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2,863 results for “Challenge”
Molecular identification of Bactrocera passiflorae (Diptera: Tephritidae): challenge and solution for DNA barcoding
<p>Fruit flies cause significant damage to crop and fruit production worldwide. Therefore it is essential to identify these organisms to species level, however, immature stages are often impossible to be identified morphologically, thus the application of DNA barcoding has greatly assisted in species identification. Nuclear, mitochondrial pseudo-COI (NUMT) can be co-amplified with mitochondrial DNA when using generic primers and therefore impair the efficacy of DNA barcoding. This study detected two types of NUMTs from<em> B. passiflorae, </em>one of them is novel. Therefore the new finding will assist future species identification by avoiding misidentification using ambiguous NUMT sequences. In addition, this study have developed primers to target the COI gene of <em>B. passiflorae</em>, not the NUMT copies. The newly designed primers have demonstrated its efficiency in amplifying the Mt-COI of <em>B. passiflorae </em>and can be used in routine diagnostics.</p>
A-AFMA Ultrasound Challenge Dataset
<p>This is the dataset for A-AFMA Ultrasound Challenge for automatic amniotic fluid measurement and analysis from ultrasound video. <strong>Each team needs to send a registration email including the team name, team members' names, and affiliations to the organizer via email: noble_pm@eng.ox.ac.uk</strong></p> <p>The challenge is hosted <a href="https://a-afma.grand-challenge.org/AAFMA_USC/">here</a>.</p> <p>Teams are required to register on the Grand Challenge site before they will be given access to the data. Each team needs to send a registration email including the team name, team members' names, and affiliations to the organizer via email: noble_pm@eng.ox.ac.uk</p> <p>No publication rights are given on this data. The data may only be used for the purpose of the challenge. This is due to constraints imposed on use of the data by the ethics approval.</p>
Understanding the challenges and requirements for facilitating iStar learning: tool prototype presentation video
<p>This is a presentation video about the tool prototype proposed by our work <i>Understanding the challenges and requirements for facilitating iStar learning: a case study with iStar beginners</i>. </p><p> </p>
Spotify Million Playlist: Recsys Challenge 2018 Dataset
<p>Spotify Million Playlist Dataset Challenge</p> <p>Summary</p> <p>The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights).</p> <p>Background</p> <p>Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes.</p> <p>Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.</p> <p>But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special.</p> <p>The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists?</p> <p>By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love.</p> <p>Dataset</p> <p>To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology.</p> <p>In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page.</p> <p>Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset.</p> <p>Dataset Contains</p> <p>1000 examples of each scenario:</p> <p>Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks</p> <p>Download Link</p> <p>Full Details: <a href="https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge">https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge</a><br> Download Link: <a href="https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files">https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files</a></p>
Validation dataset and reference code for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022.
<p>Validation dataset and reference code for <strong>Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022</strong>. </p> <p>Please refer to our website: <strong>https://vessel-wall-segmentation-2022.grand-challenge.org/</strong>.</p>
Challenges for room temperature operation of electrically pumped GeSn lasers
<p><strong>This data is accessible for bona-fide users upon request to the point of contact. </strong></p> <p>Data for the paper with the following abstract:</p> <div> <p>Recent demonstrations of room‐temperature lasing in optically pumped GeSn show promise for future CMOS compatible lasers for Si‐photonics applications. However, challenges remain for electrically pumped devices. Investigation of the processes that limit device performance is therefore vital in aiding the production of future commercial devices. In this work, a combined experimental and modelling approach is utilised to explore the dominant loss processes in current devices. By manipulating the band structure of functioning devices using high hydrostatic pressure techniques at low temperature, the dominant carrier recombination pathways are identified. This reveals that 93±5% of the threshold current is attributable to defect‐related recombination at a temperature,</p> <p>T = 85 K. Furthermore, carrier occupation of L‐valley states (carrier leakage) is responsible for 1.1± 0.3% of the threshold current, but this sharply increases to 50% with a decrease of just 30 meV in the L‐Ŵ separation energy. This indicates that thermal broadening of a similar order may reproduce these adverse effects, limiting device performance at higher temperatures. Temperature dependent calculations show that carrier occupation of indirect valley L‐states strongly affects the transparency carrier density and is therefore very sensitive to the Sn composition, leading to an effective operational temperature range for given Sn compositions and strain values. Recommendations for future device designs are proposed based on band structure and growth optimisations.</p> <p>Paper at: https://doi.org/10.1038/s41598-024-60686-3 </p> </div>
