Publications

2010
Henneken E  A, Kurtz M  J, Accomazzi A, Grant C, Thompson D, Bohlen E, Di Milia G, Luker J, Murray S  S. Finding Your Literature Match – A Recommender System, in Future Professional Communication in Astronomy II,. Cambridge, MA; 2010. Publisher's Version
Fabbiano, G.; Calzetti CDEIFGMPD ; C ;. Recommendations of the VAO-Science Council. VAO-Science Council; 2010 pp. 9. Publisher's VersionAbstract
Recommendations of the VAO-Science Council following the meeting of March 26-27, 2010. Meeting web page.
1006.2168.pdf
Kurtz M  J, Accomazzi A, Henneken E, Di Milia G, Grant C  S. Using Multipartite Graphs for Recommendation and Discovery, in Astronomical Data Analysis Software and Systems XIX.Vol 434.; 2010:155-+. Publisher's Version
Accomazzi A. Astronomy 3.0 Style {E. Isaksson, J. Lagerstrom A H, N. Bawdekar}. Library and Information Services in Astronomy VI: 21st Century Astronomy Librarianship, From New Ideas to Action [Internet]. 2010;433:273. Publisher's Version
Rodriguez MA, Pepe A, Shinavier J. The dilated triple. In Chbeir B, Hassanien A Emergent Web Intelligence: Advanced Semantic Technologies Springer; 2010. pp. 3-16. Publisher's VersionAbstract
The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.
Pepe A, Mayernik MS, Borgman CL, Sompel HVD. From Artifacts to Aggregations: Modeling Scientific Life Cycles on the Semantic Web. Journal of the American Society for Information Science and Technology [Internet]. 2010;61. Publisher's VersionAbstract
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).
2009
Goodman AA, Wong C. Bringing the Night Sky Closer: Discoveries in the Data Deluge. In The Fourth Paradigm: Data-Intensive Scientific Discovery ; 2009. Publisher's VersionAbstract
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?
Goodman AA. Seeing Science. Proceedings of the International Festival of Scientific Visualization [Internet]. 2009. Publisher's VersionAbstract
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.