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73 results for “Competition datasets”
Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2016 Competition (Alcalá, Spain).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 17 training logfiles and a subfolder with the 9 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 578 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A.; Knauth, A.; Moreira, A.; Beer, Y.; Fetzer, T.; Ta, V.-C.; Montoliu, R.; Seco, F.; Mendoza, G.; Belmonte, O.; Koukofikis, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Ebner, F.; Deinzer, F.; Vaufreydaz, D.; Dao, T.-K.; and Castelli, E. The Smartphone-based Off-Line Indoor Location Competition at IPIN 2016: Analysis and Future work Sensors Vol. 17(3), 2017. <a href="http://dx.doi.org/10.3390/s17030557">http://dx.doi.org/10.3390/s17030557</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Montoliu, R.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2791530">http://dx.doi.org/10.5281/zenodo.2791530</a></li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2016/competition-home">http://evaal.aaloa.org/2016/competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2016track3/">http://indoorloc.uji.es/ipin2016track3/</a> </li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p> </p>
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents [HisIR19] Dataset
<p>This dataset contains the training and test set used in the ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents.</p> <p>This competition investigates the performance of large-scale retrieval of historical document images based on<br> writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing<br> a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval.</p> <p>The training data set encompasses images from (i) Letters A, where each writer contributed one or three images; (ii) Manuscripts, where each writer was represented by five consecutive images from a single book.<br> In total, it contains 300 writers contributing one page, 100 writers contributing three pages, and 120 writers contributing five pages resulting in 1200 images of 520 writers.</p> <p>The test data set contains 20 000 images: About 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages.</p> <p> </p> <p>If you use this dataset, please cite:</p> <p>V. Christlein, A. Nicolaou, M. Seuret, D. Stutzmann, A. Maier: "ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents", in 15th International Conference on Document Analysis and Recognition, 2019, Sydney, Australia</p> <p> </p>
Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (bi2015b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 44 subjects playing in pair to the multi-user version of a visual P300 Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 or 2 Target, 35 or 34 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during four randomised conditions (Cooperation 1-Target, Cooperation 2-Targets, Competition 1-Target, Competition 2-Targets). The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA</a>. The ID of this dataset is <em>bi2015b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015b</em></p>
Datasets for "Intraspecific interactions in the annual legume Medicago minima are shaped by both genetic variation for competitive ability and reduced competition among kin"
<p>Datasets for “Intraspecific interactions in the annual legume <em>Medicago minima</em> are shaped by both genetic variation for competitive ability and reduced competition among kin”</p> <p>Two datasets are provided.</p> <p>root_behavior_experiment_for_ms.csv: provides data relative to a root behaviour experiment where <em>Medicago minima</em> genotypes grew either with a kin or a non kin. Direction of root growth, root length and biomass were measured.</p> <p>Medicago_minima_biomass_dataMerge.csv: provides data relative to a minicommunity experiment where <em>Medicago minima </em>genotypes were grown surrounded by three kin genotypes, or three non-kin genotypes (i.e. stranger to the central plant but identical to each other) or three stranger genotypes (stranger to the central plant and to each other). For this second experiment above-ground growth and biomass were monitored.</p> <p>Detailed information on the dataset variables are provided in the metadata file.</p> <p>Code for data wrangling and analyses is included in the manuscript as an appendix.</p>
Dataset of ICDAR 2019 Competition on Post-OCR Text Correction
