Improving the reproducibility of experiments and reusability of research outputs in complex data analysis
published: June 28, 2019, recorded: May 2019, views: 54
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The advances in science are heavily based on the premise of the concept of a trusted discovery, provided that the performed research is done correctly, and reproducible by other scientists. In order to increase the reusability of research outputs, such as developed models and produced data, they should be Findable, Accessible, Interoperable and Reusable (FAIR principles). The main point of the FAIR is to ensure that research outputs are reusable and will actually be used by others, thus becoming more valuable. The research outputs that wish to fulfil the FAIR principles must be represented with a wide accepted machine-readable framework. Currently, a popular solution to data sharing that fulfils the FAIR requirements is the use of semantic web technologies and ontologies. Complex data analysis methods, originating from machine learning and data mining, are increasingly being used in applications from various domains of science (e.g., life sciences, space research, etc). In order to provide reproducibility of experiments (e.g., executions of methods) and reuse of research outputs (e.g., predictive models), one needs to formally describe the entities involved in the process of analysis, and store them together with their descriptions (e.g., metadata) as a digital objects in a database like structure. Having a “semantically aware” stores of entities for complex data analytics enhanced with automatic reasoning capabilities would be beneficial for improving the reproducibility of experiments and reuse of research outputs. In this way, we would move closer to a FAIR data analysis process. In this talk, I will show and discuss the recent advances in the domain that are aimed towards improving the reproducibility of experiments and reusability of research outputs in complex data analysis.
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