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53 results for “Workflow Analysis”
A High-Performance Data Processing Workflow to Incorporate Effect-Directed Analysis in Suspect and Nontarget Screening [Feature Tables]
<p>This repository is supplementary to the manuscript "High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening" (DOI: 10.1021/acs.est.1c04168) and includes an overview of all measured chemical features and annotations in a waste water treatment plant (WWTP) effluent, dust standard reference material (SRM) 2585 and fetal calf serum (FCS) sample.</p> <p>Samples were measured using liquid chromatography - high resolution mass spectrometry (LC-HRMS) and fractionated into 80 micro-fractions encompassing a couple of seconds from the chromatographic run. The fractions were tested for their bioactivity in the antibiotics and the TTR-binding assay. The samples were processed separately using one, two, and three technical replicates in positive and negative ion mode. The first excel sheet includes all measured chemical features, suspect screening annotation, and corresponding bioassay responses. The second sheet includes all possible isomer annotations from the CECscreen database (DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>) for the annotated features. </p>
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Tryps-IN: A streamlined palaeoproteomics workflow enables ZooMS analysis of 10,000-year-old petrous bones from Jordan rift-valley
<p>Poor preservation of collagen in dry and/or arid environments has hindered the application of Zooarchaeology by mass spectrometry (ZooMS) analysis in many regions of the world, and as a result many zooarchaeological investigations have relied exclusively on the morphological assessment of fragmentary remains, due to the inadequate preservation of biomolecules. The climatic conditions of Southwest Asia include extreme temperature fluctuations unconducive to preservation of proteins and DNA. We performed zooarchaeological analysis of remains from the 10,000-year-old site of Shkārat Msaied in Jordan and sub-sampled twenty-eight petrous bones, the hardest bone in the mammalian skeleton, for species identification by ZooMS. Using an unconventional and simplified extraction protocol we call Tryps-IN, in which digestion was performed without removal of the demineralising EDTA, we taxonomically identified several fragments, outperforming the established ZooMS work-flow. A subset of identifications was subsequently confirmed using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) protein sequencing. The new methodology presented here opens the possibility of further bioarchaeological investigation of other fragmentary faunal assemblages within this region of archaeological significance. </p>
Elements of Style in Reproducible Workflow Creation and Analysis: An INCLUDE Training Event
<p><a href="https://github.com/NIH-NICHD/Elements-of-Style-Workflow-Creation-Maintenance/blob/main/README.md">Elements of Style Workflow Creation and Maintenance</a>: An INCLUDE Training Event</p> <p>The <a href="https://includedcc.org/">INCLUDE Data Hub</a> is a new resource that securely hosts human clinical, genomic, transcriptomic, proteomic, and other data providing a wealth of opportunities to study conditions that affect individuals with Down syndrome. Today, the approach to answering new scientific questions with these data often uses cloud-based methods accessible through web browsers.</p> <p>During a three-hour virtual training, users learn the know-how to ask scientific questions with these data using cloud platforms and workflows. Users will learn how to build and share processes that assure reproducibility, repurposablility regardless of the computational environment. While many things are possible, the user will be oriented to approaching their work in a modular, testable fashion. </p>
Test data for jga-analysis per-sample workflow
<p>Test data for jga-analysis per-sample workflow.</p> <p>Please see:</p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis">https://github.com/biosciencedbc/jga-analysis</a></p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl">https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl</a></p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE paper)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the journal "Concurrency and Computation: Practice and Experience", under the title <em>"Performance Modeling of a Geophysics Application to Accelerate the Tuning of Over-decomposition Parameters through Simulation"</em>.</p>
UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow
<p>UniSpec is a comprehensive DL spectrum predictor that can predict the intensity of the entire HCD MS/MS fragment ion series, going beyond existing tools limited to b/y ion series. </p> <p>All datasets developed for UniSpec model are shared on Zenodo as part of the UniSpec publication, "UniSpec: Deep Learning for Predicting Comprehensive Peptide Fragment Ion Series to Improve Peptide-Spectrum Matches from Shotgun Proteomics Experiments".</p> <p>This includes UniSpec datasets, downstream evaluation and analysis, and application case studies.</p> <p>1. pre-processed training, evaluation and testing data for machine learning;</p> <p> UniSpec-Datasets.7z, Readme_UniSpecDatasets.txt</p> <p>2. Streamlined input datasets based on the fragmentation dictionary;</p> <p> Streamlined_inputdatasets.7z, Readme_Streamlined_inputdatasets.txt</p> <p>3. Predictions on the validation and test sets;</p> <p> UniSpecPred_Validation-Test.7z, Readme_Predictons_ValidationTest.txt</p> <p>4. Evaluation by comparison with Prosit;</p> <p> a. Predictions: prosit_and_unispec_predictions.7z, Readme_prosit_and_unispec_predictions.txt</p> <p> b. Cosine similarity scores: prosit_vs_unispec_CS.7z, Readme_prosit_vs_unispec_CS.txt</p> <p>5. CSS for Different HCD Fragment Ion Series;</p> <p> CS_for_ion_splits.tsv</p> <p>6. Application 1: PSM rescoring;</p> <p> PSM rescoring_zipfiles.7z, PSM rescoring_readme.txt</p> <p>7. Application 2: In-silico spectral library search </p> <p> in-silico_librarysearch.7z, in-silico_librarysearch_readme.txt</p> <p> </p>
metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data - use case
<p>Data products returned by <a href="https://github.com/emo-bon/MetaGOflow">metaGOflow</a> (<a href="https://github.com/emo-bon/MetaGOflow/releases/tag/v1.0.0">v1.0.0</a>) and packed as a Research Object (RO) Crate, when performed with:</p> <ul> <li>a <strong>seawater metagenomic sample </strong>(TARA OCEAN, <a href="https://www.ebi.ac.uk/ena/browser/view/ERR599171">ERR599171</a>)</li> <li>a <strong>fish gut </strong>sample (<a href="https://www.ebi.ac.uk/ena/browser/view/ERR4765907">ERR4765907</a>)</li> <li>a<strong> human gut </strong>sample (<a href="https://www.ebi.ac.uk/ena/browser/view/SRR9654976">SRR9654976</a>)</li> </ul> <p>This Zenodo repo accompanies the metaGOflow paper and more about the analysis of this sample can be found there.</p> <p>You can also have a look at some visual components of the workflow at this <a href="https://data.emobon.embrc.eu/MetaGOflow/">GitHub page</a>. </p> <p>The source code of metaGOflow is available through <a href="http://github.com/emo-bon/MetaGOflow">GitHub</a>.</p>
Data from: Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach
Open the record for dataset details and reuse information.
