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2,090 results for “competitiveness”
H2020 Prime Fish Firm level competitiveness data Iceland Norway Newfoundland
<p>The data set contains survey data from three Norwegian, one Icelandic, one Newfoundland fish processing firm. Data are collected as part of the EU H2020 project PrimeFish (grant no 635761). The survey asks several questions concerning the firm’s evaluation of several aspects of competitiveness, following a Porter framework. The questions posed to the respondents are stated along with scoring help. All data are numeric, and on a 1-7 scale. Data were collected from the World Economic Forum 2017 competitiveness report and surveys and hard data collected in 2017.</p>
Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2017 Competition (Sapporo, Japan).</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 25 training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 7 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/Octave parser, the raster maps, the visualization of the training routes and the location of the BLE beacon (CAR) and some Wi-Fi APs (UJIUB).</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 505 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. R.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.M.; Seco, F.; Perez-Navarro, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.P.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R. and Tsao, Y. Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Sensors Vol. 18(2), 2018. <a href="http://dx.doi.org/10.3390/s18020487">http://dx.doi.org/10.3390/s18020487</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823924">http://dx.doi.org/10.5281/zenodo.2823924</a> </li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2017/2017-competition-home">http://evaal.aaloa.org/2017/2017-competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2017track3/">http://indoorloc.uji.es/ipin2017track3/</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><br> </p>
Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).</p> <p><strong>Contents:</strong></p> <ol> <li>IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) 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> <li>03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. 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>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389</li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2018/call-for-competitions">http://evaal.aaloa.org/2018/call-for-competitions</a></li> <li><a href="http://ipin-conference.org/2018/ipincompetition/">http://ipin-conference.org/2018/ipincompetition/</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>
Test results for Competition for CPU resources experiments
Open the record for dataset details and reuse information.
An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021
<p>The dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).</p> <p> </p> <p><strong>For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.</strong></p>
The ICDAR 2003 Informal Competition for the Recognition of On-line Words: The Unipen-ICROW-03 benchmark set - Version 0.0
<p>Proposal for an informal benchmark on word recognition. See for the related ImUnipen collection<br> of word images from on-line vectorial handwriting data: https://zenodo.org/record/1195059</p> <p>At the time (ICDAR 2003) there was not a lot of interest so the project was not pursued.</p> <p>Lambert Schomaker - February 2023</p> <p>_______________________________________________________________________________</p> <p>The ICDAR 2003 Informal Competition for the Recognition of On-line Words:<br> The Unipen-ICROW-03 benchmark set <br> Version 0.0</p> <p>Lambert Schomaker / International Unipen Foundation</p> <p>The ICROW suite of test files for the recognition of isolated on-line<br> free-style (handprint, mixed and cursive) words has been<br> composed. Different tablets, nationalities and languages<br> are involved. Only the ASCII set is used within word labels.</p> <p>The set contains:</p> <p> 13119 written words<br> 884 unique lexical word entries<br> 72 writers </p> <p>Language: Dutch, English, Italian.<br> Nationalities: Dutch, Irish, Italian, + mixed</p> <p>The benchmark test is a good estimator for <br> "walk-up" recognition performance.