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13,064
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13,064 results for “predictions”
Climate change amelioration by marine producers: Does dominance predict impact?
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Predicting death by the loss of intestinal function
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Data from: Genetic potential for changes in breeding systems: Predicted and observed trait changes during artificial selection for male and female allocation in a gynodioecious species
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Fine-Grained Just-In-Time Defect Prediction - Appendix
<p>Defect prediction models focus on identifying defect-prone code elements, for example to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews. While the research community has been highly active in proposing metrics and methods to predict defects on long-term periods (i.e., at release time), a recent trend is represented by the so-called short-term defect prediction (i.e., at commit-level). Indeed, this strategy represents an effective alternative in terms of effort required to inspect files likely affected by defects. Nevertheless, the granularity considered by such models might be still too coarse. Indeed, existing commit-level models highlight an entire commit as defective even in cases where only specific files actually contain defects. </p> <p>In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in a commit, that are defective. Finally, we evaluate our model in terms of (i) performance and (ii) the extent to which it decreases the effort required to diagnose a defect. Our study highlights that: (1) defective commits are frequently composed of a mixture of defective and non- defective files, (2) our fine-grained model can accurately predict defective files with an AUC-ROC up to 82% and (3) our model would allow practitioners to save inspection efforts with respect to standard just-in-time techniques.</p>
MESA Prediction models
<p>PrediXcan prediction models from MESA cohort.</p> <p>Also includes LD compilation for S-PrediXcan</p>
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: Predictive mapping of the global power system using open data
<p>Three primary global data outputs from the research:</p> <ul> <li><strong>grid.gpkg:</strong> Vectorized predicted distribution and transmission line network, with existing OpenStreetMap lines tagged in the 'source' column</li> <li><strong>targets.tif:</strong> Binary raster showing locations predicted to be connected to distribution grid. </li> <li><strong>lv.tif:</strong> Raster of predicted low-voltage infrastructure in kilometres per cell.</li> </ul> <p>This data was created with code in the following three repositories:</p> <ul> <li>https://github.com/carderne/gridfinder</li> <li>https://github.com/carderne/predictive-mapping-global-power</li> <li>https://github.com/carderne/access-estimator</li> </ul> <p>Full steps to reproduce are contained in this file:</p> <ul> <li>https://github.com/carderne/predictive-mapping-global-power/blob/master/README.md</li> </ul> <p>The data can be visualized at the following location:</p> <ul> <li>https://gridfinder.org</li> </ul>
Mitigating flicker noise in high-precision photometry. I - Characterization of the noise structure, impact on the inferred transit parameters, and predictions for CHEOPS observations
<p>This repository contains the publicly available artificial exoplanet transit light curves that has been generated in HMI observations.</p> <p>Descriptions of the data are encapsulated in the READme.dat file. For further details, please report to Sec. 3 of the paper (arXiV link: <a href="https://arxiv.org/abs/2003.07707">https://arxiv.org/abs/2003.07707</a>).</p> <p>If you find these synthetic data useful in your own research please cite the Sulis. et al., (2020) paper.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction
<p>We include samples of various data types used in the work "Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/pathway2vec">hallamlab/pathway2vec</a></p>
Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
F I G U R E 1 in Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS- CoV-2
F I G U R E 1 Structural diagrams of spike glycoproteins of SARS‐CoV, MERS‐CoV, and SARS‐CoV‐2. All spike proteins of coronaviruses contain S1 subunit and S2 subunit, which were divided by the S cleavage sites. FP, fusion peptide; HR, heptad repeat 1 and heptad repeat 2; RBD, receptor‐binding domain, contains core binding motif in the external subdomain; SP, signal peptide
F I G U R E 2 in Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS- CoV-2
F I G U R E 2 Phylogenetic analysis of sequences of coronavirus spike glycoproteins. The sequences of spike glycoproteins of SARS‐CoV‐2, bat SARS‐like CoV, pangolin SARS‐like CoV, and SARS‐CoV were analyzed. The red stars indicate pangolin SARS‐like CoV and bat SARS‐like CoV. Host flags are marked after the clusters. SARS‐CoV‐2, severe respiratory syndrome coronavirus‐2
Distribution. Known from five localities in Mozambique (Chinizuia Forest, Gerhard's Cave, Gorongosa Caves, Namapa, and Niassa Reserve) and one locality in NW Zimbabwe (Lutope-Ngolangola Confluence); predicted to occur in much of S Africa, possibly incorporating much of Zambia, Malawi, Mozambique, and Zimbabwe. in Rhinolophidae
Distribution. Known from five localities in Mozambique (Chinizuia Forest, Gerhard's Cave, Gorongosa Caves, Namapa, and Niassa Reserve) and one locality in NW Zimbabwe (Lutope-Ngolangola Confluence); predicted to occur in much of S Africa, possibly incorporating much of Zambia, Malawi, Mozambique, and Zimbabwe.
