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146 results for “data workflow”
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>
Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion
<p><span>This data includes the pre-processed dataset, along with a novel workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of machine learning and ionospheric physics science, as well as being useful to other researchers for diverse objectives. </span></p> <p><span><span>Acknowledgements:</span></span></p> <p><span><span>The WALDO database (<a href="https://waldo.world"><span>https://waldo.world</span></a>, accessed on October 1, 2023) provides VLF data. It is run collaboratively by the University of Colorado Denver and the Georgia Institute of Technology, utilizing data gathered from Stanford University and those two institutions. It has been made possible by numerous grants from the Department of Defense, NASA, and the NSF.<span> </span></span></span></p> <p> </p> <p> </p>
Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine
<p>Dataset accompanying the publication "Digitization Workflow for Data Mining in Production Technology<br>applied to a Feed Axis of a CNC Milling Machine" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.procs.2024.01.017" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.procs.2024.01.017</span></a>).</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>
IWC : Test Data For VGP Decontamination Workflow
<p>Dataset used to test the decontamination workflow published with the VGP assembly pipeline in Galaxy. </p>
A formative usability study of workflow management systems in label-free digital pathology - Data and Code
<p>This repository holds the necessary data and code as well as a descriptive Readme file that was used for our publication "A formative usability study of workflow management systems in label-free digital pathology" by Markus Jelonek et al. (2022), submitted to F1000Research.</p> <p> </p> <p>Abstract:</p> <p>We present a formative usability study that investigates the usability of different<br> workflow management systems in the field of biomedical data analysis. Specifically, we study a task in the field of so-called label-free digital pathology and investigate one graphical user interface based workflow and one script-based workflow to solve the task. Our main intention is to gain first insights into the systematic study of usability in the context of biomedical image analysis, and formulate experiences and guidelines for future usability studies dealing with workflow management systems. Embedded in a specific setup dealing with label-free digital pathology, the core question behind our contribution is how usability studies for scientific workflow management can be conducted, and how they can be used systematically to improve such tools. Further, we address specific questions about the resource utilisation and management of usability studies, including the recruitment of participants as well as the design of specific workflows to be investigated.</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>
Supplemental Data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."
<p>All supplemental data for "Adaptive Container Service: a New Paradigm for Robust and Optimized Bioinformatics Workflow Deployment in the Cloud."<br><br>Abstract:<br>We propose Adaptive Container Service (ACS), a new paradigm for deploying bioinformatics workflows in cloud computing environments. By encapsulating the entire workflow within a single virtual container, combined with automatic workflow checkpointing and dynamic migration to appropriately scaled containers, ACS-based deployment demonstrates several key advantages over alternative strategies: it enables optimal resource provision to any workflow that comprise of multiple applications with diverse computing needs; it provides protection against application-agnostic out-of-memory (OOM) errors or spot instance interruptions; and it reduces efforts required for workflow development, optimization, and management because it runs workflows with minimal or no code modifications. Proof-of-concept experiments show that ACS avoided both under- and over-provisioning in monolithic single-container deployment. Despite being deployed as a single container, it achieved comparable resource utilization efficiency as optimized Nextflow-managed, multi-modular workflows. Analysis of over 18,000 workflow runs demonstrated that ACS can effectively reduce workflow failures by two-thirds. These findings suggest that ACS frees developers from navigating the complexity of deploying robust workflows and rightsizing compute resources in the cloud, leading to significant reduction in workflow development time and savings in cloud computing costs.<br><br>Contains the following directories:<br>Fig2-bbtools: running metrics for BBTools<br>Fig3-rna-seq: running metrics for RNA-Seq<br>Fig4-Job_records: meta data and running metrics of 18,000+ jobs</p>
NGSAP-VC : Genomic Variant Calling as an Installable GALAXY Workflow Using NGS data.
