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13,064 results for “predictions”
Interception of virtual throws reveals predictive skills based on the visual processing of throwing kinematics - Dataset
<p>Dataset consists of a list of Matlab structures, one for each of the 21 participants. For each participant all the recorded trials are reported ("trials" field). For each trial, the dataset reports information about the associated experimental condition and the kinematics of the ball and the racket trajectories. Information about the experimental condition are specified in the "info" field, which provides the experimental phase (Training and Experimental), the visibility (AllVisible, ThrowerOnly, BallOnly), the target (1, 2, 3, 4), and the thrower ID (1, 2, 3, 4). The kinematics data, starting from the time of ball release, are given in the field "trajectories", which provides the time vector (in seconds), and the corresponding 3D positions of the racket and the ball (in meters) in the reference frame shown in Figure 1 of the paper.</p>
Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate
<p>These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of spatiotemporal covariates that could potentially explain pronghorn (Antilocapra americana) mortality risk in the Northern Sagebrush Steppe (NSS) ecosystem (50.0757<sup>o</sup> N, −108.7526<sup>o</sup> W). Known-fate data were collected from 170 adult female pronghorn monitored with GPS collars from 2003-2011, which were used to construct a time-to-event (TTE) dataset with a daily timescale and an annual recurrent origin of 11 November. Seasonal risk periods (winter, spring, summer, autumn) were defined by median migration dates of collared pronghorn. We linked this TTE dataset with spatiotemporal covariates that were extracted and collated from pronghorn seasonal activity areas (estimated using 95% minimum convex polygons) to form a final dataset. Specifically, average fence and road densities (km/km2), average snow water equivalent (SWE; kg/m2), and maximum decadal normalized difference vegetation index (NDVI) were considered as predictors. We tested for these main effects of spatiotemporal risk covariates as well as the hypotheses that pronghorn mortality risk from roads or fences could be intensified during severe winter weather (i.e., interactions: SWE*road density and SWE*fence density). We also compare an analogous frequentist implementation to estimate model-averaged risk coefficients. Ultimately, the study aimed to develop the first broad-scale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts.</p> <p> </p>
Fitness of evolving bacterial populations is contingent on deep and shallow history but only shallow history creates predictable patterns
<p><span>Long term evolution experiments have tested the importance of genetic and environmental factors in influencing evolutionary outcomes. Differences in phylogenetic history, recent adaptation to distinct environments, and chance events all influence the fitness of a population. However, the interplay of these factors on a population's evolutionary potential remains relatively unexplored. We tracked the outcome of 2,000 generations of evolution of four natural isolates of Escherichia coli bacteria that were engineered to also create differences in shallow history by adding previously identified mutations selected in a separate long-term experiment. Replicate populations started from each progenitor were evolved in four environments. We found that deep and shallow phylogenetic histories both contributed significantly to differences in evolved fitness, though by different amounts in different selection environments. With one exception, chance effects were not significant. Whereas the effect of deep history did not follow any detectable pattern, effects of shallow history followed a pattern of diminishing-returns whereby fitter ancestors had smaller fitness increases. These results are consistent with adaptive evolution being contingent on the interaction of several evolutionary forces but demonstrate that the nature of these interactions is not fixed and may not be predictable even when the role of chance is small.</span></p>
Black and orange coloration predict success during male-male competition in the guppy
<p>Investigating how both intrasexual competition and intersexual mate choice act within a single system is crucial to understanding the maintenance and diversity of sexually-dimorphic traits. These two processes can act in concert by selecting for the same trait, or in opposition by selecting for different extremes of the same trait; they can also act on different traits, potentially increasing overall trait complexity. We asked whether male-male competition and female mate choice act on the same male traits using Trinidadian guppies, which exhibit complex male-limited color patterns and sexual size dimorphism. We used behavioral assays to assess the relationship between color and male competitive success and then compared our results to the plethora of data on female choice and male color in our study population. We found that males initiated more contests if they were larger than their competitor. Males won contests more often if they had more black coloration than their competitor, and the effect of black was stronger when the male had less orange than his competitor. Additionally, males won more often if they had either more structural color (iridescence) and more orange, or less structural color and less orange than their competitor, suggesting multiple combinations of color traits predict success. Females from our study population exhibit strong preferences for orange coloration. Thus, traits favored in male contests differ from those favored by intersexual selection in this population. Our results suggest that mate choice and male-male competition together promote increased color pattern complexity in this species.</p>
The theory of planned behavior and the prediction of pre-service biology teachers' intention to teach evolution
