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2,863 results for “Challenge”
Experienced Challenges of Adopting Agile Scaling Frameworks: Dataset
<p>Dataset for the paper "Experienced Challenges of Adopting Agile Scaling Frameworks"</p> <p>The data is in one SPSS file, with variable descriptions.</p> <p> </p>
GENEA Challenge 2023 submitted BVH files
<p>This Zenodo repository contains 3D motion in the Biovision Hierarchy (BVH) format for all test-set motion submitted by teams participating in the GENEA Challenge 2023.</p> <p> </p> <p><strong>Contents:</strong></p> <p>The "genea_2023_test_bvh" zip file corresponds to the BVH files themselves (2GB).</p> <p>The repository also contains the time stamps for cutting out the evaluation segments we used (matched and mismatched) for the two kinds of studies we conducted (monadic and dyadic) in the files "monadic_segment_selection_info.csv" and "dyadic_segment_selection_info.csv".</p> <p> </p> <p><strong>Attribution:</strong></p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-07-11) this is our ACM ICMI 2023 paper:</p> <p>Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2023. The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI ’23). ACM.</p> <p> </p> <p>Condition NA in the data contains motion from the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2023 material on the web, please see:</p> <p>* <a href="https://genea-workshop.github.io/2023/challenge/">https://genea-workshop.github.io/2023/challenge/</a></p> <p> </p> <p>If you have any questions or comments, please contact:</p> <p>* The GENEA Challenge organisers <genea-challenge@googlegroups.com</p>
A multivariate physiological model of vagus nerve signalling during metabolic challenges in anaesthetised rats for diabetes treatment
<p>Raw recordings that support the findings of the paper. In the intact recordings, there may be more than one recoridng corresponding to the same animal. For ligated recordings, odd numbers correspond to efferent and even numbers to afferent recordings.</p>
GENEA Challenge 2023 Dataset Files
<p>This Zenodo repository contains the main dataset for the GENEA Challenge 2023, which is based on the Talking With Hands 16.2M dataset.</p> <p><strong>Notation:</strong></p> <p>Please take note of the following nomenclature when reading this document:</p> <ul> <li> <p><em>main agent</em> refers to the speaker in the dyadic interaction for which the systems generated motions. </p> </li> </ul> <ul> <li> <p><em>interlocutor</em> refers to the speaker in front of the main agent. </p> </li> </ul> <p><strong>Contents:</strong></p> <p>The “genea2023_trn" and "genea2023_val" zip files contain audio files (in WAV format), time-aligned transcriptions (in TSV format), and motion files (in BVH format) for the training and validation datasets, respectively.</p> <p>The "genea2023_test" zip file contains audio files (in WAV format) and transcriptions (in TSV format) for the test set, but no motion. The corresponding test motion is available at:</p> <p><a href="https://zenodo.org/record/8146028">https://zenodo.org/record/814602</a><a href="https://zenodo.org/record/8146027">7</a></p> <p>Each zip file also contains a "metadata.csv" file that contains information for all files regarding the speaker ID and whether or not the motion files contain finger motion.</p> <p>Note that the speech audio in the data sometimes has been replaced by silence for the purpose of anonymisation.</p> <p>In the test set, files with indices from 0 to 40 correspond to "matched" interactions (the core test set), where main agent and interlocutor data come from the same conversation, whilst file indices from 41 to 69 correspond to "mismatched" interactions (the extended test set), where main agent and interlocutor data come from different conversations.</p> <p><strong>Folder structure:</strong></p> <ul> <li>main-agent/ (main agent): Encapsulates BVH, TSV, WAV data subfolders for the main agent.</li> <li>interloctr/ (interlocutor): Encapsulates BVH, TSV, WAV data subfolders for the interlocutor.</li> <li>bvh/ (motion): Time-aligned 3D full-body motion-capture data in BVH format from a speaking and gesticulating actor. Each file is a single person, but each data sample contains files for both the main agent and the interlocutor.</li> <li>wav/ (audio): Recorded audio data in WAV format from a speaking and gesticulating actor with a close-talking microphone. Parts of the audio recordings have been muted to omit personally identifiable information.</li> <li>tsv/ (text): Word-level time-aligned text transcriptions of the above audio recordings in TSV format (tab-separated values). For privacy reasons, the transcriptions do not include references to personally identifiable information, similar to the audio files.</li> </ul> <p><strong>Data processing scripts:</strong></p> <p>We provide a number of optional scripts for encoding and processing the challenge data:</p> <p>Audio: Scripts for extracting basic audio features, such as spectrograms, prosodic features, and mel-frequency cepstral coefficients (MFCCs) can be found <a href="https://github.com/genea-workshop/Speech_driven_gesture_generation_with_autoencoder/blob/GENEA_2022/data_processing/tools.py#L105">at this link</a>.