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2,090 results for “Competition”
Offshore wind competitiveness in mature markets without subsidy - Supplementary Data
<p>This is the data set named "Supplementary Data 1" for the research paper "Offshore wind competitiveness in mature markets without subsidy". This data set also contains the raw data for reproducing Figure 1 through to Figure 4. The paper is currently under review and access is for peer-review purposes only.</p>
Figure 1. A in The green June beetle (Cotinis nitida) (Coleoptera: Scarabaeidae): local variation in the beetle's major avian predators and in the competition for mates
Figure 1. A stack of males piled up on the back of a receptive female. Note that the top male is facing in a different direction from the others and that he has everted his aedeagus, a sign of his strong sexual motivation.
Within-population sperm competition intensity does not predict asymmetry in conpopulation sperm precedence
Postcopulatory sexual selection can generate evolutionary arms races between the sexes resulting in the rapid coevolution of reproductive phenotypes. As traits affecting fertilization success diverge between populations, postmating prezygotic (PMPZ) barriers to gene flow may evolve. Conspecific sperm precedence is a form of PMPZ isolation thought to evolve early during speciation yet has mostly been studied between species. Here , we show conpopulation sperm precedence (CpSP) between Drosophila montana populations. Using Pool-seq genomic data we estimate divergence times and ask whether PMPZ isolation evolved in the face of gene flow. We find models incorporating gene flow fit the data best indicating populations experienced considerable gene flow during divergence. We find CpSP is asymmetric and mirrors asymmetry in non-competitive PMPZ isolation, suggesting these phenomena have a shared mechanism. However, we show asymmetry is unrelated to the strength of postcopulatory sexual selection acting within populations. We tested whether overlapping foreign and coevolved ejaculates within the female reproductive tract altered fertilization success but found no effect. Our results show that neither time since divergence nor sperm competitiveness predicts the strength of PMPZ isolation. We suggest that instead cryptic female choice or mutation-order divergence may drive divergence of postcopulatory phenotypes resulting in PMPZ isolation. This article is part of the theme issue 'Fifty years of sperm competition'.
Datasets and Supporting Materials for the MALIN-ANR 2019 Competition (French national research agency)
<p>This "ZENODO deposit" provides a multiple sensor dataset collected by the CyborgLOC team during the intermediate competition of the Challenge MALIN (<em>MA</em><em>îtrise</em><em> de la </em><em>L</em><em>ocalisation </em><em>IN</em><em>door</em>), which is a competition for indoor/outdoor real-time positioning. The sensors, including a GNSS receiver Ublox NEO-M8N, a Realsense D435i stereo camera, three Xsens MTi-300 and one PERSY (<strong>PE</strong>destrian <strong>R</strong>eference <strong>SY</strong>stem), are mounted on different parts of the subject’s body. The PERSY is a foot-mounted positioning device with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer as well as a GNSS receiver Ublox M8T. The two scenarios are designed in a training center of firefighters CFIS (Fire and Rescue Training Center) in Blois, France to simulate the situation of firefighters during interventions. With total distances around 2 km for each scenario, the travelled trajectories passed through challenging environments including indoor, outdoor, urban canyon. The indoor part contains different stair levels, from the underground up to the 6th floor. The travel modes are vehicles and pedestrians. Several classical activities of firefighters are realized such as walking, running, stair-climbing, side-walking, crawling, passing above/below obstacles, carrying a stretcher, ladder climbing, etc. High accurate ground truth of stationary points and enclosing volumes are provided by the organizers of the competition, i.e., the French Ministry of Defense (DGA: Direction Générale de l’Armement). Provided with raw data, they allow the evaluation of the positioning performances.