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Analysis traceability and provenance for HEP (2015)
Journal Article
Kovács, Z., McClatchey, R., Shamdasani, J., Branson, A., & Kovacs, Z. (2015). Analysis traceability and provenance for HEP. Journal of Physics: Conference Series, 664(3), https://doi.org/10.1088/1742-6596/664/3/032028

This paper presents the use of the CRISTAL software in the N4U project. CRISTAL was used to create a set of provenance aware analysis tools for the Neuroscience domain. This paper advocates that the approach taken in N4U to build the analysis suite i... Read More about Analysis traceability and provenance for HEP.

Semantic matching for the medical domain (2008)
Journal Article
Shamdasani, J., Bloodsworth, P., & McClatchey, R. (2008). Semantic matching for the medical domain. Lecture Notes in Artificial Intelligence, 5071 LNCS, 198-202. https://doi.org/10.1007/978-3-540-70504-8_21

This paper proposes some modifications to the SMatch algorithm that enables the semantic matching of medical terminologies using the Unified Medical Language System (UMLS) as a source of background knowledge. Semantic Matching is the process of disco... Read More about Semantic matching for the medical domain.

The requirements for ontologies in medical data integration: A case study (2007)
Journal Article
Hauer, T., Anjum, A., Bloodsworth, P., Branson, A., McClatchey, R., Munir, K., …Shamdasani, J. (2007). The requirements for ontologies in medical data integration: A case study. https://doi.org/10.1109/IDEAS.2007.4318120

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patient's medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scatt... Read More about The requirements for ontologies in medical data integration: A case study.

Bulk scheduling with the DIANA scheduler (2006)
Journal Article
Anjum, A., McClatchey, R., Ali, A., & Willers, I. (2006). Bulk scheduling with the DIANA scheduler. IEEE Transactions on Nuclear Science, 53(6), 3818-3829. https://doi.org/10.1109/TNS.2006.886047

Results from the research and development of a Data Intensive and Network Aware (DIANA) scheduling engine, to be used primarily for data intensive sciences such as physics analysis, are described. In Grid analyses, tasks can involve thousands of comp... Read More about Bulk scheduling with the DIANA scheduler.