LabelFlow Framework for Annotating Workflow Provenance
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 128398615
- Type
- D - Journal article
- DOI
-
10.3390/informatics5010011
- Title of journal
- Informatics
- Article number
- 5
- First page
- 11
- Volume
- 5
- Issue
- 1
- ISSN
- 2227-9709
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Computer Science
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "The paper proposes a novel technique for leveraging workflow provenance to support scientific reporting.
The PGR was hired by the PhD external examiner to transfer the technique to provenance-patterns based project for Learning Health Systems, and they are now the provenance/GDPR infrastructure specialist for the EU ELIXIR project.
The techniques have been incorporated into workflow provenance collection practice by the Common Workflow Language (endorsed by standards groups including GA4GH and IEEE P2791-2020 BioCompute Object) and the EU EOSC-Life Research Infrastructure cluster (EUR24,000,000)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
