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4,694 results for “Data Analysis”
Data from: Genomic analysis reveals limited hybridization among three giraffe species in Kenya
<p>The data deposited here was generated by and reported in Coimbra <em>et al.</em> (2023).</p> <p><em>SNP calling and linkage pruning</em></p> <ul> <li><strong>snp_calling_per_species.tar.gz:</strong> includes a genotype likelihoods (GL) file estimated with ANGSD for each giraffe species.</li> <li><strong>sampled_ld.tar.gz:</strong> contains a random sample of estimated pairwise <em>r<sup>2</sup></em> values for each species used to fit linkage disequilibrium (LD) decay curves.</li> <li><strong>ld_pruned_snps.tar.gz:</strong> contains an LD-pruned ANGSD GL file per species.</li> <li><strong>snp_calling_combined.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all sampled individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Relatedness</em></p> <ul> <li><strong>relatedness.tar.gz:</strong> contains the input and output files used with NGSremix to estimate relatedness among giraffe in the dataset.</li> <li><strong>snp_calling_combined_unrelated.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all unrelated individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Population structure and admixture</em></p> <ul> <li><strong>pcangsd.tar.gz:</strong> contains the covariance matrix generated by PCAngsd.</li> <li><strong>ngsadmix.tar.gz:</strong> includes run likelihood lists for each K value ranging from 1 to 11, as well as the admixture proportions (stored in '.qopt' files) inferred from the run with the highest log-likelihood for each K in NGSadmix.</li> <li><strong>evaladmix.tar.gz:</strong> contains the pairwise correlation of residuals between individuals estimated with evalAdmix for the NGSadmix runs with the highest log-likelihood run for each K.</li> </ul> <p><em>SNP-based phylogenomic inference</em></p> <ul> <li><strong>snp_phylogeny.tar.gz:</strong> contains the input PHYLIP file and the IQ-TREE output tree and log files.</li> </ul> <p><em>Phylogeny of mitochondrial genomes</em></p> <ul> <li><strong>mtdna_phylogeny.tar.gz:</strong> includes the 13 mitochondrial protein-coding gene alignments, the partitions file, and the IQ-TREE output tree and log files.</li> </ul> <p><em>Inference of migration events</em></p> <ul> <li><strong>admixture_graphs.tar.gz:</strong> contains the TreeMix / OrientAGraph input file ('treemix.frq.strat.gz'), the output files for all TreeMix and OrientAGraph runs, and the OptM summary table of TreeMix runs ('optm.tsv').</li> </ul> <p><em>Test for introgression</em></p> <ul> <li><strong>dsuite_introgression.tar.gz:</strong> includes the input VCF, the admixture graph topology reconstructed by OrientAGraph, and the Dsuite output files for the estimation of Patterson's D, f4-ratio, and f-branch statistics.</li> </ul> <p><em>Contemporary migration rates</em></p> <ul> <li><strong>ba3-snps.tar.gz:</strong> contains the input and output files for the BA3-SNPs-autotune and BA3-SNPs runs.</li> </ul> <p><em>Demographic reconstruction</em></p> <ul> <li><strong>demographic_inference.tar.gz:</strong> includes the SFS files generated with ANGSD and realSFS and the StairwayPlot2 blueprint and output files.</li> </ul> <p>Other:</p> <ul> <li><strong>metadata.csv:</strong> a companion file containing sample information used in conjunction with R scripts to plot the figures in the paper.</li> </ul>
Data and analysis scripts for: Recent acceleration in global ocean heat accumulation by mode and intermediate waters
<p>The folder contains the MATLAB code and data to re-create Figures 1-9 and S1-3 within the publication by <em>Li, Z., England, M. H., & Groeskamp, S. Recent acceleration in global ocean heat accumulation by mode and intermediate waters, Nature Communications</em>, 2023.</p>
Training data for 'Exome sequencing data analysis' tutorial (Galaxy Training Material)
<p>The data used in this tutorial are a subset of the data published previously in <a href="https://zenodo.org/record/3243160">Training material for the course "Exome analysis with GALAXY"</a>. Credit for uploading the original data goes to Paolo Uva and Gianmauro Cuccuru!</p> <p>Specifically, you may need the following datasets for following the tutorial:</p> <p><strong>Raw sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/father_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/father_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/father_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/mother_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/mother_R2.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R1.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R1.fq.gz</a></li> <li><a href="https://zenodo.org/record/3243160/files/proband_R2.fq.gz?download=1">https://zenodo.org/record/3243160/files/proband_R2.fq.gz</a></li> </ul> <p><strong>Premapped sequencing reads</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_father.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_father.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_mother.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_mother.bam</a></li> <li><a href="https://zenodo.org/record/3243160/files/mapped_reads_proband.bam?download=1">https://zenodo.org/record/3243160/files/mapped_reads_proband.bam</a></li> </ul> <p><strong>Reference sequence (human chromosome 8)</strong></p> <ul> <li><a href="https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz?download=1">https://zenodo.org/record/3243160/files/hg19_chr8.fa.gz</a></li> </ul> <p> </p> <p>If you would just like to play with GEMINI rather than work through the full tutorial, you'll find below a prebuilt GEMINI database (for GEMINI version 0.20.1) for the family trio. You can start exploring this database without having to run GEMINI load and, in fact, without having to install GEMINI's bundled annotation data.</p>
