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4,694
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Dataset results
4,694 results for “data analysis”
Confocal imaging raw data files of autophagy analysis in optn and p62 zebrafish mutants during Mycobacterium marinum infection
<p>Association of fluorescent Mycobacterium marinum bacteria with Ubiquitin immunolabelling and GFP-Lc3 signal in zebrafish larvae carrying mutations in the selective autophagy receptors optnineurin and p62. Data deposited are Leica LIF files belonging bioRxiv 415463; doi: https://doi.org/10.1101/415463</p>
Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'
<p>Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods". </p> <p>https://www.ncbi.nlm.nih.gov/pubmed/28212749</p> <p> </p> <p>In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences.</p> <p>MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e</p>
Raw data from datasets used in SIMON analysis
<p>Here you can find raw data and information about each of the 34 datasets generated by the <em>mulset </em>algorithm and used for further analysis in SIMON.<br> Each dataset is stored in separate folder which contains 4 files:</p> <p><em>json_info: </em>This file contains, number of features with their names and number of subjects that are available for the same dataset<br> <em>data_testing:</em> data frame with data used to test trained model<br> <em>data_training:</em> data frame with data used to train models<br> <em>results: </em>direct unfiltered data from database</p> <p>Files are written in feather format. <a href="http://gist.github.com/LogIN-/00d7628e0850f843ba84a678fac0a103">Here is an example</a> of data structure for each file in repository.</p> <p>File was compressed using 7-Zip available at https://www.7-zip.org/.</p>
Processed input data for vampire-analysis-1
<p>Input files for the analysis and plotting code for the paper "Deep generative models for T cell receptor protein sequences." </p>
Data for: ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data
<p>Data associated with the ColiCoords software paper</p>
Data for BLH_NI_CI Analysis
<p>This is the Data for the paper "An analytical framework for reservoir operation with combined natural inflow and controlled inflowAn analytical framework for reservoir operation with combined natural inflow and controlled inflow".</p>
Training material for analysis small RNA-seq data (Galaxy Training Network tutorial)
<p>The data provided here is part of the Galaxy Training Network tutorial for analysis of small RNA-seq (sRNA-seq) data using mirdeep2 and miranda. This dataset is provided by INRA (Le Rheu, France).</p>
The Effects of Cycle and Treadmill Desks on Work Performance and Cognitive Function in Sedentary Workers: data repository of a review and meta-analysis.
<p>This repository contains additional files related to the review and meta-analysis. The first dataset contains list of search terms. The second dataset contains list od studies included in the meta-analysis. The third dataset contains study evaluation using PEDro scale tool. The fourth dataset contains two additional forrest plots (Effect of cycle and treadmill desks on typing errors and Effect of cycle and treadmill desks on congruent Eriksen Flanker test).</p>
Experimental data and benchmarks used in the paper "Nucleosome Dynamics: A new tool for the dynamic analysis of nucleosome positioning"
<p>Experimental data used to illustrate the analysis with Nucleosome Dynamics pipeline. Three publicly available data sets were used:</p> <ol> <li> <p>Yeast metabolic cycle MNase-seq data downloaded from GEO under accession number GSE77631 corresponding to time points 9 and 12<br> Nocetti, N., and Whitehouse, I. (2016). Nucleosome repositioning underlies dynamic gene expression. Genes & Development 30, 660–672.</p> </li> <li> <p>MNase-seq data for S. cerevisiae cells synchronized in G1 and S phase, as described by Deniz (2016). Raw data available under accession number SAMEA2698380<br> Deniz, Ö., Flores, O., Aldea, M., Soler-López, M., and Orozco, M. (2016). Nucleosome architecture throughout the cell cycle. Scientific Reports 6, 19729.</p> </li> <li> <p>MNase-seq data for S. cerevisiae grown in different media: YPD, Gal, and EtOH. Data aligned to sacCer1 downloaded from GEO using accession numbers GSM351492, GSM351493, and GSM351494.</p> Kaplan N, Moore IK, Fondufe-Mittendorf Y, Gossett AJ et al. The DNA-encoded nucleosome organization of a eukaryotic genome. <em>Nature</em> 2009 Mar 19;458(7236):362-6. