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9,113
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9,113 results for “Sampling”
Sample datasets for Galaxy NGS tutorial
<p>Datasets in fastqsanger.gz format representing re-sequencing of human mitochondria </p>
Incorporation of QC samples within the experimental workflow
<p>A liquid chromatography - mass spectrometry experimental workflow can incorporate QC samples (blue) through various combinations with the biological samples (black).</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Umbrella Sampling Data, Crystallographic Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "Free Energy" simulations of crystallographic mode in Ref. [1]. </p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>'pullx' ('and pullx2' files, which contain data from the last 80ns) are used to constuct the free energy profile. Command for building the free energy profile is included in 'wham.sh'</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Umbrella Sampling Data, Parallel Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "Free Energy" simulations of parallel mode in Ref. [1]. </p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>'pullx' ('and pullx2' files, which contain data from the last 80ns) are used to constuct the free energy profile. Command for building the free energy profile is included in 'wham.sh'</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.</strong></p>
Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"
<p>In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data".</p> <p>This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.</p>
Precomputed results for Geodesic Slice Sampling on the Sphere (GeoSSS) paper
<p>Precomputed results used in the publication titled "<a href="https://arxiv.org/abs/2301.08056">Geodesic Slice Sampling on the Sphere</a>". This can be used with our package on Github: <a href="https://github.com/microscopic-image-analysis/geosss">https://github.com/microscopic-image-analysis/geosss</a></p> <p>Follow the below steps:</p> <p>1. Download and unzip all the files. </p> <p>2. Clone the github repository and navigate to the root directory.</p> <pre><code>git clone https://github.com/microscopic-image-analysis/geosss.git cd geosss</code></pre> <p>3. Move the data to a new <em>"results"</em> directory inside the repository. After unzipping folders from "<em>mixture_vMF_d10_K5_kappa50" </em>to <em>"mixture_vMF_d10_K5_kappa500" </em>(10 folders), please move them to a new <em>"results/mix_vMF_d10_K5"</em> directory.</p> <p>4. You can use the "geosss/scripts" directory to use these precomputed results and plot them, see the info on the <a href="https://github.com/microscopic-image-analysis/geosss/tree/main">GitHub repository</a> for installing the package. </p>
Sample video of 2pL droplet generation.
<p>Sample video of 2pL droplet generation. Can be opened using ImageJ.</p>
Sample video of 25 um diameter droplet sorting
<p>Sample video of 25 um diameter droplet sorting. </p>
Sample video of picoinjection into 20 um diameter droplets.
<p>Sample video of picoinjection into 20 um diameter droplets.</p>
Sample Stripped Pre-supernova Progenitors for open-source code CHIPS (Complete History for Interaction-Powered Supernovae)
<p>Inlists, mainly based on the test suite "example_make_pre_ccsn" in r12778, with slight amendments for removal of hydrogen (and helium, for Ic progenitors) envelope at core hydrogen (helium) exhausion.</p><p>For details: https://ui.adsabs.harvard.edu/abs/2023arXiv230810785T/abstract</p>
Sample export from Feodo Tracker 5 Oct 2023
<p>Export of a blocklist of botnet C&C servers from 5 Oct 2023</p>
Sample reddit posts 5 Oct 2023
<p>Reddit posts from technology related subreddits from 5 Oct 2023</p>
Antibody panel used for multiplexed antibody-based imaging of Human Pancreas Analysis Program (HPAP) samples by CODEX
<p>This data file details antibodies applied to human pancreas tissue samples from the Human Pancreas Analysis Program (HPAP; RRID:SCR_016202) of the <a href="https://hirnetwork.org/">Human Islet Research Network</a> (HIRN; RRID:SCR_014393). Images will be uploaded for interactive analysis on <a href="https://pancreatlas.org/datasets">Pancreatlas</a> (RRID:SCR_018567) and made available for download via <a href="https://hpap.pmacs.upenn.edu/">PANC-DB</a>. Workflow is documented on protocols.io: <a href="https://dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1">dx.doi.org/10.17504/protocols.io.36wgq7dryvk5/v1</a>.</p><p>Table format adapted from Radtke AJ, Quardokus EM, Saunders DC (2022), <a href="https://doi.org/10.5281/zenodo.7386417">SOP: Construction of Organ Mapping Antibody Panels for Multiplexed Antibody-Based Imaging of Human Tissues</a>. See also: Saunders D; Reihsmann R. <a href="https://doi.org/10.48539/HBM754.BHVR.258">OMAP-13: Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Pancreas with CODEX, v1.0</a>.</p>
