This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of an overt interdisciplinary research group focused on sensor networks and wireless communication. The popular leading eigenvector community detection algorithm was employed to assign a structural community to each scholar in the network. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson's \$\chi^2\$ test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result indicates that, contrary to the common conception of a multi-institutional interdisciplinary research group, collaboration is primarily driven by scholar expertise and physical proximity.

At CENS, various efforts aimed at the preservation and dissemination of scientific material have emerged over time, resulting in data being collected in three main repository systems: 1), CENSDC, a database service that provides access to CENS deployments in a centralized web location, 2) Sensorbase.org, a data warehouse for raw sensor data and 3) CDL eScholarship Repository, a digital library service for articles, technical reports and similar scholarly material. We anticipate forthcoming data repositories to include, among others, a directory of CENS people and a sensor software library. Despite the heterogeneity of this content, we believe that these information resources are all building blocks of the same scholarly production chain. With this concept in mind, we are designing a framework that allows the creation, discovery, ingest and publication of aggregated information resources via simple web services.

This article presents and validates a clustering-based method for creating cultural ontologies for community-oriented information systems. The introduced semiautomated approach merges distributed annotation techniques, or subjective assessments of similarities between cultural categories, with established clustering methods to produce cognate ontologies. This approach is validated against a locally authentic ethnographic method, involving direct work with communities for the design of fluid ontologies. The evaluation is conducted with of a set of Native American communities located in San Diego County (CA, US). The principal aim of this research is to discover whether distributing the annotation process among isolated respondents would enable ontology hierarchies to be created that are similar to those that are crafted according to collaborative ethnographic processes, found to be effective in generating continuous usage across several studies. Our findings suggest that the proposed semiautomated solution best optimizes among issues of interoperability and scalability, deemphasized in the fluid ontology approach, and sustainable usage.

Scientists and engineers working with embedded networked sensing systems in the environmental sciences are acquiring data at unprecedented rates. Scientific data do not emerge from a vacuum. There is considerable contextual information that surrounds the process of data acquisition that is critical to interpret and analyze data. Current techniques for data sharing involve considerable manual effort to prepare, describe, and transfer this contextual information along with the data itself. This paper reports on a study of the UCLA-based Center for Embedded Networked Sensing (CENS), an interdisciplinary NSF research center that supports collaborations to develop and implement innovative wireless sensor networks. We report here on the development of the CENS Deployment Center, a database for CENS deployment information. The goals of the CENSDC are to facilitate better deployment organization, and to provide a central location for key information that describes the context of data capture. We also describe our plans within CENS to use a tri-partite approach to capture and share sensor data resources using technical recommendations currently being developed within the context of the Open Archive Initiative for Object Reuse and Exchange (OAI-ORE).

Micro-blogging is a form of online communication by which users broadcast brief text updates, or tweets. This arti- cle explores the temporal component of micro-blogging ac- tivity by emphasizing its narrative nature: an individual tweet is an expression of personal online presence at a given time, yet it necessarily embodies the context of a broader developing story. We present Twit ick, a digital media platform that blends a continuous stream of real-time text updates from Twitter with related user-uploaded images hosted on Flickr. Twit ick acts as a space in which dis- tributed, temporally-authentic personal narratives, in the form of photographs and text, reinforce, extend, and even misrepresent each other. The visualizations provided by Twitflick capture the quotidian rhythms of online social exchange and draw attention to the poetic potential of web 2.0.

Scientists and engineers working with embedded networked sensing systems in the environmental sciences are acquiring data at unprecedented rates. Scientific data do not emerge from a vacuum. There is considerable contextual information that surrounds the process of data acquisition that is critical to interpret and analyze data. Current techniques for data sharing involve considerable manual effort to prepare, describe, and transfer this contextual information along with the data itself. This paper reports on a study of the UCLA-based Center for Embedded Networked Sensing (CENS), an interdisciplinary NSF research center that supports collaborations to develop and implement innovative wireless sensor networks. We report here on the development of the CENS Deployment Center, a database for CENS deployment information. The goals of the CENSDC are to facilitate better deployment organization, and to provide a central location for key information that describes the context of data capture. We also describe our plans within CENS to use a tri-partite approach to capture and share sensor data resources using technical recommendations currently being developed within the context of the Open Archive Initiative for Object Reuse and Exchange (OAI-ORE).