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2,863 results for “Challenge”
MICCAI 2021 MSSEG-2 challenge quantitative results
<p>This dataset includes all quantitative results of all metrics for challengers of the MICCAI MSSEG2 2021 challenge on new MS lesions detection. It also includes the ranking excel file obtained on the challenge day.</p> <p>Please refer to the README for more details.</p> <p>Second version adds missing SNAC team results and ITU team results update.</p> <p>Third version provides corrected results after correction of ground truth.</p>
MICCAI 2021 MSSEG-2 challenge demographics data
<p>This dataset will include all demographics and dataset constitution information for the MICCAI 2021 challenge.</p>
The artifact for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.
<p>This artifact is for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.</p> <p>More detail and update please refer to https://github.com/ZHYfeng/Dependency.</p>
Data Corpus for the IEEE-AASP Challenge on the Acoustic Characterization of Environments (ACE)
<p>The aim of this challenge was to evaluate state-of-the-art algorithms for blind acoustic parameter estimation from speech and to promote the emerging area of research in this field.</p> <p>Several established parameters and metrics have been used to characterize the acoustics of a room. The most important are the Direct-To-Reverberant Ratio (DRR), the Reverberation Time (<em>T60</em>) and the reflection coefficient. The acoustic characteristics of a room based on such parameters can be used to predict the quality and intelligibility of speech signals in that room. Recently, several important methods in speech enhancement and speech recognition have been developed that show an increase in performance compared to the predecessors but do require knowledge of one or more fundamental acoustical parameters such as the <em>T60</em>. Traditionally, these parameters have been estimated using carefully measured Acoustic Impulse Responses (AIRs). However, in most applications it is not practical or even possible to measure the acoustic impulse response. Consequently, there is increasing research activity in the estimation of such parameters directly from speech and audio signals.</p> <p><strong>Documentation and software</strong></p> <ul> <li>Corpus instructions including software operating instructions</li> <li>Software to generate new datasets from the corpus materials (Matlab)</li> <li><em>T</em>60 and DRR measurements in fullband and <a href="http://www.iso.org/iso/catalogue_detail.htm?csnumber=1350">ISO-266</a> preferred frequency bands</li> <li>Room dimensions and approximate positions of microphones and sources</li> </ul> <p><strong>Anechoic speech</strong></p> <p>Comprising Development (Dev): 4 male talkers, 2 utterances each, and Evaluation (Eval): 5 male and 5 female talkers, 5 utterances each, recorded using the anechoic chamber at <a href="http://www.tudelft.nl/en/">TU Delft</a> at <em>fs</em>=48 kHz in 16-bit format. Plain text (.txt) transcriptions of each .wav file are included.</p> <p><strong>RIRs and noise by microphone configuration</strong></p> <p>Each archive below contains the set of <em>fs</em>=48 kHz 16-bit RIRs, ambient, fan and babble noise .wav files for each room and microphone position for that microphone configuration, recorded in 7 different rooms in the <a href="http://www3.imperial.ac.uk/electricalengineering">Dept. of Electrical and Electronic Engineering at Imperial College London</a>.</p> <p>The corpus comprises the following components:</p> <ul> <li>Single-channel (based on cruciform channel 1) 417 MB</li> <li>2-channel laptop 1.05 GB</li> <li>3-channel mobile 1.59 GB</li> <li>5-channel cruciform 2.84 GB</li> <li>8-channel linear 4.24 GB</li> <li>32-channel spherical 14.2 GB</li> </ul> <p>The corpus and the ACE Challenge are described in the following <a href="https://www.researchgate.net/publication/303854321_Estimation_of_room_acoustic_parameters_The_ACE_Challenge">journal paper</a>:</p> <ul> <li>J. Eaton; N. D. Gaubitch; A. H. Moore; P. A. Naylor, "Estimation of room acoustic parameters: The ACE Challenge," in <em><a href="http://ieeexplore.ieee.org/document/7486010/">IEEE/ACM Transactions on Audio, Speech, and Language Processing</a></em>, vol. 24, no.10, pp.1681-1693, Oct. 2016.</li> </ul> <p>Please cite this whenever you use any part of the corpus. BibTeX references are available here for the <a href="http://www.commsp.ee.ic.ac.uk/~sap/uploads/data/ACE/ACE_IEEE_ref.bib">journal paper</a> and <a href="http://www.commsp.ee.ic.ac.uk/~sap/uploads/data/ACE/ACE_Tech_ref.bib">technical report</a>.</p> <ul> </ul>