</p> <p>To facilitate the use of our dataset under Rosbag format, a toolkit of python scripts named <em>MALIN Data Processing Tools</em> is provided on GitHub (<a href="https://github.com/4g-group/malin_data_processing_tools">https://github.com/4g-group/malin_data_processing_tools</a>). It allows merging Rosbags, converting Rosbag files to CSV files as well as republishing camera’s topics as decompressed data. Details about these processing tools could be found in the Readme file on the Github page. </p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</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>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, 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> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 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.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </p>
Behavioral data and analyses of competitive interactions between invasive and native ant species [from Cordonnier et al. 2021, Animals]
<p>This README accompanies the files "data_Cordonnier_Animals.txt" & "script_Cordonnier_Animals.txt"</p> <p> </p> <p>Associated publication : </p> <p>The native ant <em>Lasius niger</em> can limit the access to resources of the invasive Argentine ant</p> <p>M. Cordonnier, O. Blight, E. Angulo, and F. Courchamp</p> <p>Published in <em>Animals</em></p> <p> <br> ********************************** CONTENTS *****************************<br> The data are in table form with TABs as variables field delimiters so they can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted otherwise. </p> <p> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p> </p> <p>Status_Lh status of Linepithema humile (Colonizer or Resident) </p> <p>opp species of the opponent</p> <p>combirc combination of status and species interacting</p> <p>temp temperature during the test</p> <p>hygro hygrometry during the test</p> <p>categ interacting species combination</p> <p>n_deadtot_opp total number of dead opponent workers</p> <p>t_50dead_opp time when 50% of the opponent mortality load have been diagnosed</p> <p>t_interact time of the first interaction between L. humile and opponent workers</p> <p>t_maxfights time when the maximal number of simultaneous fights occurs</p> <p>ET_fights standard deviation of the numbers of fights over time</p> <p>mean_fights mean number of simultaneous fights during the contest</p> <p>n_deadtot_Lh total number of dead workers of L. humile</p> <p>t_50dead_Lh time when 50% of the L. humile mortality load have been diagnosed</p> <p>t_arena_opp time of the opponent entrance in the arena</p> <p>t_bait_opp time of opponent resources’ discovery</p> <p>t_maxarena_opp time when the max. number of opponent workers occurs in the arena</p> <p>mean_arena_opp mean number of opponent workers simultaneously present in the whole arena</p> <p>t_maxbait_opp time when the maximal number of opponent workers on the bait occurs</p> <p>mean_bait_opp mean number of opponent workers on the bait</p> <p>t_arena_Lh time of the entrance in the arena of L. humile</p> <p>t_maxarena_Lh time when the max. number of workers of L. humile occurs in the arena</p> <p>mean_arena_Lh mean number of L. humile workers simultaneously present in the whole arena</p> <p>n_totprey_Lh total number of preys brought by L. humile</p> <p>t_bait_Lh time of resources’ discovery by L. humile</p> <p>t_maxbait_Lh time when the maximal number of L. humile individuals on the bait occurs</p> <p>ETbait_Lh standard deviation of the numbers of L. humile workers on the bait over time</p> <p>mean_bait_Lh mean number of L. humile workers on the bait</p> <p>t_50prey_Lh time when 50% of the final prey load</p> <p> </p> <p>******************************** CONTACT *********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************</p> <p> </p>
Results of the 10th Intl. Competition on Software Verification (SV-COMP 2021)
<p>Competition Results</p> <p>This file describes the contents of an archive of the 10th Competition on Software Verification (SV-COMP 2021).<br> <a href="https://sv-comp.sosy-lab.org/2021/">https://sv-comp.sosy-lab.org/2021/</a></p> <p>The competition was run by Dirk Beyer, LMU Munich, Germany.<br> More information is available in the following article:<br> Dirk Beyer. <em>Software Verification: 10th Comparative Evaluation (SV-COMP 2021).</em> In Proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2021, Luxembourg, March 27 - April 1), 2021. Springer.