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Dataset results
77 results for “predictive processing”
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
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Space resource utilization of dominant species integrates abundance- and functional-based processes for better predictions of plant diversity dynamics
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Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects"
<p>Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects" with README included</p>
Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
1.Functional diversity (FD) metrics are widely used to assess invasion ecosystem impacts, but we have limited theory to predict how FD should respond to invasion. A key challenge to effectively using FD metrics is the complexity of conceptualizing alterations to multi-dimensional trait space, making it difficult to select a priori the most appropriate metric for specific ecological questions. 2.Here, we provide expectations on how invasion should change four commonly used FD metrics—functional richness (FRic), evenness (FEve), divergence (FDiv), and dispersion (FDis)—and then test these expectations in a lab decomposition experiment. We simulate invasion of a forest by understory plants by adding leaf litter from 18 natives and nonnatives to a representative canopy tree litter mixture to test changes in FD and decomposition. 3.All four metrics changed predictably with invasion. Species that were more functionally unique or when added at greater proportions had larger impacts on FD. Overall, FRic, FEve, and FDiv were poor choices for understanding impacts of nonnative species. FDis was the only metric that both changed predictably with addition of understory litter and correlated intuitively with changes in carbon mineralization. Furthermore, ranking species based upon how much they changed FDis of the litter mixture provided a fair assessment of which species had the largest impact on decomposition. As such, functional dispersion may be a key tool for predicting a priori which nonnatives will have the greatest impact on ecosystem processes. 4.Synthesis: We highlight the need to assess the suitability of each FD metric for the specific ecological question at hand. Our work reveals the pitfalls of considering multiple metrics or randomly choosing a single metric without suitability assessments. At the same time, it suggests a framework for metric assessment that should help lead to selection of a metric or metrics that provide robust a priori insights into how invasion by nonnative species can impact ecosystem processes.
How manual object exploration is associated with 7- to 8-month-old infants' visual prediction abilities in spatial object processing.
<p>Data set of Kubicek, C., Jovanovic, B., & Schwarzer, G. (in press). How manual object exploration is associated with 7- to 8-month-old infants’ visual prediction abilities in spatial object processing. <em>Infancy.</em></p>
Supplementary data: Predicting grid frequency short-term dynamics with Gaussian processes and sequence modeling
<p>This repository contains data and result files for the paper "Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelling". The code to generate the models and reproduce the results of the comparative study in the above paper is available on this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository</a></p> <p><strong>Supplementary data</strong>:</p> <p>- The <strong>trained_models</strong> folder contains the results of the trained models.</p> <p>- The folder <strong>data</strong> contains data needed for for the comparative study for the year 2019 in the paper above. This data set (except knn_point_predictions.npy) is generated with the code in this <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">github repository</a>. knn_point_predictions.npy is generated with the code in this <a href="https://github.com/bolin-liu/sequence-model-and-gaussian-process-for-frequency-prediction">github repository </a>.</p>
Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability
<p>Data supporting the findings in:</p> <p>Lubinus, C., Einhäuser, W., Schiller, F., Kircher, T., Straube, B., & van Kemenade, B. M. (2022). Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability. <em><strong>NeuroImage</strong>. DOI: XXX</em></p> <p>Details are specified in the readme file.</p>
Interpretable semi-supervised population prediction and disaggregation using ancillary data: processed dataset
<p>Dataset ready for use for the tool "cross_validator.py", that runs the experiments.</p> <p> </p> <p>The data cannot be redistributed as-is as it includes non-redistributable work. Contact authors for access.</p>
The source code for a new capillary and adsorption‒force model predicting hydraulic conductivity of soil during freeze‒thaw processes
<p>The source code is related to "A New Capillary and Adsorption‒Force Model Predicting Hydraulic Conductivity of Soil during Freeze‒thaw Processes" (Shufeng Qiao, Rui Ma, Yunquan Wang, Ziyong Sun, Helen Kristine French, Yanxin Wang)</p>
Pragmatic prediction in the processing of referring expressions containing scalar quantifiers
<p>Data and analysis scripts pertaining to a study by Macuch Silva & Franke (submitted).</p>
Validation prediction: a flexible protocol to increase efficiency of automated acoustic processing for wildlife research
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Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
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Dataset: Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning
<p>Supplementary Data for <em><strong>Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning</strong></em><em><strong> </strong></em>article</p> <p>Dataset associated with the following publication:</p> <p>Maurer, L. K., Joch, M., Hegele, M., Maurer, H. & Müller, H. (2019). Predictive error processing distinguishes between relevant and irrelevant errors after visuomotor learning. <em>Journal of Vision</em>, <em>19(4):18</em>, 1-13. https://doi.org/10.1167/19.4.18</p>
Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
The relation between crawling and non-crawling 9-month-old infants' visual prediction abilities in spatial object processing.
<p>The data set Kubicek et al._JECP_DataSet.sav containts the data of the paper from Kubicek, C., Jovanovic, B., & Schwarzer, G. (2017). The relation between crawling and non-crawling 9-month-old infants' visual prediction abilities in spatial object processing. Journal of Experimental Child Psychology, 158, 64–76.</p> <p> </p>
Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining
<p>The Supplementary Material of the paper "Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining" includes: <br> 1) APPENDIX 1: SQL Statements for data extraction. Appendix 2: Interview for operating Staff.<br> 2) The DataSet of the normalized data to define the predictive model.</p>
Data set used for comparison of different machine learning approaches, for predicting aircraft departure delays, due to the circumstances of the defrosting process.
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Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Data for article entitled 'Prediction in SVO and SOV languages: Processing and typological considerations', published in Linguistics
<p>see the publication for the description of the data and analysis</p>
Gene expression profiling predicts processing properties in short-term imbibition of wheat
GEO Series GSE116933. Triticum aestivum. 8 samples. Type: Expression profiling by array.
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.