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/19092803">19092803</a></li> </ol> <p>Each tar file contains two folders:</p> <ul> <li>inputs: bam/RData files can be used to run Nucleosome Dynamics analyses. bigWig files contain nucleosome coverage and can be used to visualise in a genome browser. </li> <li>outputs: results from all analyses (nucleR, NFR, TSS, Periodicity, Stiffness, NucDyn)</li> </ul> <p> </p> <p>Simulated data used to benchmark nucleosome positioning by nucleR, and nucleosome dynamics by NucDyn, DANPOS and Dimnp.</p> <p><strong>Figure </strong><strong>2B: </strong>synthetic data simulated for comparison of nucleR and Danpos to detect a second family of nucleosomes. Each folder pX contains simulations when the second nucleosome is present in X% of the families.</p> <p><strong>Figure 2C</strong>: Distance between the dyads identified by nucleR and DANPOS to the dyad position in the true synthetic nucleosome map for fuzzy and well positioned nucleosomes.</p> <p><strong>Figure 2D</strong>: Synthetic data used to compute sensitivity of the EVICTION prediction for NucDyn, DANPOS and Dimnp. Evictions were simulated removing reads from a given percentage of families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with point_log2FC < -1 and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software), and with default parameters for Dimnp </p> <p><strong>Figure 2E</strong>: Synthetic data used to compute sensitivity of the SHIFT prediction. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%). </p> <p>For each simulated data:</p> <ul> <li><em> *.RData</em> files can be used to run nucleR or NucDyn (*mod* corresponds to the modified reads: eviction or shift introduced)</li> <li><em>*.bed </em>files can be used to run DANPOS or Dimnp (*mod* corresponds to the modified reads: eviction introduced)</li> <li>results/ folder contains results from DANPOS</li> <li><em>NR.gff</em> contains the results from nucleR (*mod* corresponds to the results for modified reads: eviction or shift introduced)</li> <li><em>ND.gff</em> contains the results from NucDyn</li> <li><em>res_dimnp_*</em> contains the results from Dimnp</li> </ul> <p><strong>FigSupDanposShift:</strong> Synthetic data used to compute sensitivity of the SHIFT prediction for DANPOS. Shifts were introduced displacing reads from 1 to 5 DNA turns (i.e. 10-50 bp) and modifying different percentages of the families (10%, 20%, …, 90%) and were identified from DANPOS output as a nucleosome with treat2control_dis-10 larger than the given displacement and point_diff_FDR < 0.01 (point with highest difference in the two samples, as reported by the software).</p> <p>For each simulated data:</p> <ul> <li><em> *.bed</em> files contain the modified nucleosome positions</li> <li><em>results</em> folder contains output from DANPOS</li> </ul>
SNase Diffraction Data and Analysis
<p>This upload contains data and analysis scripts used in the manuscript "Towards computational design of improved protein crystal resolution". In this dataset, Figures 3 and 9 can be generated using the analyze_xscale.R script; source data for Figures 4, 5, and 6 can be found in the convert-map-for-pymol/ subdirectories; Figure 8 can be generated using the "plot_crystal_scores.R" script.</p>
Data and analysis code for 'Monovision and the misperception of motion'
<p>Data from the publication 'Monovision and the misperception of motion. J Burge, V Rodriguez-Lopez, C Dorronsoro'. to be published.</p> <p>Preprint version of the manuscript in <a href="https://www.biorxiv.org/content/10.1101/591560v1">https://www.biorxiv.org/content/10.1101/591560v1</a>.</p> <p>The file includes Matlab code to plot the data of the paper. Please, follow the instructions in the readme.pdf file to plot the data.</p> <p>For any other request, contact Johannes Burge (jburge@psych.upenn.edu).</p>
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
Trimmed RNASeq pair for the Galaxy Training Network tutorial - "Metatranscriptomics analysis using microbiome RNASeq data"
<p>Functional microbiome analysis which estimates the functional groups expressed by microbial community enables researchers to look beyond taxonomic composition and correlation with the condition under study. Using microbial community RNA-Seq data and subsequent metatranscriptomics workflows to elucidate the functional complement of the microbiome is gaining interest in the field. <br> This Galaxy training network tutorial will introduce researchers to the basic concepts and tools from the published ASaiM workflow (Batut et al, <em>GigaScience</em> (2018), 7 (6),<a href="http://dx.doi.org/10.1093/gigascience/giy057"> http://dx.doi.org/10.1093/gigascience/giy057</a>). </p> <p>The dataset is a trimmed version of one of the time points from a cellulose degradation biogas reactor dataset. The dataset has been trimmed to facilitate running the workflows for this tutorial. Any biological interpretation from the results would be incorrect, due to the trimmed version of the dataset.</p>