<p><strong>Corpus for the ICDAR2019 Competition on Post-OCR Text Correction (October 2019)</strong><br> Christophe Rigaud, Antoine Doucet, Mickael Coustaty, Jean-Philippe Moreux<br> <a href="http://l3i.univ-larochelle.fr/ICDAR2019PostOCR">http://l3i.univ-larochelle.fr/ICDAR2019PostOCR</a><br> -------------------------------------------------------------------------------</p> <p>These are the supplementary materials for the ICDAR 2019 paper <em><a href="https://zenodo.org/record/3459116">ICDAR 2019 Competition on Post-OCR Text Correction</a></em></p> <p>Please use the following citation:</p> <pre><code>@inproceedings{rigaud2019pocr,</code> <code> </code><code>title="ICDAR 2019 Competition on Post-OCR Text Correction",</code> <code> </code><code>author={Rigaud, Christophe and Doucet, Antoine and Coustaty, Mickael and Moreux, Jean-Philippe},</code> <code> </code><code>year={2019},</code> <code> </code><code>booktitle={Proceedings of the 15th International Conference on Document Analysis and Recognition (2019)}</code> <code> </code><code>}</code></pre> <p> </p> <p><strong>Description</strong><br> The corpus accounts for 22M OCRed characters along with the corresponding Gold Standard (GS). The documents come from different digital collections available, among others, at the National Library of France (BnF) and the British Library (BL). The corresponding GS comes both from BnF's internal projects and external initiatives such as Europeana Newspapers, IMPACT, Project Gutenberg, Perseus and Wikisource.</p> <p><strong>Repartition of the dataset</strong><br> - <em>ICDAR2019_Post_OCR_correction_training_18M.zip</em>: 80% of the full dataset, provided to train participants' methods.<br> - <em>ICDAR2019_Post_OCR_correction_evaluation_4M</em>: 20% of the full dataset used for the evaluation (with Gold Standard made publicly after the competition).<br> - <em>ICDAR2019_Post_OCR_correction_full_22M</em>: full dataset made publicly available after the competition.</p> <p><strong>Special case for Finnish language</strong><br> Material from the National Library of Finland (<em>Finnish dataset FI > FI1</em>) are not allowed to be re-shared on other website. Please follow these guidelines to get and format the data from the original website.</p> <p>1. Go to <a href="https://digi.kansalliskirjasto.fi/opendata/submit?set_language=en">https://digi.kansalliskirjasto.fi/opendata/submit?set_language=en</a>;<br> 2. Download <em>OCR Ground Truth Pages (Finnish Fraktur) [v1](4.8GB)</em> from <em>Digitalia (2015-17)</em> package;<br> 3. Convert the Excel file "<em>~/metadata/nlf_ocr_gt_tescomb5_2017.xlsx</em>" as Comma Separated Format (.csv) by using <em>save as</em> function in a spreadsheet software (e.g. Excel, Calc) and copy it into "<em>FI/FI1/HOWTO_get_data/input/</em>";<br> 4. Go to "<em>FI/FI1/HOWTO_get_data/</em>" and run "<em>script_1.py</em>" to generate the <em>full</em> "<em>FI1</em>" dataset in "<em>output/full/</em>";<br> 4. Run "<em>script_2.py</em>" to split the "<em>output/full/</em>" dataset into "<em>output/training/</em>" and "<em>output/evaluation/</em>" sub sets.<br> At the end of the process, you should have a "<em>training</em>", "<em>evaluation</em>" and "<em>full</em>" folder with 1579528, 380817 and 1960345 characters respectively.</p> <p><br> <strong>Licenses: free to use for non-commercial uses, according to sources in details</strong><br> - BG1: IMPACT - National Library of Bulgaria: CC BY NC ND<br> - CZ1: IMPACT - National Library of the Czech Republic: CC BY NC SA<br> - DE1: Front pages of Swiss newspaper NZZ: Creative Commons Attribution 4.0 International (<a href="https://zenodo.org/record/3333627">https://zenodo.org/record/3333627</a>)<br> - DE2: IMPACT - German National Library: CC BY NC ND<br> - DE3: GT4Hist-dta19 dataset: CC-BY-SA 4.0 (<a href="https://zenodo.org/record/1344132">https://zenodo.org/record/1344132</a>)<br> - DE4: GT4Hist - EarlyModernLatin: CC-BY-SA 4.0 (<a href="https://zenodo.org/record/1344132">https://zenodo.org/record/1344132</a>)<br> - DE5: GT4Hist - Kallimachos: CC-BY-SA 4.0 (<a href="https://zenodo.org/record/1344132">https://zenodo.org/record/1344132</a>)<br> - DE6: GT4Hist - RefCorpus-ENHG-Incunabula: CC-BY-SA 4.0 (<a href="https://zenodo.org/record/1344132">https://zenodo.org/record/1344132</a>)<br> - DE7: GT4Hist - RIDGES-Fraktur: CC-BY-SA 4.0 (<a href="https://zenodo.org/record/1344132">https://zenodo.org/record/1344132</a>)<br> - EN1: IMPACT - British Library: CC BY NC SA 3.0<br> - ES1: IMPACT - National Library of Spain: CC BY NC SA<br> - FI1: National Library of Finland: no re-sharing allowed, follow the above section to get the data. (<a href="https://digi.kansalliskirjasto.fi/opendata">https://digi.kansalliskirjasto.fi/opendata</a>)<br> - FR1: HIMANIS Project: CC0 (<a href="https://www.himanis.org/">https://www.himanis.org</a>)<br> - FR2: IMPACT - National Library of France: CC BY NC SA 3.0<br> - FR3: RECEIPT dataset: CC0 (<a href="http://findit.univ-lr.fr/">http://findit.univ-lr.fr</a>)<br> - NL1: IMPACT - National library of the Netherlands: CC BY<br> - PL1: IMPACT - National Library of Poland: CC BY<br> - SL1: IMPACT - Slovak National Library: CC BY NC</p> <p>Text post-processing such as cleaning and alignment have been applied on the resources mentioned above, so that the Gold Standard and the OCRs provided are not necessarily identical to the originals.