<p>We developed the project to identify and analyze variables that promote or hinder prospective biology teachers’ intentions to teach evolution. We adopted the model of the theory of planned behavior (TPB). We extended it to include additional variables described by teacher education research as key determinants of behavioral intention to teach evolution. We initially hypothesized that attitudes toward teaching evolution, subjective norms, perceived behavioral control, personal religious beliefs, perceived usefulness, and knowledge about evolution would determine a person’s behavioral intentions. To test the hypotheses, we developed an online questionnaire and conducted a quantitative cross-sectional survey in the field of teacher education. The data included information on <em>N</em> = 309 participants. Because we initially analyzed the data using a two-stage structural equation model (SEM), we uploaded two data files that were created in subprocesses of our original analyses (for more information, see the original publication). The dataset “data3” contains 77 variables and has missing values. Since we wanted to use complete data for the SEM, we trimmed the data set “data3” to include only the 67 variables necessary for the SEM, then applied an expectation-maximum (EM) algorithm with multiple imputations, and obtained the data set “data4”. </p>
1-km Antarctic net snow accumulation predictions
<p><strong>Overview</strong></p> <p>We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using a machine learning approach. Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty.</p> <p>For a thorough description of how the predictions were generated generated see Medley et al. (2022). </p>
On the Application of Machine Learning Models to Assess and Predict Software Reusability
<p>This is the dataset, results and notebook for the submission into Maltesque 2022 conference.</p>
Can diet composition estimates using stable isotope analysis of feathers predict growth and condition in nestling mountain bluebirds (Sialia currucoides)
<p>Insectivorous birds breeding in seasonal environments provision their dependent young during periods when prey diversity and abundance vary. Consequently, the composition and nutritional value of diets parents feed to their offspring may differ within and among broods, potentially affecting the condition of nestlings. In a population of mountain bluebirds (<i>Sialia currucoides</i>), we used two methods to estimate diet composition for individual nestlings: direct observation of provisioning using video recordings at 5 and 9 days post-hatch, and stable isotopes of the δ<sup>13</sup>C and δ<sup>15</sup>N in nestling feathers and prey followed by analysis with mixing models. We determined the macronutrient content (% fat and lean mass) and estimated the metabolized energy from each type of prey. We evaluated whether different methods of estimating diet composition would produce similar results, and if the types of prey nestlings ate at one or both ages affected their morphology, growth rates, or blood ketone concentration. We found that bluebirds fed their young 5 main types of prey: beetles, cicadas, grasshoppers, insect larvae, and spiders. Both observational and mixing model estimates of diet composition indicated that larvae are traded-off with grasshoppers, and that fewer larvae are provided to nestlings as the season progresses. In evaluating how diet influences individual growth and condition, estimates from direct observations had greater explanatory power than those from mixing models, indicating that diets rich in the most energy-dense prey (greatest fat content; cicadas and larvae) were associated with larger size and higher body condition, and faster rate of mass gain and growth of tarsus. Lower value prey had more limited, specific effects on nestlings, but may still be important dietary components. While isotopic methods produced estimates of diet composition that were generally informative, when applied to explain the growth and condition of nestlings they proved less useful. </p>
Interpretable AI for drug response prediction
<p>This dataset contains input data needed to run the models evaluated in our study "Interpretable deep learning architectures for improving drug response prediction: myth or reality?". The data required to run each model is organized into sub-folders, with the folder name being the corresponding model name. The data in 'model_agnostic_data' is shared by all models. For details regarding each data file, please refer to the documentation ('input_data_doc') included. The code for this study is available at <a href="https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP">https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP</a></p> <p> </p>
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity, published at Geoderma, 2023 (https://doi.org/10.1016/j.geoderma.2023.116418).</p> <p>Soil property and Ksat maps for Austria. The digital soil maps were generated based on a Machine Learning and PTF-based approach (indirect approach) and a pure Machine Learning based approach (direct approach). By downloading the datasets, you agree that we nor the provider of the used source datasets cannot be liable for the data provided.</p> <p>This study was funded by the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), the Austrian Academy of Science (Project RechAUT) and the Austrian Science Fund project P 31213.</p> <p> </p>
Figure 5 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 5. Moving colonies to imperialist in culture and language axes (AtashpazGargari et al. 2008).
Figure 2 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 2. Generalized semivariogram showing the range of spatial dependence, nugget effect (C0) variability associated with spatial dependence (C), and sill (C + C0).
Figure 5 in Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Iran
Figure 5. Tetranychus urticae distribution maps in actual (b, d and f) and classified conditions by MLPNN (c, e and a). The maps of a, c, e and b, d, f have been drawn according to economic threshold of 4, 8 and 12, respectively.
