Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,064
datasets available to search
ShareScore release 0.9.0
Dataset results
13,064 results for “predictions”
CNNpredIM - Dataset for Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network
<p>The <strong>dataset</strong> available here is the dataset used in the <a href="https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggaa233/5836721"><strong>paper</strong> <em>"Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network".</em></a></p> <p>The <strong>abstract</strong> of the <strong>paper</strong>:</p> <blockquote> <p>This study describes a deep convolutional neural network (CNN) based technique for the prediction of intensity measurements (IMs) of ground shaking. The input data to the CNN model consists of multistation 3C broadband and accelerometric waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0. We find that the CNN is capable of predicting accurately the IMs at stations far from the epicenter and that have not yet recorded the maximum ground shaking when using a 10 s window starting at the earthquake origin time. The CNN IM predictions do not require previous knowledge of the earthquake source (location and magnitude). Comparison between the CNN model predictions and the predictions obtained with Bindi et al. (2011) GMPE (which require location and magnitude) has shown that the CNN model features similar error variance but smaller bias. Although the technique is not strictly designed for earthquake early warning, we found that it can provide useful estimates of ground motions within 15-20 sec after earthquake origin time depending on various setup elements (e.g., times for data transmission, computation, latencies). The technique has been tested on raw data without any initial data pre-selection in order to closely replicate real-time data streaming. When noise examples were included with the earthquake data, the CNN was found to be stable predicting accurately the ground shaking intensity corresponding to the noise amplitude.</p> </blockquote>
MiRoR4_P1_A systematic review describes models for recruitment prediction at the design stage of a clinical trial
<p>This dataset is related to the publication "A systematic review describes models for recruitment prediction at the design stage of a clinical trial".</p>
Fig. 6 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)
Fig. 6. Kruskall-Wallis results of the lapillus length and weight in each Prochilodus lineatus age; ages 2 and 7 were not included in analysis because they only have one sample.
Fig. 1 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)
Fig. 1. Study area and location of sampling sites in the floodplain of the Upper Paraná River (Baía River – 1; Ivinhema Ri- ver – 2; Paraná River – 3; lagoa guaraná – 4; lagoa dos patos – 5; lagoa das garças – 6; lagoa do Osmar – 7; ressaco do paú véio – 8; lagoa fechada – 9; lagoa ventura – 10).
Fig. 3 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)
Fig. 3. Fits of the linear regressions between lapillus otolith and standard length of Prochilodus lineatus: a. Otolith Length (r2 = 0.80) and b. Otolith Weight (r2 = 0.82); the shaded area represents the confidence interval of 95% of the estimate; loess fit of raw residuals are in right panels.
Anonymized Dataset for "Do Programmers Prefer Predictable Code"
<p>This package contains the anonymized dataset, R notebook results, and R code for processing the meaning preserving transformations and human subject study. Note that the title has been changed from the earlier version on arvix which was "Do People Prefer 'Natural' Code?".</p> <p>See the README file for more details.</p>
ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Label-free prediction (fnet) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: 3D paired microscopy images (fluorescence and transmitted light)</p> <p><strong>Microscopy data type</strong>: Confocal microscopy data (TOM20 labeled with Alexa Fluor 594)</p> <p><strong>Microscope</strong>: Leica SP8, HC PL APO 63x 1.40 NA oil objective </p> <p><strong>Cell type</strong>: HeLa (fixed using an organelle-preserving protocol)</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 x 32 (Pixel size: x and y: 90 nm pixel size, z: 150 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn</p> <p><strong>Contact email</strong>: c.spahn@chemie.uni-frankfurt.de, heilemann@chemie.uni-frankfurt.de</p> <p><strong>Affiliation</strong>: Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><br> <strong>Funding body(ies)</strong>: M.H. and C.S.: German Science Foundation (grant nr. SFB1177). C.S.: European Molecular Biology Organization (short term fellowship 8589)</p>
Data for: Brain structural connectivity predicts brain functional complexity
<p>Data used in analyses for "Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal"</p>
Prediction of nucleosome dyads for the K562 cell line in the hg19 genome assembly
<p><strong><a href="https://andre-rendeiro.com/2015/05/12/predicting_dyads_from_mnase">Predicting dyads from MNase-seq data</a></strong></p> <p>I needed the location of nucleosomal dyads in the K562 cell line (ENCODE tier 1 line). Surprisingly, although plenty of MNase-seq data for that cell line is available, no nucleosome and dyad prediction exists.</p> <p><strong>Running NuMap</strong></p> <p>I found the <a href="http://www-hsc.usc.edu/~valouev/NuMap/NuMap.html">NuMap</a> software by Anton Valouev to do exactly what I intended.</p> <p>Since it is in a somewhat obscure page and this seemed to be the only place where this software was, I have <a href="https://github.com/afrendeiro/NuMap">uploaded it into a Github repository</a> for the sake of preservation (<a href="https://github.com/orphancode/NuMap">https://github.com/orphancode/NuMap</a>).</p> <p>Predicting dyads from MNase-seq data with NuMap seemed trivial: I downloaded the <a href="http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeSydhNsome/">K562 MNase-seq data set</a> (11 replicates ~85Gb!!), combined all replicates and ran NuMap on the data(instructions on the Github README).</p> <p>From NuMap output there are <a href="https://www.dropbox.com/s/asmp7bi40lrvtjb/K562_dyads.bed?dl=0">dyad positions in bed format</a> and you can also produce several metrics to evaluate how good the prediction was.</p> <p><strong>Distograms & phasograms</strong></p> <p>Valouev describes two measurements of the frequencies of distances between MNase-seq reads. The frequency of distances between reads mapping to opposite strands can be used to build a “distogram”, which ilustrates the expected nucleosome length (147 bp) - this is consistent across most eukaryotic cells. The frequency of distances between reads mapping to the same strand gives a measurement of the distance between nucleosomes, as they’re separated by some linker DNA - (Valouev calls this plot a “phasogram”). This measurement, on the other hand tends to be species and cell-type specific.</p> <p><strong>K562 predictions:</strong></p> <p>The expected 147 bp nucleosome length in K562 cells.</p> <p>The average distance between dyads in K562 cells seems to be 185 bp.</p> <p><strong>References:</strong></p> <p>Valouev, A., Johnson, S. M., Boyd, S. D., Smith, C. L., Fire, A. Z., Sidow, A. (2011). Determinants of nucleosome organization in primary human cells. Nature, 474(7352), 516–520. <a href="http://doi.org/10.1038/nature10002">http://doi.org/10.1038/nature10002</a></p>
Data set related to the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons"
<p>Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript.</p>
Dataset, Survey, and R Notebooks for "Does Surprisal Predict Code Comprehension Difficulty?"