</p> <p><br> <strong>Structure</strong><br> - **Content** [<em>./lang_type/sub_folder/#.txt</em>]<br> - "<em>[OCR_toInput] </em>" => Raw OCRed text to be de-noised.<br> - "<em>[OCR_aligned] </em>" => Aligned OCRed text.<br> - "<em>[ GS_aligned] </em>" => Aligned Gold Standard text.</p> <p>The aligned OCRed/GS texts are provided for training and test purposes. The alignment was made at the character level using "<em>@</em>" symbols. "<em>#</em>" symbols correspond to the absence of GS either related to alignment uncertainties or related to unreadable characters in the source document. For a better view of the alignment, make sure to disable the "word wrap" option in your text editor.</p> <p>The Error Rate and the quality of the alignment vary according to the nature and the state of degradation of the source documents. Periodicals (mostly historical newspapers) for example, due to their complex layout and their original fonts have been reported to be especially challenging. In addition, it should be mentioned that the quality of Gold Standard also varies as the dataset aggregates resources from different projects that have their own annotation procedure, and obviously contains some errors.</p> <p><br> <strong>ICDAR2019 competition</strong><br> Information related to the tasks, formats and the evaluation metrics are details on :<br> <a href="https://sites.google.com/view/icdar2019-postcorrectionocr/evaluation">https://sites.google.com/view/icdar2019-postcorrectionocr/evaluation</a></p> <p><br> <strong>References</strong><br> - IMPACT, European Commission's 7th Framework Program, grant agreement 215064<br> - Uwe Springmann, Christian Reul, Stefanie Dipper, Johannes Baiter (2018). Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin.<br> - <a href="https://digi.nationallibrary.fi/">https://digi.nationallibrary.fi</a> , Wiipuri, 31.12.1904, Digital Collections of National Library of Finland<br> - EU Horizon 2020 research and innovation programme grant agreement No 770299</p> <p><br> <strong>Contact</strong><br> - christophe.rigaud(at)univ-lr.fr<br> - antoine.doucet(at)univ-lr.fr<br> - mickael.coustaty(at)univ-lr.fr<br> - jean-philippe.moreux(at)bnf.fr</p> <p>L3i - University of la Rochelle, <a href="http://l3i.univ-larochelle.fr/">http://l3i.univ-larochelle.fr</a><br> BnF - French National Library, <a href="http://www.bnf.fr/">http://www.bnf.fr</a></p>
Dataset belonging to Fragmented micro-growth habitats present opportunities for alternative competitive outcomes
<p>This Dataset contains the raw data, processed data and script/code underlying the results and figures presented in the manuscript "Fragmented micro-growth habitats present opportunities for alternative competitive outcomes", (deposited at BioRXiv 10.1101/2024.01.26.577336)</p> <p>by: Maxime Batsch<sup>1</sup>, Isaline Guex<sup>2</sup>, Helena Todorov<sup>1</sup>, Clara M. Heiman<sup>1</sup>, Jordan Vacheron<sup>1</sup>, Julia A. Vorholt<sup>3</sup>, Christoph Keel<sup>1</sup>, and Jan Roelof van der Meer<sup>1*</sup></p> <p>1) Department of Fundamental Microbiology, University of Lausanne, CH-1015 Lausanne, Switzerland</p> <p>2) Department of Mathematics, University of Fribourg, CH-1700 Fribourg, Switzerland</p> <p>3) Institute for Microbiology, Swiss Federal Institute of Technology (ETH Zürich), CH-8049 Zürich, Switzerland</p> <p>The raw data consists of microscopy droplet/cell image data, 96-well plate reader data, and flow cytometry cell counts.<br>Processed data are organised by Figure in the manuscript and/or the Supplementary figures belonging to the manuscript. Figure folders contain the main output, the input data, the code and scripts to produce the output from the input, and relevant statistical tests.</p>
ICFHR 2016 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset
