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13,064 results for “predictions”
Training and Test Datasets, Pretrained Weights and Predictions for HIDRA3
<div>HIDRA3 is a state-of-the-art deep neural model for multi-point sea-level prediction based on past sea level observations and future tidal and geophysical forecasts. Published data contain HIDRA3 pretrained weights, predictions for all 50 ensembles, geophysical training and evaluation data and SSH observations from Koper (Slovenia).</div> <div> </div> <div>The structure of the data is described in README.md.</div>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></p>
Data from: Community ecology in a bottle: Leveraging eDNA metabarcoding data to predict occupancy of co-occurring species
<p>Detecting environmental DNA (eDNA) of numerous organisms from the same samples has been revolutionized by metabarcoding. However, utilizing the vast amounts of data generated from metabarcoding to predict occupancy probabilities for co-occurring species is currently rare. Here, we demonstrate how metabarcoding data can be used to advance community ecology research through a case study using replicate stream water samples and Bayesian occupancy models to test hypotheses of eDNA occurrence for a native fish (brook trout, Salvelinus fontinalis), its major ectoparasite (gill lice, Salmincola edwardsii), and an introduced potential competitor (brown trout, Salmo trutta). Gill lice DNA occupancy was positively associated with brook trout biomass determined via electrofishing conducted near eDNA sampling sites, suggesting gill lice occupancy is dependent on host density. Leveraging site-specific molecular operational taxonomic units identified from metabarcoding, DNA occupancy of trout and gill lice was often positively predicted by species richness of aquatic insect orders trout commonly feed on, which are also environmental quality indicators. Thus, high-quality habitat that environmentally sensitive salmonids and their primary prey rely on may promote higher fish occupancy rates, further facilitating the spread of fish parasites. An increasing amount of community-level data is being generated from global metabarcoding efforts, and we suggest our framework could be broadly implemented to enhance understanding of factors impacting distributions of co-occurring species, reveal new ecological phenomena, and support management and conservation efforts.</p>
Ranger models for predicting isoform abundance from UTR sequence features
<p>Each RDS file contains a ranger object, trained on transcripts after removing those associated with the held-out genes in one of the five cross-validation folds. The day and replicate number in the file name corresponds to the neuronal differentiation sample on which the model was trained. The file gene_folds.txt indicates the fold from which each gene was excluded during model training. The file transcript_gene_associations.txt contains transcript-gene associations. The file predictors.RDS contains the matrix of predictor variables.</p>
FIGURE 14 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 14. Detailed comparison of model versions.
FIGURE 13 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 13. Refined model results.
FIGURE 9 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 9. Revised weighted suitability analysis results.
FIGURE 10 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 10. Number of cells assigned to each fossil potential value for the revised model.
FIGURE 7 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 7. Photos of the 10 test sites. Numbers correspond to those in Table 5.
FIGURE 6 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 6. Weighted suitability analysis results.
FIGURE 4 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 4. Simplified flowchart showing methodology.
FIGURE 3 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 3. Slope and Aspect data for the Cedar Mountain Formation.
FIGURE 2. Landsat 8 in A fossil locality predictive model using weighted suitability analysis for the Early Cretaceous Cedar Mountain Formation, Utah, USA
FIGURE 2. Landsat 8 natural color composite image centered on the Cedar Mountain Formation.
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Leaf area predicts conspecific spatial aggregation of woody species
<p><strong>Aim:</strong> Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co-existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention.</p> <p><strong>Location:</strong> Global synthesis.</p> <p><strong>Time period:</strong> 1997 - 2022.</p> <p><strong>Major taxa studied:</strong> Woody angiosperms and conifers.</p> <p><strong>Methods:</strong> We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat.</p> <p><strong>Results:</strong> We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observe aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza.</p> <p><strong>Main Conclusions:</strong> Compared to climatic variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area.</p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers - Dataset
<p>This dataset contains data from 43 subjects following two authentic manufacturing protocols and their self-reported fatigue score. The system employs 6 wearable sensors to continuously track vital and locomotive signs from multiple body locations.</p> <p> </p> <p>The goal of the experiment is to predict fatigue trends in a subject, while they are asked to perform pre-defined manual tasks simulating a manufacturing environment, using data from soft, flexible, wearable sensors and a vision system. The tasks in this study are repetitive and physically exerting involving intricate steps taken in real manufacturing settings. The iterative nature of the tasks facilitates comparative analyses of distinct temporal segments to characterize fatigue. The two manufacturing tasks are (1) Task Composite: Composite Sheet Layup, and (2) Task Harnessing: Wire Harnessing. The task protocol requires the subject to wear sensors to monitor vital and locomotive signs continuously. Additionally, we incorporate a weighted vest to exaggerate the induced fatigue in a reasonable duration for the study to mimic a full shift for a manufacturing worker. Each task consists of two rest periods of 5 minutes each at the start and end, as well as five segments of physical tasks. On average, each task takes a total of 1 hour. Before each data segment, the subject fills out a survey form to indicate their current fatigues as per the Borg scale. </p>
Amazon Package Length Prediction Training Dataset
<p>This dataset was originally published by Amazon company. I have just uploaded it here for my project. </p>
Fig. 1 in Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippiensis, daudin 1802), and the prediction of intermediate hosts
Fig. 1. Collecting localities of alligators in Georgia and Florida.
Fig. 4 in Gastric nematode diversity between estuarine and inland freshwater populations of the American alligator (Alligator mississippiensis, daudin 1802), and the prediction of intermediate hosts
Fig. 4. Prey stomach contents categorized to taxonomic class levels.
Data and code of "Germline mutation rate predicts cancer mortality across 37 vertebrate species" article
<p>Data and code used in this article.</p> <p>The "Data.csv" file contains information about each species class, common name, average yearly mutation rate, trophic level, number of animals per species that died from cancer, records of mortality per species, the minimum confidence interval (CI) for the cancer mortality values, the maximum CI for the cancer mortality values, the standard error (SE) of the records of mortality per species, cancer mortality, average parental age (measured in months), maximum lifespan (measured in months), average parental age (measured in months), and whether a species is reported as domesticated/semidomesticated or not. The way the authors obtained these data, and the original sources of these data, are described in the methodology section of the article.</p> <p>The "Regression_analyses.R" file is the code we used in the regression analyses conducted in this article. The .phy file is the phylogenetic tree used in the regression analyses.</p> <p>The .txt, .nwk, .Rproj, and "germFit.R" files are the data and code we used for comparing the different models of phenotype evolution (comparison of 3 different models: Ornstein–Uhlenbeck, Brownian, and Early Burst).</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.