Training Data for 'ewas_suite' Analysis
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes EWAS data from a study published by Hugo, Willy, et al., 2015 (DOI: <a href="https://doi.org/10.1016/j.cell.2015.07.061">10.1016/j.cell.2015.07.061</a>) to identify differentially methylated regions and positions associated with melanoma MAPKi resistance.</p>
Data from: Genome-wide selection components analysis in a fish with male pregnancy
Open the record for dataset details and reuse information.
Data and analysis code from: Micro-scale geography of synchrony in a serpentine plant community
This package includes data and code to reproduce analyses of micro-scale geography of synchrony in the plant community at Jasper Ridge Biological Preserve. Plant cover and soil depth data come from long-term experimental plots established by Richard Hobbs. Plant cover is aggregated into 36 1m2 plots across three treatments (control, gopher exclosure, rabbit exclosure) from 1983 to 2015; included herein are data on the 6 most abundant species (Plantago erecta, Bromus hordeaceous, Lasthenia californica, Microseris douglasii, Vulpia microstachys, and Calycadenia multiglandulosa), total plant cover across all species, and records of gopher disturbance in the plots. The data package also includes time series of monthly precipitation and growing season Palmer’s Drought Severity Index for the same time period. An R Markdown file is included that reproduces all analyses described in the manuscript and reproduces all data figures. Data to support: Walter, Hallett et al. in review “Micro-scale geography of synchrony in a serpentine plant community
Data for study Conventional land-use intensification reduces species richness and increases production: A global meta-analysis
Most current research on land‐use intensification addresses its potential to either threaten biodiversity or to boost agricultural production. However, little is known about the simultaneous effects of intensification on biodiversity and yield. To determine the responses of species richness and yield to conventional intensification, this dataset was created and a global meta‐analysis on it was carried out, thus synthesizing 115 studies. The dataset consists of 449 cases that cover a variety of areas used for agricultural (crops, fodder) and silvicultural (wood) production. It was found that across all production systems and species groups, conventional intensification is successful in increasing yield (grand mean + 20.3%), but it also results in a loss of species richness (−8.9%). However, analysis of sub‐groups revealed inconsistent results. Within high‐intensity systems species losses were non‐significant but yield gains were substantial (+15.2%). Conventional intensification within medium intensity systems revealed the highest yield increase (+84.9%) and showed the largest loss in species richness (−22.9%). Production systems differed in their magnitude of richness response, with insignificant changes in silvicultural systems and substantial losses in crop systems (−21.2%). In addition, this meta‐analysis identifies a lack of studies that collect robust biodiversity (i.e. beyond species richness) and yield data at the same sites and that provide quantitative information on land‐use intensity. These findings suggest that, in many cases, conventional land‐use intensification drives a trade‐off between species richness and production. However, species richness losses were often not significantly different from zero, suggesting even conventional intensification can result in yield increases without coming at the expense of biodiversity loss. These results, which were published in a paper titled Conventional land‐use intensification reduces species richness and increa
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))
<p>Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))</p>
Table S3. List of Locustella sound recordings included in bioacoustic analysis surrounding description of the Taliabu Grasshopper-Warbler. The table provides information on sound library sources and sampling localities of recordings as well as raw data on all 11 bioacoustic parameters measured (see Supplementary Materials section SM3 for more details on parameters). Recordings whose source is labeled as "private recording" were obtained by colleagues and are available upon demand from the corresponding author.