<p>The ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) provided a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition.</p> <p>If you use this dataset, please cite:</p> <p>Florence Cloppet, Véronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, "ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script", <em>Proceedings of International Conference on Frontiers in Handwriting Recognition</em>, Los Alamos : IEEE, 2016, p. 590-595. [<a href="https://doi.org/10.1109/ICFHR.2016.0113">https://doi.org/10.1109/ICFHR.2016.0113</a>]</p> <p>At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set of 2000 images with their ground truths. In the first task of script crisp classification, the classifiers have been evaluated on a test set of 1000 single-type manuscripts. In the second task of “Fuzzy Classification”, the classifiers have been carried out on a set of 2000 multi-script-type manuscripts.</p> <p>The present dataset contains the training dataset, both test datasets (task 1 and task 2) and the matrices provided by the competitors. It was first published on a https://clamm.irht.cnrs.fr/icfhr2016-clamm/ in Oct. 2016.</p> <p> </p>
SRBench Competition 2023 - Datasets, Scripts, and Results
<p>Source-codes for: generating artificial datasets, evaluating the results and repositories for the SRBench Competition 2023:</p> <p>https://cavalab.org/srbench/competition-2023/</p>
Datasets and Supporting Materials for the IPIN 2023 Competition Track 4 (Foot-Mounted IMU based Positioning, offsite-online)
<p>This package contains the datasets and supplementary materials used in the IPIN 2023 Competition.</p> <p><strong>Contents:</strong><br> - IPIN2023_Track4_CallForCompetition_v2.2.pdf: Call for competition including the technical annex describing the competition</p> <p>- 01-Logfiles: This folder contains 2 files for each Trials (Testing, Scoring01, Scoring02)<br> - IPIN2023_T4_xxx.txt : data file containing ACCE, ROTA, MAGN, PRES, TEMP, GSBS, GOBS, POSI frames<br> - IPIN2023_T4_xxx_gnss_ephem.nav : for trajectory estimation.<br> <br> - 02-Supplementary_Materials: This folder contains the datasheet files of the different sensors, a static logfile of about 12 hours that can be used for sensors bias estimation (Allan Variance) and a logfile of about 1 minute that can be used to calibrate the magnetometer sensor (Magnetometer Calibration).</p> <p>- 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on all evaluation points. It requires Matlab Mapping Toolbox. We also provide ground truth of the 2 scoring trials as 2 MAT and KML files. It contains samples of reported estimations and the corresponding results. Just run script_Eval_IPIN2023.mat</p> <p>We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competition</p> <p> </p> <p><strong>Citation Policy:</strong><br> Please, cite the following works when using the datasets included in this package:</p> <p>Ortiz, M.; Zhu, N.; Ziyou L. ; Renaudin, V. Datasets and Supporting Materials for the IPIN 2023 Competition Track 4 (Foot-Mounted IMU based Positioning, offsite-online), Zenodo 2023<br> <a href="https://doi.org/10.5281/zenodo.8399764">https://doi.org/10.5281/zenodo.8399764</a></p> <p>Check the citation policy at: <a href="https://doi.org/10.5281/zenodo.8399764">https://doi.org/10.5281/zenodo.8399764</a></p> <p> </p> <p><strong>Contact:</strong><br> For any further questions about the database and this competition track, please contact:</p> <p> Miguel Ortiz (<a href="mailto:miguel.ortiz@univ-eiffel.fr">miguel.ortiz@univ-eiffel.fr</a>) at the University Gustave Eiffel, France.<br> Ni Zhu (<a href="mailto:ni.zhu@univ-eiffel.fr">ni.zhu@univ-eiffel.fr</a>) at the University Gustave Eiffel, France.</p> <p> </p> <p><strong>Acknowledgements:</strong><br> We thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Joaquín Torres-Sospedra from Universidade do Minho and Francesco Potortì and Antonino Crivello from ISTI-CNR Pisa, for their support in collecting the datasets.</p> <p>We extend our appreciation to the staff at the Museum for Industrial Culture (Museum Industriekultur) for their unwavering patience and invaluable support throughout our collection days.</p>
Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, 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>IPIN2019_Call4Competition: Call for competition and main rules</li><li>IPIN2019_Track03_TechnicalAnnex: Technical annex describing the Track 3 of the competition.</li><li>01-Logfiles: This folder contains a subfolder with the 50 (40 + 10) training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li><li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li><li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li></ol><p><strong>Please, cite the following works when using the datasets included in this package:</strong></p><ul><li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a></li><li>Potorti, F.; Park, S.; Palumbo, F.; Girolami, M.; Barsocchi, P.; Lee, S.; Torres-Sospedra, J.; Jimenez Ruiz, A. R.; Perez-Navarro, A.; Mendoza-Silva, G. M.; Seco, F.; Ortiz, M.; Perul, J.; Renaudin, V.; Kang, H.; Park, S. Y.; Lee, J. H.; Park, C. G.; Ha, J.; Han, J.; Park, C.; KIM, K.; Lee, Y.; GYE, S.; Lee, K.; Kim, E.; Choi, J.