Announcement: RoSE (Research-oriented Social Environment)
RoSE is a research-oriented social environment for tracking and integrating relations between authors and documents in a combined “social-document graph.” It allows users to learn about an author or idea from the evolving relationships between people-and-documents, people-and-people, and documents-and-documents.
Unique features of RoSE include:
- Fine-grained and flexible relationship types and tags. Not everyone is just an “author of” or “friend of.”
- Historical “dead” people have their own profile pages. In true research, the influence between the past and present evolves in both directions.
- Visualizations of relations between people and documents. Social-network and other diagrams allow users to notice orbits and clusters of knowledge.
- User-definable “contexts” for entering or filtering data. One can enter or search for information entered in the context of a course, conference, research project, etc.
-an approach that provides implicit local contexts in which to judge goals, priorities, and information quality.
- Potential for interaction with other document repositories or social networks-e.g., through algorithmic harvesting of information or automated output into other biblio-social systems, visualization applications, etc. Though it currently does not access full-texts of documents, RoSe may in the future be wedded to full-text repositories.
RoSe is currently a demonstration project in early development by the UC Transliteracies Project, which focuses on the digital reading in today’s socially-networked digital environments. As a demonstration project, its limited goal is to suggest what is possible and to offer a hands-on way of thinking about some of the critical issues that would need to be confronted if RoSE were to be implemented as a production-scale system. These issues—which map the frontier where older document-centric modes of knowledge are extending into new socially-networked digital environments—include: expertise and networked public knowledge, data-mining and visualization of social networks, information credibility, fluid ontologies and metadata for social and historical research, and online reading and research environments.