<p>supplement to Rheindt, Frank E., Prawiradilaga, Dewi M., Ashari, Hidayat, Suparno, Gwee, Chyi Yin, Lee, Geraldine W. X., Wu, Meng Yue, Ng, Nathaniel S. R. (2020): A lost world in Wallacea: Description of a montane archipelagic avifauna. Science 367: 167-170, DOI: 10.1126/science.aax2146</p>
Supplementary data: Computational analysis of mechanical stress in colonic diverticulosis
<p>The data set contains code, source data, and derivatives data for the results presented in our research paper titled "Computational analysis of mechanical stress in colonic diverticulosis".</p> <p>The "code" contains Abaqus (SIMULIA, Providence, RI) files for the simulations presented in the paper (tested with Abaqus version 6.13) and jupyter notebooks developed to analyze simulation results.</p> <p>The "sourcedata" folder contains Excel files with parameters used for the simulations as well as data directly extracted from the simulation results.</p> <p>The "derivatives" folder contains secondary data calculated based on the files from "sourcedata".</p> <p>The README document included in the dataset contains a more detailed description of the files and folders.</p>
Data from Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies
<p>This dataset contains three text files in RIS format. They represent the screening process during study selection for "Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies" (<a href="https://doi.org/10.1111/liv.14179">https://doi.org/10.1111/liv.14179</a>). The file DOKU_All TiAb-Screening_20200116_cap contains all 3453 records (merged from original and update search) that were subjected to title-abstract screening. The file DOKU_All FT-Screening_20200116_cap contains all 174 records that were subjected to full-text screening. The file DOKU_All Included_20200116_cap contains all 26 records that were included into the final review.</p> <p>In addition, a PRISMA flow diagram (Fig. 1 in the paper) is available in TIF format.</p>
Output data for manuscript "Tidal analysis of GNSS reflectometry applied for coastal sea level sensing in Antarctica and Greenland"
<p>We retrieve sea levels in polar regions via GNSS reflectometry (GNSS-R), using signal-to-noise ratio (SNR) observations from eight POLENET GNSS stations. Although geodetic-quality antennas are designed to boost the direct reception from GNSS satellites and to suppress indirect reflections from natural surfaces, the latter can still be used to estimate the sea level in a stable terrestrial reference frame. Here, typical GNSS-R retrieval methodology is improved in two ways, 1) constraining phase-shifts to yield more precise reflector heights and 2) employing an extended dynamic filter to account for the second-order height rate of change (vertical acceleration). We validate retrievals over a 4-year period at Palmer Station (Antarctica), where there is a co-located tide gauge (TG). Because ice contaminates the long-period tidal constituents, we focus on the main tidal species (daily and subdaily), by employing a deseasonalization filter. The difference between sub-hourly GNSS-R retrievals of the ocean surface and TG records has a root-mean-square error (RMSE) of 15.4 cm and a correlation of 0.903, while the tidal prediction has a RMSE of 1.9 cm and a correlation of 0.998. There is excellent millimetric agreement between the two sensors for most eight major tidal constituents, with the exception of luni-solar diurnal (<em>K<sub>1</sub></em>), principal solar (<em>S<sub>2</sub></em>), and luni-solar semidiurnal (<em>K</em><sub>2</sub>) components, which are biased in GNSS-R due to the leakage of the GPS orbital period. We also compare the GNSS-R tidal constituents from seven additional POLENET sites, without co-located TG, to global and local ocean tide models. We find that the root-sum-square-error (RSSE) of eight major constituents varies between 26.0 cm and 56.9 cm for different models. Given that the agreement in tidal constituents between the TG and GNSS-R was better at Palmer Station, we conclude that assimilating the GNSS-R retrievals into tidal models would improve their accuracy in Antarctica and Greenland, provided that care is exercised to avoid the orbital period overtones and also sea ice.</p>
Constraining the dense matter equation of state with joint analysis of NICERand LIGO/Virgo measurements: Data for generating plots
<p>In this repository you will find a Jupyter notebook with code to generate the plots from the paper <em>Constraining the dense matter equation of state with joint analysis of NICER and LIGO/Virgo measurements</em> by Raaijmakers et al. (2020).</p>