Dataset and tools for the PSST Challenge on Post-Stroke Speech Transcription
<p>Initial Release, for archival/DOI purposes.</p>
Raw data of sequencing results of our study: Bovine milk microbiota: Evaluation of different DNA extraction protocols in challenging samples
<p>Clean reads of the repeated milk samples with used Primer Pairs V1V2 and V3V4</p> <p>Raw data of sequencing results (amplicon single variants)</p>
Interview Data on Challenges in big data based communication amid C19
<p><strong>Transcriptions of Interview Data that follows the Semi-Structured Interview Guide</strong></p> <p>Introduction</p> <p>Prior to beginning the interview, the research participant is given the option to accept or decline the interview being recorded. The interviewers then explained that the interview is entirely confidential and any references to company names, colleague names, or product names will be redacted from the transcripts and kept anonymous. The research participant’s name will not be used or recorded in the transcript or in the final manuscript. </p> <p>Background</p> <ol> <li> <p>Can you tell us about your education and professional background?</p> </li> </ol> <p>Current role and responsibilities</p> <ol> <li> <p>What is your current role/position? </p> </li> <li> <p>What industry is your company part of?</p> </li> <li> <p>What department are you part of? </p> </li> <li> <p>What area of the business do you support? </p> </li> <li> <p>What types of communications are you creating (e.g. communications channels, deliverables, platforms, etc.)?</p> </li> <li> <p>Are you utilizing RPA, AI, or data visualizations tools to communicate data? If so, how? </p> </li> <li> <p>Who are your stakeholders (who is the audience)?</p> </li> </ol> <p>Data and communications</p> <ol> <li> <p>When communicating data, what are the communication objectives/goals?</p> <ol> <li> <p>What communications problems are seeking to solve?</p> </li> </ol> </li> <li> <p>What type of data do you use in your role? </p> </li> <li> <p>How do you determine which datasets to use and communicate? </p> <ol> <li> <p>How do you assess the datasets for variety, volume, velocity, veracity?</p> </li> </ol> </li> <li> <p>How do you use it? (To describe, predict, diagnose, or make recommendations?)</p> </li> <li> <p>What has been the outcome or impact of using data in communications?</p> </li> <li> <p>How do you measure the impact of your communications?</p> </li> <li> <p>What are the challenges in communicating data?</p> </li> <li> <p>Do you use these communications across regions and international markets?</p> <ol> <li> <p>What are the challenges in communicating data across regions and international markets?</p> </li> <li> <p>What adjustments are required or what factors are taken into consideration when communicating data across countries? </p> </li> </ol> </li> <li> <p>Are these communications shared or utilized across business units, functions, and departments?</p> </li> </ol> <p>Conclusion </p> <ol> <li> <p>What value do your communications add to achieving business objectives? </p> </li> </ol>
Radiomics in medical imaging: pitfalls and challenges in clinical management
<p>We uploaded theimages of the manuscript "Radiomics in medical imaging: pitfalls and challenges in clinical management"</p>
Pulmonary lymphangitis pose a major challenge for radiologists in an oncological setting during the COVID-19 pandemic