The motivation for citizens' involvement in life sciences research is predicted by age and gender
<p>Data for publication <em><strong>The motivation for citizens’ involvement in life sciences research is predicted by age and gender</strong></em></p>
Biomedical associations predictions
<p>Resulting predictions</p>
OpenPREDICT: Open and FAIR implementation of the PREDICT method for drug repurposing
<p>This dataset is used for Open and FAIR implementation of the PREDICT method. It is described in the paper titled "PREDICT: a method for inferring novel drug indications with application to personalized medicine.", Gottlieb A, Stein GY, Ruppin E, Sharan R. Mol Syst Biol. 2011;7:496. Published 2011 Jun 7. doi:10.1038/msb.2011.26</p>
Predicting disease in transition dairy cattle based on behaviors measured before calving
<p>Data set and code for the article "Predicting disease in transition dairy cattle based on behaviors measured before calving"</p>
Fig. 3 in Predicting the global mammalian viral sharing network using phylogeography
Fig. 3 Taxonomic and geographic patterns of mean predicted viral sharing link numbers (degree centrality). Top row: all viral sharing links; middle row: viral sharing links with species in the same order; bottom row: viral sharing links with species in another order. a, c, e Average species-level viral sharing link numbers for mammalian orders in our dataset. Bars represent means; error bars represent standard errors. b, d, f Geographic distributions of mean viral sharing link numbers. Distributions were derived by summing the viral sharing link numbers of all species inhabiting a 25 km2 grid square and dividing them by the number of species inhabiting the grid square, giving mean degree number at the grid level.
Fig. 1 Viral sharing GAMM outputs and data distribution. a in Predicting the global mammalian viral sharing network using phylogeography
Fig. 1 Viral sharing GAMM outputs and data distribution. a Predicted viral sharing probability increases with increasing phylogenetic relatedness; the different coloured lines represent different geographic overlap values. b Predicted viral sharing probability increases with increasing geographic overlap; the different coloured lines represent different phylogenetic relatedness values. c The geographic overlap:phylogenetic similarity interaction surface, where the darker colours represent increased probability of viral sharing. White contour lines denote 10% increments of sharing probability. Labels have been removed from some contours to avoid overplotting. d Hexagonal bin chart displaying the data distribution, which was highly aggregated at low values of phylogenetic similarity and especially of geographic overlap.
Dataset for "Predicting the influence of particle size on the glass transition temperature and viscosity of secondary organic material"
<p>The archive file predictingInfluenceParticleSize2020.zip contains scripts and data to generate the figures in the manuscript. Datafiles are in the src/Data folder. Scripts are written in the Julia language. </p> <p>The file predicting_influence_particle2020.tar.gz contains a Docker container. The docker container is a virtual machine that contains all software and dependencies needed to execute the code. The freely available Docker engine must be installed on the local computer (https://docs.docker.com/install/). To install the container run</p> <p>docker load < predicting_influence_particle2020.tar.gz</p> <p>It can be started through the command:</p> <p>docker run -it -p 8888:8888 mdpetters/predicting_influence_particle2020:final</p> <p>Please refer to the supplement of the paper for further instructions.</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.