</p> <p>Copyright (C) Dirk Beyer<br> <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p> <p>SPDX-License-Identifier: CC-BY-4.0<br> <a href="https://spdx.org/licenses/CC-BY-4.0.html">https://spdx.org/licenses/CC-BY-4.0.html</a></p> <p>To browse the competition results with a web browser, there are two options:</p> <ul> <li>start a local web server using php -S localhost:8000 in order to view the data in this archive, or</li> <li>browse https://sv-comp.sosy-lab.org/2021/results/ in order to view the data on the SV-COMP web page.</li> </ul> <p>Contents</p> <ul> <li><code>index.html</code>: directs to the overview web page</li> <li><code>LICENSE.txt</code>: specifies the license</li> <li><code>README.txt</code>: this file</li> <li><code>results-validated/</code>: results of validation runs</li> <li><code>results-verified/</code>: results of verification runs and aggregated results</li> </ul> <p>The folder <code>results-validated/</code> contains the results from validation runs:</p> <ul> <li><code>*.xml.bz2</code>: XML results from BenchExec</li> <li><code>*.logfiles.zip</code>: output from tools</li> <li><code>*.json.gz</code>: mapping from files names to SHA 256 hashes for the file content</li> </ul> <p>The folder <code>results-verified/</code> contains the results from verification runs and aggregated results:</p> <ul> <li><code>index.html</code>: overview web page with rankings and score table</li> <li><code>*.xml.bz2</code>: XML results from BenchExec</li> <li><code>*.merged.xml.bz2</code>: XML results from BenchExec, status adjusted according to the validation results</li> <li><code>*.logfiles.zip</code>: output from tools</li> <li><code>*.json.gz</code>: mapping from files names to SHA 256 hashes for the file content</li> <li><code>*.xml.bz2.table.html</code>: HTML views on the detailed results data as generated by BenchExec’s table generator</li> <li><code>*.All.table.html</code>: HTML views of the full benchmark set (all categories) for each tool</li> <li><code>META_*.table.html</code>: HTML views of the benchmark set for each meta category for each tool, and over all tools</li> <li><code><category>*.table.html</code>: HTML views of the benchmark set for each category over all tools</li> <li><code>iZeCa0gaey.html</code>: HTML views per tool</li> <li> <p><code>validatorStatistics.html</code>: Statictics of the validator runs</p> </li> <li><code>quantilePlot-*</code>: score-based quantile plots as visualization of the results</li> <li><code>quantilePlotShow.gp</code>: example Gnuplot script to generate a plot</li> <li> <p><code>score*</code>: accumulated score results in various formats</p> </li> </ul> <p>The hashes of the file names (in the files *.json.gz) are useful for</p> <ul> <li>validating the exact contents of a file and</li> <li>accessing the files from the witness store.</li> </ul> <p>Other Archives</p> <p>Overview over archives from SV-COMP 2021 that are available at Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4459196">https://doi.org/10.5281/zenodo.4459196</a> Witness store (containing the generated verification witnesses)</li> <li><a href="https://doi.org/10.5281/zenodo.4458215">https://doi.org/10.5281/zenodo.4458215</a> Results (XML result files, log files, file mappings, HTML tables)</li> <li><a href="https://doi.org/10.5281/zenodo.4459126">https://doi.org/10.5281/zenodo.4459126</a> Verification tasks, version svcomp21</li> <li><a href="https://doi.org/10.5281/zenodo.4317433">https://doi.org/10.5281/zenodo.4317433</a> BenchExec, version 3.6</li> </ul> <p>All benchmarks were executed for SV-COMP 2021 <a href="https://sv-comp.sosy-lab.org/2021/">https://sv-comp.sosy-lab.org/2021/</a><br> by Dirk Beyer, LMU Munich, based on the following components:</p> <ul> <li><a href="https://gitlab.com/sosy-lab/sv-comp/archives-2021">https://gitlab.com/sosy-lab/sv-comp/archives-2021</a> svcomp21-0-g08c7a98</li> <li><a href="https://gitlab.com/sosy-lab/software/sv-benchmarks">https://gitlab.com/sosy-lab/software/sv-benchmarks</a> svcomp21-0-g4cc6b6d96a</li> <li><a href="https://gitlab.com/sosy-lab/software/benchexec">https://gitlab.com/sosy-lab/software/benchexec</a> 3.6-0-gb278ebbb</li> <li><a href="https://gitlab.com/sosy-lab/benchmarking/competition-scripts">https://gitlab.com/sosy-lab/benchmarking/competition-scripts</a> svcomp21-0-g8339740</li> <li><a href="https://gitlab.com/sosy-lab/sv-comp/bench-defs">https://gitlab.com/sosy-lab/sv-comp/bench-defs</a> svcomp21-0-ga57fe48</li> </ul> <p>Contact</p> <p>Feel free to contact me in case of questions: <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p>
