Recently, there has been a push among both funders and journals to share raw data. The response has been positive and that in itself has created a unique challenge. Voluminous amount of data have been produced. There is too much data for humans to sift through. The semantic confusion is not a simple one-to-one correspondence. It is multidimensional as it includes spatial, temporal, and methodological differences, along with functional definitions. An obvious response to overcome this challenge is to use machine learning. However, it is an enormous semantic challenge to parse hundreds of bespoke terms to facilitate machine learning. In this scenario, can ontologies offer a way out?
Maryann Martone, a neuroscience professor at the University of California, a specialist in ontologies and the co-author of FAIR Guiding Principles for scientific data management and stewardship, is optimistic that ontologies might offer a solution to this intractable problem.
According to Martone, “Ontologies are a critical tool for creating and managing knowledge bases that help us communicate and verify the knowledge we think we have. If someone says X is in a motor region, how does a machine know what a motor region is? There’s an ontologic structure that says some features obligatorily appear together.”
“In an ontology, individual terms are tagged to central concepts called Uniform Resource Identifiers (URIs), with no weight to the tagging. Dog, Canis lupus familiaris, and Mr. Fluffy all map to the same URI. Because ideas can overlay each other, explains Martone, “ontologies allow you to construct a reasonable theory. Previously, we couldn’t even bring all the data together because we first had to navigate all the terms.”
So, how do ontologies help scientists? Ontologies help them visualize beyond buckets of data. It can help scientists compare patterns hidden in the tranche of data and analyze them. In the new world of data sharing, ontologies can help scientists move away from meta-analysis to mega-analysis. Instead of stopping with comparing published literature to decide what the evidence suggested, scientists can now compare pools of raw data, which show correlations among variables collected across multiple experiments. This will increase the depth and breadth of human understanding.
Click here to read the complete article written by Karen Heyman, a former science and development writer for the Salk Institute.
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