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).
Throughout history, astronomers have been accustomed to data falling from the sky. But our relatively newfound ability to store the sky's data in "clouds" offers us fascinating new ways to access, distribute, use, and analyze data, both in research and in education. Here we consider three interrelated questions: (1) What trends have we seen, and will soon see, in the growth of image and data collection from telescopes? (2) How might we address the growing challenge of finding the proverbial needle in the haystack of this data to facilitate scientific discovery? (3) What visualization and analytic opportunities does the future hold?
The ability to represent scientific data and concepts visually is becoming increasingly important due to the unprecedented exponential growth of computational power during the present digital age. The data sets and simulations scientists in all fields can now create are literally thousands of times as large as those created just 20 years ago. Historically successful methods for data visualization can, and should, be applied to today's huge data sets, but new approaches, also enabled by technology, are needed as well. Increasingly, "modular craftsmanship" will be applied, as relevant functionality from the graphically and technically best tools for a job are combined as-needed, without low-level programming.