A Linked Science Investigation: Enhancing Climate Change Data Discovery with Semantic Technologies

Area of Science:

• Environmental Science
• Data Science
• Computational Science

Background:

• Linked Science facilitates the sharing and reuse of scientific data through interconnected workflows.
• Much scientific data resides in multi-institutional data centers, not the cloud, requiring specialized tools for access and analysis.
• Ontologies, as formal machine-readable descriptions, are crucial for enabling effective data search and discovery.

Purpose of the Study:

• To advocate for the use of scientific scenarios in Linked Science practices.
• To propose a river-channel transport scenario integrating biogeochemical and climate simulation data.
• To demonstrate how ontologies can enhance data search and retrieval within distributed data systems.

Main Methods:

• Utilized a river-channel transport scenario requiring diverse data sources.
• Focused on employing ontologies for data search and discovery.
• Augmented the Mercury tool with ontologies from the Semantic Web for Earth and Environmental Terminology (SWEET) collection.
• Prototyped a component for accessing SWEET ontology terms within Mercury.
• Evaluated the coverage of SWEET for the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC).

Main Results:

• Demonstrated the integration of experimental and climate model data through a specific scientific scenario.
• Successfully augmented Mercury's search capabilities with ontology-driven access.
• Showcased the utility of SWEET ontologies for enhancing metadata retrieval.
• Provided an evaluation of SWEET's applicability to the ORNL DAAC.

Conclusions:

• Scientific scenarios are valuable for driving Linked Science initiatives.
• Ontology integration significantly improves the searchability and accessibility of distributed scientific data.
• Tools like Mercury, enhanced with ontologies, can effectively bridge data silos and support complex scientific inquiries.