WDS-RDA Publishing Data Workflows Working Group Analysis sheet
<p>The information was further refined by adding color: pink indicates “project” (4 entries), blue shows “repository” (14 entries), yellow is “journal” (7 entries) and green for “hybrid” (1 entry). Future versions of the spreadsheet are planned, which can be filtered via other categories, for example: discipline-specific vs discipline-agnostic, funding model, level of editing/intervention, etc.</p> <p>The collection and analysis of data took place between 1 February and 30 June 2015</p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet FINAL
<p><strong>NB: This dataset is superseded by:</strong></p> <p>Murphy, Fiona et al.. (2015). WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED. Zenodo. 10.5281/zenodo.33899</p> <p>Data Publishing Workflows collected and analysed between 1 February - 30 June 2015. Fields were populated using a combination of consultation and desk research. This is a refined version of the previous spreadsheet also lodged in Zenodo: </p> <p>Murphy, Fiona et al.. (2015). WDS-RDA Publishing Data Workflows Working Group Analysis sheet. Zenodo. 10.5281/zenodo.19107</p> <p>Publication date: 29 June 2015</p> <p>Keyword(s): <strong>data publishing, workflows, journals, repositories, research data</strong></p>
AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis: example data and analysis source-code
<p>This repository contains:</p> <ul> <li>Annotated TCR-seq data files named <em>tripod-XX-XXXX</em></li> <li>The metadata corresponding to the annotated files</li> <li>The RepSeqExperiment object, which integrates the annotated files and the metadata and was used in the analysis pipeline</li> <li>The analysis script to generate the plots of the different figures</li> </ul>
Data for BY-COVID Pathways to MINERVA Analysis Workflow
<p>Source data (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE182152">GSE182152</a>) was analysed with <a href="https://workflowhub.eu/workflows/688">WFHub:688</a> to generate these datasets</p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"
<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts. The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>
Demo dataset for: SPACEc, a streamlined, interactive Python workflow for multiplexed image processing and analysis
<p>Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.</p> <p>The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user's system or to familiarize oneself with the pipeline.</p>
Data of "A workflow to study the microbiota profile of piglet's umbilical cord blood: from sampling to data analysis".
<p>The present study proposes a workflow – from the sampling method to DNA extraction, bioinformatics and data analysis – that characterises the bacterial profile of umbilical cord blood samples, taking into account the contaminants found throughout the procedure of bacterial DNA extraction and amplification.</p> <p>Ps_umbilical.rds: A phyloseq object file of data containing the amplicon sequences variants (ASVs) of thirteen umbilical cord samples and two negative control samples, created by DADA2.</p> <p>R-script.doc: A word document containing the scripts used to characterize the taxonomical composition of the fifteen umbilical cord samples and two negative control samples before and after the application of Decontam R-package (Davis et al., 2018).</p> <p>metadata.docx: meta data for R-script.doc</p>
Comparison between the results from JGA analysis somatic short variant discovery workflow and those from the compatible Terra workflow
<p>Files starting from <code>HCC1143.somatic</code> are the results from <a href="https://github.com/ddbj/jga-analysis/tree/main/somatic-short-variant">JGA analysis somatic short variant discovery workflow</a>. Files starting from <code>submissions_</code> are the results from the compatible Terra workflow.</p> <p>VCFs are identical between two workflows except for the header lines. MAFs are also identical except for the header lines.</p>
Comparison between the results from JGA analysis mitochondrial short variant discovery workflow and those from the compatible Terra workflow
<p>Files starting from <code>NA12878.chrM</code> are the results from <a href="https://github.com/ddbj/jga-analysis/tree/mitocondrial-variant">JGA analysis mitochondrial short variant discovery workflow</a>. Files starting from <code>submissions_</code> are the results from the compatible Terra workflow.</p> <p>VCFs are identical between two workflows except for the header lines.</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.