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13,064 results for “predictions”
Predicting time of failure of Internet of Things devices using Bayesian workflow
<p>Repository includes the environment details in ```energies_iot_env.yml``` file.</p> <p>All code for model analysis is included in the ```iot_tests_refactor.ipynb``` notebook.</p> <p>Code for computing simulation based calibration is in the ```compute_sbc.py``` file, and can be run by ```just_csv.ipynb``` notebook.</p> <p> </p> <p>The data set was created in the project NCN OPUS "Process Fault Prediction and Detection" (UMO-2021/41/B/ST7/03851)</p>
Experimental Database of deterministic wave prediction built from synchronous measurements from an X-band pulse radar and met-ocean sensors deployed on the Floatgen floating wind turbine and its vicinity on SEM-REV test site.
<p>This dataset is a deliverable of the FLOATECH project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007142.<br> The aim of this dataset is a result of the field experiments carried out at the Floatgen FOWT located at the SEM-REV test site.</p>
Data from: Predictions about reward outcomes in rhesus monkeys
<p>Human infants and nonhuman animals respond to surprising events by looking longer at unexpected-than-expected situations. These looking responses provide core cognitive evidence that nonverbal minds make predictions about possible outcomes and detect when these predictions fail to match reality. We propose that this phenomenon has crucial parallels with the processes of reward prediction error, indexing the difference between expected and actual reward outcomes. Most work on reward prediction errors to date involves neurobiological techniques that cannot be implemented in many relevant populations, so we developed a novel behavioral task to assess monkeys' predictions about reward outcomes using looking time responses. In Study 1, we tested how semi-free-ranging monkeys (n = 210) responded to positive error (more rewards than expected), negative error (less rewards than expected), and number control. We found that monkeys looked longer at a given reward when it was unexpectedly large or small, compared to when the same quantity was expected. In Study 2, we compared responses in the positive error condition in monkeys ranging from infancy to old age (n = 363), to assess lifespan changes in sensitivity to reward predictions. We found that adolescent monkeys showed heightened responses to unexpected rewards, similar to patterns seen in humans, but showed no changes during aging. These results suggest that monkeys' looking responses can be used to track their predictions about rewards and that monkeys share some developmental signatures of reward sensitivity with humans, providing a new approach to accessing cognitive processes underlying reward-based decision-making.</p>
Libraries generated in: Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes
<p>Libraries generated in the manuscript: "<strong>Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes"</strong>. The libraries can be generated locally by running the code provided on <a href="https://github.com/TheFreiLab/RutheniumML">GitHub</a>, but are also provided here free to download.</p>
Supplementary Datasets for "Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints"
<p>Supplementary Datasets for "Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints". The deposition contains training/testing datasets used in the article.</p>
Reproduction package for the paper "The Impact of Hard and Easy Negative Training Data on Vulnerability Prediction Performance"
<p>This Reproduction package contains the datasets, code and results for the paper "The Impact of Hard and Easy Negative Training Data on Vulnerability Prediction Performance" for other researchers to use for reproducing or improving our work. </p>
Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure
<p>Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222</p> <p> https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1</p> <p> </p>
Natal forest fragment size does not predict fledgling, pre-migration, or apparent annual survival in Wood Thrushes
<p>Determining the drivers and mechanisms for first-year survival of migratory songbirds has been an understudied area in population dynamics due to the difficulty in tracking juveniles once they have dispersed from the natal site. With the advancement in miniaturization of radio-tags (battery life ~400 days) and the development of the Motus Wildlife Tracking System, we tracked 189 Wood Thrush nestlings through independence and to fall migration departure, and their return the following spring. Natal forest fragment size was not a good predictor of survival at any of the main life stages and onset of fall migration was predicted by fledge date but not natal fragment size. The percent forest cover in the landscape (at 2-km scale) had only a weak effect on fledgling survival. Survival probability was lowest for fledglings on their natal territory (70%, or 0.86 weekly survival probability), very high for juveniles as they explored the local landscape prior to fall migration (89%, or 0.99 weekly survival probability) and low during their first migration and wintering season (26%, or 0.95 weekly survival probability). To our knowledge, this is the first study to directly estimate annual apparent juvenile survival in a migratory songbird using year-round radio-tracking. Our study suggests that small forest fragments are important for the conservation for forest songbirds because they can support high survival of juveniles. </p>
RESPEL Prediction - Dataset
<p>For the prediction of electrical and electronic waste generation (2021-2025) according to its behavior recorded by IDEAM in the Animated Bases (transformed databases) (2015-2020); the dataset is presented in 5 stages.</p> <ol> <li>Anonymized databases collected by IDEAM (transformed databases).</li> <li>Data understanding, field mapping, identification of the main cities in Colombia and CIIU Codes.</li> <li>Cleaning, filtering and categorization of the anonymized bases (2015-2020) for subsequent use in prediction.</li> <li>Prediction by means of linear regression and neural network (RRNN).</li> <li>Understanding of the data by means of graphs.</li> </ol>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
Data --- "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions"
<p>Data to reproduce the results of the manuscript entitled "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions" submitted to Geophysical Research Letters. The companion jupyter notebook can be found in DOI: <a href="https://doi.org/10.5281/zenodo.8387558">10.5281/zenodo.8387558</a></p>
Magnetic elastomers as specific soft actuators – predicting particular modes of deformation from selected configurations of magnetizable inclusions
<p>This dataset contains the underlying data and python programs to generate the plots in the manuscript</p> <p><em>L. Fischer and Andreas M. Menzel</em><br>Magnetic elastomers as specific soft actuators – predicting particular modes of deformation from selected configurations of magnetizable inclusions<br>J. Magn. Magn. Mater. <strong>591</strong>, 171695 (2024) (DOI: <a href="https://doi.org/10.1016/j.jmmm.2023.171695" target="_blank" rel="noopener">10.1016/j.jmmm.2023.171695</a>).<br>Part of the special issue "ICMF 2023".</p> <p>arXiv Version: <a href="https://arxiv.org/abs/2310.16833" target="_blank" rel="noopener">arXiv:2310.16833</a>.</p> <p>For more information, please see the included "Readme.txt" in the dataset "Zenodo.zip".</p>
Study of Innovative Multimodal Imaging Biomarkers to Predict Anatomical Outcome in Naive Patients With wAMD Treated With Brolucizumab.
ClinicalTrials.gov study NCT04774926. IPD Sharing: YES. Countries: 1. Publications: 1.
Prediction of pCR by Preoperative Biopsy in Breast Cancer With cCR After Neoadjuvant Chemotherapy.
ClinicalTrials.gov study NCT03273426. IPD Sharing: YES. Countries: 1. Publications: 1.
Predicted asymmetrical effects of warming on nocturnal and diurnal soil-dwelling ectotherms
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Data for: Local adaptation of seed and seedling traits along a natural aridity gradient may both predict and constrain adaptive responses to climate change
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Climate change is predicted to reduce sympatry among North American wood-warblers
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Intraspecific diversity poorly predicts invasibility and invasiveness in communities: A meta-analysis of experimental invasions
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Predicting the distribution of serotonergic axons: A supercomputing simulation of reflected fractional Brownian motion in a 3D-mouse brain model
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Functional traits of young seedlings predict trade-offs in seedling performance in three neotropical forests
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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.