<p>(Version 1.1 Update)</p> <p>- Removed anonymization after reviewing period ended and paper was accepted at Cogsci 2020.</p> <p>- Added Qualtrics Survey in exported form</p> <p>Dataset and R analysis scripts for the paper "Does Surprisal Predict Code Comprehension Difficulty?". For more details, see "ComprehensionREADME.md" in the included zip file.</p>
A Dataset of Pull Requests and A Trained Random Forest Model for predicting Pull Request Acceptance
<p>A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variables, code snippet for creating a Random Forest model for predicting pull request acceptance, and a pre-trained Random Forest model (in R). The dataset is for the ESEM-2020 paper: "Impact of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem" (<a href="https://arxiv.org/abs/2007.04816">https://arxiv.org/abs/2007.04816</a>). </p> <p>Citation:</p> <pre>@inproceedings{dey2020effect, title={Effect of technical and social factors on pull request quality for the npm ecosystem}, author={Dey, Tapajit and Mockus, Audris}, booktitle={Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)}, pages={1--11}, year={2020} }</pre>
Dataset: Accuracy of Motor Error Predictions for Different Sensory Signals
<p>Supplementary Data for <em><strong>Accuracy of Motor Error Predictions for Different Sensory Signals </strong></em>article</p> <p>Dataset associated with the following publication:</p> <p>Joch, M., Hegele, M., Maurer, H., Müller, H., & Maurer, L. K. (2018). Accuracy of Motor Error Predictions for Different Sensory Signals. <em>Frontiers in Psychology</em>, <em>9 </em>(August), 1–13. https://doi.org/10.3389/fpsyg.2018.01376</p>
Volatile organic compound analysis, a new tool in the quest for preterm birth prediction – an observational cohort study
<p>Vaginal swabs were taken in pregnancy in high risk asymptomatic women attending a preterm prevention clinic. Women in the study attended the clinical due to a history of preterm birth or midtrimester pregnancy loss, or due to a history of cervical surgery. Individualised management plans were made depending upon individual patient risk factors. During their attendance to the clinic vaginal swabs were taken for VOC analysis. Swabs were taken between 15 and 28 weeks gestation. Women consented to vaginal swabs at each of their visits to the clinic. The dataset contains GC-IMS VOC data from a G.A.S. GC-IMS and includes a Spreadsheet of demographics.</p>
Dataset for "Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"
<p>Datasets for preprint (https://doi.org/10.1101/2020.07.10.190785) entitled:</p> <p>"Intraspecies diversity reveals a subset of highly variable plant immune receptors and predicts their binding sites"</p> <p>Contains:</p> <p>- All data files for scripts quoted in the preprint and deposited at https://github.com/krasileva-group/hvNLR</p> <p>- Clade Membership Tables</p> <p>- Clade Alignment Files</p> <p>- Clade Trees</p> <p>- Excel files for Figure S1, Figure S2, and Table 1</p>
A Replication Package of Learning Features that Predict Developer Responses for iOS App Store Reviews
<p>This replication package contains the dataset and script used in our paper "<em>Learning Features that Predict Developer Responses for iOS App Store Reviews.</em>" The paper has been accepted at the ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2020. For further modification and versioning of the dataset (as well as the preprint) please go to <a href="https://github.com/Kamonphop/ESEM20-Replication">https://github.com/Kamonphop/ESEM20-Replication</a></p>
Data from: Can the genomics of ecological speciation be predicted across the divergence continuum from host races to species? A case study in Rhagoletis
<p>Studies assessing the predictability of evolution typically focus on short-term adaptation within populations or the repeatability of change among lineages. A missing consideration in speciation research is to determine whether natural selection predictably transforms standing genetic variation within populations into differences between species. Here, we test whether host-related selection on diapause timing anticipates genome-wide differentiation during ecological speciation by comparing ancestral hawthorn and newly formed apple-infesting host races of <i>Rhagoletis pomonella </i>to their sibling species <i>R. mendax</i> that attacks blueberries. The responses of 57,857 single nucleotide polymorphisms in a diapause study on the hawthorn race strongly predicted the direction and magnitude of genomic divergence among the three flies at a field site in Fennville, Michigan, USA. As anticipated, the apple race and <i>R. mendax</i> show parallel changes in the frequencies of putative inversions on three chromosomes associated with the earlier fruiting times of apples and blueberries compared to hawthorns. A diapause experiment on <i>R. mendax</i> revealed compensatory mutations throughout the genome accounting for the earlier eclosion of blueberry, but not apple flies. Thus, a degree of predictability, although not complete, exists in the genomics of diapause across the ecological speciation continuum in <i>Rhagoletis</i>. The generality of this result is placed in the context of other similar systems.</p>
