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Dataset results
70 results for “Quality prediction”
Online water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Online water quality data though the drinking water distribution network. More than 1 year of data.</p> <p>SCADA data source.</p> <p>Provide water quality of the whole system at selected locations.</p>
Predicting Bug-Inducing Commits Using Software Quality Metrics
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Supplementary Materials for 'Spatiotemporal Prediction of Air Quality Using Machine Learning Techniques'
<p>This package includes supplementary materials used to implement air quality prediction in the city of Madrid. It consists of two main subdirectories: Data and Code. The Data directory contains Raw-Data (air quality, meteorological and traffic data from the period of January-June 2019 and January-June 2020, and the location of air quality and meteorological monitoring stations and traffic measurement points of the city of Madrid) and Processed-Data (the output after raws data has gone through the workflow to meet the requirements corresponding to the implementation of the proposed forecasting approaches). The Code directory contains Process Raw Data, Chapter4-ConvLSTM, Chapter5-BiConvLSTM, and Chapter6-A3T_GCN, which provides the procedure for constructing and implementing the proposed approaches.</p>
Assessing and predicting the quality of peer reviews: a text mining approach
<p>Dataset</p>
Personalized Audio Quality Preference Prediction Dataset
<p>Dataset for the following paper. Please cite this paper if our dataset is used in your research.</p> <p>Chung-Che Wang, Yu-Chun Lin, Yu-Teng Hsu, and Jyh-Shing Roger Jang, "Personalized Audio Quality Preference Prediction", APSIPA ASC 2023.</p> <p>Here is a brief description of our dataset. For more details, please see our paper.</p> <p>This dataset is designed for personalized audio quality preference prediction. It includes recordings from 5 different mobile phones playing 7 distinct song segments at 2 volume settings. The played audio is recorded by using a binaural microphone and a computer interface. For each volume type, 70 pairs of recorded audio files are formed, where each of the two audio files in one pair corresponds to same song segment played by different mobile phones. For each volume type, each subject is asked to compare at least 14 of the 70 pairs. Subject information, which includes age, gender, and headphone/earphone specifications such as impedance, frequency response range, and sensitivity, are also collected.</p>
Impact of a Predictive Score of Bowel Preparation Quality in Clinical Practice
ClinicalTrials.gov study NCT03830489. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Predicting the Quality of Response to Specific Treatments in Patients With cGVHD, PQRST Study
ClinicalTrials.gov study NCT04431479. IPD Sharing: NO. Countries: 2. Publications: 1.
The Predictive Capacity of Peripheral Muscle Function on Quality of Life Impairment at 1 Year in Subjects With Obesity
ClinicalTrials.gov study NCT05479396. IPD Sharing: YES. Countries: 1. Publications: 3.
Preoperative Prediction Model of Pathological Outcomes (Mesorectum Quality and Positive Circumferential Resection Margin) in Patients With Mid-low Rectal Cancer
ClinicalTrials.gov study NCT03107650. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Environmental quality predicts optimal egg size in the wild
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Data from: Mite load predicts the quality of sexual color and locomotor performance in a sexually dichromatic lizard
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Data from: The utility of normalized difference vegetation index for predicting African buffalo forage quality
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Herbivore phenology can predict response to changes in plant quality by livestock grazing
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Unobtrusive tracking of interpersonal orienting and distance predicts the subjective quality of social interactions
<p>Interpersonal coordination of behavior is essential for smooth social interactions. Measures of interpersonal behavior, however, often rely on subjective evaluations, invasive measurement techniques or gross measures of motion. Here, we constructed an unobtrusive motion tracking system that enables detailed analysis of behavior at the individual and interpersonal levels, which we validated using wearable sensors. We evaluate dyadic measures of joint orienting and distancing, synchrony and gaze behaviors to summarize data collected during natural conversation and joint action tasks. Our results demonstrate that patterns of proxemic behaviors, rather than more widely used measures of interpersonal synchrony, best predicted the subjective quality of the interactions. Increased distance between participants predicted lower enjoyment, while increased joint orienting toward each other during cooperation correlated with increased effort reported by the participants. Importantly, the interpersonal distance was most informative of the quality of interaction when task demands and experimental control were minimal. These results suggest that interpersonal measures of behavior gathered during minimally constrained social interactions are particularly sensitive for the subjective quality of social interactions and may be useful for interaction-based phenotyping for further studies.</p>
Using predictive models to evaluate the quality of a test suite at class and method level.
<p>Vídeo de apresentação para o Workshop de Teses e Dissertações do CBSoft (WTDSoft).</p>
Dataset for collaborative prediction of web service quality based on user preferences and services
<p><span><span><span><span><span><span><span><span><span><span><span><span>The prediction of<b> </b>web service quality plays an important role in improving user services; it has been one of the most popular topics in the field of Internet services. In traditional collaborative filtering methods, differences in the personalization and preferences of different users have been ignored. In this paper, we propose a prediction method for<b> </b>web service quality based on different types of quality of service (QoS) attributes. Different extraction rules are applied to extract the user preference matrices from the original web data, and the negative value filtering-based top-K method is used to merge the optimization results into the collaborative prediction method. Thus, the individualized differences are fully exploited, and the problem of inconsistent QoS values is resolved. The experimental results demonstrate the validity of the proposed method. Compared with other methods, the proposed method performs better, and the results are closer to the real values.</span></span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Predictions of response to temperature are contingent on model choice and data quality
The equations used to account for the temperature dependence of biological processes, including growth and metabolic rates are the foundations of our predictions of how global biogeochemistry and biogeography change in response to global climate change. We review and test the use of 12 equations used to model the temperature dependence of biological processes across the full range of their temperature response, including supra- and sub-optimal temperatures. We focus on fitting these equations to thermal response curves for phytoplankton growth, but also tested the equations on a variety of traits across a wide diversity of organisms. We found that many of the surveyed equations have comparable abilities to fit data and equally high requirements for data quality (number of test temperatures and range of response captured), but lead to different estimates of cardinal temperatures and of the biological rates at these temperatures. When these rate estimates are used for biogeographic predictions, differences between the estimates of even the best fitting models can exceed the global biological change predicted for a decade of global warming. As a result, studies of the biological response to global changes in temperature must make careful consideration of model selection and of the quality of the data used for parametrizing these models.
Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta-analysis"
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Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction
<p>In hard winter wheat breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association mapping (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard winter wheat. Advanced breeding lines (n=462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time; most of which were within or in tight linkage to glutenin and gliadin loci, and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits; thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25 to 0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward-prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.</p>
Online machine learning algorithms to predict link quality in community wireless mesh networks
<p>FunkFeuer raw topology data from 15-01-2016 to 28-01-2016 retrieved on 17-02-2016 from http://opendata.confine-project.eu/dataset/funkfeuer-topology-data. This dataset was employed in "Miguel L. Bote-Lorenzo, Eduardo Gómez-Sánchez, Carlos Mediavilla-Pastor, Juan I. Asensio-Pérez, Online machine learning algorithms to predict link quality in community wireless mesh networks, Computer Networks, Volume 132, 2018, Pages 68-80, https://doi.org/10.1016/j.comnet.2018.01.005."</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)
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.