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73 results for “Competition datasets”
Dataset from Competition and drought affect cleistogamy in a non-additive way in the annual ruderal Lamium amplexicaule
<p>Data associated with the manuscript 23147-TS1R1 published in AoB Plants. For detailed information about files and their content see ReadMe</p>
Dataset The relative importance of body size and UV coloration in influencing male-male competition in a Lacertid lizard
<p>This is the dataset of the paper "The relative importance of body size and UV coloration in influencing male-male competition in a Lacertid lizard" published in Behavioral Ecology and Sociobiology by Names et al. (2019). It includes a metadata statement and five data spreadsheets.</p> <p><strong>Abstract of the paper</strong></p> <p>Communication via color signals is common in natural systems. Ultraviolet (UV)-blue patches located on the outer-ventral scales of some Lacertid lizards are thought to be involved in male-male competition. However, the mechanisms that maintain their honesty remain unknown. Here, we use the common wall lizard <em>Podarcis muralis</em> to<br> test whether the lateral UV-blue spots are conventional signals, the honesty of which is guaranteed by receiver-dependent costs, and discuss their potential role as an amplifier of body size. We first described the morphology and reflectance properties of lateral blue spots in common wall lizards and investigated how they influence male-<br> male competition. Spot size and number, UV chroma, and conspicuousness (calculated using vision models) were significantly greater in adult males relative to adult females and adult males relative to juveniles. Total spot area (and not spot number) of adult males was positively correlated with body size. We conducted staged competition encounters between focal males and smaller or larger rivals with control or manipulated spots. Spots were enlarged in small rivals and reduced in large rivals to disrupt the phenotypic correlation between spot area and body size. Aggressiveness and dominance were positively influenced by body size in control encounters. Spot manipulations resulted in greater submission and less aggressiveness in focal males. These results contradict the predictions associated with conventional signals and amplifiers, but suggest that spots contributed to opponent evaluation during short-distance encounters between competing males.</p>
E-scooters: competition with shared bicycles and relationship to public transport (processed datasets)
<p>Processed datasets for article "E-scooters: competition with shared bicycles and relationship to public transport" by Łukasz Nawaro (University of Warsaw, Faculty of Economic Sciences).</p>
Dataset for "Polyploidy impacts population growth and competition with diploids: Multigenerational experiments reveal key life history tradeoffs"
<p>Datasets associated with the manuscript "Polyploidy impacts population growth and competition with diploids: Multigenerational experiments reveal key life history tradeoffs".</p>
Indoor Location Competition 2.0 Dataset
<p>This is the dataset of our Mobicom 2023 paper titled "The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization Competition". We organized an indoor location competition in 2021. 1446 contestants from more than 60 countries making up 1170 teams participated in this unique global event. In this competition, a first-of-its-kind large-scale indoor location benchmark dataset (60 GB) was released. The dataset for this competition consists of dense indoor signatures of WiFi, geomagnetic field, iBeacons etc. as well as ground truth locations collected from hundreds of buildings in Chinese cities. Here we upload a sample data to Zenodo, and the whole dataset can be found at https://www.kaggle.com/c/indoor-location-navigation.</p>
EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)
<p>This dataset was a part of the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (<a href="https://ijcai-23.org/competitions/">IJCAI'23 Official Website</a>). This dataset repository is divided into 3 versions:</p> <ul> <li><strong><em>Version 1: </em>Training data + Metadata</strong></li> <li><strong><em>Version 2: </em>Test data</strong></li> <li><strong>Version 3: Complete dataset (EEG + EMG) </strong></li> </ul> <p><strong>For more detailed information about the competition, please visit our <a href="http://ijcai-23.dfki-bremen.de/competitions/inter-hri/">competition webpage</a>.</strong></p> <p>This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. </p> <p>The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (<a href="https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/%7D%7D.">https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/).</a> For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). </p> <p><strong>If you use our dataset, cite our paper.</strong></p> <p>Frontiers in Human Neuroscience DOI: <a href="https://doi.org/10.3389/fnhum.2024.1304311">10.3389/fnhum.2024.1304311</a></p> <p>BibTeX citation:</p> <div> <div>@ARTICLE{10.3389/fnhum.2024.1304311,</div> <div>AUTHOR={Kueper, Niklas and Chari, Kartik and Bütefür, Judith and Habenicht, Julia and Rossol, Tobias and Kim, Su Kyoung and Tabie, Marc and Kirchner, Frank and Kirchner, Elsa Andrea},</div> <div>TITLE={EEG and EMG dataset for the detection of errors introduced by an active orthosis device},</div> <div>JOURNAL={Frontiers in Human Neuroscience},</div> <div>VOLUME={18},</div> <div>YEAR={2024},</div> <div>URL={https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311},</div> <div>DOI={10.3389/fnhum.2024.1304311},</div> <div>ISSN={1662-5161}</div> <div>}</div> </div>
Dataset for: Mating competition and adult sex ratio in wild Trinidadian guppies
Open the record for dataset details and reuse information.