In silico prediction of ARB resistance: A first step in creating personalized ARB therapy
<p><strong>AT1R Model preparation</strong><br> The crystal structure of human AT1R bound to olmesartan (PDB: 4ZUD) was downloaded from the RCSB Protein Data Bank. 4ZUD contains apocytochrome b562RIL fused to the amino terminus, and many of the flexible regions, as well as helix 8, are not resolved. In order to generate an appropriate starting structure, olmesartan and the apocytochrome b562RIL fusion were removed from 4ZUD, and the missing regions were added to the protein with MOE software (Chemical Computing Group ULC, Montreal, Canada). Specifically, the N-Terminus (residues 1 to 25), intracellular loop 2 (residues 134 to 140), extracellular loop 2 (residues 186 to 188), intracellular loop 3 (residues 223 to 234), and helix 8 (residues 305 to 316) were added to the AT1R in accordance to the human AT1R sequence and PDB:4YAY. The remaining carboxyl-tail of the AT1R (residues 317 to 359) was not modeled. The AT1R model then underwent an energy minimization within MOE using the Amber10:Extended Huckel Theory (EHT) force field.</p> <p><strong>Molecular dynamic (MD) simulations and analysis</strong><br> The MOE minimized AT1R was loaded into CHARMM-GUI. An 80 Å by 80 Å lipid bi-layer composed of 13% cholesterol and 87% Phosphatidylcholine (POPC) was generated around the receptor. Water was packed 17.5 Å above and below the lipid bi-layer, and 150 mM Na+ and Cl- ions were added to the system via Monte-Carlo ion placing. The all-atom CHARMM C36 force field for proteins and ions, and the CHARMM TIP3P force field for water were selected. A hard non-bonded cutoff of 8.0 angstroms was utilized. All molecular dynamics simulations were performed using the PMEMD module of the AMBER16 package with support for MPI multi-process control and GPU acceleration code. Orthorhombic periodic boundary conditions with a constant pressure of 1 atm was set via the NPT ensemble and temperature was set to 310.15°K (37°C) using Langevin dynamics. The SHAKE algorithm was used to constrain bonds containing hydrogens. The dynamics were propagated using Langevin dynamics with Langevin damping coefficient of 1 ps-1 and a time step of 2 fs. Before the production run, the AT1R model was minimized for 5000 steps using the steepest descent method and then equilibrated for 600 ps. The protein coordinates were saved in 10 ps intervals. The production run lasted 150 ns, at which point all three replicas were stable for at least the last 20 ns.</p>
Magnitude and predictability of pH fluctuations shape plastic responses to ocean acidification
<p>Phenotypic plasticity is expected to facilitate the persistence of natural populations as global change progresses. The attributes of fluctuating environments that favor the evolution of plasticity have received extensive theoretical investigation, yet empirical validation of these findings is still in its infancy. Here, we combine high-resolution environmental data with a laboratory-based experiment to explore the influence of habitat pH fluctuation dynamics on the plasticity of gene expression in two populations of the Mediterranean mussel, <i>Mytilus galloprovincialis</i>. We linked differences in the magnitude and predictability of pH fluctuations in two habitats to population-specific gene expression profiles in ambient and stressful pH treatments. The results presented demonstrate population-based differentiation in gene expression plasticity, whereby mussels native to a habitat exhibiting a large magnitude of pH fluctuations with low predictability display reduced phenotypic plasticity between experimentally imposed pH treatments. This work validates recent theoretical findings on evolution in fluctuating environments using an ecologically important marine bivalve, and suggests that populations inhabiting regions exposed to unpredictably fluctuating selection pressures may exhibit reduced plasticity as global change progresses.</p>
Xpresso: Predicting gene expression levels from genomic sequences
<p>Xpresso: Predicting gene expression levels from genomic sequences<br> <br> More info at:<br> Publication: https://doi.org/10.1016/j.celrep.2020.107663<br> Website: https://xpresso.gs.washington.edu/<br> Github: https://github.com/vagarwal87/Xpresso</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.