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73 results for “Competition datasets”
Seabirds mediate intraguild and competitive interactions in a shark community dataset
<p>R scripts with corresponding data sheets to run network, residency and temporal analyses. </p>
Dataset for: Differential responses to fertilization and competition among invasive, non-invasive alien and native Bidens species
Open the record for dataset details and reuse information.
GECCO Industrial Challenge 2015 Dataset: A heating system dataset for the 'Recovering missing information in heating system operating data' competition at the Genetic and Evolutionary Computation Conference 2015, Madrid, Spain
<p>Dataset of the 'Industrial Challenge: Recovering missing information in heating system operating data' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 11th-15th 2015, Madrid, Spain</p> <p> </p> <p>The task of the competition was to recover (impute) missing information in heating system operation time series'.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of heating system operational time series with missing values</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, A. Fischbach, C. Schlitt, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Major German heating systems supplier (S. Moritz)</p> <p> </p> <p> </p> <p>Industrial Challenge: Recovering missing information in heating system operating data</p> <p> </p> <p>The Industrial Challenge will be held in the competition session at the Genetic and Evolutionary Computation Conference. It poses difficult real-world problems provided by industry partners from various fields. Highlights of the Industrial Challenge include interesting problem domains, real-world data and realistic quality measurement</p> <p>Overview</p> <p>In times of accelerating climate change and rising energy costs, increasing energy efficiency and reducing expenses becomes a high priority goal for businesses and private households alike. Modern heating systems record detailed operating data and report this data to a central system. Here, the operating data can be correlated and analyzed to detect potential optimization opportunities or anomalies like unusually high energy consumption. Due to various difficulties this data might be incomplete which makes accurate forecasting even harder.</p> <p>Goal of the GECCO 2015 Industrial Challenge is to develop capable procedures to recover missing information in heating system operating data. Adequate recovery of the missing data enables more accurate forecastings which allow for intelligent control of the heating systems, and therefore contributes to a positive energy balance and reduced expenses.</p> <p> </p> <p><strong>Submission deadline:</strong><br> June 22, 2015</p> <p><strong>Official Webpage:</strong><br> <a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2015/">www.spotseven.de/gecco-challenge/gecco-challenge-2015/</a></p> <p> </p>
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Dataset associated to "Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis"
<p>This dataset includes all the codes necessary to reproduce the simulations presented in the paper "Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis" published in Journal of Ecology. More precisely, it includes the version of the codes used to generate the results for the vegetation model (ED2), the analysis platform (PEcAn), as well as the meterological drivers (Drivers) and the initial conditions (IC) of the model and the priors and posterior parameter distributions.</p>
NeurIPS 2022 Cell Segmentation Competition Dataset
<p>The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images.</p> <p>https://neurips22-cellseg.grand-challenge.org/</p> <p>Please cite the following paper if this dataset is used in your research. </p> <p> </p> <pre><code><span>@article</span><span>{</span><span>NeurIPS</span><span>-</span><span>CellSeg</span><span>,</span> <span>title</span> <span>=</span> <span>{</span><span>The</span> <span>Multi</span><span>-</span><span>modality</span> <span>Cell</span> <span>Segmentation</span> <span>Challenge</span><span>:</span> <span>Towards</span> <span>Universal</span> <span>Solutions</span><span>}</span><span>,</span> <span>author</span> <span>=</span> <span>{</span><span>Jun</span> <span>Ma</span> <span>and</span> <span>Ronald</span> <span>Xie</span> <span>and</span> <span>Shamini</span> <span>Ayyadhury</span> <span>and</span> <span>Cheng</span> <span>Ge</span> <span>and</span> <span>Anubha</span> <span>Gupta</span> <span>and</span> <span>Ritu</span> <span>Gupta</span> <span>and</span> <span>Song</span> <span>Gu</span> <span>and</span> <span>Yao</span> <span>Zhang</span> <span>and</span> <span>Gihun</span> <span>Lee</span> <span>and</span> <span>Joonkee</span> <span>Kim</span> <span>and</span> <span>Wei</span> <span>Lou</span> <span>and</span> <span>Haofeng</span> <span>Li</span> <span>and</span> <span>Eric</span> <span>Upschulte</span> <span>and</span> <span>Timo</span> <span>Dickscheid</span> <span>and</span> <span>José</span> <span>Guilherme</span> <span>de</span> <span>Almeida</span> <span>and</span> <span>Yixin</span> <span>Wang</span> <span>and</span> <span>Lin</span> <span>Han</span> <span>and</span> <span>Xin</span> <span>Yang</span> <span>and</span> <span>Marco</span> <span>Labagnara</span> <span>and</span> <span>Vojislav</span> <span>Gligorovski</span> <span>and</span> <span>Maxime</span> <span>Scheder</span> <span>and</span> <span>Sahand</span> <span>Jamal</span> <span>Rahi</span> <span>and</span> <span>Carly</span> <span>Kempster</span> <span>and</span> <span>Alice</span> <span>Pollitt</span> <span>and</span> <span>Leon</span> <span>Espinosa</span> <span>and</span> <span>Tâm</span> <span>Mignot</span> <span>and</span> <span>Jan</span> <span>Moritz</span> <span>Middeke</span> <span>and</span> <span>Jan</span><span>-</span><span>Niklas</span> <span>Eckardt</span> <span>and</span> <span>Wangkai</span> <span>Li</span> <span>and</span> <span>Zhaoyang</span> <span>Li</span> <span>and</span> <span>Xiaochen</span> <span>Cai</span> <span>and</span> <span>Bizhe</span> <span>Bai</span> <span>and</span> <span>Noah</span> <span>F</span><span>.</span> <span>Greenwald</span> <span>and</span> <span>David</span> <span>Van</span> <span>Valen</span> <span>and</span> <span>Erin</span> <span>Weisbart</span> <span>and</span> <span>Beth</span> <span>A</span><span>.</span> <span>Cimini</span> <span>and</span> <span>Trevor</span> <span>Cheung</span> <span>and</span> <span>Oscar</span> <span>Brück</span> <span>and</span> <span>Gary</span> <span>D</span><span>.</span> <span>Bader</span> <span>and</span> <span>Bo</span> <span>Wang</span><span>}</span><span>,</span> <span>journal</span> <span>=</span> <span>{</span><span>Nature</span> <span>Methods</span><span>}</span><span>,</span><span><br> volume={21},<br> pages={1103–1113},<br> <span>year</span> <span>=</span> <span>{</span><span>2024</span><span>}</span>,</span> <span>doi</span> <span>=</span> <span>{</span><span>https</span><span>:</span><span>//</span><span>doi</span><span>.</span><span>org</span><span>/</span><span>10.1038</span><span>/</span><span>s41592</span><span>-</span><span>024</span><span>-</span><span>02233</span><span>-</span><span>6</span><span>}</span> <span>}</span></code></pre> <p> </p> <p>This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning.</p> <p> </p> <p>The images are provided with original formats, including tiff, tif, png, jpg, bmp... The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats.</p> <p> </p> <p><strong>We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website!</strong></p> <p> </p> <p><strong>Dataset License: CC-BY-NC-ND</strong></p>
Codalab ML competition - No GPS, No problem! - Training dataset
<p>See competition <a href="https://epfl-enac.github.io/topo-ml-competition">website</a></p>
Codalab ML competition - No GPS, No problem! - test dataset
<p>See competition <a href="https://epfl-enac.github.io/topo-ml-competition">website</a></p>
Datasets and Supporting Materials for the IPIN 2022 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2022 Competition.</p> <p><strong>Contents:</strong></p> <ul> <li>Track-3_TA-2022.pdf: Technical annex describing the competition (Version 2)</li> <li>01 Logfiles: This folder contains a subfolder with the 89 training trials a subfolder with the 24 testing trials (validation), and a subfolder with the 3 blind scoring trials (test) as provided to competitors.</li> <li>02 Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization.</li> <li>03 Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 31|61|61 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results.</li> </ul> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2022 Competition Track 3 (Smartphone-based, off-site), Zenodo 2022. http://dx.doi.org/10.5281/zenodo.7612915</li> </ul>
SIAM 2007 Text Mining Competition dataset
**Subject Area:** Text Mining **Description:** This is the dataset used for the SIAM 2007 Text Mining competition. This competition focused on developing text mining algorithms for document classification. The documents in question were aviation safety reports that documented one or more problems that occurred during certain flights. The goal was to label the documents with respect to the types of problems that were described. This is a subset of the Aviation Safety Reporting System (ASRS) dataset, which is publicly available. **How Data Was Acquired:** The data for this competition came from human generated reports on incidents that occurred during a flight. **Sample Rates, Parameter Description, and Format:** There is one document per incident. The datasets are in raw text format. All documents for each set will be contained in a single file. Each row in this file corresponds to a single document. The first characters on each line of the file are the document number and a tilde separats the document number from the text itself. **Anomalies/Faults:** This is a document category classification problem.