<p>Implementation of genomic variants calling as an installable GALAXY workflows using NGS data. Repository contains two separate sets of simulated ebola test data. One for SNPs and INDELs calling and another for Structural Variants calling.</p>
Data Science Tasks used in "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"
<p>This entry contains the supplementary files for a scientific article. </p> <p>The dataset contains the necessary files for the two data science tasks used in the experiment study from scientific article "AI Support for Data Scientists: An Empirical Study on Workflow and Alternative Code Recommendation"</p> <p> </p>
Demo-Dataset for publication "FAIR workflows in Earth system modelling: a use case with semantic data management"
<p>This demodataset is intended to be used to test the workflow described in the publication by Lennartz & Schlemmer "FAIR workflows in Earth System modelling: a use case with semantic data management". It contains example model output for an arbitrary biogeochemical model tracer (here: dissolved organic carbon, DOC) from an ocean model as a 4-dimensional dataset (latitude, longitude, depth, time), the corresponding grid point locations as well as a textfile specifying parameter inputs for the model. The file structure is adapted for seamless integration into the workflow described in Lennartz & Schlemmer, which builds on the open source semantic research data management system LinkAhead. The dataset contains the following structure: The folder DataAnalysis stores data required for data analysis, such as the grid point locations in the file TMM_grid_v2018a.mat. The folder SimulationData stores model output in the folder 2022_TMM, containing the parameter input file nl_in.txt and the model output TR_monthly.mat. Related instructions can be accessed here: https://gitlab.com/salexan/fairworkflows-demodataset .</p>
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
<p>The European weather loach (<i>Misgurnus fossilis</i>) is classified as highly endangered in several countries of Central Europe. Populations of <i>M. fossilis</i> are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, <i>M. fossilis</i> was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the Threshold cycle (Ct) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of <i>M. fossilis</i>. We tested several method combinations for long-term sample preservation, DNA capture and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural <i>M. fossilis</i> population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of <i>M. fossilis </i>did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of <i>M. fossilis</i>, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of <i>M. fossilis</i>.</p>
IWC : Test data for VGP workflows v2.0
<p>Test data for VGP workflows in <a href="https://github.com/galaxyproject/iwc">iwc</a>. Pipeline VGP assembly v2.0</p>
Research Workflows and Open Science - Data Set
<p>Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.</p> <p>The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘<strong>workflow ID</strong>’ and ‘<strong>URL</strong>’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively.</p> <p>The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified:</p> <ul> <li> <p>'<strong>used/open science infrastructure/virtual</strong>'</p> <ul> <li> <p>If a workflow relies on a virtual open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open science infrastructure/physical</strong>'</p> <ul> <li> <p>If a workflow relies on a physical open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow relies on open source software (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open hardware</strong>'</p> <ul> <li> <p>If a workflow relies on open hardware (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow (re)uses open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow (re)uses open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/(open access) scientific publication</strong>'</p> <ul> <li> <p>If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>transparency/transparency type</strong>'</p> <ul> <li> <p>degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise)</p> </li> </ul> </li> <li> <p>'<strong>transparency/sharing type</strong>'</p> <ul> <li> <p>workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/collaboration implementation</strong>'</p> <ul> <li> <p>If a workflow implements collaborative practices (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdfunding</strong>'</p> <ul> <li> <p>If a workflow envisages crowdfunding (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdsourcing</strong>'</p> <ul> <li> <p>If a workflow envisages crowdsourcing (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/scientific volunteering</strong>'</p> <ul> <li> <p>If a workflow envisages scientific volunteering (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/citizen and participatory science</strong>'</p> <ul> <li> <p>If a workflow envisages citizen and participatory science (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/indigenous peoples</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/marginalised scholars</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/local communities</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with local communities (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>assessment</strong>'</p> <ul> <li> <p>If a workflow implements assessment processes for the evaluation of the research products created (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>automation</strong>'</p> <ul> <li> <p>If a workflow includes automated processes (yes/no)</p> </li> </ul> </li> </ul>