</p> <p>[Note: some of the writers (NIC-Pc95*.dat set) are present in the<br> UNIPEN R01/V07 distribution, but the actual words are unseen <br> outside of the Int. Unipen Foundation.]</p> <p>Please note the Copyright notice in the <br> accompanying file 'Copyright'</p> <p>Wed Jul 16 21:20:10 CEST 2003</p> <p>Lambert Schomaker</p> <p>---------------------------------------------------------------------------</p> <p>Instructions for the ICDAR 2003 informal competition for<br> the recognition of on-line words.</p> <p>1 - unpack the .tgz file<br> 2 - use the UNIPEN files as input for your recognizer.<br> 3 - report, for each writer, a file <writer-id>.res</p> <p> Example: do-my-recognizer < NIC-Hi93b-marc.dat > NIC-Hi93b-marc.res</p> <p>Format of the .res file.</p> <p>No XML for this moment: simplicity does it.</p> <p>We assume that the recognizer is able to produce a top-10 list<br> of likely words, sorted from most likely to least likely.<br> The output for each word is on a single line. The correct<br> target word is in the first column.</p> <p><targetword 1> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp><br> <targetword 2> <best word hyp.> <2nd-best word hyp.> ... <10th-best word hyp></p> <p>Example with two words:</p> <p>summertime slumbertime slipknot summertime somatome spumante simulative semitone schoolmate sermonette semimature<br> Aberdeen Adamson Aberdeen Addison Armageddon Abyssinian Araban Albanian Alabamian Abraham Adelaide</p> <p><br> 4 - pack the *.res files in a .tgz or .zip file and send them<br> to schomaker@ai.rug.nl<br> All *.dat files need to be processed.</p> <p>LS.<br> </p> <p> </p>
DUDE competition train - validation - test splits ground truth
<p>This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/).</p> <p> </p> <p><strong>V1.0.7 release</strong>: 41454 annotations for 4974 documents (train-validation-test)</p> <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23728 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6315 }) test: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 11402 }) }) ++update on answer_type +++formatting change to answers_variants ++++stricter check on answer_variants & rename annotations file <strong>+ blind test set (no ground truth answers provided) </strong>++ removed duplicates from test set: </pre> <blockquote> <p> "92bd5c758bda9bdceb5f67c17009207b_ac6964cbdf483e765b6668e27b3d0bc4",</p> <p> "6ee71a16d4e4d1dbd7c1f569a92d4e08_549f2a163f8ff3e9f0293cf59fdd98bc",</p> <p> "e6f3855472231a7ca6aada2f8e85fe5a_827c03a72f2552c722f2c872fd7f74c3",</p> <p> "e3eecd7cca5de11f1d17cd94ae6a8d77_6300df64e4cf6ba0600ac81278f68de2",</p> <p> "107b4037df8127a92ee4b6ae9b5df8fb_d7a60e7a9fc0b27487ea39cd7f56f98e",</p> <p> "300cc3900080064d308983f958141232_6a7cf1aad908d58a75ab8e02ddc856f4",</p> <p> "fdd3308efacddb88d4aa6e2073f481d4_138cb868ecc804a63cc7a4502c0009b2",</p> <p> "1f7de256ff1743d329a8402ba0d132e7_95b6e8758533a9817b9f20a958e7b776",</p> <p> "4f399b8c526ffb6a2fd585a18d4ed5ec_51097231bc327c26c59a4fd8d3ff3069",</p> </blockquote> <p> </p>
Electoral competition and strategic intra-coalition oversight in parliament: the case of the bipolar Belgian polity (replication data)
<p>Replication dataset for B. de Vet (2023). Electoral competition and strategic intra-coalition oversight in parliament: the case of the bipolar Belgian polity <em>(In: Political Studies Review)</em></p>
Datasets used for the manuscript: "Sibling competition, dispersal and fitness outcomes in humans"
<p>Datasets used for the manuscript: “Sibling competition, dispersal and fitness outcomes in humans”, 10.1038/s41598-023-33700-3</p>
Datasets and Supporting Materials for the IPIN 2023 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2023 Competition.</p><p><strong>Contents</strong></p><ul><li><i>Track-3_TA-2023.pdf: </i>Technical annexe describing the competition (Version 2)</li><li><i>01 Logfiles: </i>This folder contains a subfolder with the 54 training trials, a subfolder with the 4 testing trials (validation), and a subfolder with the 2 blind scoring trials (test) as provided to competitors.</li><li><i>02 Supplementary_Materials: </i>This folder contains the Matlab/octave parser, the raster maps, the files for the Matlab tools and the trajectory visualization.</li><li><i>03 Evaluation: </i>This folder contains the scripts we used to calculate the competition metric, the 75th percentile on the 69 evaluation points. It requires the Matlab Mapping Toolbox. We also provide the ground truth as 2 CSV files. It contains samples of reported estimations and the corresponding results.