MuSe-Humor 2024 (MuSe-Challenge 2024)
<p><strong>Important Information: </strong>Due to the large number of requests, unsolicited requests will be rejected immediately. The data will be provided exclusively to the MuSe-Humor Sub-Challenge registered participants. To register for the challenge, please use the following link: <a href="https://www.muse-challenge.org/challenge/participate">https://www.muse-challenge.org/challenge/participate</a></p> <p><strong>Description</strong>: Predicting the presence of humor in football press conference recordings. Available modalities: audio and video, transcriptions. The dataset offers a cross-cultural setting: all press conferences in the training and development partition are held in German, and all videos in the test partition are taken from English Premier League press conferences.</p> <p><strong>Important</strong>: <strong>this package does not include the recordings themselves. Please find them here</strong>: <a href="../deposit/7843460">https://zenodo.org/record/7843460</a></p> <p><strong>Labels</strong>: windows of 2 seconds are given a binary label, indicating the presence and absence of humor. At least 7 human annotators labeled each video.</p> <p><strong>Dataset</strong>: MuSe-Humor is based on the Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset (<a href="https://arxiv.org/pdf/2209.14272.pdf">paper</a>). It includes press conference recordings of 10 different German Bundesliga football coaches collected in 2017. Moreover, for the MuSe 2023 challenge, recordings of 6 Premier League coaches have been added. The data provided here only includes segments in which the respective coach is speaking. The training data set contains recordings of 7 German-speaking coaches, development data consists of videos of 3 German-speaking coaches, and the test data set comprises recordings of 6 English-speaking coaches (one native speaker).</p> <p><strong>General</strong>: The <a href="http://muse-challenge.org/">5th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2024</a> addresses research questions that are of interest to affective computing, machine learning, and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.</p>
MuSe-Perception (MuSe Challenge 2024)
<p><strong>Important Information: </strong>Due to the large number of requests, unsolicited requests will be rejected immediately. The data will be provided exclusively to the MuSe-Perception Sub-Challenge registered participants. To register for the challenge, please use the following link: <a href="https://www.muse-challenge.org/challenge/participate">https://www.muse-challenge.org/challenge/participate</a></p> <p><strong>Description</strong>: For the Social Perception Sub-Challenge (MuSe-Perception, participants are tasked to predict 16 different social attributes of individuals, such as ‘assertiveness,‘ ‘dominance,’ ‘likability,‘ and ‘sincerity‘ based on the provided audiovisual data.</p> <p><strong>General</strong>: The <a href="http://muse-challenge.org/">5th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2024</a> addresses research questions that are of interest to affective computing, machine learning, and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.</p>
Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"
<p>Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"</p>
Advancements and challenges in strained group-IV-based optoelectronic materials stressed by ion beam treatment
<p><strong>This data is accessible for bona-fide users upon request to the point of contact.</strong></p> <p> </p> <p>Data for the paper with the following abstract:</p> <p>In this perspective article, we discuss the application of ion implantation to manipulate strain (by either neutralising or inducing compressive or tensile states) in suspended thin films. Emphasising the pressing need for a high-mobility silicon-compatible transistor or a direct bandgap group-IV semiconductor that is compatible with complementary metal-oxide-semiconductor technology, we underscore the distinctive features of different methods of ion beam-induced alterations of material morphology. The article examines the precautions needed during experimental procedures and data analysis and explores routes for potential scalable adoption by the semiconductor industry. Finally, as an example of an application we briefly discuss how this highly controllable strain-inducing technique can facilitate enhanced manipulation of impurity-based spin quantum bits (qubits).</p> <p> </p> <p>Paper published as: </p> <p>Mateus G Masteghin <em>et al</em> 2024 <em>J. Phys.: Condens. Matter</em> <strong>36</strong> 431501</p> <p><strong>DOI</strong> 10.1088/1361-648X/ad649f</p>
DL-Spectral Challenge data and information