</p> <p>Text: A script to encode text transcriptions to word vectors using FastText is available here: <a href="https://gist.github.com/youngwoo-yoon/c9e5d5b04994478011911f88eb15caa4">tsv2wordvectors.py</a></p> <p>Motion: If you wish to encode the joint angles from the BVH files to and from an exponential map representation, you can use scripts by <a href="https://github.com/simonalexanderson">Simon Alexanderson</a> based on the <a href="https://omid.al/projects/pymo/">PyMo library</a>, which are available here:</p> <ul> <li> <p><a href="https://github.com/genea-workshop/Speech_driven_gesture_generation_with_autoencoder/blob/GENEA_2022/data_processing/bvh2features.py">bvh2features.py</a></p> </li> <li> <p><a href="https://github.com/genea-workshop/Speech_driven_gesture_generation_with_autoencoder/blob/GENEA_2022/data_processing/features2bvh.py">features2bvh.py</a></p> </li> </ul> <p><strong>Attribution:</strong></p> <p>If you use this material, please cite our latest paper on the GENEA Challenge 2023. At the time of writing (2023-07-25) this is our ACM ICMI 2023 paper:</p> <p>Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2023. The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI ’23). ACM.</p> <p>Also, please cite the paper about the original dataset from Meta Research:</p> <p>Gilwoo Lee, Zhiwei Deng, Shugao Ma, Takaaki Shiratori, Siddhartha S. Srinivasa, and Yaser Sheikh. 2019. Talking With Hands 16.2M: A large-scale dataset of synchronized body-finger motion and audio for conversational motion analysis and synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV ’19). IEEE, 763–772.</p> <p> </p> <p>The motion and audio files are based on the Talking With Hands 16.2M dataset at <a href="https://github.com/facebookresearch/TalkingWithHands32M/">https://github.com/facebookresearch/TalkingWithHands32M/</a>. The material is available under a CC BY NC 4.0 Attribution-NonCommercial 4.0 International license, with the text provided in LICENSE.txt.</p> <p> </p> <p>To find more GENEA Challenge 2023 material on the web, please see:</p> <ul> <li><a href="https://genea-workshop.github.io/2023/challenge/">https://genea-workshop.github.io/2023/challenge/</a></li> </ul> <p> </p> <p>If you have any questions or comments, please contact:</p> <ul> <li>The GENEA Challenge organisers <genea-challenge@googlegroups.com></li> </ul>
GERDAT020-Dataset for- Challenges of caring for older patients with multimorbidity including cancer
<p>This dataset shows the data underlying the publication Challenges of caring for older patients with multimorbidity including cancer, published in JGO on 14 July 2023, which can be found at <a href="https://doi.org/10.1016/j.jgo.2023.101588">https://doi.org/10.1016/j.jgo.2023.101588</a></p>
Supplementary Materials for "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850"
<p><strong>Supplementary Materials for</strong>:</p> <p>R. Connolly, W. Soon, M. Connolly, S. Baliunas, J. Berglund, C.J. Butler, R.G. Cionco, A.G. Elias, V. Fedorov, H. Harde, G.W. Henry, D.V. Hoyt, O. Humlum, D.R. Legates, N. Scafetta, J.-E. Solheim, L. Szarka, V.M. Velasco Herrera, H. Yan and W.J. Zhang (<strong>2023</strong>). "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850". <em>Research in Astronomy and Astrophysics</em>. <a href="https://doi.org/10.1088/1674-4527/acf18e">https://doi.org/10.1088/1674-4527/acf18e</a></p> <p><strong>Description:</strong></p> <p>A dataset for the detection and attribution of Northern Hemisphere surface temperature trends since 1850.</p> <table> <caption>Contents of dataset</caption> <thead> <tr> <th scope="col">Tab</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>NH temperatures</td> <td>5 different Northern Hemisphere (NH) annual surface temperature (ST) estimates in temperature anomalies (°C) relative to 1901-2000 averages.</td> </tr> <tr> <td>TSI 1850-2018</td> <td>27 different Total Solar Irradiance (TSI) reconstructions. At 1 Astronomical Unit (W/m2) relative to 1901-2000 average.</td> </tr> <tr> <td>IPCC AR6 forcings</td> <td>IPCC AR6 radiative forcings (RF) time series (for the 1850-2018 period) in units of W/m2</td> </tr> <tr> <td>Fitting statistics</td> <td>Fitting statistics and results from all fits</td> </tr> <tr> <td>Key results</td> <td>Summary statistics and results</td> </tr> <tr> <td>Fits - Rural-only</td> <td>Multi-linear regression fitting results for Rural-only ST</td> </tr> <tr> <td>Fits - Urban & rural</td> <td>Multi-linear regression fitting results for Urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - SST</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for Sea surface temperature (SST)</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Tree-rings</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for tree-ring proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Glaciers</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for glacier-length proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Rural-only</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to Rural-only ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Urban & rural</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - SST</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to SST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Tree-rings</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to tree-ring proxy-based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Glaciers</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to glacier-length proxy-based ST</td> </tr> </tbody> </table> <p> </p>
