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Improving the reproducibility of experiments and reusability of research outputs in complex data analysis
Published on Jun 28, 201963 Views
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 t
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Chapter list
Improving the reproducibility of experiments and reusability of research outputs in complex data analysis00:00
Outline01:34
Reproducibility in Science 02:45
Reproducibility in Artificial Intelligence (AI)04:55
Reusable research outputs and FAIR06:53
FAIR data principles09:55
Adding semantics to research outputs: Ontologies12:18
Semantic web technologies14:37
Complex data analysis18:23
Example task: Structured output prediction20:40
How can we improve the reproducibility and reusability (1)23:58
How can we improve the reproducibility and reusability (2)27:04
How can we improve the reproducibility and reusability (3)30:02
Storing experiments, research outputs and their annotations32:01
Relevant ontological representations for machine learning and data mining34:47
OntoDM : a set of modular ontologies for data mining36:17
Generic ontology of datatypes OntoDT37:02
Examples of different datatypes in data mining37:47
Example of dataset taxonomy constructed using OntoDT40:08
Ontology of core data mining entities OntoDM core41:14
OntoDM core structure41:45
Representation of complex analysis tasks43:42
How do we represent complex tasks?43:47
OntoDM KDD44:04
OntoDM KDD structure44:25
IMPERATRIX project44:52
Project objectives46:45
Vision for the IMPERATRIX prototype system47:55
Current work48:57
Prototype system50:09
Outline of the system prototype50:36
Summary50:50