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13,064 results for “predictions”
Prediction of Compounds in Different Local SAR Environments using ECP
<p>SD files of 15 data sets reported in the manuscript are uploaded. Each data set is represented by its CHEMBL Target ID. The file format is provided in the file 'description.txt'.</p>
Neutralization Data and Aligned ENV Sequences for Predicting Antibody Affinities using Artificial Neural Networks
<p>Sample file with neutralization data (IC<sub>50</sub>) for different antibodies and viral strains, adapted from J. Huang, G. Ofek, L. Laub, M. K. Louder, N. A. Doria-Rose, N. S. Longo, H. Imamichi, R. T. Bailer, B. Chakrabarti, S. K. Sharma, S. M. Alam, T. Wang, Y. Yang, B. Zhang, S. A. Migueles, R. Wyatt, B. F. Haynes, P. D. Kwong, J. R. Mascola, and M. Connors, “Broad and potent neutralization of HIV-1 by a gp41-specific human antibody.,” <em>Nature</em>, vol. 491, no. 7424, pp. 406–12, Nov. 2012.</p> <p> </p> <p>Aligned ENV sequences downloaded from the HIV Sequence Database (www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). There are 4907 sequences and the alignment length is 1369.</p>
Data and software associated with PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources
<p>Data and software associated with the paper:</p> <p>PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources</p>
Supporting data for: A close look at protein function prediction evaluation protocols
<p>Data files and predictions associated with the paper: A close look at protein function prediction evaluation protocols.</p>
Host-pathogen protein interactions predicted using structure
<p>This dataset accompanies a manuscript describing a method to predict host-pathogen protein interactions using structure:</p> <p>Host-pathogen protein interactions predicted by comparative modeling.<br /> Davis FP, Barkan DT, Eswar N, McKerrow JH, Sali A. Protein Sci (2007) 16:2585-2596.<br /> http://www.proteinscience.org/cgi/doi/10.1110/ps.073228407</p> <p>The files contain predictions made for 10 human pathogens including species of Mycobacterium, Apicomplexa, and Kinetoplastida. The species.all.zip files contain all interactions predictions for each species along with the filter criteria that each interaction passed. The species.filter.zip files contains the same information, but only for the subset of interactions that passed the biological and network-level filters. These files can be viewed in any spreadsheet program, such as Excel.</p> <p> </p> <p>Predictions were made for interactions between human and </p> <ol> <li>Mycobacterium tuberculosis: mtuber</li> <li>Mycobacterium leprae: mleprae</li> <li>Leishmania major: lmajor</li> <li>Trypanosoma brucei: tbrucei</li> <li>Trypanosoma cruzi: tcruzi</li> <li>Cryptosporidium hominis: chominis</li> <li>Cryptosporidium parvum: cparvum</li> <li>Plasmodium falciparum: pfalciparum</li> <li>Plasmodium vivax: pvivax</li> <li>Toxoplasma gondii: tgondi</li> </ol>
CTCF loop analysis and prediction scripts and datasets
<p>Additional files accompanying the study of experimental and predicted CTCF loops in human cell lines by Oti et al.</p> <p>ctcfloopscripts_v1.0.zip: Scripts used in the analysis of CTCF-mediated loops in human cell lines; both experimental ChIA-PET loops as well as predicted loops based on ChIP-seq data. Zipped file.</p> <p>ctcf_predictedloops_ENCODE_chipseq_datasets.tar.gz: Predicted CTCF loops for 100 ENCODE ChIP-seq datasets. Gzipped tar file.</p> <p>fimo_ctcfmotifs_MA0139_hg19_2.5e-4.bed.gz: genome-wide CTCF motifs in human genome (hg19) detected by FIMO tool.</p>
LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements
<p>Dataset relative to the following publication:</p> <p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2016). LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements. <em>Journal of Neurophysiology, </em>in press</p> <p>Each folder contains the data relative to one experiment and the script that was used to generate them. Please refer to "Description on data format.txt" for the usage of the data.</p> <p>Additional information can be deducted from the experimental scripts.</p>
WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction - A Model-Driven Approach for Session-Based Application Systems.
<p>Supplementary material for the paper: "WESSBAS: Extraction of Probabilistic Workload Specifications for Load Testing and Performance Prediction".</p> <p>Included in the supplementary material are the evaluation results.</p> <p>The WESSBAS software relevant to the paper is available via https://github.com/Wessbas/</p> <p>The WESSBAS UI is available as a password-protected (password: wessbasui) ZIP file:</p> <p>https://dl.dropboxusercontent.com/u/81621779/wessbas.ui.zip (--- WESSBAS GUI (license confirmation pending, i.e., not on GitHub, yet))</p>
Cortical idiosyncrasies predict the perception of object size
<p>This archive contains preprocessed pRF data from our manuscript "Cortical idiosyncrasies predict the perception of object size" (originally name "Mapping the subjective experience of object size"). This has been published in Nature Communications in 2016: http://www.nature.com/ncomms/2016/160630/ncomms12110/full/ncomms12110.html. Stimulus presentation and data analysis code is available on FigShare: https://dx.doi.org/10.6084/m9.figshare.1579442.v5</p>
Data from "Spontaneous alpha power lateralization predicts detection performance in an un-cued signal detection task", PlosOne2016
<p>32-channel(+6) data (Biosemi Active two, 10-20, 2048Hz) unfiltered from the 2016 Plos ONE paper "Spontaneous alpha power lateralization predicts detection performance in an un-cued signal detection task"</p>
Predicting the sensory consequences of one's own action: First evidence for multisensory facilitation
<p>Dataset relative to the following publication:</p> <p>van Kemenade, B.M., Arikan, B.E., Kircher, T., Straube, B. (2016). Predicting the sensory consequences of one’s own action: First evidence for multisensory facilitation. <em>Attention, Perception, Psychophysics</em> 78:2515–2526</p>
Myeloperoxidase can differentiate between sepsis and non-infectious SIRS and predicts mortality in intensive care patients with SIRS.