</li></ul><p>We provide additional information on the competition at: https://evaal.aaloa.org/2023/call-for-competition</p><p><strong>Citation Policy</strong> </p><p>Please cite the following works when using the datasets included in this package:</p><p><i>Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2023</i><br><i>Competition Track 3 (Smartphone-based, off-site), Zenodo 2023</i><br><i>http://dx.doi.org/10.5281/zenodo.8362205</i></p><p>Check the updated citation policy at: http://dx.doi.org/10.5281/zenodo.8362205</p><p><strong>Contact</strong></p><p>For any further questions about the database and this competition track, please contact: </p><p>Joaquín Torres-Sospedra <br>Centro ALGORITMI,<br>Universidade do Minho, Portugal<br>info@jtorr.es - jtorres@algoritmi.uminho.pt<br> <br>Antonio R. Jiménez <br>Centre of Automation and Robotics (CAR)-CSIC/UPM, Spain <br>antonio.jimenez@csic.es</p><p>Antoni Pérez-Navarro<br>Faculty of Computer Sciences, Multimedia and Telecommunication, Universitat Oberta de Catalunya, Barcelona, Spain<br>aperezn@uoc.edu</p><p><strong>Acknowledgements</strong></p><p>We thank Maximilian Stahlke and Christopher Mutschler at Fraunhofer ISS, as well as Miguel Ortiz and Ziyou Li at Université Gustave Eiffel, for their invaluable support in collecting the datasets. And last but certainly not least, Antonino Crivello and Francesco Potortì for their huge effort in georeferencing the competition venue and evaluation points.</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><p>We are also grateful to Francesco Potortì, the ISTI-CNR team (Paolo, Michele & Filippo), and the Fraunhofer IIS team (Chris, Tobi, Max, ...) for their invaluable commitment to organizing and promoting the IPIN competition.</p><p>This work and competition belong to the IPIN 2023 Conference in Nuremberg (Germany). </p><p>Parts of this work received the financial support received from projects and grants: </p><ul><li>ORIENTATE (H2020-MSCA-IF-2020, Grant Agreement 101023072)</li><li>GeoLibero (from CYTED)</li><li>INDRI (MICINN, ref. PID2021-122642OB-C42, PID2021-122642OB-C43, PID2021-122642OB-C44, MCIU/AEI/FEDER UE)</li><li>MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)</li><li>TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER)</li><li>SmartLoc(CSIC-PIE Ref.201450E011)</li><li>LORIS (TIN2012-38080-C04-04)</li></ul>
Flow discharge impact competition for food and shelter between two overlapping species of crayfish
Competition between aquatic organisms is heavily influenced by abiotic factors in the environment, specifically flow regime in aquatic systems. Flow regime has been shown to significantly affect the way in which a species uses the environmental resources and alterations in flow can exasperate competitive advantages by congenerics. However, little work has concentrated on the competitive outcome between native and invasive organisms as a function of flow regime. Here, we sought to uncover how competition between two crayfish species (the native virile crayfish [Faxonius virilis] and invasive rusty crayfish [Faxonius rusticus]) was affected by varying flow rates. To do this, we size-matched crayfish and quantified three separate behaviors (food use, shelter use, and fights) of crayfish in four different discharge levels (no discharge—0 cm3/s; low discharge—116 cm3/s; intermediate discharge—345 cm3 /s; high discharge—450 cm3/s). We found that the number of foraging bouts was significantly (p < 0.001) influenced by discharge levels for both species, where rusty crayfish foraged more often in no and discharge. The number of times crayfish sheltered also varied between species and was significantly (p < 0.001) altered by discharge level, where rusty crayfish sheltered more often in no discharge and virile crayfish sheltered more in low and intermediate discharge levels. Similarly, the number of fights that crayfish engaged in also significantly (p < 0.001) depended upon species and discharge levels. Rusty crayfish engaged in more agonistic activities in no and high discharge levels while virile crayfish participated most often in low and intermediate discharge levels
Relyea, R. A. 2000. Trait-mediated indirect effects in larval anurans: Reversing competition with the threat of predation. Ecology 81:2278-2289.