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>
Triple-resolution of spectral phases via semi-relativistic ab-initio RABBITT simulations DATA & WORKFLOW
<p>This dataset contains the necessary atomic structure and input files to use the <a href="https://gitlab.com/Uk-amor/RMT/rmt">R-Matrix with Time-dependence code suite</a> (open source and freely available) to replicate the results presented in "Triple-resolution of spectral phases via semi-relativistic ab-initio RABBITT simulations".</p> <p>Additionally, the output photoelectron momentum spectra data output from the RMT simulations are provided, to allow replication of the post-processing and spectral phase extraction processes in the absence of access to a large HPC cluster.</p> <p>Finally, a link is provided to a <a href="https://gitlab.com/lukeroantree/argon_rabbitt_scripts">git repository</a> hosted on gitlab.com where post-processing, spectral phase extraction, and visualisation tools are available to operate on these momentum spectra, and an interactive example is provided via a webhosted (via mybinder) Python Jupyter notebook.</p>
Workflow-Based Spatio-Temporal Data Analytics
<p>In the biodiversity domain, researchers often have to combine a large variety of heterogeneous spatio-temporal data sources. For example, the loss of biodiversity can be quantified by analyzing occurrence observations of various species across time. To find the root cause of that loss, occurrence data may need to be combined with satellite images to find possible correlations with climate variables. To facilitate an exploratory approach for this combination of data sources, it is essential to provide researchers with workflow-based tools such that each step during the formulation of a research hypothesis can be tracked. In this presentation, we will discuss Geo Engine, a workflow-based analysis platform for spatio-temporal data analytics, and its place within FAIR data spaces.</p>
Data Management Workflows with CaosDB
<p>A figure illustrating how the open source research data management system CaosDB can be integrated into data management workflows. It is shown that data is typically integrated into the system using a file crawler. Afterwards data can be accessed using e.g. the web frontend or other client interfaces.</p>
Supporting data for "Software pipelines for RNA-Seq, ChIP-Seq and Germline Variant calling analyses in Common Workflow Language (CWL)"
<p>Datasets produced during the validation of CWL-based pipelines, designed for the analysis of data from RNA-Seq, ChIP-Seq and germline variant calling experiments. Specifically, the workflows were tested using publicly available High-throughput (HTS) data from published studies on Chronic Lymphocytic Leukemia (CLL) (accession numbers: E-MTAB-6962, GSE115772) and Genome in a Bottle (GIAB) project samples (accession numbers: SRR6794144, SRR22476789, SRR22476790, SRR22476791).</p> <p>The supporting data include:</p> <ul> <li>Differential transcript and gene expression results produced during the analysis with the CWL-based RNA-Seq pipeline</li> <li>Bigwig and narrowPeak files, differential binding results, table of consensus peaks and read counts of EZH2 and H3K27me3, produced during the analysis with the CWL-based ChIP-Seq pipeline</li> <li>VCF files containing the detected and filtered variants, along with the respective hap.py () results regarding comparisons against the GIAB golden standard truth sets for both CWL-based germline variant calling pipelines</li> </ul>
DLC networks from: Application of a novel deep learning based 3D videography workflow to bat flight data
<p>Studying the detailed biomechanics of flying animals relies on producing accurate three-dimensional coordinates for key anatomical landmarks. Traditionally, this is achieved through manual digitization of animal videos, a labor-intensive task that grows more so with increasing frame rates and numbers of cameras. In this study, we present a workflow that combines deep learning-powered automatic digitization with intelligent filtering and correction of mislabeled points using 3D information. We tested our workflow using a particularly challenging scenario – bat flight. First, we documented bats flying steadily in a wind tunnel. We compared the results from manually digitizing bats with markers applied to anatomical landmarks against using our automatic workflow on the same bats without markers. In our second test case, we compared manual digitization against our automated workflow for bats exhibiting complex maneuvers in a large flight arena. We found that the variation between the 3D coordinates from our workflow and those from manual digitization was less than a millimeter larger than the variation between 3D coordinates resulting from two different human digitizers. The reduced reliance on manual digitization stemming from this work has the potential to significantly increase the scalability of studies into the detailed biomechanics of animal flight.</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.