<p>I uploaded the images of the manuscript "Pulmonary lymphangitis pose a major challenge for radioLogists in an oncological setting during the COVID-19 pandemic".</p>
Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems
<div> <p><span>Coastal </span><span>methane (CH<sub>4</sub>) emissions dominate the global ocean CH<sub>4</sub> budget and can offset the "blue carbon" storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets </span><span>sensitive to statistical assumptions and uncertainties</span><span>. Using continuous CH<sub>4</sub> concentrations, δ<sup>13</sup>C-CH<sub>4</sub> values, and CH<sub>4</sub> sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CH<sub>4 </sub>distribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH<sub>4 </sub>concentrations ranging two orders of magnitude (i.e., 6 – 460 nM CH<sub>4</sub>) with habitat-specific seasonal and diurnal patterns. We observed (1) δ<sup>13</sup>C-CH<sub>4</sub> signatures that revealed habitat-specific CH<sub>4</sub> production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH<sub>4 </sub>distribution signified by apparent activation energies of </span><span>∼</span><span>1 eV that drove seasonal changes. </span><span>Bootstrapping simulations show that scaling the CH<sub>4</sub> distribution from few samples involves large errors,</span><span> and that </span><span>∼</span><span>50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH<sub>4</sub> sources (i.e., releasing ≥100 μmol CH<sub>4</sub> m<sup>−2</sup> day<sup>−1</sup> in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH<sub>4</sub> estimates and confine the habitat-specific contribution to regional and global CH<sub>4</sub> budgets.</span></p> </div>
Dynamic deformation calculation of articular cartilage and cells using resonance-driven laser scanning microscopy - Osmotic Challenge and Validation Testing Dataset
<p>This archive contains 3-D image stacks obtained over time of articular cartilage undergoing osmotic swelling. These were acquired with a resonance scanning protocol, which allows for fast scanning but with decreased image quality. The Python package, resonant_lsm, was developed to segment and analyze the deformation of cells in such images. The archive also contains validation testing data of this software. Included README files document the archive contents and how to reproduce the analyses. </p>
Figure 6 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 6. Specimens of the Synallaxis omissa showing the absence of correlation between plumage variation and geography, from left to right, lateral and ventral views: MPEG 38616, collected at Alto Turiaçu, Maranhão, Brazil, on 2 October 1986, with rufous present on the breast, abdomen and back; MPEG 36916, from Carutapera, river Curupi, Maranhão, Brazil, collected on 5 November 1984, with rufous present on the breast, abdomen and back; MPEG 38618, collected at Alto Turiaçu, Maranhão, Brazil, on 2 October 1986, the same day of MPEG 38616, without any trace of rufous.
Figure 4 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 4. Plumage distribution and geographic variation in the Synallaxis rutilans group. Dark grey circles, specimens with grey-patterned plumage; pale grey circles, specimens with olive-patterned plumage; black circles, specimens with rufous-patterned plumage. Concerning intermediate individuals, any specimen with rufous present on the upperparts was classified in the 'specimens with rufous-patterned plumage' group. *Illustrations of birds with the typical plumage of: S. caquetensis (on the left); S. dissors (centre above); S. omissa (right). Area of endemism (AE) and distribution of species of the Synallaxis rutilans group: purple, Guiana EA, S. dissors; red, Napo EA, S. caquetensis; orange, Xingu EA, S. rutilans; yellow, Belém EA, S. omissa; and green, Inambari EA, blue, Rondônia EA and turquoise, Tapajós EA, S. amazonica. *Illustrations from del Hoyo J, Elliott A, Sargatal J, Christie DA, de Juana E, eds. 2017. Handbook of the birds of the world alive. Barcelona: Lynx Edicions (retrieved on 10.11.2017 from http://www.hbw.com).