Ecologists recently have been focusing on the role that trait-mediated indirect effects can have on community structure and composition. To date, this work has primarily focused on the effects of predator-induced behavioral plasticity on communities. However, predator-induced morphological plasticity, which has been documented in many taxa, might also lead to trait-mediated indirect effects. Here, I examined how predators altered the behavior and morphology of larval wood frogs (Rana sylvatica) and leopard frogs (R. pipiens) and how these phenotypic changes altered the outcome of competition between the two species. Competition in the absence of caged predators was asymmetric; when reared separately, leopard frogs grew more than wood frogs, but when competing (without predators), wood frogs grew faster than leopard frogs. The presence of caged predators reversed the outcome of competition between the two anuran prey. In the presence of larval dragonflies (Anax spp.) or caged mudminnows (Umbra limi), leopard frogs grew faster than wood frogs while total tadpole biomass production remained unchanged. Thus, there was a predator-mediated indirect effect. Because predators alter both the behavior and morphology of larval anurans and both of these traits are known to affect resource consumption and growth, both are potential mechanisms to explain the change in competitive outcome. Changes in behavior were not related to changes in growth, but changes in morphology (specifically mouth width and tail length) were related to changes in growth. When competitors were added (without predators), wood frogs increased their mouth width by 10% and their tail length by 3%, while leopard frogs increased their mouth width by 5% and did not change their tail length. The greater increase in mouth width for wood frogs should increase their forage intake, since tadpoles feed by scraping periphyton; the importance of a 3% longer tail in competitive ability is unknown. The presence of the p
Experiment on Competition and the Local Distribution of the Grass Stipa neomexicana, Arizona, 1979 - 1986
This dataset contains the results on an experiment whose aim was to determine whether competitive displacement accounted for a species' local distribution. Within a grassland in southern Arizona, Stipa neomexicana, a C3 grass, was found to occur only on dry ridge crests with low total grass cover, while total grass cover is greater below the ridge crests in moister, low—lying areas. It was hypothesized that Stipa neomexicana was limited to these dry ridges by competitive exclusion. This hypothesis was tested by removal experiments conducted at three positions along the topographic gradient. The responses of Stipa were compared with those of Aristida glauca and other neighboring grass species, all of which are C4 grasses (Stipa being the only C3 grass in that area). This experiment was carried out from 1980 to 1983, in a grassland near Sonoita, Santa Cruz County, Arizona, USA.
The Interaction between Competition and Predation: A Meta-analysis of Field Experiments
Ecologists working with a range of organisms and environments have carried out manipulative field experiments that enable us to ask questions about the interaction between competition and predation (including herbivory) and about the relative strength of competition and predation in the field. Evaluated together, such a collection of studies can offer insight into the importance and function of these factors in nature. Therefore, this dataset was created by combining the results of 20 articles reporting on 39 published field experiments on the interaction between competition and predation. These experiments tested whether the presence of predators affects the intensity of competitive effects. The combined data was then analyzed using a factorial meta-analysis technique, the results of which were published in the study titled The Interaction between Competition and Predation: A Meta‐analysis of Field Experiments (Gurevitch et al., 2000).
Emerging fungal pathogen of an invasive grass: Implications for competition with native plant species
This data package includes data and code from an experiment testing the effects of a leaf spot fungal infection and competition from the invasive (to the U.S.) grass Microstegium vimineum on the performance of three native grass species: Dichanthelium clandestinum, Elymus virginicus, and Eragrostis spectabilis. The experiment was performed between June and September of 2019 in a greenhouse on the University of Florida campus in Gainesville, FL, USA. The leaf spot infection is caused by the fungal pathogen Bipolaris gigantea, which has recently emerged on populations of M. vimineum in the U.S. We tested the hypothesis that infection of B. gigantea would both directly and indirectly affect the native grass species by measuring the change in biomass of each species with and without pathogen inoculation (direct effects) and by measuring the effect of pathogen inoculation on M. vimineum competition through changes in native grass biomass across a density gradient of M. vimneum (indirect effects). The code includes statistical analyses and figures. The code was run using R (version 4.0.1).
Drought and herbivory effects on woody plant seedling establishment and grass competition in the Jornada Basin, 2016-2018
This dataset contains observations of seedling establishment and grass competition under precipitation manipulations, and herbivory and granivory exclosure treatments, in the Chihuahuan desert of southern New Mexico, USA. The experiment took place at the Jornada Basin LTER site. We used a rainfall manipulation system and various herbivore exclosures in a factorial design, to test hypotheses about how precipitation (PPT), competition between grasses and shrub seedlings, and predation affect the germination and first-year survival of Mesquite (Prosopis glandulosa), a shrub that has encroached in Southern Great Plains and Chihuahuan Desert grasslands. Data collected in these files include seedling counts in each treatment over observation years 2016 to 2018. This data supports the related publication in Ecological Applications (Weber-Grullon et al. in press). The dataset is complete.
