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41 results for “control events”
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. A 100 yr old thermal erosion event under control conditions.
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for a 100yr old TEF response to N fertilization over 25 years.
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>
Hacettepe University Event (HUE) Dataset - Controlled
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
A Randomized Controlled Trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly People
ClinicalTrials.gov study NCT01259297. IPD Sharing: Not stated. Countries: 18. Publications: 1.
The role of shear fabric in controlling breakdown processes during laboratory slow slip events
<p>Understanding the physical mechanisms at the origin of slow slip events has been proven a very challenging task. In particular little is known on the role of fault heterogeneity during slow slip. In this study we provide evidence that fault fabric controls slip velocity time histories during slow slip events generated in the laboratory under the specific boundary condition of imposing a stiffness ratio K=k/k<sub>c</sub>~1. We have performed experiments using a double-direct biaxial shear apparatus and two different fault gouges, homogeneous quartz powder and heterogeneous anhydrite/dolomite mixture. We measure fine details of fault slip to resolve the slip velocity time history and volumetric deformation that, coupled with an analysis of the resulting microstructure, allow us to infer the mechanical processes at play. Our results show that slow slip events can be generated for both fault gouges when k~k<sub>c</sub> with similar values of breakdown work. The shear fabric exerts a strong influence during the co-seismic breakdown stage of instabilities. In quartz, where most of the slip occurs on a localized (1 µm) slipping surface, the peak slip velocity is attained near the final stage of friction breakdown and therefore a relevant amount of the mechanical work is absorbed during slip acceleration to peak slip velocity. In anhydrite/dolomite mixture, the peak slip velocity is suddenly reached after a relatively small drop in friction, accompanied by fault dilation, implying that most of the mechanical work is absorbed during slip deceleration. For anhydrite/dolomite mixture these results are likely related to heterogeneous slip distribution along the observed foliation. Taken together these observations suggest that the mechanics of slow slip events depends on shear zone fabric. The slip velocity function contains the necessary dynamic information to characterize the evolution of shear stress.</p> <p>The data are uploaded are structured as follow:</p> <p>1) a .txt file of the datafile that is recorded from the machine (raw data)</p> <p>2) a reduction file (_r) that is used to elaborate the raw data</p> <p>3) a file in .txt format containing the elaborated data (data_rp) </p> <p>The data are analyzed using rawPy that can be found at <a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Marco Maria Scuderi at marco.scuderi@uniroma1.it.</p>
Results of Simulation Study II for "New weighting methods when cases are only a subset of events in a nested case-control study"
<p>This file includes the result of Simulation Study II in Section 4.3 of the manuscript.</p>
Dataset related to the paper submitted in Journal of Geophysical Research : Solid Earth, named : A Controlled-Source Physical Model for Long Period Events
Open the record for dataset details and reuse information.
Ciência Vitae controlled vocabulary - Type of event
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Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control
<p>If you use any of this data, please cite the following publication:</p> <p><span>@inproceedings{monforte2023fast,</span><br><span> title={Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control},</span><br><span> author={Monforte, Marco and Gava, Luna and Iacono, Massimiliano and Glover, Arren and Bartolozzi, Chiara},</span><br><span> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},</span><br><span> pages={4035--4043},</span><br><span> year={2023}</span><br><span>}</span></p> <p>Event-based datasets of synthetic and real trajectories of a bouncing ball.</p> <p>The synthetic trajectories were obtained converting frames taken using Unreal Engine to events. The ground truth is provided along with objects and camera settings.</p> <p>The real trajectories wer dumped from a real event camera located in front of the robot workspace.</p> <p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p> <p>Specifically use the functions to import .log files:</p> <p>data = importIitYarpBinaryDataLog(filePathOrName=input_path)<br> </p> <p> </p> <p> </p>
Controlled Trial to Demonstrate a Reduction in the Number of Oliguria Events in Patients Being Managed in a Critical Care Unit, Following Cardiac Surgery, When a Novel Oliguria Prediction Tool (STABIL
ClinicalTrials.gov study NCT05001503. IPD Sharing: NO. Countries: 1. Publications: 2.
Real-time Continuous Glucose Monitoring for Reduced Adverse Complications and Events in Women With Gestational Diabetes (GRACE): a Multicentre International Randomized Controlled Trial
ClinicalTrials.gov study NCT03981328. IPD Sharing: NO. Countries: 1. Publications: 2.
Controlling Hypertension After Severe Cerebrovascular Event
ClinicalTrials.gov study NCT02982655. IPD Sharing: UNDECIDED. Countries: 1. Publications: 15.
Controlled Trial of Early Intervention With Children and Adolescents Exposed to Nonrelational Traumatic Events
ClinicalTrials.gov study NCT02299583. IPD Sharing: Not stated. Countries: 1. Publications: 37.
Aspirin Continuation or Interruption in Patients at Moderate Risk for Cardiovascular Events Undergoing Colonoscopy and/or Polypectomy; a Placebo-controlled Trial
ClinicalTrials.gov study NCT07052799. IPD Sharing: UNDECIDED. Countries: 0. Publications: 11.
Placebo Controlled Study to Generate Data Characterising Clinical Events, Physiological Responses and Immune Responses
ClinicalTrials.gov study NCT02523287. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effect of Acute Concurrent Exercise on Inhibitory Control: An Event-related Potential Study
ClinicalTrials.gov study NCT06370286. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Participant Centered Active Surveillance for Adverse Event Following Measles Immunization in Gedeo Zone, Ethiopia, 2022. A Multi-center Open-label Randomized Control Trial
ClinicalTrials.gov study NCT05803538. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Role of Colonic Events on Metabolism and Appetite Control: A Synbiotic Approach
ClinicalTrials.gov study NCT01718418. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Controlled-Source Physical Model for Long Period Events
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Protoves M1® Syrup in Controlling Adverse Event During HIVEC® Instillations
ClinicalTrials.gov study NCT04148677. IPD Sharing: Not stated. Countries: 0. Publications: 6.
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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
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.