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13,064 results for “predictions”
Attitude and CTM predictions for From Tribal Polarization to Socio-Economic Disparities: Exploring the Landscape of Vaccine Hesitancy on Twitter paper
<p>The presented data pertains to predictions of Attitudes and CTM, generated through the application of Machine Learning models. These models have been extensively elucidated in the research paper titled "From Tribal Polarization to Socio-Economic Disparities: Exploring the Landscape of Vaccine Hesitancy on Twitter". The aforementioned data is available to the public.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 1919 through 2103
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1919 through 2103 in .fits format.</p>
Predicting the Future of AI with AI
<p>This dataset augments the paper "Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network" by Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp.</p> <p>GitHub: https://github.com/artificial-scientist-lab/FutureOfAIviaAI</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2284 through 2488
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2284 through 2488 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2104 through 2283
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2104 through 2283 in .fits format.</p>
Active Region Magnetograms for Solar Flare Prediction: Full Resolution Dataset Images for ARs 2489 through 2731
<p>This dataset is the images associated with Dryad dataset https://doi.org/10.5061/dryad.dv41ns23n. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2489 through 2731 in .fits format.</p>
Figure 3 in Sexually dimorphic claws predict courtship and mating sequence in the intertidal oribatid mite Fortuynia atlantica (Acari, Oribatida)
Figure 3 Hypothetical process of courtship and sperm transfer in Fortuynia atlantica. 1) 'attraction′:
Figure 2 in Sexually dimorphic claws predict courtship and mating sequence in the intertidal oribatid mite Fortuynia atlantica (Acari, Oribatida)
Figure 2 Graphical depiction (dorsal view) of measured morphological features. (a) distance to bridge, when clasping both handles simultaneously; x - distance from line of ′handles' to the posterior end of the male, y - distance from rostrum to insertion first leg, z - length of leg I. If x+y> z no physical contact with both legs possible, if x+y
Figure 1 in Sexually dimorphic claws predict courtship and mating sequence in the intertidal oribatid mite Fortuynia atlantica (Acari, Oribatida)
Figure 1 (a) schematic drawing with landmarks and obtained measurements on first leg claw. (b-d) box-plots showing the differences in body length, claw length and claw curvature between males and females of F. atlantica. The line in the middle of each box represents the median for each group examined.
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1064 through 1527
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1064 through 1527 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data
<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic 15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate resolution (about 1/12° for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region</p> <p>by Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 2470 through 2731
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 2470 through 2731 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1981 through 2469
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1981 through 2469 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations
<p>This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as concatenated raw results from S-PrediXcan. Files belong to the research paper "Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations" published at HGG Advances (2023, doi: 10.1016/j.xhgg.2023.100216). Please refer to the README file for a more detailed description.</p>
Active Region Magnetograms for Solar Flare Prediction: Extra Dataset Images for ARs 1528 through 1980
<p>This dataset is the extra images associated with Dryad dataset <a href="https://doi.org/10.5061/dryad.qjq2bvqmj">https://doi.org/10.5061/dryad.qjq2bvqmj</a>. These images are consistently sized images of active region magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). These data are the full sized images (600x600 pixels) for active regions (ARs) 1528 through 1980 in .fits format. These are images that were removed from the preconfigured dataset https://doi.org/10.5061/dryad.jq2bvq898.</p>
Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"
<p><strong>Supplements for "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process files (in "PDBs.zip") and produce the dataset ("Dataset_v02.csv")</li> <li>PDBs.zip --- PDB files include filtered structures processed by three programs</li> <li>Dataset_v02.csv --- final filtered version of dataset produced in R language (by "Main_v02.R"). </li> <li>Dataset key.txt --- key to column names in Dataset_v02.csv </li> </ul> <p>The article featuring this dataset is published in:</p> <p>Journal: Journal of Chemical Information and Modeling<br>Title: "Solvent Accessibility Promotes Rotamer Errors During Protein Modelling with Major Side-Chain Prediction Programs"<br>Author(s): Hameduh, Tareq; Mokry, Michal ; Miller, Andrew ; Heger, Zbynek; Haddad, Yazan</p> <p><a href="https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpubs.acs.org%2Fdoi%2F10.1021%2Facs.jcim.3c00134&data=05%7C01%7C%7C4876a5c33e5f4bd8daf008db7e55a28d%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C638242678496577396%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=NE%2F0dYvc4m5%2B8T0YQtqxY21yk1pA9ZpiADqzx3vEJ4Q%3D&reserved=0">https://pubs.acs.org/doi/10.1021/acs.jcim.3c00134</a></p> <p> </p>
Dataset for surface waves height prediction through the video and image processing
<p>Image-based study of surface waves is a long lasting topic in ocean science and remote sensing. We believe that modern computers and new programming techniques can make a break-through in this area.</p> <p> </p> <p>This dataset provides some video files of surface wind waves of two kinds. First is a video snapshot of a quite large area. Second one is a zoom-in video of a spar-buoy (a stick) located in this field. According to the zoom-in video we may see the actual height of the wave in this particular point. This should be treated as a reliable data and so it can be used to calibrate the brightness field. I.e. the users of this dataset are welcome to train their model to obtain the height of the wave out of its brightness on the zoom-out large-area videos.</p> <p> </p> <p>All video files are readable by a conventional software. Records were taken at mild wind conditions in a gulf (fjord or skerry) of the Ladoga Lake. See "readme.pdf" for the details</p>
Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize
<p>Relevant Data and Code for <em>Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize </em>by Taylor Ferebee and Edward Buckler.</p> <p><strong>Input Data</strong></p> <p>The inputs of the models are enclosed in <em>Input_data-2022-001.zip</em></p> <p><strong>Output Data</strong></p> <p>The outputs of the models are enclosed in <em>Output_Results-2022-001.zip</em></p> <p><strong>Relevant Code </strong></p> <p>The code for all analyses is enclosed in <em>Code_Archive.zip </em></p>
Active region magnetograms for solar flare prediction: Extra images dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset contains those images that were removed from the preconfigured datasets (see usage notes below).</p>
Active region magnetograms for solar flare prediction: Full resolution dataset
<p>In this dataset, we provide a comprehensive collection of magnetograms from the National Aeronautics and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The dataset incorporates data from three sources and provides SDO Helioseismic and Magnetic Imager (HMI) magnetograms of solar active regions as well as labels of corresponding flaring activity. This dataset will be useful for image analysis or solar physics research related to magnetic structure, its evolution over time, and its relation to solar flares. The dataset will be of interest to those researchers investigating automated solar flare prediction methods, including supervised and unsupervised machine learning (classical and deep), binary and multi-class classification, and regression. This dataset is a minimally processed, user configurable dataset of consistently sized images of solar active regions that can serve as a benchmark dataset for solar flare prediction research. This dataset consists of full resolution images (see usage notes below).</p>
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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.