Dataset S1: Results of modeled spectral competition between Synechococcus type IV chromatic acclimaters (CA4) and blue and green light-harvesting specialists
Open the record for dataset details and reuse information.
Datasets and Supporting Materials for the IPIN 2019 Competition Track 4 (Foot-Mounted IMU based Positioning, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa , Italy).</p> <p><strong>Contents:</strong></p> <ol> <li>track4_ipin2019competition.pdf: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains 2 zip files.<br> - HKB08.zip : for sensors bias estimation.<br> - HKB21.zip : for trajectory estimation.<br> Each archive contains 4 files :<br> - HKBxx_mag.csv : magnetometer data<br> - HKBxx_sti.csv : inertial data<br> - HKBxx_ublox.ubx : GNSS data<br> - HKBxx_INFO.txt : info file<br> see track4_ipin2019competition.pdf for more details.</li> <li>02-Supplementary_Materials: This folder contains the datasheet files of the different sensors.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on all evaluation points. The ground truth is provided csv file.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Ortiz, M.; Perul, J.; Torres-Sospedra, J. Renaudin, V. Datasets and Supporting Materials for the IPIN 2019 Competition Track 4 (Foot-Mounted IMU based Positioning, off-site), Zenodo 2019 <a href="http://dx.doi.org/10.5281/zenodo.3937220">http://dx.doi.org/10.5281/zenodo.3937220</a></li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://ipin-conference.org/2019/competition.html">http://ipin-conference.org/2019/competition.html</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact to: </strong></p> <ul> <li> <p>Miguel Ortiz (<a href="mailto:miguel.ortiz@univ-eiffel.fr">miguel.ortiz@univ-eiffel.fr</a>) at the University Gustave Eiffel, France.</p> <p> </p> </li> </ul>
ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments (HisFrag20) Dataset
<p>This competition investigates the performance of large-scale retrieval of historical document fragments based on writer recognition. The analysis of historic fragments is a difficult challenge commonly solved by trained humanists.<br> We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as fragment or writer retrieval. Therefore, we created a large dataset consisting of more than 120000 fragments.<br> The goal is then to find similar patches of the same page or manuscript. contains ~100 000 fragments using the Historical-IR19 as base dataset, they should all contain some text, however some fragments are quite small.</p> <p>Training-set: contains ~100 000 fragments using the Historical-IR19 as base dataset, they should all contain some text, however some fragments are quite small.</p> <p>Test-set: contains about 20 000 new fragments</p> <p>Naming-convention: WID_PID_FID.jpg , where WID=writer id, PID: page id, FID= fragment id</p> <p>For more information visit: <a href="https://lme.tf.fau.de/research/competitions/hisfragir20/">https://lme.tf.fau.de/research/competitions/hisfragir20/</a></p>
The development dataset of the AI composition recognition competition, CSMT2020
<p>The development dataset contains 6000 MIDI files with monophonic melodies generated by artificial intelligence algorithms. The tempo is between the 68bpm and 118bpm (beat per minute). The length of each melody is 8 bars, and the melody does not necessarily include complete phrase structures. There are two datasets with different music styles used as the training dataset of a certain number of algorithms, where the melodies in the development dataset are generated.</p> <p>The website of the challenge:</p> <p><a href="http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020/">http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020</a> (Chinese instruction)</p> <p><a href="https://ai-composition-recognition2020.github.io/english.html">https://ai-composition-recognition2020.github.io/english.html</a> (English instruction)</p>
AirSim building99 competition dataset
<p>MISTLAB</p> <p>dataset for using learning method in AirSim environment, 2 agents' competition</p>
The evaluation dataset of the AI composition recognition competition, CSMT2020
<p>The evaluation dataset contains 4000 MIDI files with exact configurations of development dataset with two exceptions: 1) A number of melodies composed by human composers are added, some of which are published, and some of which are composed for this competition. The music style of the human composed melodies are the same as the styles of music in the training set. This was confirmed by musicologists. 2) There are a number of melodies generated by algorithms with minor algorithmic or parameter changes compared to the algorithms in the development dataset.</p> <p>The development dataset:</p> <p><a href="https://zenodo.org/record/3944685#.Xza9DegzY2x">https://zenodo.org/record/3944685#.Xza9DegzY2x</a></p> <p>The website of the challenge:</p> <p><a href="http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020/">http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020</a> (Chinese instruction)</p> <p><a href="https://ai-composition-recognition2020.github.io/english.html">https://ai-composition-recognition2020.github.io/english.html</a> (English instruction)</p>
Dataset: A Comparison of Individual and Group Behavior in a Competition with Cheating Opportunities
<p>This is the dataset and corresponding do-file to reproduce the results from the paper:</p> <p>Dannenberg, Astrid, and Elina Khachatryan. 2020. “A comparison of individual and group behavior in a competition with cheating opportunities.” Journal of Economic Behavior & Organization, 177: 533–47.</p>
Test A for the ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p><strong>Test A. </strong>A batch of page images annotated with baselines.</p>
Competitive Metaheuristic Algorithms for Building a Performance Database of a Dual-Band Combline Bandpass Filter with Microstrip Connection (Version 17) [Dataset]. Zenodo.