ICDAR 2019 Competition on Baseline Detection (cBAD)
<p>This dataset contains the training, evaluation, and test set for the ICDAR 2019 Competition on Baseline Detection (cBAD).</p> <p>A newly created freely available real world dataset consisting of 3021 annotated document page images that are collected from seven European archives and form the basis of cBAD. The baselines in all images were manually annotated. The training and the evaluation sets contain PAGE XMLs with annotated text regions and baselines.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/11/</p>
Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging
<p>Raw data used in Kumar et al. 2020: Barley shoot biomass responds strongly to N:P stoichiometry and intraspecific competition, whereas roots only alter their foraging</p>
Results of the 1st International Competition on Software Testing (Test-Comp 2019)
<p>This file describes the contents of an archive of the<br> 1st Competition on Software Testing (Test-Comp 2019)<br> <a href="https://test-comp.sosy-lab.org/2019/">https://test-comp.sosy-lab.org/2019/</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. First International Competition on Software Testing: Test-Comp 2019.<br> International Journal on Software Tools for Technology Transfer, 2020.</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> </p> <p>To browse the competition results with a web browser, there are two options:<br> - start a local web server using<br> php -S localhost:8000<br> in order to view the data in this archive, or<br> - browse <a href="https://test-comp.sosy-lab.org/2019/results/">https://test-comp.sosy-lab.org/2019/results/</a><br> in order to view the data on the Test-Comp web page.</p> <p><br> Contents:</p> <p>index.html directs to the overview web page<br> LICENSE.txt specifies the license<br> README.txt this file<br> results-validated/ results of validation runs<br> results-verified/ results of verification runs and aggregated results</p> <p><br> The folder results-validated/ contains the results from validation runs:</p> <p>- *.xml.bz2 XML results from BenchExec<br> - *.logfiles.zip output from tools<br> - *.json.gz mapping from files names to SHA 256 hashes for the file content</p> <p><br> The folder results-verified/ contains the results from test-generation runs and aggregated results:</p> <p>index.html overview web page with rankings and score table<br> design.css HTML style definitions<br> *.xml.bz2 XML results from BenchExec<br> *.merged.xml.bz2 XML results from BenchExec, status adjusted according to the validation results<br> *.logfiles.zip output from tools<br> *.json.gz mapping from files names to SHA 256 hashes for the file content<br> *.xml.bz2.table.html HTML views on the detailed results data as generated by BenchExec's table generator<br> *.All.table.html HTML views of the full benchmark set (all categories) for each tool<br> META_*.table.html HTML views of the benchmark set for each meta category for each tool, and over all tools<br> <category>*.table.html HTML views of the benchmark set for each category over all tools<br> iZeCa0gaey.html HTML views per tool</p> <p>quantilePlot-* score-based quantile plots as visualization of the results<br> quantilePlotShow.gp example Gnuplot script to generate a plot<br> score* accumulated score results in various formats</p> <p><br> The hashes of the file names (in the files *.json.gz) are useful for<br> - validating the exact contents of a file and<br> - accessing the files from the witness store.</p> <p> </p> <p>Overview over archives from Test-Comp 2019 that are available at Zenodo:</p> <p><a href="https://doi.org/10.5281/zenodo.3856669">https://doi.org/10.5281/zenodo.3856669</a> Witness store (containing the generated test suites)<br> <a href="https://doi.org/10.5281/zenodo.3856661">https://doi.org/10.5281/zenodo.3856661</a> Results (XML result files, log files, file mappings, HTML tables)<br> <a href="https://doi.org/10.5281/zenodo.3856478">https://doi.org/10.5281/zenodo.3856478</a> Test tasks, version testcomp19<br> <a href="https://doi.org/10.5281/zenodo.2561835">https://doi.org/10.5281/zenodo.2561835</a> BenchExec, version 1.18</p> <p>All benchmarks were executed<br> for Test-Comp 2019, <a href="https://test-comp.sosy-lab.org/2019/">https://test-comp.sosy-lab.org/2019/</a><br> by Dirk Beyer, LMU Munich<br> based on the components<br> git@github.com:sosy-lab/sv-benchmarks.git testcomp19-0-g6a770a9c1<br> git@gitlab.com:sosy-lab/test-comp/bench-defs.git testcomp19-0-g1677027<br> git@github.com:sosy-lab/benchexec.git 1.18-0-gff72868</p> <p><br> Feel free to contact me in case of questions:<br> <a href="https://www.sosy-lab.org/people/beyer/">https://www.sosy-lab.org/people/beyer/</a></p> <p> </p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.