffVC, and FreeVC. We did not use text to speech methods for our video deepfakes, since the speech they produce is not synchronized with the lip movements in the video. For YourTTS, HiFiVC, and DiffVC methods, we used the pretrained models provided by the authors. HiFiVC was pretrained on VCTK, DiffVC on LibriTTS, and YourTTS on both VCTK and LibriTTS datasets. For FreeVC, we generated audio deepfakes for several variants: using the provided pretrained models (for 16Hz with and without pretrained speaker encoder and for 24Hz with pretrained speaker encoder) as is and by tuning 16Hz model either from scratch or starting from the pretrained version for different number of iterations on the mixture of VCTK and SWAN data. In total, SWAN-DF contains 12 different variations of audio deepfakes: one for each of YourTTS, HiFiVC, and DiffVC and 9 variants of FreeVC.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>If you use this database, please cite the following publication:</p> <p>Pavel Korshunov, Haolin Chen, Philip N. Garner, and Sébastien Marcel, "Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes", IEEE International Joint Conference on Biometrics (IJCB), September 2023.<br> <a href="https://publications.idiap.ch/publications/show/5092">https://publications.idiap.ch/publications/show/5092</a></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.
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International Brain Laboratory public data
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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.