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Fig. 2 in Morphology and ecological setting of the basal echinoid genus Rhenechinus from the early Devonian of Spain and Germany
Fig. 2. Stratigraphical succession of the Aguión Formation at Arnao Platform showing lithological units, their faunal composition and types of communities. The level bearing echinoids is indicated by an arrow. From Arbizu et al. (1995).
Nitrate trend reversal in Dutch dual-permeability chalk springs, evaluated by tritium-based groundwater travel time distributions [Data set]
<p>Data set covering measured nitrate and sulfate data of 90 springs, the tritium and nitrate input data for the convolution models and the age distributions metrics for the base case model and the stringent denitrification model, which were described in the publication in Science of the Total Environment (2024); doi: <em>10.1016/j.scitotenv.2024.175250</em></p>
Hearing the Voice of Software Practitioners on Technical Debt Monitoring: Understanding Monitoring Practices and the Practices' Avoidance Reasons: Data set
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Traning set for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"
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Detection of HBV mRNA in a liver scRNA-seq data set
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Data_set_collected
<p><strong>※Please make sure to cite appropriately from the journal site when referencing.</strong></p> <p>This is the data of the following article.</p> <p>Ishii, Y. (2025). Applicability and reflection of three-phase model of language comprehension on the listening skill of Japanese EFL (English as Foreign Language) learners. <em>Linguistica online; miscellanea XVI., 28</em>., 1-61.</p> <p>You can access this article on this Journal page.</p> <p>Journal site: <a href="https://www.phil.muni.cz/linguistica/">https://www.phil.muni.cz/linguistica/</a></p> <p>The article: <a href="https://www.phil.muni.cz/linguistica/art/ishi/ish-001.pdf">https://www.phil.muni.cz/linguistica/art/ishi/ish-001.pdf</a></p>
Spontaneous reversal of spin chirality and competing phases in the topological magnet EuAl4 - Data set
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DraftSeptember 16, 2024 (v1)DatasetOpen Exploring the conformational space of proteins with an efficient set of TlCA features based on GoMartini 3 descriptors
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AnnData set for single cell analysis tutorial
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LnBS2* basis sets
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Training set
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Data set: Comparison of the predictive capacity of the erotophobia-erotophilia and the attitudes toward sexual behaviors in the sexual experience of young adults
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Video RONDO-TEST set up
<p>Setting up of the RONDO-TEST.</p>
Image sets for OmicsFootPrint publication
<p> Image sets used in the OmicsFootPrint paper. All generated from public data of TCGA. </p>
Codecfake dataset - development set
<p>This dataset is the development set of the Codecfake dataset, corresponding to the manuscript "<strong>The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection</strong>".</p> <h1>Abstract</h1> <p>With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)<br>models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose<br>the CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.</p> <h1>Codecfake Dataset</h1> <p>Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:</p> <table> <tbody> <tr> <td>Codecfake dataset</td> <td>description</td> <td>link</td> </tr> <tr> <td>training set (part 1 of 3) & label</td> <td>train_split.zip & train_split.z01 - train_split.z05</td> <td><a href="https://zenodo.org/records/13838106">https://zenodo.org/records/13838106</a></td> </tr> <tr> <td>training set (part 2 of 3)</td> <td>train_split.z06 - train_split.z10</td> <td><a href="https://zenodo.org/records/13841652">https://zenodo.org/records/13841652</a></td> </tr> <tr> <td>training set (part 3 of 3)</td> <td>train_split.z11 - train_split.z16</td> <td><a href="https://zenodo.org/records/13853860">https://zenodo.org/records/13853860</a></td> </tr> <tr> <td>development set</td> <td>dev_split.zip & dev_split.z01 - dev_split.z02</td> <td><a href="https://zenodo.org/records/13841216"><strong>https://zenodo.org/records/13841216</strong></a></td> </tr> <tr> <td>test set (part 1 of 2)</td> <td>Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip</td> <td><a href="https://zenodo.org/records/13838823">https://zenodo.org/records/13838823</a></td> </tr> <tr> <td>test set (part 2 of 2)</td> <td>Codec unseen test: C7.zip</td> <td><a href="https://zenodo.org/records/11125029">https://zenodo.org/records/11125029</a></td> </tr> </tbody> </table> <h1>Countermeasure</h1> <p>The source code of the countermeasure and pre-trained model are available on GitHub <a href="https://github.com/xieyuankun/Codecfake">https://github.com/xieyuankun/Codecfake</a>.</p> <p> </p> <p>The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.</p>
