From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web

TL;DR

This paper proposes a semantic web-based model for representing scientific artifacts and their life cycles, emphasizing relationships and persistent identifiers to improve discovery and understanding of scientific data and practices.

Contribution

It introduces a conceptual implementation of the OAI-ORE data model to represent scientific life cycles and relationships in sensor-based research, enhancing data management and discovery.

Findings

Demonstrates how to model scientific artifacts and their relationships using OAI-ORE.

Shows improved discovery and understanding of scientific practices through semantic relationships.

Facilitates new scientific research and learning methods.

Abstract

In the process of scientific research, many information objects are generated, all of which may remain valuable indefinitely. However, artifacts such as instrument data and associated calibration information may have little value in isolation; their meaning is derived from their relationships to each other. Individual artifacts are best represented as components of a life cycle that is specific to a scientific research domain or project. Current cataloging practices do not describe objects at a sufficient level of granularity nor do they offer the globally persistent identifiers necessary to discover and manage scholarly products with World Wide Web standards. The Open Archives Initiative's Object Reuse and Exchange data model (OAI-ORE) meets these requirements. We demonstrate a conceptual implementation of OAI-ORE to represent the scientific life cycles of embedded networked sensor applications in seismology and environmental sciences. By establishing relationships between publications, data, and contextual research information, we illustrate how to obtain a richer and more realistic view of scientific practices. That view can facilitate new forms of scientific research and learning. Our analysis is framed by studies of scientific practices in a large, multi-disciplinary, multi-university science and engineering research center, the Center for Embedded Networked Sensing (CENS).