The Hume Vocal Burst Competition Dataset (H-VB) | Raw Data [ExVo: updated 02.28.22]
<p>This package includes the raw data for a subset of the Hume Vocal Burst (Hume-VB) dataset used for the <a href="https://www.competitions.hume.ai/exvo2022">ExVo 2022 Competition</a>.<br> <br> <strong>The competition is now over</strong>, please fill out <a href="https://forms.gle/JCsnQZ2wd7tUMmbYA">this form </a>to request access to the full Hume VB dataset.</p> <p>More information on the competition and dataset can be found in the competition <a href="https://arxiv.org/abs/2205.01780">white paper</a>.<br> -- <br> This dataset contains <strong>59,201 audio recordings of vocal bursts from 1,702 speakers</strong>, from 4 cultures, the <strong>U.S.A, South Africa, China, </strong>and<strong> Venezuela</strong>, ranging in age from <strong>20 to 39.5 years old</strong>.</p> <p>The total duration of data in this version of <strong>Hume-VB is 36 Hours </strong> (Mean: 02.23 sec). A subset of 10 emotional intensity labels is also available. Data has been partitioned into equal training, validation, and test sets, and the test set is blind. </p> <p><strong>Emotion Labels: </strong><br> <em>Awe, Excitement, Amusement, Awkwardness, Fear, Horror, Distress, Triumph, Sadness, Surprise</em><br> <br> </p>
The Hume Vocal Burst Competition Dataset (H-VB) | Raw Data [A-VB: updated 03.01.22]
<p>This package includes the raw data for a subset of The Hume Vocal Burst Database (H-VB).</p> <p>This dataset contains <strong>59,201 audio recordings of vocal bursts from 1,702 speakers</strong>, from 4 cultures, the <strong>U.S.A, South Africa, China, </strong>and<strong> Venezuela</strong>, ranging in age from <strong>20 to 39.5 years old</strong>.</p> <p>The total duration of data in this version of <strong>H-VB is 36 Hours </strong> (Mean: 02.23 sec). A subset of 10 emotional intensity labels is also available. Data has been partitioned into equal training, validation, and test sets, and the test set is blind. </p> <p><strong>Emotion Labels: </strong><br> <em>Awe, Excitement, Amusement, Awkwardness, Fear, Horror, Distress, Triumph, Sadness, Surprise, Valence, Arousal</em></p> <p>For further questions about the data contact: <strong>competitions@hume.ai</strong></p>
"DO THE NON-VERBAL BEHAVIORS OF SPRINTERS BEFORE THE COMPETITION AFFECT THEIR PERFORMANCE?" Dataset
<p>The dataset of the article titled "DO THE NON-VERBAL BEHAVIORS OF SPRINTERS BEFORE THE COMPETITION AFFECT THEIR PERFORMANCE?"</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.