Data of "Accurate photonic temporal mode analysis with reduced resources"
<p>Data published in "<em>Accurate photonic temporal mode analysis with reduced resources</em>".</p> <p>Phys. Rev. A <strong>101</strong>, 013801</p>
10 Women of Digital Humanities: An Analysis of Linked Open Data
<p>This is a spreadsheet of linked open data, including tweets of the 10 female scholars randomly chosen for this project. This is an experimental study, and was prepared for my own learning. Presentation prepared for a Masters seminar in Information Science at uOttawa in Winter 2020. </p> <p>Linked Open Data in the Humanities was taught by Prof. Constance Crompton.</p> <p>10 Women of Digital Humanities: An Analysis of Linked Open Data</p> <p>tags: dh, digital humanities, feminist dh, computational analysis, Voyant, word clouds, vizualization, digital identifiers, open scholarship</p>
Forensic Exchange Analysis of Contact Artifacts on Data Hiding Timestamps-ADS Experiment Supplementary Files
<p>Da-Yu Kao is an Associate Professor at the Department of Information Management, Central Police University, Taiwan. He was a detective and forensic police officer at Taiwan's Criminal Investigation Bureau (under the National Police Administration). With a Master's degree in Information Management and a Ph.D. degree in Crime Prevention and Correction, he had led several investigations in cooperation with police agencies from other countries for the past 20 years. He is now the director of Computer Crime Investigation Lab at Central Police University and the webmaster of Cybercrime Investigation and Digital Forensics in the Facebook Group.</p>
Data for: Cardiac and Respiratory Self-Gating in Radial MRI using an Adapted Singular Spectrum Analysis (SSA-FARY)
<p>Magnetic Resonance Imaging measurement data used in our paper about self-gating with SSA-FARY (DOI: <a href="https://doi.org/10.1109/TMI.2020.2985994">10.1109/TMI.2020.2985994</a>). Cardiac data was obtained from eight volunteers with no known illness using single-slice radial (SS), simultaneous multi-slice radial (SMS), and stack-of-stars (SoS) FLASH and bSSFP sequences and is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p>
Supplementary Data: A Frequentist Analysis of Three Right-Handed Neutrinos with GAMBIT
<p><strong>Supplementary Data</strong><br> <em> A Frequentist Analysis of 3 Right-Handed Neutrinos with GAMBIT.</em></p> <p>The files in this record contain data for the frequentist global fit of a model with three right-handed neutrinos using the <a href="http://gambit.hepforge.org">GAMBIT</a> tool.</p>
Data to Richter et al. 2020 Comparative analysis of worker head anatomy of Formica and Brachyponera (Hymenoptera: Formicidae) in Arthropod Systematics & Phylogeny
<p>This dataset contains the ant head µCT-Sanning Datasets used for the article "Comparative analysis of worker head anatomy of <em>Formica</em> and <em>Brachyponera</em> (Hymenoptera: Formicidae)" by Richter et al. published in <em>Arthropod Systematics & Phylogeny </em>2020. In addition, the image plates from that article (in highest resolution as TIF files) and a series of supplementary 3D-volume render video files are deposited.</p> <p>The datasets for CASENT0709409, CASENT0709411 and CASENT0790267 are deposited as they were used in this study (with transformation to adjust the orientation of the ant head to the global axes and cropped to reduce file size) while CASENT0709419 is deposited as the original scanning result as it was not modified for the work in this article.</p> <p>Scanning details can be found in the materials and methods section of the article. Scanning parameters were as follows:</p> <p>Power (W): 3W all specimens</p> <p>Voltage (kV): 40kV all specimens</p> <p>Voxel Size: CASENT0709409: 1,1557 µm<sup>3</sup>; CASENT0709419: 1,2244 µm<sup>3</sup>; CASENT0709411: 2,5527µm<sup>3 </sup>; CASENT0790267: 2,8337 µm<sup>3</sup> </p> <p>Exposure Time (s): CASENT0709409: 25 s; CASENT0709419: 15 s; CASENT0709411: 7 s<sup> </sup>; CASENT0790267: 5 s</p> <p>Source Distance (mm): CASENT0709409: -9,5038 mm; CASENT0709419: -9,5321 mm; CASENT0709411: 10,038 mm<sup> </sup>; CASENT0790267: 13,0037 mm</p> <p>Detector Distance (mm): CASENT0709409: 46 mm; CASENT0709419: 43,0166 mm; CASENT0709411: 16,5043 mm<sup> </sup>; CASENT0790267: 18,0015 mm</p>
ScienceDex guides
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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.