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83 results for “Computational methods”
Dataset: Methods for computing the maximum performance of computational models of fMRI responses.
<p>Accompanying data for the revised version of the manuscript: Methods for computing the maximum performance of computational models of fMRI responses. written by Agustin Lage-Castellanos, Giancarlo Valente, Elia Formisano, Federico De Martino, submitted for publication in Plos Computational Biology, November 2018.</p> <p>The dataset (01.rar) contains the fMRI time series for one subject in Nifti format acquired on an actively shielded MAGNETOM 7T whole body system driven by a Siemens console at Scannexus (<a href="http://www.scannexus.nl)">www.scannexus.nl)</a>. Every folder (24 runs, one folder per run) contains 150 Nifti files, one Nifti file for each fMRI volume. Preprocessing consisted of slice scan-time correction (with sinc interpolation), 3-dimensional motion correction, and temporal high pass filtering (removing drifts of 4 cycles or less per run).</p> <p>The matlab file dmS01_24runs.mat contains a 24-length cell array of fMRI design matrices, one for every run. Every fMRI design matrix is size 150 volumes x 51 covariates. The first 42 columns correspond to the stimuli presented (42 sounds per run). Columns 43, and 44, correspond to the run mean and the linear trend covariates. The rest of the columns correspond to the covariates obtained with GLMdenoise. The matlab variable <em>stimulus</em> of size 24 x 42 contains the index of the sounds presented at every run. A total of 168 sounds were presented, each sound was presented 6 times across the 24 fMRI runs.</p> <p>The file SPMgls0.rar contains the Beta images in Nifti format for every column of the fMRI design matrix, including noise covariates, for every fMRI run. This model was estimated assuming i.i.d fMRI noise (OLS). The codes for computing the noise ceiling are available in the file nccodes.rar, together with a two of examples of their use. SPM is required.</p>
Figures for "New method for computing post-seismic deformations in a realistic gravitational viscoelastic Earth model"
<p>Here are all the figures used in the paper "New method for computing post-seismic deformations in a realistic gravitational viscoelastic Earth model". </p>
Systematic benchmarking of computational methods to identify spatially variable genes: Part 1
<div> <div> <div> <p>Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran’s I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks. </p> <p> </p> </div> </div> </div>
Fig. 2. A in Structure elucidation and absolute configuration of metabolites from the soil-derived fungus Dictyosporium digitatum using spectroscopic and computational methods
Fig. 2. A: Key HMBC and COSY correlations of 1. B: Key NOESY correlations of 1. C: Mosher's ester analysis of MTPA-1 (irregular ΔδS−R signs in bold). D: Key HMBC and COSY correlations for dictyosporin C (3). E: Key NOESY correlations of 3. F: Octant rules applied for 3. G: Key HMBC and COSY correlations of dictyosporin D (4). H: Key NOESY correlations of 4. I: Experimental ECD spectrum of 4 and calculated ECD spectra of (1S, 10S)-4 and (1R, 10R)-4.
The Best Parameters for Imaging Agent Injection and Scanning Methods in Computed Tomography Angiography
ClinicalTrials.gov study NCT04832633. IPD Sharing: NO. Countries: 1. Publications: 1.
Comparison of Computer-Assisted and Conventional Local Anesthesia Methods in Mandibular Impacted Third Molar Surgery
ClinicalTrials.gov study NCT06852066. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Can Methods From Computational Psychology be Used to Phenotype Individuals Most Likely to be Non-adherent to Fitness Goals?
ClinicalTrials.gov study NCT04783298. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Is computer-assisted instruction more effective than other educational methods in achieving ECG competence amongst medical students and residents? A systematic review and meta-analysis.
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Data from: Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity and synchronization in growing cells
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Evaluation datasets and pre-computed scores for: "CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations"
<p>CAPICE is a computational method for predicting the pathogenicity of SNVs and InDels.</p> <p>This new repository added index for CAPICE v1.0 (build37) precomputed files.</p> <p><strong>Repository description:</strong></p> <p>1) "paper_datasets.tar.gz" contains all datasets used in the CAPICE paper;</p> <p>2) "capice_v1.0_build37_indels.tsv.gz" contains the precomputed scores for InDels in genome build 37</p> <p>3) "capice_v1.0_build37_indels.tsv.gz.tbi" contains the index for file"capice_v1.0_build37_indels.tsv.gz"</p> <p>4) "capice_v1.0_build37_snvs.tsv.gz" contains the precomputed scores for all possible SNVs in genome build 37</p> <p>5) "capice_v1.0_build37_snvs.tsv.gz.tbi" contains the index for file "capice_v1.0_build37_snvs.tsv.gz"</p> <p> </p>
Automated X-ray computer tomography segmentation method for finite element analysis of non-crimp fabrics reinforced composites
<p>Data behind the publications:</p> <p>Auenhammer, R.M., Mikkelsen, L.P., Asp, L., Blinzler, B. Automated X-ray computer tomography segmentation method for finite element analysis of non-crimp fabric reinforced composites. <em>Composite Structures, </em><strong>256</strong>, 113136, <a href="https://doi.org/10.1016/j.compstruct.2020.113136">https://doi.org/10.1016/j.compstruct.2020.113136</a>, 2021.</p> <p>Auenhammer, Robert M., Lars P. Mikkelsen, Leif E. Asp, Brina J. Blinzler, Dataset of non-crimp fabric reinforced composites for an X-ray computer tomography aided engineering process, <em>Data in Brief, </em><strong>33</strong>, 106518, <a href="https://doi.org/10.1016/j.dib.2020.106518">https://doi.org/10.1016/j.dib.2020.106518</a>, 2020.</p> <p>Auenhammer, R.M., L.P. Mikkelsen, L.E. Asp, B.J. Blinzler, X-ray tomography based numerical analysis of stress concentrations in non-crimp fabric reinforced composites - assessment of segmentation methods. <em>IOP Conf. Ser.: Mater. Sci. Eng.</em> <strong>942</strong>, 012038, <a href="https://doi.org/10.1088/1757-899X/942/1/012038">https://doi.org/10.1088/1757-899X/942/1/012038</a>, 2020</p> <p>The data-set contain data from three samples: A, E and G. </p> <p>For each sample the data are saved in the follow format</p> <ul> <li>X-ray scan: nii-files</li> <li>SEM scan: tif-files</li> <li>Abaqus files: inp-files </li> <li>X-ray setting: pdf-files</li> <li>SEM settings: hdr-ascii files</li> </ul> <p> </p>
Data from: The Shortlist Method for fast computation of the Earth Mover's Distance and finding optimal solutions to transportation problems
Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method.