Figure 1 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 1. Map showing the distribution of sequenced individuals, phylogenetic time tree and plumage analyses for the Synallaxis rutilans group. The areas of endemism recognized by Silva et al. (2005) are highlighted on the map, and distribution points are numbered in accordance with individuals in the tree. The phylogenetic time tree is based on 1539 bp of concatenated ND2 and COI genes. Posterior probability values and the 95% HPD are indicated at each node. Thr, throat; Rec, rectrices; For, forehead; Sup, supercilium; Fac, face; Win, wing-coverts; Rem, remiges; 1st, first colour (main colour); 2nd, second colour (variation ±); 36 (e.g.), colours in Smithe's catalogue; *, specimen damaged or immature; **, specimen analysed without Smithe's catalogue; MPEG A, spirit collection specimen; dark grey circles in map and patches in table, specimens with grey plumage pattern; light grey circles and patches, specimens with olive plumage pattern; black circles and patches, specimens with rufous plumage pattern; purple star, type locality of S. r. dissors; red star, type locality of S. r. caquetensis; red star with white spot, type locality of S. r. confinis; blue star, type locality of S. r. amazonica; blue star with a white spot, type locality of S. r. tertia; orange star, type locality of S. r. rutilans; yellow star, type locality of S. r. omissa.
Figure 5 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 5. Specimens of the Synallaxis rutilans group showing the grey (left-hand bird in each image) and olive patterns in juvenile plumage, from left to right, ventral, lateral and dorsal views: MZUSP 44653, Capim, Pará, Brazil; and MZUSP 93965, Boa Vista, Roraima, Brazil.
Figure 2 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 2. Scatterplots illustrating the results of the PCA for the morphometric data pertaining to populations of the Synallaxis rutilans group as clades resulting from the cladistic analysis performed using mtDNA.
Figure 3 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 3. Specimens of the Synallaxis rutilans group illustrating the patterns grey, olive, and rufous in plumage from left to right: ventral, lateral and dorsal views: MPEG 38618, Alto Turiaçu, Maranhão, Brazil; MPEG 59487, Barcelos, Amazonas, Brazil; and MPEG 56645, Juruti, Pará, Brazil.
Morphology Challenge Datasets
<p>Datasets of the analysis of the Euclid Morpho Challenge</p>
Dataset: Co-administration of adjuvanted recombinant Ov-103 and Ov-RAL2 vaccines confer protection against natural challenge in a bovine O. ochengi infection model of human onchocerciasis
<p class="MsoNormal"><span>[Abstract from accompanying manuscript]</span></p> <p class="MsoNormal"><span>Onchocerciasis (river blindness), caused by the filarial nematode <em>Onchocerca volvulus</em>, is a neglected tropical disease endemic to sub-Saharan Africa. Worldwide, an estimated 20.9 million individuals live with infected and a further 205 million are at risk of disease. Current control methods rely on mass drug administration of ivermectin to suppress microfilarial development. Presently, there are no viable treatment options effective against the long-lived adult worms. The identification and development of efficacious vaccine candidates as complementary tools to support ongoing elimination efforts are therefore a major objective of onchocerciasis research. </span></p> <p class="MsoNormal"><span>We evaluated the immunogenicity and protective effects of co-administering two leading <em>O. volvulus</em>-derived recombinant vaccine candidates (<em>Ov</em>-103 and <em>Ov</em>-RAL2) in cattle with subsequent natural exposure to <em>Onchocerca ochengi, </em>a close phylogenetic relative of <em>O. volvulus</em>.</span></p> <p class="MsoNormal"><span>Immunisation induced strong IgG responses against both vaccine candidates. Following natural exposure to <em>O. ochengi</em>, vaccination status and resulting serum antibody response was associated with delayed nodule development and onset of microfilaridermia. Peptide arrays identified several <em>Ov</em>-103 and <em>Ov</em>-RAL2-specific epitopes recognised by IgG from immunised cattle which warrant further investigation. Our data provide supporting evidence for ongoing development of a recombinant vaccine for control of human onchocerciasis.</span></p>
DCASE 2022 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" for the <a href="https://dcase.community/challenge2022/task-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring"><strong>DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques</strong>"</a>.</p> <p> </p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following three papers. </p> <ul> <li>Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, <em>Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, </em>2022. [<a href="https://arxiv.org/abs/2206.05876">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> <em>In arXiv e-prints: 2205.13879</em>, 2022. [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
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.