Results of the 3rd Intl. Competition on Software Testing (Test-Comp 2021)
<p>Competition Results</p> <p>This file describes the contents of an archive of the 3rd Competition on Software Testing (Test-Comp 2021).<br> <a href="https://test-comp.sosy-lab.org/2021/">https://test-comp.sosy-lab.org/2021/</a></p> <p>The competition was run by Dirk Beyer, LMU Munich, Germany.<br> More information is available in the following article:<br> Dirk Beyer. <em>Status Report on Software Testing: Test-Comp 2021.</em> In Proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering (FASE 2021, Luxembourg, March 27 - April 1), 2021. Springer.</p> <p>Copyright (C) Dirk Beyer<br> <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p> <p>SPDX-License-Identifier: CC-BY-4.0<br> <a href="https://spdx.org/licenses/CC-BY-4.0.html">https://spdx.org/licenses/CC-BY-4.0.html</a></p> <p>To browse the competition results with a web browser, there are two options:</p> <ul> <li>start a local web server using php -S localhost:8000 in order to view the data in this archive, or</li> <li>browse <a href="https://test-comp.sosy-lab.org/2021/results/">https://test-comp.sosy-lab.org/2021/results/</a> in order to view the data on the Test-Comp web page.</li> </ul> <p>Contents</p> <ul> <li><code>index.html</code>: directs to the overview web page</li> <li><code>LICENSE.txt</code>: specifies the license</li> <li><code>README.txt</code>: this file</li> <li><code>results-validated/</code>: results of validation runs</li> <li><code>results-verified/</code>: results of test-generation runs and aggregated results</li> </ul> <p>The folder <code>results-validated/</code> contains the results from validation runs:</p> <ul> <li><code>*.xml.bz2</code>: XML results from BenchExec</li> <li><code>*.logfiles.zip</code>: output from tools</li> <li><code>*.json.gz</code>: mapping from files names to SHA 256 hashes for the file content</li> </ul> <p>The folder <code>results-verified/</code> contains the results from test-generation runs and aggregated results:</p> <ul> <li><code>index.html</code>: overview web page with rankings and score table</li> <li><code>design.css</code>: HTML style definitions</li> <li><code>*.xml.bz2</code>: XML results from BenchExec</li> <li><code>*.merged.xml.bz2</code>: XML results from BenchExec, status adjusted according to the validation results</li> <li><code>*.logfiles.zip</code>: output from tools</li> <li><code>*.json.gz</code>: mapping from files names to SHA 256 hashes for the file content</li> <li><code>*.xml.bz2.table.html</code>: HTML views on the detailed results data as generated by BenchExec’s table generator</li> <li><code>*.All.table.html</code>: HTML views of the full benchmark set (all categories) for each tool</li> <li><code>META_*.table.html</code>: HTML views of the benchmark set for each meta category for each tool, and over all tools</li> <li><code><category>*.table.html</code>: HTML views of the benchmark set for each category over all tools</li> <li> <p><code>iZeCa0gaey.html</code>: HTML views per tool</p> </li> <li><code>quantilePlot-*</code>: score-based quantile plots as visualization of the results</li> <li><code>quantilePlotShow.gp</code>: example Gnuplot script to generate a plot</li> <li> <p><code>score*</code>: accumulated score results in various formats</p> </li> </ul> <p>The hashes of the file names (in the files <code>*.json.gz</code>) are useful for</p> <ul> <li>validating the exact contents of a file and</li> <li>accessing the files from the witness store.</li> </ul> <p>Other Archives</p> <p>Overview over archives from Test-Comp 2021 that are available at Zenodo:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4459466">https://doi.org/10.5281/zenodo.4459466</a> Witness store (containing the generated test suites)</li> <li><a href="https://doi.org/10.5281/zenodo.4459470">https://doi.org/10.5281/zenodo.4459470</a> Results (XML result files, log files, file mappings, HTML tables)</li> <li><a href="https://doi.org/10.5281/zenodo.4459132">https://doi.org/10.5281/zenodo.4459132</a> Test tasks, version testcomp21</li> <li><a href="https://doi.org/10.5281/zenodo.4317433">https://doi.org/10.5281/zenodo.4317433</a> BenchExec, version 3.6</li> </ul> <p>All benchmarks were executed for Test-Comp 2021 <a href="https://test-comp.sosy-lab.org/2021/">https://test-comp.sosy-lab.org/2021/</a><br> by Dirk Beyer, LMU Munich, based on the following components:</p> <ul> <li><a