Automated Author ProfileCrusoe, Michael R.
0000-0002-2961-9670
Crusoe, Michael R.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 312.5 (sum of 22 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Analysis of runcrate. v1: runcrate 0.4.0v2: runcrate 0.5.0v3: runcrate 0.5.0 (updated README.md, fixed some mistakes in the results)See also https://github.com/RenskeW/runcrate-analysis
Authors
- de Wit, Renske ;
- Crusoe, Michael R.
Analysis of runcrate. v1: runcrate 0.4.0v2: runcrate 0.5.0v3: runcrate 0.5.0 (updated README.md, fixed some mistakes in the results)See also https://github.com/RenskeW/runcrate-analysis
Authors
- de Wit, Renske ;
- Crusoe, Michael R.
RO-Crate for the manuscript that describes Workflow Run Crate, includes mapping to PROV using SKOS/SSSOM.
Authors
- Leo, Simone ;
- Crusoe, Michael R ;
- Rodríguez-Navas, Laura ;
- Sirvent, Raül ;
- Kanitz, Alexander ;
- De Geest, Paul ;
- Wittner, Rudolf ;
- Pireddu, Luca ;
- Garijo, Daniel ;
- Fernández, José M. ;
- Colonnelli, Iacopo ;
- Gallo, Matej ;
- Ohta, Tazro ;
- Suetake, Hirotaka ;
- Capella-Gutierrez, Salvador ;
- de Wit, Renske ;
- de Paula Kinoshita, Bruno ;
- Soiland-Reyes, Stian
RO-Crate for the manuscript that describes Workflow Run Crate, includes mapping to PROV using SKOS/SSSOM.
Authors
- Leo, Simone ;
- Crusoe, Michael R ;
- Rodríguez-Navas, Laura ;
- Sirvent, Raül ;
- Kanitz, Alexander ;
- De Geest, Paul ;
- Wittner, Rudolf ;
- Pireddu, Luca ;
- Garijo, Daniel ;
- Fernández, José M. ;
- Colonnelli, Iacopo ;
- Gallo, Matej ;
- Ohta, Tazro ;
- Suetake, Hirotaka ;
- Capella-Gutierrez, Salvador ;
- de Wit, Renske ;
- de Paula Kinoshita, Bruno ;
- Soiland-Reyes, Stian
Analysis of runcrate. v1: runcrate 0.4.0v2: runcrate 0.5.0See also https://github.com/RenskeW/runcrate-analysis
Authors
- de Wit, Renske ;
- Crusoe, Michael R.
Analysis of runcrate 0.4.0See also https://github.com/RenskeW/runcrate-analysis
Authors
- de Wit, Renske ;
- Crusoe, Michael R.
Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. Figure 1 shows a workflow for detecting variants in genome sequences. The workflow is specified in the Common Workflow Language, displayed using the CWL Viewer. Any CWL compliant WfMS execution engine should be able to execute this standardized workflow description and obtain the same results independent of their underlying infrastructure, for instance using Toil executed on SLURM, or REANA on Kubernetes.
Authors
- Goble, Carole ;
- Cohen-Boulakia, Sarah ;
- Soiland-Reyes, Stian ;
- Garijo, Daniel ;
- Gil, Yolanda ;
- Crusoe, Michael R. ;
- Peters, Kristian ;
- Schober, Daniel
The automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable Automation, Scaling, Adaption and Provenance support (ASAP). However, there are still several challenges associated with the effective sharing, publication and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different technical (software) platforms. Based on best practice recommendations identified from literature on workflow design, sharing and publishing, we define a hierarchical provenance framework to achieve uniformity in the provenance and support comprehensive and fully re-executable workflows equipped with domain-specific information. To realise this framework, we present CWLProv, a standard-based format to represent any workflow-based computational analysis to produce workflow output artefacts that satisfy the various levels of provenance. We utilise open source community-driven standards; interoperable workflow definitions in Common Workflow Language (CWL), structured provenance representation using the W3C PROV model, and resource aggregation and sharing as workflow-centric Research Objects (RO) generated along with the final outputs of a given workflow enactment. We demonstrate the utility of this approach through a practical implementation of CWLProv and evaluation using real-life genomic workflows developed by independent groups. The underlying principles of the standards utilised by CWLProv enable semantically-rich and executable Research Objects that capture computational workflows with retrospective provenance such that any platform supporting CWL will be able to understand the analysis, re-use the methods for partial re-runs, or reproduce the analysis to validate the published findings.
Authors
- Khan, Farah, Zaib ;
- Soiland-Reyes, Stian ;
- Sinnott, Richard, O. ;
- Lonie, Andrew ;
- Goble, Carole ;
- Crusoe, Michael, R.
This FAIRsharing record describes: Common Workflow Language (CWL) is an open standard for describing how to run command line tools and connect them to create workflows.Tools and workflows described using CWL are portable across a variety of platforms that support the CWL standards. Using CWL, it easy to scale complex data analysis and machine learning workflows from a single developer's laptop up to massively parallel cluster, cloud and high performance computing environments.
Authors
- FAIRsharing Team ;
- Crusoe, Michael
The Common Workflow Language (CWL) is an informal, multi-vendor working group consisting of various organizations and individuals that have an interest in portability of data analysis workflows. Our goal is to create specifications that enable data scientists to describe analysis tools and workflows that are powerful, easy to use, portable, and support reproducibility.
CWL builds on technologies such as JSON-LD and Avro for data modeling and Docker for portable runtime environments. CWL is designed to express workflows for data-intensive science, such as Bioinformatics, Medical Imaging, Chemistry, Physics, and Astronomy.
This is draft 3 of the CWL tool and workflow specification, released on 2016-02-05.
The specification, in HTML format, is in the draft-3/docs folder.
Authors
- Amstutz, Peter ;
- Andeer, Robin ;
- Chapman, Brad ;
- Chilton, John ;
- Crusoe, Michael R. ;
- Guimerà, Roman Valls ;
- Hernandez, Guillermo Carrasco ;
- Sinisa Ivkovic ;
- Kartashov, Andrey ;
- Kern, John ;
- Leehr, Dan ;
- Ménager, Hervé ;
- Mikheev, Maxim ;
- Pierce, Tim ;
- Randall, Josh ;
- Soiland-Reyes, Stian ;
- Stojanovic, Luka ;
- Nebojša Tijanić