<p>Dataset of "Myeloperoxidase can differentiate between sepsis and non-infectious SIRS and predicts mortality in intensive care patients with SIRS."</p>
Issue close time: datasets + prediction classifiers
<p>This project contains experiments on predicting the amount of time required to close issue reports in software repositories. Namely, it contains (a) issue lifetime datasets from 10 large software projects and (b) experiment scripts to generate decision tree classifiers that predict issue close time.</p> <p><strong>To run the cross-validation experiment:</strong><br> 1. Compile the Java classes by running "make" or "make compile-java" on the command line<br> 2. Configure the experimental setup by changing the variables at the top of run.sh<br> 3. Run "bash run.sh" on the command line<br> 4. Results can be found in out/</p> <p><strong>To run the round robin experiment:</strong><br> 1. Compile the Java classes by running "make" or "make compile-java" on the command line<br> 2. Run "bash roundRobin.sh" on the command line<br> 3. Results can be found in out/roundRobin</p> <p> </p> <p>The latest version of this project can be found on GitHub: https://github.com/reesjones/issueCloseTime</p>
Predicting the multisensory consequences of one's own action: BOLD suppression in auditory and visual cortices
<p><strong><em>Data related to the following publication:</em></strong></p> <p>Straube, B., Kemenade, B. M. van, Arikan, B. E., Fiehler, K., Leube, D. T., Harris, L. R., & Kircher, T. (2017). Predicting the Multisensory Consequences of One’s Own Action: BOLD Suppression in Auditory and Visual Cortices. <em>PLOS ONE</em>, <em>12</em>(1), e0169131. http://doi.org/10.1371/JOURNAL.PONE.0169131</p> <p>Further information can be found in the data_description.docx file.</p>
Development of predictive models of the kinetics of a hydrogen abstraction reaction combining quantum-mechanical calculations and experimental data
<p>The files contain the electronic structure calculations for all the levels of theory tested in this work.</p>
DTI predicts Mandarin Learning
<p>The nifti files, bvals, bvecs of each deidentified participants are included.</p> <p>A separate csv file describes basic demographic information and these participants' learning scores reported in the paper: </p> <p>Qi Z., Han M., Garel K., Chen E. S., & Gabrieli J. D. E. (2014). White-matter structure in the right hemisphere predicts Mandarin Chinese learning success. <em>Journal of Neurolinguistics</em>, 33: 14-28.</p>
Defect Prediction: SPE
<p>Software process evaluation is essential to improve software development and the quality of software products in an organization. Conventional approaches based on manual qualitative evaluations (e.g., artifacts inspection) are deficient in the sense that (i) they are time-consuming, (ii) they suffer from the authority constraints, and (iii) they are often subjective. To overcome these limitations, this paper presents a novel semi-automated approach to software process evaluation using machine learning techniques. In particular, we formulate the problem as a sequence classification task, which is solved by applying machine learning algorithms. Based on the framework, we define a new quantitative indicator to objectively evaluate the quality and performance of a software process. To validate the efficacy of our approach, we apply it to evaluate the defect management process performed in four real industrial software projects. Our empirical results show that our approach is effective and promising in providing an objective and quantitative measurement for software process evaluation.</p> <p><strong>Reference: </strong>Chen, Ning, Steven CH Hoi, and Xiaokui Xiao. "Software process evaluation: A machine learning approach." <em>Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering</em>. IEEE Computer Society, 2011.</p>
mvaziri/PredictingActions_RawData: Second release of the Predicting Actions raw data
<p>Raw data for the manuscript titled "Predicting actions from subtle preparatory movements" in the journal "Cognition".</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>
Predictive and sensory signals systematically lower peak forces in the exploration of softer objects
<p>An object’s softness is stereotypically judged by pressure movements indenting the surface [1]. In exploration without movement constraints, participants repeat such indentation movements. We investigated how people modulate executed peak forces for different indentations depending on stimulus softness. Participants performed a 2AFC discrimination task for stimulus pairs from one of 4 softness categories. We assumed that movement control at different exploration moments is based on variations in the predictive and sensory signals available. We manipulated availability of predictive signals on softness category, by presenting either stimuli of the same category in a blocked fashion (high predictability) or by randomly mixing stimuli from different categories (low predictability). Effects of sensory signals were examined by contrasting first and last indentation, as sensory signals are hardly available when initiating exploration but gathered during exploration. The results show that participants systematically apply lower forces when sensory or predictive signals indicate softer objects as compared to harder objects. We conclude that softness exploration can be considered as a sensorimotor control loop, in which predictive and sensory signals determine movement control. Further, the results indicate a high importance of predictive processes throughout the entire exploration, as effects of predictive signals maintain in the last indentation.</p> <p><strong>Lezkan</strong>, A. & <strong>Drewing</strong>, K. (2015). Predictive and sensory signals systematically lower peak forces in the exploration of softer objects. World Haptics Conference, 2015 IEEE (pp. 69-74). IEEE.</p> <p>The Zip file contains all data relative to the publication. For each participant code and each session exists a *txt and</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</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.