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146 results for “data workflow”
Figure 1. General Workflow of Algorithm-.Data Conflict Resolution among Same Entities in Web of Data
<p>The Page Rank algorithm which is widely used in most search engines such as Google could<br> be easily used to rank linked data. By starting from a point and random surfing, this algorithm<br> evaluates the probability of finding any given page. The algorithm assumes a link between a page i<br> to a page j demonstrates the importance of page j. In addition, the importance of page j is associated<br> to the importance of page i itself and inversely proportional to the number of pages i point to. To<br> adapt this algorithm to web of data, any page considered as a dataset and links between pages<br> considered as links between datasets.</p>
NEUBIAS TS7 - data used in the workflow deconstruction session on quantifying monolayer cell migration
<p>Training session details (including slides): https://github.com/miura/NEUBIAS_AnalystSchool2018/tree/master/Assaf</p> <p>Matlab source code: https://github.com/assafzar/MonolayerKymographs</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>
The demo data set for the meta16S-Seq workflow using Qiime2
<p>The demo data used in the introduction of meta16S-Seq workflow using Qiime2. The original manuscript of the workflow introduction is written by Yuh Shiwa. The workflow is translated in Common Workflow Language by Tazro Ohta.</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>
data for BioImage.IO workflows
<p>Test data and cover images for workflows implemented in https://github.com/bioimage-io/workflows-bioimage-io-python</p> <p><br> References:<br> stardist_chatty_frog.npy derived from https://doi.org/10.5281/zenodo.6338615<br> dask_inference_cover.svg/png: raw data from https://cremi.org/, predictions from https://bioimage.io/#/?id=10.5281%2Fzenodo.5874741, dask icon: https://dask.org</p> <p> </p>
Data for manuscript, "An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples"
<p>Clinical BALF samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, MS based proteomic analysis of BALF is impeded by the dynamic range of protein abundance, and potential for interfering contaminants. We have developed a workflow that eliminates these challenges. By combining high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, our workflow is compatible with both qualitative and quantitative MS-based proteomic analysis. The workflow includes collection of endogenous peptides for peptidomic analysis of BALF, if desired, as well as amenability to offline semi-preparative or microscale fractionation of peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We show the effectiveness of this workflow on BALF samples from COPD patients. Overall, our workflow should allow MS-based proteomics to be applied to a wide variety of studies focused on BALF clinical samples. </p> <p>Note: Due to the nature of some of the files, file <em>wendt005_ostr0103_18260_20210831_BALF_FAIMS_MS2_TMT16.msf, wendt005_ostr0103_18976_20230202_quantReport.msf, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_1R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_2R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_3R.raw and cmsptc_higgi022_18988_20230203_18976DW_Eclipse_noFAIMS_quantReport.msf</em> were zipped into compressed folders before uploading.</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>
Supplementary Datasets for: 'A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages'
<p>The repository contains four independent datasets that are a part of the publication (<a href="https://arxiv.org/abs/2302.14629">arXiv:2302.14629</a>), which delineates the capabilities of the Pycroscopy ecosystem of packages. The details of the individual datasets can be found below. </p> <p>1) bfo_iv_final.hf5: Dataset of I-V curves captured by conductive atomic force microscopy on a BiFeO3 sample. The data has been transformed so that we plot not the log of the current density (J) as a function of the square root of the electric field. The dataset was originally presented in the paper 10.1038/s41467-017-01334-5 </p> <p>2) bto_atomic.dm3: Atomically resolved data BaTiO3 thin film acquired with scanning transmission electron microscopy. These were originally captured in the dm3 file format. This dataset was a part of the publication: doi.org/10.1002/adma.202106426</p> <p>3) EELS_STO.dm3: Scanning transmission electron microscope (STEM)-Electron energy loss spectroscopy (EELS) dataset of SrTiO3.</p> <p>4) STO-stack.h5: High-angle annular dark-field imaging (HAADF) scanning transmission electron microscope (STEM) image stack of SrTiO3. This image stack contains 25 images.</p> <p> </p>
specleanr: An R package for automated flagging of environmental outliers in ecological data for modeling workflows
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Data from: Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach
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LipidQuant 1.0: Automated data processing in lipid class separation - mass spectrometry quantitative workflows
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BIDS Data for "An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging"
<p>Base data package for the “An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging” article, formatted corresponding to the Brain Imaging Data Structure.</p>
IPBES Data Management Tutorials - Session 4.2: Recommendations for workflow establishment and data management
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> Recommendations for workflow establishment and data management</em>, provides recommendations for data management including the generation of workflows and data storage. </p>
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-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED
<p>Final, revised version of http://dx.doi.org/10.5281/zenodo.33413 (published 6 November 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>
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>
Research Data Management Workflow
<p>This diagram illustrates essential ideas and useful tools during the RDM cycle.</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.