Data and Analysis Artifacts for Service-Based Evolvability Patterns (Experiment and Metrics)
<p>Two functionally equivalent service-based web-shop systems (one version with selected service-based patterns, one without) were analyzed with a controlled experiment as well as with structural maintainability metrics. This repo contains all analysis artifacts.</p>
Original Data of Paper: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis
<p>Paper title: Novel lncRNA-panel as biomarkers for prognosis in breast cancer via Ce-RNA Network analysis. Our paper was submitted to PeerJ recently. This data file is the original data of this study which contains all the original data involved in this work.</p>
VERTEX simulator snapshot with pre-generated data and analysis scripts for electric field stimulation simulations
<p>This dataset contains a snapshot of the VERTEX Matlab toolbox for simulating spiking neural networks, along with Matlab files for pre-generated results from simulations run using VERTEX. The scripts and results files here are to accompany the manuscript 'Predicting the impact of electric field stimulation in a detailed computational model of cortical tissue' and pertain to simulations of electric field stimulation on a cortical tissue model. Analysis scripts for the simulation results along with scripts for setting up and running simulations are included.</p>
Microscope Image Analysis Course Sept 2109 -- Image Data siRNA Screen
<p>Contains 380 Fluorescence images of DAPI stained HeLa nuclei, acquired in 42 wells of a 384 well plate. individual wells were treated with siRNA according to the layout file. Images were acquired with an Olympus ScanR system at 10x magnification, maetadata is given in the experiment_descriptor.xml file.</p>
Supplemental data to Incense Road' from Petra to Gaza: an analysis using GIS and cost functions
<p>Base data and results in GIS format (shapefiles) of the research 'Incense Road’ from Petra to Gaza: an analysis using GIS and cost functions' </p>
Developing Implementable Climatic Input Data and Moisture Boundary Conditions for Pavement Analysis and Design
<p>Corresponding data set for Tran-SET Project No. 18POKS03. Abstract of the final report is stated below for reference:</p> <p>"The main objective of this study is to develop a practical and implementable numerical model for predicting the moisture (suction) regime within the pavement subgrade system. The research quality and uniformly-dispersed climate data over short distances from Oklahoma Mesonet and the Mitchell based moisture (suction) prediction methods establish the main background of the research study. The study involved numerical modeling and statistical analysis of climatic weather data. The proposed moisture variation model predicts the suction distribution throughout the soil subgrade by solving the diffusion equation and incorporates the measured suction from the Oklahoma Mesonet to estimate the diffusion coefficient. The research study resulted in a practical prediction model that could be used to determine the moisture boundary conditions within the pavement structure."</p>
Human sequence alignment data set used for analysis of SPDI algorithm and tools
<p>Collection of alignment segments produced on October 30, 2019. The ADS currently consists of over 2,680,000 pairwise alignment segments generated from over 350,000 distinct input sequences. </p> <ul> <li> <p>Old assembly to current Genome Reference Consortium (GRC) <a href="http://f1000.com/work/citation?ids=111899&pre=&suf=&sa=0">(Church et al., 2011)</a> primary assemblies (e.g. GRCh36(hg18) or GRCh37(hg19) with GRCh38(hg38))</p> </li> </ul> <ul> <li> <p>Patches, alternative loci, or pseudoautosomal regions (PAR) to GRC primary assembly</p> </li> <li> <p>RefSeq <a href="http://f1000.com/work/citation?ids=2599029&pre=&suf=&sa=0">(O’Leary et al., 2016)</a> and select GenBank <a href="http://f1000.com/work/citation?ids=6183037&pre=&suf=&sa=0">(Benson et al., 2018)</a> transcripts to selected RefSeq genomic regions, also known as RefSeqGene (NG), a member of the Locus Reference Genome (LRG) collaboration <a href="http://f1000.com/work/citation?ids=3225699&pre=&suf=&sa=0">(Dalgleish et al., 2010)</a>.</p> </li> <li> <p>Current RefSeq transcripts (NM/NR/XM/XR) and RefSeq genomic regions (NG) to the latest Assembly</p> </li> <li> <p>Previous versions of NG and RefSeq transcripts (NM/NR) to GRC primary assembly</p> </li> </ul>
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.