Robust evidence for bats as reservoir hosts is lacking in most African virus studies – a review and call to optimize sampling and conserve bats
<p><span>Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus-bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoirs and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded, and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus-host relationships by evidence type and quality, using the contrasting available evidence for </span><em>Orthoebolavirus</em> (formerly <em>Ebolavirus</em>) versus <em>Orthomarburgvirus</em> (formerly <em>Marburgvirus</em>) as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human-bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.</p>
Species delimitation with limited sampling: an example from rare trapdoor spider genus Cyclocosmia (Mygalomorphae, Halonoproctidae)
<p>The outcome of species delimitation depends on many factors, including conceptual framework, study design, data availability, methodology employed, and subjective decision-making. Obtaining sufficient taxon sampling in endangered or rare taxa might be difficult, particularly when non-lethal tissue collection cannot be utilized. The need to avoid overexploitation of the natural populations may thus limit the methodological framework available for downstream data analyses and bias the results. We test species boundaries in rare North American trapdoor spider genus <em>Cyclocosmia</em> Ausserer (1871) inhabiting the Southern Coastal Plain biodiversity hotspot with the use genomic data and two multispecies coalescent model methods. We evaluate the performance of each methodology within a limited sampling framework. To mitigate the risk of species over-splitting, common in taxa with highly structured populations, we subsequently implement a species validation step via genealogical diversification index (gdi), which accounts for both genetic isolation and gene flow. We delimited eight geographically restricted lineages within North American <em>Cyclocosmia</em>, suggesting that major river drainages in the region are likely barriers to dispersal. Our results suggest that utilizing BPP in the species discovery step might be a good option for datasets comprising hundreds of loci, but fewer individuals, which may be a common scenario for rare taxa. However, we also show that such results should be validated via gdi, in order to avoid over-splitting.</p>
Appendix_Results_of_quantitative_qualitative_analysis (42-language sample)
<p>A dataset showing results of quantitative and qualitative analysis on the basis of a 42-language sample.</p>
Appendix_results_qual_analysis_summarized (42-language sample)
<p>A .pdf file that plots verb scores (1 to 3) in main and adverbial clauses in the sample languages covered by the CIEP (42-language sample).</p>
Supplementary Table 1 - GISAID Accession Numbers of Samples analysed in the first and second waves of SARS-CoV-2 cases in Irish hospitals
<p>The Supplementary Table 1 contains the GISAID accession numbers of samples sequenced in the context of the AIID biobank in the Republic of Ireland during the first and second wave of SARS-CoV-2 cases in hospitals of Dublin.</p>
Raw sequencing data and ngsfilters for snow track eDNA samples
<p>Continued advancements in environmental DNA (eDNA) research have made it possible to access intraspecific variation from eDNA samples, opening new opportunities to expand non-invasive genetic studies of wildlife populations. However, the use of eDNA samples for individual genotyping, as typically performed in non-invasive genetics, still remains elusive. We present the first successful individual genotyping of eDNA obtained from snow tracks of three large carnivores: brown bear (<em>Ursus</em> <em>arctos</em>), European lynx (<em>Lynx</em> <em>lynx</em>) and wolf (<em>Canis</em> <em>lupus</em>). DNA was extracted using a protocol for isolating water eDNA and genotyped using amplicon sequencing of short tandem repeats (STR) and, for brown bear, a sex marker, on a high-throughput sequencing platform. Individual genotypes were obtained for all species, but genotyping performance differed among samples and species. The proportion of samples genotyped to individuals was higher for brown bear samples (5/7) than for wolf (7/10) and lynx (4/9), but locus genotyping success was greater for brown bear (0.88). Results for three species show that reliable individual genotyping, including sex identification, is now possible from eDNA in snow tracks, underlining its vast potential to complement the non-invasive genetic methods used for wildlife. To fully leverage the application of snow track eDNA, improved understanding of the ideal species- and site-specific sampling conditions, as well as laboratory methods promoting genotyping success are needed. This will also inform efforts to retrieve and type nuclear DNA from other eDNA samples, thereby advancing eDNA–based individual and population-level studies.</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.