-S.; Choi, Y.-S.; Talwar, S.; Cho, S. Y.; Ben-Moshe, B.; Sansano, E.; Chidlovskii, B.; Kronenwett, N.; Prophet, S.; Landay, Y.; Marbel, R.; Peng, A.; Wu, B.; MA, C.; Poslad, S.; Selviah, D.; Wu, W.; Ma, Z.; Zhang, W.; Wei, D.; Yuan, H.; Jiang, J.-B.; Liu, J.-W.; Su, K.-W.; Leu, J.-S.; Nishiguchi, K.; Bousselham, W.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.-I.; Cortés, V.; Lungenstrass, T.; Ashraf, I.; Lee, C.; Usman Ali, M.; Im, Y.; Kim, G.; Eom, J.; Hur, S.; Park, Y.; Opiela, M.; Moreira, A.; Nicolau, M. J.; Pendão, C.; Silva, I.; Meneses, F.; Costa, A.; Trogh, J.; Plets, D.; Chien, Y.-R.; Chang, T.-Y.; Fang, S.-H.; Tsao, Y. The IPIN 2019 Indoor Localisation Competition - Description and Results IEEE Access Vol. 8, pp. 206674-206718, 2020. https://doi.org/10.1109/ACCESS.2020.3037221</li></ul><p><strong>Additional information can be found at:</strong></p><ul><li><a href="http://evaal.aaloa.org/2019/call-for-competitions">http://evaal.aaloa.org/2019/call-for-competitions</a></li></ul><p><strong>For any further questions about the database and this competition track, please contact: </strong></p><ul><li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>,<a href="mailto:torres@ubikgs.com?subject=Contact%20from%20Zenodo%203606765">torres@ubikgs.com</a>) UBIK Geospatial Solutions S.L., Spain<br>Institute of New Imaging Technologies, Universitat Jaume I, Spain.</li><li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.</li></ul>
FORCE 2020 Well well log and lithofacies dataset for machine learning competition
<p>This well log dataset from 118 wells in the Norwegian Sea that has been used in the FORCE 2020 machine learning competition with seismic and wells to predict the lithofacies using machine learning models. </p> <p>The well logs have been slightly cleaned up and partially despiked.</p> <p>The lithofacies and lithology interpretation has been hand crafted using skilled geoscientists (Thanks to Explocrowd for excellent work). For citation in addition to the DOI please also refer to the github repository where the documentation and trained models reside</p> <p><a href="https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition">https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition </a></p> <p> </p> <p>The original well log data comes form the Norwegian government and is provided by a NOLD 2.0 license</p> <p> </p>
ORE 2014 Reasoner Competition Dataset
<p>The ORE 2014 Reasoner Competition Dataset is a set of ontologies, curated for DL Reasoner benchmarking. All files are serialised as OWL Functional syntax.</p>
Test-B1 ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<ul> <li><strong>Test-B1</strong>: a batch of page images annotated with the geometry of regions where to detect text line and recognize.</li> </ul>
Test-B2 ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p><strong>Test-B2</strong>: a batch of page images annotated with the geometry of regions where to detect text line and recognize.</p>
Dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p><strong>Train-A:</strong> Dataset of pages with manually revised baselines and the corresponding transcripts associated to them. This batch is small, 50 pages. Please, keep in mind that only the baselines have been manually corrected, The polygons associated to each line have not been manually reviewed.</p> <p><strong>Train-B:</strong> Dataset of pages without any layout or text line information. The corresponding transcripts are provided at page level with line breaks. It has 10k pages, though for convenience it is divided into two 5k page batches. This information is provided in PAGE format.</p> <p><strong>Test A:</strong> Dataset of pages with manually revised baselines. This batch has 65 pages. The polygons associated to each line have not been manually reviewed.</p> <p><strong>Test-B1:</strong> The same dataset of pages of the Test A, but annotated only with the geometry of regions. Text line information is not provided. </p> <p><strong>Test-B2:</strong> Dataset of page images annotated with the geometry of regions where to detect text line and recognize. It has 57 pages.</p> <p><strong>Baseline.tgz:</strong> Baseline system trained using the first 40 pages of Train-A. The system is based on the deep learning toolkit to transcribe handwritten text images called Laia.</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p> <p> </p>