href="https://gitlab.com/sosy-lab/test-comp/archives-2021">https://gitlab.com/sosy-lab/test-comp/archives-2021</a> testcomp21-0-gdacd4bf</li> <li><a href="https://gitlab.com/sosy-lab/software/sv-benchmarks">https://gitlab.com/sosy-lab/software/sv-benchmarks</a> testcomp21-0-gefea738258</li> <li><a href="https://gitlab.com/sosy-lab/software/benchexec">https://gitlab.com/sosy-lab/software/benchexec</a> 3.6-0-gb278ebbb</li> <li><a href="https://gitlab.com/sosy-lab/benchmarking/competition-scripts">https://gitlab.com/sosy-lab/benchmarking/competition-scripts</a> testcomp21-0-g8339740</li> <li><a href="https://gitlab.com/sosy-lab/test-comp/bench-defs">https://gitlab.com/sosy-lab/test-comp/bench-defs</a> testcomp21-0-g9d532c9</li> </ul> <p>Contact</p> <p>Feel free to contact me in case of questions: <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p>
Competitive growth experiments with a high-lipid Chlamydomonas reinhardtii mutant strain and its wild-type to predict industrial and ecological risks
<p>Key microalgal species are currently being exploited as biomanufacturing platforms using mass cultivation systems. The opportunities to enhance productivity levels or produce non-native compounds are increasing as genetic manipulation and metabolic engineering tools are rapidly advancing. Regardless of the end product, there are both environmental and industrial risks associated to open pond cultivation of mutant microalgal strains. A mutant escape could be detrimental to local biodiversity and increase the risk of algal blooms. Similarly, if the cultivation pond is invaded by a wild-type microalgae or the mutant reverts to wild-type phenotypes, productivity could be impacted. To investigate these potential risks, a response surface methodology was applied to determine the competitive outcome of two <em>Chlamydomonas reinhardtii</em> strains, a wild-type (CC-124) and a high-lipid accumulating mutant (CC-4333), grown in mixotrophic conditions, with differing levels of nitrogen and initial wild-type to mutant ratios. Results of the growth experiments show that mutant cells have double the exponential growth rate of the wild-type in monoculture. However, due to a slower transition from lag phase to exponential phase, mutant cells are outcompeted by the wild-type in every co-culture treatment. This suggests that, under the conditions tested, outdoor cultivation of the <em>C. reinhardtii</em> cell wall-deficient mutant strains does not carry a significant environmental risk to its wild-type in an escape scenario. Furthermore, lipid results show the mutant strain accumulates over 200% more TAGs per cell, at 50 mg/L NH<sub>4</sub>Cl, compared to the wild-type, therefore, the fragility of the mutant strain could impact on overall industrial productivity.</p>
ICDAR 2015 Competition HTRtS: Handwritten Text Recognition on the tranScriptorium Dataset
<p>This dataset comprises the dataset used for the ICDAR 2015 Competition on Handwritten Text Recognition on the tranScriptorium Dataset. The handwritten images for this contest were drawn from the English “Bentham collection” dataset used in the TRAN SCRIPTORIUM project. The selected data has been written by several hands and entails significant variabilities and difficulties regarding the quality of text images, writing styles and crossed-out text. This contest is clearly more difficult than the the first edition both for training and for testing. A portion of the training dataset and the full test dataset were provided in the form of carefully segmented line images, along with the corresponding transcripts. Another portion of the training dataset was provided as raw images and their corresponding transcripts at region level.<br> </p> <p>ICDAR 2015 competition HTRtS: handwritten text recognition on the tranScriptorium dataset<br> JA Sánchez, AH Toselli, V Romero, E Vidal. In International Conference on Document Analysis and Recognition (ICDAR), pp. 1166-1170, 2015.</p>
Raw data for CPC2015: A Choice Prediction Competition for decisions under risk, under Ambiguity, and from experience
<p>This is the raw data collected as part of a choice prediction competition organized by the three authors for decisions under risk, under ambiguity, and from experience. It includes over 330,000 consequential choices of human participants between two risky and/or uncertain prospects. More details about the competition and the data can be found in http://departments.agri.huji.ac.il/cpc2015.</p> <p>If you use this data, please cite the paper describing the competition: Erev, I., Ert, E., Plonsky, O., Cohen, D., & Cohen, O. (2017). From anomalies to forecasts: Toward a descriptive model of decision under risk, under ambiguity, and from experience. <em>Psychological Review,</em> 124(4), 369-409.<em> </em>DOI: 10.1037/rev0000062</p>