<p>This record contains the materials for the DL-sparse-view CT Challenge</p> <p>----------------------------------------------------------------------------------------<br>CONTENTS of data/<br>----------------------------------------------------------------------------------------</p> <p>The training data for developing the neural networks is in the subfolder "data".<br>All files are compressed with gzip in order to facilate faster downloads.<br>Data are partitioned into four batches, which also facilates downloading of the<br>individual files. Data are in python numpy's .npy format.<br>After uncompressing with gunzip the .npy files can be read into python<br>with the numpy.load command, yielding single precision floating point arrays<br>of the proper dimensions.</p> <p>In the "data" folder are:<br>Phantom_batch?.npy<br>These arrays are 1000x512x512.<br>1000 images of pixel dimensions 512x512.<br>These are the true images.</p> <p>FBP128_batch?.npy<br>These arrays are 1000x512x512.<br>1000 images of pixel dimensions 512x512.<br>These are the FBP reconstructed images from the 128-view sinograms.</p> <p>Sinogram_batch?.npy<br>These arrays are 1000x128x1024.<br>1000 sinograms of 128 projections over 360 degree scanning onto a 1024-pixel linear detector. </p> <p>There are four batches. Thus 4000 sets of data/image pairs are available for training<br>the neural networks for image reconstruction.<br>The goal is to train a network that accepts the FBP128 image (and/or the 128-view sinogram)<br>to yield an image that is as close as possible to the corresponding Phantom image.</p> <p>----------------------------------------------------------------------------------------<br>CONTENTS of validation-data/<br>----------------------------------------------------------------------------------------<br>Data is in the same arrangement as in the "data/" folder except that there are only 10 cases.<br>As a result they data are not split into batches and they are not compressed.<br>Phantom_validation.npy<br>These arrays are 10x512x512.<br>10 images of pixel dimensions 512x512.<br>These are the true images for the validation stage. !!!! KEEP THIS SECRET !!!!</p> <p>FBP128_validation.npy<br>These arrays are 10x512x512.<br>10 images of pixel dimensions 512x512.<br>These are the FBP reconstructed images from the 128-view sinograms.</p> <p>Sinogram_validation.npy<br>These arrays are 10x128x1024.<br>10 sinograms of 128 projections over 360 degree scanning onto a 1024-pixel linear detector. </p> <p><br>----------------------------------------------------------------------------------------<br>Contents of this folder<br>----------------------------------------------------------------------------------------<br>"smallbatch" data<br>metrics.py<br>converMatlab.py<br>README (you're reading this now)</p> <p>A "smallbatch" set of data is in this folder, containing only 10 phantoms, fbp images, and sinograms.</p> <p>These data files are for viewing and are used to demonstrate the<br>metrics that will be used to evaluate the submitted images for this Grand Challenge.<br>Running the program metrics.py will compare the FBP128 images against the ground truth (Phantom images).<br>Hopefully your network will yield images that have lower RMSEs!<br>The two metrics are mean image RMSE, and worst-case ROI RMSE for a 25x25 pixel ROI.<br>The formulas for these metrics are in [put appropriate url link here],<br>and the metrics.py code can also be inspected to see how the calculation is performed.</p> <p>The contest data are in numpy's .npy format and test image submission should also use<br>this format. For matlab users, a script "convertMatlab.py" is included that shows how<br>to convert the "smallbatch" data to matlab's .mat format. Also, converting back to .npy<br>is shown in this script.</p>
GEO corpus for the Aalto ASR challenge 2024
<p>Speech corpora for the ASR course competition at Aalto University.</p> <p>The data was originally part of the CommonVoice Esperanto set, on the transcription side random corruptions were employed to create a synthetic challege.</p>
Interference suppression techniques for OPM-based MEG: Opportunities and challenges
<p>Datasets to run the example data analysis tutorials presented in: Seymour et al., (2021). Interference suppression techniques for OPM-based MEG: Opportunities and challenges.<em> In Prep.</em></p>
Sword 30 Minute Model and Texture challenge
Source: Objaverse 1.0 / Sketchfab
Transcriptomic response of goat mammary epithelial cells to Mycoplasma agalactiae challenge – a preliminary study
GEO Series GSE30379. Capra hircus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic blood responses to mycobacteria in a BCG challenge model
GEO Series GSE84867. Bos taurus. 213 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome sequencing to unravel the molecular mechanisms underlying the cuticle liquefaction of Antheraea pernyi following Antheraea pernyi nucleopolyhedrovirus challenge
GEO Series GSE125821. Antheraea pernyi. 6 samples. Type: Expression profiling by high throughput sequencing.
Wood kraft pulp supplementation alters the rumen fermentation characteristics and epithelial transcriptomes in Holstein cattle during the high-grain diet challenge
GEO Series GSE128461. Bos taurus. 8 samples. Type: Expression profiling by array.
Early Transcriptional Responses to Human Paratyphoid Challenge
GEO Series GSE224006. Homo sapiens. 39 samples. Type: Expression profiling by high throughput sequencing.
RBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data
GEO Series GSE227455. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
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.