Stian Soiland-ReyesFORCE11 Member since August 6, 2013
- Organization/Institution: eScience Lab, The University of Manchester
- CV: View [profile2:user:profile-member:field_first_name] [profile2:user:profile-member:field_last_name]'s C.V.
- ORCID ID: http://orcid.org/0000-0001-9842-9718
- Twitter: @soilandreyes
- Website: http://soiland-reyes.com/stian/work/
- Skype: soiland
Open Source research software engineer with interests in Linked Data, RESTful web services, provenance, annotations, open science, reproducible research results.
Stian Soiland-Reyes is a senior Research Software Engineer who joined Goble’s eScience Lab group in Manchester in 2006 after obtaining his MSc on Reinforcement Learning at the Norwegian University of Technology and Science (NTNU) and University of Birmingham. His research and development interests are in semantic technologies and distributed computing for reproducible and shared science. He has been instrumental in the core development and adoption of the Taverna workflow system and oversaw its transition into the Apache Foundation, where he has also been engaged in open source initiatives for Linked Data, such as Commons RDF, Jena and JSON-LD. He led the adoption of Taverna in the US NIH CaBIG Cancer Grid project (using Globus). Previous projects include FP7-funded Wf4Ever, where he co-led specifications for preserving and publishing workflow-based Research Objects, including the general scientific workflow model wfdesc, as well as development of the formal workflow language for Taverna. He is a co-author of the W3C PROV-O standard for provenance, as well as the PAV provenance ontology and was part of starting the W3C Web Annotation Data Model. He is currently part of the H2020-funded BioExcel Centre of Excellence with attention to workflows in HPC and HTC environments for biomolecular simulation and modelling. He has also been in charge of the public data ingest pipelines and deployment of the successful Open PHACTS Linked Data warehouse for pharmacological data, focusing on reproducibility using Docker. He has experience in a range of workflow systems and workflow models, including Common Workflow Language (CWL), Apache Taverna, KNIME, Kepler and Galaxy, with a particular focus on workflow interoperability, as shown in his engagement with development of the CWL standard, which is based on the Taverna and the Wf4ever workflow models.