Creative Person and the Development of Creativity: A Challenge For Contemporary Society
<p>Creative Person and the Development of Creativity: A Challenge For Contemporary Society, International Conference, Lobachevsky University of Nizhni Novgorod, University of Rome Foro Italico, January, 19, 2023. Scientific Committee: Irina Petrova, Irina Leonova, Emanuele Isidori</p>
MULTIPLIERS project: Making school science relevant to real-world challenges
<p>MULTIPLIERS promotes Open Schooling across Europe, a new way to learn that makes science more meaningful and directly relevant to everyday life & real-world challenges.</p> <p>Find out more at: https://multipliers-project.org/</p> <p>Follow MULTIPLIERS on Twitter: https://twitter.com/MULTIPLIERS_</p> <p>Follow MULTIPLIERS on Instagram: https://www.instagram.com/multipliers_project/</p> <p>This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 101006255.</p>
Sophie Gebeil and Federico Nanni: Comparing online transnational events: a methodological challenge
<p>Sophie Gebeil and Federico Nanni: Comparing online transnational events: a methodological challenge</p> <p>WARCnet Luxembourg meeting Thursday 5 November 2020</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 5)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 5)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <p>$ zip -s 0 train.zip --out train_unsplit.zip</p> <p> </p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p>$ unzip train_unsplit.zip</p> <p> </p> <p><br> The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p> </p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p><br> Remember that to be part of the challenge you need to register in the webpage above.</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 4)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 4)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <p>$ zip -s 0 train.zip --out train_unsplit.zip</p> <p>[Click and drag to move]</p> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <p>$ unzip train_unsplit.zip</p> <p>[Click and drag to move]</p> <p><br> The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p><br> Remember that to be part of the challenge you need to register in the webpage above.</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 3)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 3)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <pre><code class="language-bash">$ zip -s 0 train.zip --out train_unsplit.zip </code></pre> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <pre><code class="language-bash">$ unzip train_unsplit.zip</code></pre> <p> </p> <p>The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p>Remember that to be part of the challenge you need to register in the webpage above.</p>
Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)
<p>Train Dataset for the ICASSP-2024 3D-CBCT challenge (Part 2)</p> <p>https://sites.google.com/view/icassp2024-spgc-3dcbct/home</p> <p>Please download all files (of all parts, 1-5) into one folder, and then merge them together with:</p> <pre><code class="language-bash">$ zip -s 0 train.zip --out train_unsplit.zip </code></pre> <p>This will create a new zip file, "train_unsplit.zip" that then you can unzip with your favorite tool, e.g.</p> <pre><code class="language-bash">$ unzip train_unsplit.zip</code></pre> <p> </p> <p>The CBCT geometry required to be used</p> <p>image size : [300 300 300] mm<br> image shape : [256 256 256] voxels<br> voxel size : [1.171875 1.171875 1.171875] mm<br> detector shape : [256 256] pixels<br> detector size : [600, 600] mm<br> pixel size : [2.34375, 2.34375] mm<br> distance source origin (axis of rotation, center of image) : 575 mm<br> distance source to detector : 1050 mm</p> <p>We recommend zenodo_get to download the files:<br> <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a></p> <p>Remember that to be part of the challenge you need to register in the webpage above.</p>
Supplemental Material for "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development"
<p>This package contains the research materials that were used for the paper "Objectives and Key Results in Software Teams: Challenges, Opportunities and Impact on Development".</p> <ul> <li><strong>Survey on Goals and Objectives.docx</strong> contains the survey instrument that was used for the study.</li> </ul>
Medieval Ornament Axe (Texturing Challenge)
My more traditional entry for the challenge Source: Objaverse 1.0 / Sketchfab
AFRI Childhood Obesity Prevention Challenge Area
ClinicalTrials.gov study NCT04469816. IPD Sharing: NO. Countries: 1. Publications: 4.
A BCG Challenge Model Study to Assess Anti-mycobacterial Immunity Induced by BCG and a Candidate TB Vaccine, MVA85A
ClinicalTrials.gov study NCT01194180. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Screening of Healthy Volunteers for Investigational Antimalarial Drugs, Malaria Vaccines, and Controlled Human Malaria Challenge
ClinicalTrials.gov study NCT02639299. IPD Sharing: NO. Countries: 1. Publications: 1.
Interest of the Second Phase of the Oral Challenge Test in Patients With Suspected Long-standing Penicillin Allergy
ClinicalTrials.gov study NCT04513938. IPD Sharing: Not stated. Countries: 1. Publications: 7.
The Effects Of Methacholine Challenge Administered Using Three Different Nebulizers On Small Airways Function
ClinicalTrials.gov study NCT00529477. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.