Codecfake dataset - test set (part 1 of 2)
<p>This dataset is the test set (part 1 of 2) of the Codecfake dataset , corresponding to the manuscript "<strong>The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection</strong>".</p> <h1>Abstract</h1> <p>With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)<br>models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose<br>the CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.</p> <h1>Codecfake Dataset</h1> <p>Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:</p> <table> <tbody> <tr> <td>Codecfake dataset</td> <td>description</td> <td>link</td> </tr> <tr> <td>training set (part 1 of 3) & label</td> <td>train_split.zip & train_split.z01 - train_split.z05</td> <td><a href="https://zenodo.org/records/13838106">https://zenodo.org/records/13838106</a></td> </tr> <tr> <td>training set (part 2 of 3)</td> <td>train_split.z06 - train_split.z10</td> <td><a href="https://zenodo.org/records/13841652">https://zenodo.org/records/13841652</a></td> </tr> <tr> <td>training set (part 3 of 3)</td> <td>train_split.z11 - train_split.z16</td> <td><a href="https://zenodo.org/records/13853860">https://zenodo.org/records/13853860</a></td> </tr> <tr> <td>development set</td> <td>dev_split.zip & dev_split.z01 - dev_split.z02</td> <td><a href="https://zenodo.org/records/13841216">https://zenodo.org/records/13841216</a></td> </tr> <tr> <td>test set (part 1 of 2)</td> <td>Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip</td> <td><a href="https://zenodo.org/records/13838823"><strong>https://zenodo.org/records/13838823</strong></a></td> </tr> <tr> <td>test set (part 2 of 2)</td> <td>Codec unseen test: C7.zip</td> <td><a href="https://zenodo.org/records/11125029">https://zenodo.org/records/11125029</a></td> </tr> </tbody> </table> <h1>Countermeasure</h1> <p>The source code of the countermeasure and pre-trained model are available on GitHub <a href="https://github.com/xieyuankun/Codecfake">https://github.com/xieyuankun/Codecfake</a>.</p> <p> </p> <p>The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.</p>
Codecfake dataset - training set (part 3 of 3)
<p>This dataset is the training set (part 3 of 3) of the Codecfake dataset , corresponding to the manuscript "<strong>The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection</strong>".</p> <h1>Abstract</h1> <p>With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)<br>models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose<br>the CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.</p> <h1>Codecfake Dataset</h1> <p>Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:</p> <table> <tbody> <tr> <td>Codecfake dataset</td> <td>description</td> <td>link</td> </tr> <tr> <td>training set (part 1 of 3) & label</td> <td>train_split.zip & train_split.z01 - train_split.z05</td> <td><a href="https://zenodo.org/records/13838106">https://zenodo.org/records/13838106</a></td> </tr> <tr> <td>training set (part 2 of 3)</td> <td>train_split.z06 - train_split.z10</td> <td><a href="https://zenodo.org/records/13841652">https://zenodo.org/records/13841652</a></td> </tr> <tr> <td>training set (part 3 of 3)</td> <td>train_split.z11 - train_split.z16</td> <td><strong><a href="https://zenodo.org/records/13853860">https://zenodo.org/records/13853860</a></strong></td> </tr> <tr> <td>development set</td> <td>dev_split.zip & dev_split.z01 - dev_split.z02</td> <td><a href="https://zenodo.org/records/13841216">https://zenodo.org/records/13841216</a></td> </tr> <tr> <td>test set (part 1 of 2)</td> <td>Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip</td> <td><a href="https://zenodo.org/records/13838823">https://zenodo.org/records/13838823</a></td> </tr> <tr> <td>test set (part 2 of 2)</td> <td>Codec unseen test: C7.zip</td> <td><a href="https://zenodo.org/records/11125029">https://zenodo.org/records/11125029</a></td> </tr> </tbody> </table> <h1>Countermeasure</h1> <p>The source code of the countermeasure and pre-trained model are available on GitHub <a href="https://github.com/xieyuankun/Codecfake">https://github.com/xieyuankun/Codecfake</a>.</p> <p>The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.</p>
