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650 results for “Workflow”
Data Processing for a Small-Scale Long-Term Coastal Ocean Observing System Near Mobile Bay, Alabama: A Geoscience Papers of the Future (GPF) Workflow Diagram
<p>The Dauphin Island Sea Lab (DISL) has been operating a permanent moored oceanographic station at 30 05.410'N, 88 12.694'W, 25 km southwest of the entrance to Mobile Bay, Alabama, since 2004. It collects hydrographic and current velocity data.</p> <p>This diagram shows the processing steps for data from the instruments at this mooring, from initial download to initial scientific analysis.</p> <p>The file has been prepared as supplementary material for a Geoscience Paper of the Future (GPF) in prep for publication at Earth and Space Science, as part of the OntoSoft GPF Initiative.</p>
GitHub_workflow_failure_log
<p>There are logs of 15,535 github workflow failures that we collected.</p>
Dataset related to the article "An optimized MRM-based workflow of the L-arginine/nitric oxide pathway metabolites revealed disease- and sex-related differences in the cardiovascular field"
<p>This record contains raw data related to the article "An optimized MRM-based workflow of the L-arginine/nitric oxide pathway metabolites revealed disease- and sex-related differences in the cardiovascular field"</p> <p>Clinical data indicate that low circulating L-homoarginine (HArg) concentrations are associated with cardiovascular (CV) disease, CV mortality, and all-cause mortality. A high number of LC-based analytical methods for the quantification of HArg, in combination with the L-arginine (Arg)-related pathway metabolites, have been reported. However, these methods usually consider a limited panel of analytes. Thus, in order to achieve a comprehensive picture of the Arg metabolism, we described an improved targeted metabolomic approach based on a multiple reaction monitoring (MRM) mass spectrometry method for the simultaneous quantification of the Arg/nitric oxide (NO) pathway metabolites. This methodology was then employed to quantify plasma concentrations of these analytes in a cohort of individuals with different grades/types of coronary artery disease (CAD) in order to increase knowledge about the role of HArg and its associated metabolites in the CV field. Our results showed that the MRM method here implemented is suitable for the simultaneous assessment of a wide panel of amino acids involved in the Arg/NO metabolic pathway, in plasma samples from patients with CV disease. Further, the findings highlighted an impairment of the Arg/NO metabolic pathway, and suggest a sex-dependent regulation of this metabolic route.</p>
Traces of scientific workflows
<p>This is an anonymized dataset containing the traces we obtained when we executed our scientific workflows.</p>
QIIME2 Workflow
<p>QIIME2 1.0 Workflow for FCAV Project.</p>
Dataset related to article "Deep learning and atlas-based models to streamline the segmentation workflow of Total Marrow and Lymphoid Irradiation"
<p>This record contains raw data related to article “Deep learning and atlas-based models to streamline the segmentation workflow of Total Marrow and Lymphoid Irradiation"</p> <p>Abstract:</p> <p><strong>Purpose: </strong>To improve the workflow of Total Marrow and Lymphoid Irradiation (TMLI) by enhancing the delineation of organs-at-risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.</p> <p><strong>Materials and Methods:</strong> Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients and a semi-automatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR.</p> <p><strong>Results: </strong>The two DL models achieved a median Dice Similarity Coefficient (DSC) of 0.84 [0.73;0.92] and 0.84 [0.77;0.93] across the OARs. The absolute median dose (Dmedian) difference between manual and the two DL models was 2% [1%;5%] and 1% [0.2%;1%]. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmedian differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 hours the time required to complete the entire segmentation process.</p> <p><strong>Conclusion: </strong>DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.</p> <p> </p> <p><strong>Statements & Declarations</strong></p> <p><strong>Funding:</strong> This work was funded by the Italian Ministry of Health, grant AuToMI (GR-2019-12370739).</p> <p><strong>Competing Interests:</strong> The authors have no conflict of interests to disclose.</p> <p><strong>Author Contributions:</strong> All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by D.D., N.L., L.C., R.C.B., D.L., and P.M. The first draft of the manuscript was written by D.D. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.</p> <p><strong>Ethics approval:</strong> The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of IRCCS Humanitas Research Hospital (ID 2928, 26 January 2021). ClinicalTrials.gov identifier: NCT04976205.</p> <p><strong>Consent to participate: </strong>Informed consent was obtained from all individual participants included in the study.</p>
Workflow Diagram for GeoSoft "Geoscience Papers of the Future" (GPF) project - Tzeng
<p>This is a diagram for data provenance for a paper in progress, as part of a software sharing project. It is an earlier version of a the final diagram used in the paper.</p>
Workflow memoization traces for a walk in the graph
<p>This is a collection of anonymized traces describing the execution of workflows on a POWER8 and a x86-64 cluster. These traces are the results of the experimental evaluation of the workflow memoization method that the paper "A walk in the graph: Fast, flexible, and high-fidelity cache keys for workflow memoization" introduces.</p>
Molecule dataset used in workflow memoization experiments
<p>The dataset contains 80 molecules in Simplified Molecular Input Line Entry System (SMILES) format.</p>
Example workflow
<p>This is a trial to upload data to zenodo</p>
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