Train-B dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR). Batch 1 and Batch 2.
<p>Train-B Dataset. 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>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439807#.WOIBZ3WLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p> <p> </p>
Train-A dataset for ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset (ICDAR2017 HTR)
<p>Train-A 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>This dataset is complementary to this other dataset:</p> <p>https://zenodo.org/record/439811#.WOIF9HWLSkA</p> <p>More information at:</p> <p>https://scriptnet.iit.demokritos.gr/competitions/~icdar2017htr/</p>
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</p>
Intensive male competition caused severity of trauma in female genital tracts predicts female reproductive success and longevity in strictly monandrous wolf spiders
<p>This is the raw data for the manuscript of Dr. Shichang Zhang from Hubei University entitled: <strong>Intensive male competition caused severity of trauma in female genital tracts predicts female reproductive success and longevity in strictly monandrous wolf spiders. </strong></p>
Code and sequence data pertaining to: A phylogenomic perspective on interspecific competition
<p>Evolutionary processes may have substantial impacts on community assembly, but evidence for phylogenetic relatedness as a determinant of interspecific interaction strength remains mixed. In this perspective, we consider a possible role for discordance between gene trees and species trees in the interpretation of phylogenetic signal in studies of community ecology. Modern genomic data show that the evolutionary histories of many taxa are better described by a patchwork of histories that vary along the genome rather than a single species tree. If a subset of genomic loci harbor trait-related genetic variation, then the phylogeny at these loci may be more informative of interspecific trait differences than the genome background. We develop a simple method to detect loci harboring phylogenetic signal and demonstrate its application through a proof of principle analysis of Penicillium genomes and pairwise interaction strength. Our results show that phylogenetic signal that may be masked genome-wide could be detectable using phylogenomic techniques and may provide a window into the genetic basis for interspecific interactions.</p>
Experimental evolution under varying sex ratio and behavioral plasticity in response to perceived competitive environment independently affect calling effort in male crickets
<p>The operational sex ratio (OSR) is a key component influencing the magnitude of sexual selection driving the evolution of male sexual traits, but males often also retain the ability to plastically modulate trait expression depending on the current environment. Here we employed an experimental evolution approach to determine whether the OSR affects the evolution of male calling effort in decorated crickets, a costly sexual trait, and whether plasticity in calling effort is altered by the OSR under which males have evolved. Calling effort of males from two selection regimes maintained at different OSRs over 18–20 generations (male- versus female-biased) was recorded at two different levels of perceived competition, in the absence of rivals or in the presence of an experimentally muted competitor. The effect of the OSR on the evolution of male calling effort was modest and in the opposite direction predicted by theory. Instead, the immediate competitive environment strongly influenced male calling effort as males called more in the presence of a rival, revealing considerable plasticity in this trait. This increased calling effort came at a cost, however, as males confined with a muted rival experienced significantly higher mortality.</p>
An integrated population model reveals source-sink dynamics for competitively subordinate African wild dogs linked to anthropogenic prey depletion