Codecfake dataset - training set (part 1 of 3)
<p>This dataset is the training set (part 1 of 3) of the Codecfake dataset , corresponding to the manuscript "<a href="https://arxiv.org/abs/2405.04880"><strong>The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection</strong></a>".</p> <h1>Abstract</h1> <p>With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)<br>models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose<br>the CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.</p> <h1>Codecfake Dataset</h1> <p>Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:</p> <table> <tbody> <tr> <td>Codecfake dataset</td> <td>description</td> <td>link</td> </tr> <tr> <td>training set (part 1 of 3) & label</td> <td>train_split.zip & train_split.z01 - train_split.z05</td> <td><a href="https://zenodo.org/records/13838106"><strong>https://zenodo.org/records/13838106</strong></a></td> </tr> <tr> <td>training set (part 2 of 3)</td> <td>train_split.z06 - train_split.z10</td> <td><a href="https://zenodo.org/records/13841652">https://zenodo.org/records/13841652</a></td> </tr> <tr> <td>training set (part 3 of 3)</td> <td>train_split.z11 - train_split.z16</td> <td><a href="https://zenodo.org/records/13853860">https://zenodo.org/records/13853860</a></td> </tr> <tr> <td>development set</td> <td>dev_split.zip & dev_split.z01 - dev_split.z02</td> <td><a href="https://zenodo.org/records/13841216">https://zenodo.org/records/13841216</a></td> </tr> <tr> <td>test set (part 1 of 2)</td> <td>Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip</td> <td><a href="https://zenodo.org/records/13838823">https://zenodo.org/records/13838823</a></td> </tr> <tr> <td>test set (part 2 of 2)</td> <td>Codec unseen test: C7.zip</td> <td><a href="https://zenodo.org/records/11125029">https://zenodo.org/records/11125029</a></td> </tr> </tbody> </table> <h1>Countermeasure</h1> <p>The source code of the countermeasure and pre-trained model are available on GitHub <a href="https://github.com/xieyuankun/Codecfake">https://github.com/xieyuankun/Codecfake</a>.<br><br>The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.</p>
Codecfake dataset - training set (part 2 of 3)
<p>This dataset is the training set (part 2 of 3) of the Codecfake dataset , corresponding to the manuscript "<strong>The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection</strong>".</p> <h1>Abstract</h1> <p>With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)<br>models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we propose<br>the CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.</p> <h1>Codecfake Dataset</h1> <p>Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:</p> <table> <tbody> <tr> <td>Codecfake dataset</td> <td>description</td> <td>link</td> </tr> <tr> <td>training set (part 1 of 3) & label</td> <td>train_split.zip & train_split.z01 - train_split.z05</td> <td><a href="https://zenodo.org/records/13838106">https://zenodo.org/records/13838106</a></td> </tr> <tr> <td>training set (part 2 of 3)</td> <td>train_split.z06 - train_split.z10</td> <td><strong><a href="https://zenodo.org/records/13841652">https://zenodo.org/records/13841652</a></strong></td> </tr> <tr> <td>training set (part 3 of 3)</td> <td>train_split.z11 - train_split.z16</td> <td><a href="https://zenodo.org/records/13853860">https://zenodo.org/records/13853860</a></td> </tr> <tr> <td>development set</td> <td>dev_split.zip & dev_split.z01 - dev_split.z02</td> <td><a href="https://zenodo.org/records/13841216">https://zenodo.org/records/13841216</a></td> </tr> <tr> <td>test set (part 1 of 2)</td> <td>Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip</td> <td><a href="https://zenodo.org/records/13838823">https://zenodo.org/records/13838823</a></td> </tr> <tr> <td>test set (part 2 of 2)</td> <td>Codec unseen test: C7.zip</td> <td><a href="https://zenodo.org/records/11125029">https://zenodo.org/records/11125029</a></td> </tr> </tbody> </table> <h1>Countermeasure</h1> <p>The source code of the countermeasure and pre-trained model are available on GitHub <a href="https://github.com/xieyuankun/Codecfake">https://github.com/xieyuankun/Codecfake</a>.</p> <p> </p> <p>The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Hand Screw Clamp Part 2
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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.