Dataset (II) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist
<p>MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. <a href="https://doi.org/10.1016/j.csbj.2022.06.020">https://doi.org/10.1016/j.csbj.2022.06.020</a></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</p> <p>dataset I: systems SRL+Co, C-100 and BAY-1797</p> <p>dataset I: each file contains all branched replicas and the main replica.</p> <p>dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).</p> <p>dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.</p> <p>dataset I: system SRL+Co ;each file contains one main replica</p> <p>dataset I: system BAY-1797; each file contains one main replica</p> <p>dataset II:system SRL ; each file contains one main replica.</p>
Dataset (I) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist
<p>MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. <a href="https://doi.org/10.1016/j.csbj.2022.06.020">https://doi.org/10.1016/j.csbj.2022.06.020</a></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</p> <p>dataset I: systems SRL+Co, C-100 and BAY-1797</p> <p>dataset I: each file contains all branched replicas and the main replica.</p> <p>dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).</p> <p>dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.</p> <p>dataset I: system SRL+Co ;each file contains one main replica</p> <p>dataset I: system BAY-1797; each file contains one main replica</p> <p>dataset II:system SRL ; each file contains one main replica.</p>
Dataset for: Mating competition and adult sex ratio in wild Trinidadian guppies
<p><span>Most experimental tests of mating systems theory have been conducted in the laboratory, using operational sex ratios (ratio of ready-to-mate male to ready-to-mate female) that are often not representative of natural conditions. Here, we first measured the range of adult sex ratio (proportion of adult males to adult females; ASR) in two populations of Trinidadian guppies (<em>Poecilia reticulata</em>) differing in ambient predation risk (high vs. low). We then explored, under semi-wild conditions, the effect of ASR (i.e. 0.17, 0.50, 0.83) on mating competition patterns in these populations. ASR in the wild was female-biased and did not significantly differ between the two populations. The range of ASR in our experiment was representative of natural ASRs. As expected, we observed an increase in intrasexual aggression rates in both sexes as the relative abundance of competitors increased. In support of the risky competition hypothesis, all measured behaviors had lower rates in a high vs. low predation-risk population, likely due to the costs of predation. In terms of mating tactics, a male-biased ASR did not lead males to favor forced mating over courtship,</span><span> </span><span>indicating that males did not compensate for the cost of competition by switching to a less costly alternative mating tactic</span><span>. </span><span>Overall, this study highlights the need for field experiments using natural ranges of ASRs to test the validity of mating systems theory in a more complex, ecologically relevant context.</span></p>
Dataset (III) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist
<p>MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. <a href="https://doi.org/10.1016/j.csbj.2022.06.020">https://doi.org/10.1016/j.csbj.2022.06.020</a></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files)</p> <p>dataset I: systems SRL+Co, C-100 and BAY-1797</p> <p>dataset I: each file contains all branched replicas and the main replica.</p> <p>dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b).</p> <p>dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches.</p> <p>dataset I: system SRL+Co ;each file contains one main replica</p> <p>dataset I: system BAY-1797; each file contains one main replica</p> <p>dataset II:system SRL ; each file contains one main replica.</p> <p>dataset III:system C-100+Co (compound 100 in presence of SRC-1 coactivator) ; each file contains one main replica.</p>
ICDAR 2015 Competition HTRtS: Handwritten Text Recognition on the tranScriptorium Dataset Rerelease
<p>A new release of the dataset used in the ICDAR 2015 HTR competition in which all Page XML files are based on the same 2013-07-15 schema. It only contains page level images, Page XML files for train and test (including the ground truth transcripts for the test and train batch 1) and plain text files for train batch 2 that have the page level ground truth transcripts. The original version of this dataset can be found at http://doi.org/10.5281/zenodo.248733<br> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.