Data from: Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation
1. In a recent paper, Bradburd et al. (Evolution, 67, 2013, 3258) proposed a model to quantify the relative effect of geographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways. 2. We modify the covariance model so as to fit better with mainstream geostatistical models and avoid mathematically ill-behaved covariance functions. We extend the model – initially implemented only for co-dominant bi-allelic markers such as single nucleotide polymorphisms – to encompass highly polymorphic markers such as microsatellites. We implement and test a model selection procedure that allows users to assess which model (e.g. with or without an environment effect) is most suited. We code all our MCMC algorithms in a mix of compiled languages which allows us to decrease computing time by at least one order of magnitude. We propose an approximate inference and model selection method allowing us to deal with genomic data sets (several hundred thousands loci). 3. We also illustrate the potential of the method by re-analysing three data sets, namely harbour porpoises in Europe, coyotes in California and herrings in the Baltic Sea. 4. The computer program developed here is freely available as an r package called sunder. It takes as input georeferenced allele counts at the individual or population level for co-dominant markers. Program homepage: http://www2.imm.dtu.dk/~gigu/Sunder/.
Data from: Computational performance and statistical accuracy of *BEAST and comparisons with other methods
Under the multispecies coalescent model of molecular evolution, gene trees have independent evolutionary histories within a shared species tree. In comparison, supermatrix concatenation methods assume that gene trees share a single common genealogical history, thereby equating gene coalescence with species divergence. The multispecies coalescent is supported by previous studies which found that its predicted distributions fit empirical data, and that concatenation is not a consistent estimator of the species tree. *BEAST, a fully Bayesian implementation of the multispecies coalescent, is popular but computationally intensive, so the increasing size of phylogenetic data sets is both a computational challenge and an opportunity for better systematics. Using simulation studies, we characterize the scaling behavior of *BEAST, and enable quantitative prediction of the impact increasing the number of loci has on both computational performance and statistical accuracy. Follow-up simulations over a wide range of parameters show that the statistical performance of *BEAST relative to concatenation improves both as branch length is reduced and as the number of loci is increased. Finally, using simulations based on estimated parameters from two phylogenomic data sets, we compare the performance of a range of species tree and concatenation methods to show that using *BEAST with tens of loci can be preferable to using concatenation with thousands of loci. Our results provide insight into the practicalities of Bayesian species tree estimation, the number of loci required to obtain a given level of accuracy and the situations in which supermatrix or summary methods will be outperformed by the fully Bayesian multispecies coalescent.
METHODS OF TEACHING STUDENTS TO THINK INDEPENDENTLY IN THE PROCESS OF LEARNING COMPUTER TECHNOLOGIES.
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Accelerating the Conjugate Gradient Method on Distributed-Memory Computers: Software and Experiment Data
<p>This Zenodo record consists of supplementary material for journal article 'Accelerating the Conjugate Gradient Method on Distributed-Memory Computers' by Kozický, Šimeček and Rúra. Archive 'software.zip' contains the source code of the software we used to perform the experiments in our article, archive 'data-experiments.zip' contains the raw data from our experiments and archive 'data-manuscript.zip' contains the result data presented in our article.</p>
Screening and mechanism of novel angiotensin-I-converting enzyme inhibitory peptides in X. sorbifolium seed meal: A computer-assisted experimental study method
<p>本文档是补充材料</p>
A Computer Graphics Approach to Creating New Method for Generating 3D Gesture Animations in Bangla Sign Language via HamNoSys to SiGML Conversion
<p>To prepare the system, we employed 94 classes of data. In this dataset, there are 13 Bangla numerical data classes, 36 Bangla alphabet data classes, and 41 Bangla word data classes. Every class of data contains different data types like Hand Shape, Hand Orientation, Hand Movement, Notations, etc. These data were created in SiGML tags. Every class of data is unique and different from others. The system was prepared using BdSL. And BdSL is an uncommon and unique sign language, among others. That's why every class of data is unique and created by us. We search HamNoSys notations for Bangla alphabets, words, and numbers in English HamNoSys datasets (almost 6,000 data). However, we find only 20% of the data, which is quite similar to BdSL. We create 80% HamNoSys notation for BdSL and we modify the matching 20% notations. Then, we converted them into SiGML and made the data classes. This was a big challenge in our research. </p>
scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data
<p><span>In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, specific evaluation for different contamination levels is lacking. Here, we show that DecontX and CellBender under-correct highly-contaminating genes, while SoupX and scAR over-correct lowly-/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly-contaminating genes while avoiding over-correction of other genes.</span></p>
Appendix to the paper titled: Which teamwork challenges do computing students face in a project-based learning course in research methods?
<p>Instructions on reflection reports, interview guide, and example student projects.</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.