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Data set used for comparison of different machine learning approaches, for predicting aircraft departure delays, due to the circumstances of the defrosting process.
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PlaceGain example data sets
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Codecfake dataset - test set (part 2 of 2)
<p>This dataset is the test set (part 2 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">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><strong><a href="https://zenodo.org/records/11125029">https://zenodo.org/records/11125029</a></strong></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>
Candidates for test set of NPLIB1
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SAT-based Decision Tree Learning for Large Data Sets - Results and Source code
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Data set for article''Digital health intervention in patients undergoing cardiac rehabilitation: systematic review and meta-analysis''
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data set
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Data set - Laboratory experiments of extreme wave transformation on a coral reefs with a steep slope
<p><strong>Data set - Laboratory experiments of extreme wave transformation on a coral reef with a steep slope</strong></p> <p>This data set includes measurements of wave transformation and wave runup on a physical model (scale 1:60) of a coral reef with a very steep slope (1/2.26). The fore reef morphology is based on the reef at Teahupo'o, Tahiti, French Polynesia according to Harris et al. (2019). The bathymetry is extended with a reef lagoon and planar slope (1/9.64) inland. Wave conditions from the study area are employed to estimate extreme wave conditions.</p> <p>Laboratory experiments were carried out at the Coastal Processes and Engineering Laboratory, IINGEN UNAM, in Sisal, Yucatan. The data set is aimed to assess the performance of numerical models to predict hihgly nonlinear wave transformation over steep slopes.</p> <p>The dataset consists of:</p> <p><strong>-Waves: </strong>free surface elevation time series were aquired at 50 Hz using resistive wave gauges. Moreover, a 2-m long sensor was installed parallel to the beach in order to measure wave runup. Irregular, regular, and solitary waves were tested. The information is recorded in .txt files. The files are in the folders Irregular_waves.zip, Regular waves.zip and Solitary waves.zip, corresponding to irregular, regular, and solitary waves, respectively.</p> <p><strong>-Horizontal velocity:</strong> An electromagnetic current meter was installed near the breakpoint to measure the horizontal current velocity at 16 Hz. The information corresponding to each test is recorded in .txt files. The files are in the folder Electromagnetic current meter.zip.</p> <p><strong>-Bathymetry:</strong> A .txt file is included with the x-z coordinates of the reef profile used (Profile_Teahupoo.txt).</p> <p><strong>-Simulated cases:</strong> An Excel file with the laboratory tests and the sensor locations for each test is included (Cases.xlsx).</p> <p><strong>-ERA-5 data: </strong>A .txt file is included with the ERA-5 wave reanalysis data used (-18° S, -149.5° W) (ERA5_data.txt).</p>
data set
<p>DATA</p>
Pollination methods for watermelon (Citrullus lanatus (Thunb.)) fruit setting and yield
<p>Pollination is an essential ecosystem function to sustain food security. Human made agroecosystem led pollinators to decline which ultimately reduced fruit setting and yield of watermelon production. A field experiment was conducted at Manahari Rural Municipality from March 2023 to July 2023 to study the effect of pollination methods on fruit setting and yield of two watermelon varieties. The experiment was laid out in two factorial randomized complete block design (RCBD) having two watermelon varieties (Saraswati and Vishala) and three pollination methods (bee pollination, hand pollination, open pollination, and control) which were replicated thrice. The data regarding foraging activity of domesticated bee pollinators (<em>Apis cerana </em>F. and <em>Apis mellifera </em>L.) from open field condition and yield attributing parameters from experimental plot were recorded. In open field condition, <em>Apis mellifera </em>L<em>.</em> recorded the highest abundance (5.12 bees/m<sup>2</sup>/5 min.), foraging speed on male (5.14 second/flower) and female flower (6.17 second/flower). <em>Apis cerana </em>F. recorded the highest foraging rate (9.31 flowers/bee/min.). The foraging activity was more active in the morning hours (8-10 a.m.) than at midday (12-2 p.m.) and evening hours (4-6 p.m.) for both the bee species. From the experimental plot, variety Saraswati recorded highest number of fruits per plant (2.52), fruit girth (14.45 cm), fruit weight (1.82 kg), seeds per fruit (122.53), and yield per plant (4.91 kg) while highest fruit length (18.58 cm) and 1000 seed weight (49.37 g). The highest fruit set (74.51%), number of fruits per plant (3.15), healthy fruit (84.83%), fruit girth (15.81 cm), fruit weight (2.21 kg), seeds per fruit (141.04), and yield per plant (6.98 kg) were recorded in bee pollinated plots. The result suggests that the use of <em>A. mellifera</em> bees in the pollination of watermelon is useful to increase fruit set, production, and productivity. </p>
The adaption sets of ADD 2022
<p>Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (<a href="http://addchallenge.cn/add2022">http://addchallenge.cn/add2022</a>) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.</p> <p>The ADD 2022 dataset is publicly available.</p> <p>This data set is licensed with a CC BY-NC-ND 4.0 license.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:<br>ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220</p> </blockquote>
MarTREC 6017: Development of Rapid Setting Soil-Cement Mixture Designs and Performance Testing
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Deformation data set along China-Pakistan railway
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Data set of surface deformation in Lanzhou main city from 2018 to 2023
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Raw dat sets for research
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Divide and Remaster v3: Yoruba Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>Yoruba variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Multilingual Validation & Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>metadata, validation set audio, and test set audio of the</strong> <strong>multilingual variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Japanese Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>Japanese variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Dravidian Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>Dravidian variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Agriculture and Livestock Metadata Elements Set (Almes Core)
<p>This document was published for citation purposes only. To access the current version of the Almes Core schema, please refer to <a href="https://w3id.org/AlmesCore/">https://w3id.org/AlmesCore#</a></p> <p>Serializations:</p> <p>HTML | SHACL | JSON-LD</p> <p>Related publication:</p> <p>Soares, F. M., Corrêa, F. E., Pires, L. F., da Silva Santos, L. O. B., Drucker, D. P., Braghetto, K. R., de Abreu Moreira, D., Delbem, A. C. B., da Silva, R. F., da Silva Lopes, C. O., & Saraiva, A. M. (2022). <a href="https://ceur-ws.org/Vol-3235/paper26.pdf">Building a Community-Based FAIR Metadata Schema for Brazilian Agriculture and Livestock Trading Data</a>. In SEMPDW 2022 (CEUR workshop proceedings; Vol. 3235). </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.