Data for "High-accuracy determination of Paul-trap stability parameters for electric-quadrupole-shift prediction", J. Appl. Phys. 132, 124401 (2022)
<p>Data required to reproduce the key results in: "High-accuracy determination of Paul-trap stability parameters for electric-quadrupole-shift prediction", J. Appl. Phys. <strong>132</strong>, 124401 (2022). <a href="https://doi.org/10.1063/5.0106633">https://doi.org/10.1063/5.0106633</a></p> <ul> <li>The file "sec_freq.dat" contains measured secular frequencies, the rf frequency, the applied bias voltages, and the MJD of the measurement: <ul> <li>Figures 4-5 use rows 31-33 of this data.</li> <li>Figure 6 uses all data corresponding to -1.1e-3 < <em>a</em><sub>x</sub> < -0.6e-3.</li> <li>Figure 7(a) uses all the data.</li> </ul> </li> <li>The file "data2021-12-21_MJD.txt" contains the secular frequency data used to derive Eq. (16) and plot Figure 8.</li> <li>The file "RF_monitor_rectifier_1d.txt" contains the rectified monitor voltage used in Figure 8.</li> <li>The file "Temperature_108_1d.txt" contains the helical-resonator temperature used in Figure 8.</li> <li>The file "Fig9_EQS.dat" contains the measured electric quadrupole shift (EQS) used for Figure 9.</li> <li>The file "interleavedEQS.dat" contains the data from the interleaved EQS measurement used to determine a lower value of 1070 for the cancellation factor.</li> </ul>
Evolutionary effects of nitrogen are not easily predicted from ecological responses
<p>Anthropogenic nitrogen (N) addition might alter the evolutionary trajectories of plant populations, in part because it alters the abiotic and biotic environment by increasing aboveground primary productivity, light asymmetry, and herbivory intensity, and reducing plant species diversity. Such evolutionary impacts could be caused by N altering patterns of natural selection (i.e., trait-fitness relationships) and the opportunity for selection (i.e., variance in relative fitness). Because at the community level N addition favors species with light acquisition strategies (e.g., tall species), we predict that N would also increase selection favoring those same traits. We also hypothesize that N could alter the opportunity for selection via its effects on mean fitness and/or competitive asymmetries.To investigate these evolutionary consequences of N, we quantified the strength of selection and the opportunity for selection in replicated populations of the annual grass Setaria faberi Herrm. (giant foxtail) growing in a long-term N addition experiment. We also correlated our measures of selection and opportunity for selection with light asymmetry, diversity, and herbivory intensity to identify the proximate causes of any N effects on evolutionary processes. N addition increased aboveground productivity, light asymmetry, and reduced species diversity. Contrary to expectations, N addition did not strengthen selection for trait values associated with higher light acquisition such as greater height and specific leaf area (SLA); rather, it strengthened selection favoring lower SLA. Increased light asymmetry was associated with stronger selection for lower SLA and lower species diversity was associated with stronger selection for greater height and lower SLA, suggesting a role for these factors in driving N-mediated selection. The opportunity for selection was not influenced by N addition (despite increased mean fitness) but was negatively associated with species diversity. Our results indicate that anthropogenic N enrichment can affect evolutionary processes, but that evolutionary changes in plant traits within populations are unlikely to parallel the shifts in plant traits observed at the community level.</p>
Predicted Arctic Beaded Stream Locations
<p>This dataset contains predicted beaded stream locations from an analysis of high-resolution satellite imagery, using both decision tree regression and object detection from You-Only-Look-Once algorithm (v5). Beaded streams are low-order streams found in high latitude regions near permafrost that form unique 'beads-on-a-string' morphology in a pool-chute structure and represent an important habitat for Arctic grayling fish.</p> <p>Included here are estimated locations of catchments that contain beaded streams from a decision tree classification (MERIT_possible_beads.zip) based on a MERITHydro-Vector dataset (<a href="https://doi.org/10.6084/m9.figshare.13377329">https://doi.org/10.6084/m9.figshare.13377329</a>), circumpolar permafrost dataset (https://doi.org/10.7265/skbg-kf16), and a previous study on Arctic beaded streams (Arp et al., 2015; https://doi.org/10.5194/bgd-11-11391-2014).</p> <p>Also included is a list of catchments that were manually classified or classified by the Arp et al. (2015) paper as beaded (training_catchments.csv), and predicted boxes from the object detection analysis (YOLOv5_prediction_boxes.zip). </p> <p>Finally, code to reproduce some of these results are located in Beaded_Streams-1.0.0.zip, or can be found on github: <a href="https://github.com/merrittharlan/Beaded_Streams/releases/tag/v1.0.0">Release Predicted Arctic Beaded Stream Locations · merrittharlan/Beaded_Streams (github.com)</a>. </p> <p>Please contact the author (mharlan@umass.edu; merritt.e.harlan@gmail.com) with any questions. </p>
The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability
<p>Data set of the article: The Influence of Active and Passive Motion Experience on Infants’ Visual Prediction Ability</p>
Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model
<p>The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R<sup>2</sup> score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen.</p>
Calibration Data for Choice Prediction Competition 2018 (CPC18)
<p>These are the complete raw data for the Calibration Set of a choice prediction competition for decisions under risk, under ambiguity, and from experience (titled CPC18), organized by the authors and their colleagues. The data includes 510,750 consequential choices of human decision makers choosing between two risky and/or uncertain prospects with up to 10 possible outcomes each.</p> <p>Part of these data (published herein at: https://zenodo.org/record/321652#.WZlniSgjE2z) were previously used in CPC15 (a previous choice prediction competition), and are described in the paper: 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> <p>More details about the new competition (CPC18) can be found at https://cpc18.wordpress.com/</p> <p>Keywords: Choice between gambles; Decision making; Prospect theory; Decisions from experience; Description-experience gap Decisions under uncertainty</p>
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
ScienceDex guides
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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.