<p>To run the files, remove the prefixes (e.g. figA- or fig5A-)</p>
Dataset for: Differential responses to fertilization and competition among invasive, non-invasive alien and native Bidens species
<p class="manuscript">Comparative studies of invasive, non-invasive alien, and native congenic plant species can identify plant traits that drive invasiveness. In particular, functional traits associated with rapid growth rate and high fecundity likely facilitate invasive success. As such traits often exhibit high phenotypic plasticity, characterizing plastic responses to anthropogenic environmental changes such as eutrophication and disturbance is important for predicting the invasive success of alien plant species in the future. Here, we compared trait expression and phenotypic plasticity at the species level among invasive, non-invasive alien, and native <i>Bidens</i> species. Plants were grown under nutrient addition and competition treatments, and their functional, morphological, and seed traits were examined. Invasive <i>B. frondosa</i> exhibited higher phenotypic plasticity in most measured traits than did the alien non-invasive <i>B. pilosa</i> or native <i>B. bipinnata</i>. However, differential plastic responses to environmental treatments rarely altered the rank of trait values among the three <i>Bidens</i> species, except for the number of inflorescences. The achene size of <i>B. frondosa</i> was larger, but its pappus length was shorter than that of <i>B. pilosa</i>. Two species demonstrated opposite plastic responses of pappus length to fertilization. These results suggest that the plasticity of functional traits does not significantly contribute to the invasive success of <i>B. frondosa</i>. The dispersal efficiency of <i>B. frondosa</i> is expected to be lower than that of <i>B. pilosa</i>, suggesting that long-distance dispersal is likely not a critical factor in determining invasive success.</p>
Dataset for Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
Open the record for dataset details and reuse information.
ERL Service Robot Competition Dataset FBM1 and FBM2 (Lisbon 2017 Tournament)
<p>Dataset for ERL Service Robot Competition (Major competition in Lisbon 2017)</p> <p>FBM1:Object Perception and FBM2:Navigation Functionality benchmarks.</p> <p>Log files are in Rosbag format and include two files per trial of the benchmark:</p> <p>1) Internal Robot data (information logged by the robot such as onboard sensors and control commands)</p> <p>2) RSBB data (Information From the Referee box that contains benchmark related information as well as the ground truth data measured by motion capture system)</p> <p> </p>
ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset
<p>The ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) followed a competition at ICFHR2016 and provided a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition, containing information on date of production and class of script.</p> <p>If you use this upload, please cite:</p> <p>Florence Cloppet, Véronique Eglin, Marlène Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, "ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script", in <em>14th IAPR International Conference on Document Analysis and Recognition</em>. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. <a href="https://doi.org/10.1109/ICDAR.2017.224">https://doi.org/10.1109/ICDAR.2017.224</a></p> <p>We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground<br> truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color<br> representation.</p> <p>The present dataset contains the training dataset, both test datasets (tasks 1 and 3, and tasks 2 and 4) and the matrices provided by the competitors. It was first published on <a href="https://clamm.irht.cnrs.fr/icdar-2017/">https://clamm.irht.cnrs.fr/icdar-2017/</a> in Nov. 2017.</p>
ScienceDex guides
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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.