<ol> <li>Many African large carnivore populations are declining due to decline of the herbivore populations on which they depend. The densities of apex carnivores like the lion and spotted hyena correlate strongly with prey density, but competitive subordinates like the African wild dog benefit from competitive release when the density of apex carnivores is low, so the expected effect of a simultaneous decrease in resources and dominant competitors is not obvious. </li> <li>Wild dogs in Zambia's Luangwa Valley Ecosystem occupy four ecologically similar areas with well-described differences in the densities of prey and dominant competitors, due to spatial variation in illegal offtake.</li> <li>We used long-term data to fit a Bayesian integrated population model (IPM) of the demography and dynamics of wild dogs in these four regions. The IPM used Leslie projection to link a Cormack-Jolly-Seber model of area-specific survival (allowing for individual heterogeneity in detection), a zero-inflated Poisson model of area-specific fecundity, and a state-space model of population size that used estimates from a closed mark-capture model as the counts from which (latent) population size was estimated.</li> <li>The IPM showed that both survival and reproduction were lowest in the region with the lowest density of preferred prey (puku, <em>Kobus vardonii</em>, and impala, <em>Aepyceros</em> <em>melampus</em>), despite little use of this area by lions. Survival and reproduction were highest in the region with the highest prey density, and intermediate in the two regions with intermediate prey density. The population growth rate (λ) was positive for the population as a whole, strongly positive in the region with the highest prey density, and strongly negative in the region with the lowest prey density.</li> <li>It has long been thought that the benefits of competitive release protect African wild dogs from the costs of low prey density. Our results show that the costs of prey depletion overwhelm the benefits of competitive release and cause local population decline where anthropogenic prey depletion is strong. Because competition is important in many guilds and humans are affecting resources of many types, it is likely that similarly fundamental shifts in population limitation are arising in many systems.</li> </ol>
Data for: Microbe-induced plant resistance alters aphid inter-genotypic competition leading to rapid evolution with consequences for plant growth and aphid abundance
<p>Plants and insect herbivores are two of the most diverse multicellular groups in the world, and both are strongly influenced by interactions with the belowground soil microbiome. Effects of reciprocal rapid evolution on ecological interactions between herbivores and plants have been repeatedly demonstrated, but it is unknown if (and how) the soil microbiome could mediate these eco-evolutionary processes on a shared host plant. We tested the role of a plant-beneficial soil bacterium (<em>Acidovorax radicis</em>) in altering eco-evolutionary interactions between different aphid genotypes (Sitobion avenae; genotypes Sickte and Fescue) feeding on barley (<em>Hordeum vulgare</em>). We measured fecundity, longevity and population growth of two aphid genotypes reared separately or together (population mixture) on three different barley varieties that were inoculated with or without <em>A. radicis</em>. Results showed that across all plant varieties <em>A. radicis</em> increased plant growth and suppressed aphid populations via reduced longevity and fecundity. The strength of effect was dependent on aphid genotype and barley variety, while the direction of effect was altered by aphid population mixture. Using Lotka-Volterra modelling, we demonstrated that while <em>A. radicis</em> inoculation decreased growth rates for both aphid genotypes it increased the competitiveness of one genotype against the other. In general, in the presence of <em>A. radicis</em>, the Fescue aphid genotype became more inhibitory of Sickte aphids, while Sickte aphids facilitated the growth of Fescue aphids. Our work demonstrates that plant rhizosphere microbiomes exert community-level influences by mediating eco-evolutionary interactions between herbivores and host plants. By altering competitive interaction outcomes among aphids and thus impacting processes such as rapid evolution, soil microbes contribute to the short- and long-term structure and functioning of terrestrial habitats.</p>
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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.