E-Research is intended to facilitate collaboration through distributed access to content, tools, and services. Findings from two large, long-term digital library research projects are used to illustrate ways in which access to such resources does and does not facilitate collaboration. Both the Alexandria Digital Earth Prototype Project (ADEPT) and Center for Embedded Networked Sensing (CENS) project on data management leverage scientific research data for use in teaching. Two types of collaboration are considered: direct collaboration, in which faculty work together, and indirect or serial collaboration, in which faculty use or contribute shared content such as teaching resources, ontologies, or research data. Implications for collaboration in e-Research are divided into five categories: (1) differences in use based on discipline or specialty, (2) incentives to use e- Learning and e-Research technologies, (3) differences in use of information by role, (4) selecting and sharing of information, and (5) functionality and architecture requirements. Reuse and repurposing of content from research to teaching are proving to be even more complex than anticipated. Better tools and services to manage content can improve capture, management, and preservation. Making content more shareable increases the likelihood that it will be shared. Significant barriers and disincentives to sharing exist, including scientific priority, intellectual property, lack of standards, and the effort to implement systems compared to perceived value. Personal digital libraries offer a middle ground between private control and public release of content. We are just beginning to understand how e-Research can facilitate collaboration. The next step is to understand why.

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.

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).

e-Science promises to increase the pace of science via fast, distributed access to computational resources, analytical tools, and digital libraries. “Big science” fields such as physics and astronomy that collaborate around expensive instrumentation have constructed shared digital libraries to manage their data and documents, while “little science” research areas that gather data through hand-crafted fieldwork continue to manage their data locally. As habitat ecology researchers begin to deploy embedded sensor networks, they are confronting an array of challenges in capturing, organizing, and managing large amounts of data. The scientists and their partners in computer science and engineering make use of common datasets but interpret the data differently. Studies of this field in transition offer insights into the role of digital libraries in e-Science, how data practices evolve as science becomes more instrumented, and how scientists, computer scientists, and engineers collaborate around data. Among the lessons learned are that data on the same variables are gathered by multiple means, that data exist in many states and in many places, and that publication practices often drive data collection practices. Data sharing is embraced in principle but little sharing actually occurs, due to interrelated factors such as lack of demand, lack of standards, and concerns about publication, ownership, data quality, and ethics. We explore the implications of these findings for data policy and digital library architecture. Research reported here is affiliated with the Center for Embedded Networked Sensing.

As data become scientific capital, digital libraries of data become more valuable. To build good tools and services, it is necessary to understand scientists’ data practices. We report on an exploratory study of habitat ecologists and other participants in the Center for Embedded Networked Sensing. These scientists are more willing to share data already published than data that they plan to publish, and are more willing to share data from instruments than hand-collected data. Policy issues include responsibility to provide clean and reliable data, concerns for liability and misappropriation of data, ways to handle sensitive data about human subjects arising from technical studies, control of data, and rights of authorship. We address the implications of these findings for tools and architecture in support of digital data libraries.

The seemingly simple task of reusing data for science education relies on the presence of scientific data, scientists willing to share, infrastructure to provide access, and mechanisms to share between the two disparate communities of scientists and science students. What makes sharing between scientists and science students a special case of data sharing, is that all of the implicit knowledge attending the data must pass along this same vector. Our work at the Center for Embedded Networked Sensing studying aspects of this data reuse problem has shown us a rough outline of how the future of this data sharing will look. Our approach is to start from the prospective of the scientists, looking for opportunities to support scientific research, and then leveraging the data for reuse by education. The investment needed to capture high quality scientific data necessitates the consideration of reuse by the general population as well as other interested scientific parties.

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).

CENS collects data for scientific applications using nascent embedded sensing system technology on real-world deployments. Data collection occurs through a complex set of interactions and relationships between people, instruments, and the physical setting. In order to fully understand and evaluate data that results from real-world deployments, it is necessary to be aware of the context of data collection. CENS deployment documentation varies widely, existing both electronically and in lab or field notebooks, and is typically stored separate from the data. We are developing the CENS Deployment Center, a web accessible database for deployment information to provide CENS research groups with a centralized location for pre-deployment planning and post-deployment knowledge. We hope that the CENSDC will promote deployment efficiency and continuity as research personnel changes over time and CENS technology is deployed more widely.

This poster reports findings from a pilot study of a preliminary version of a CENS middle school inquiry module. The purpose of the pilot was to elicit students’ conceptions of plant evolution, especially leaf structure-function relationships in different environments. Pre-tests of students’ knowledge about plants and plant evolution confirmed our expectations that they lacked knowledge about leaf structure and function and about plant evolution. Post-tests, following approximately 13 hours of instruction, showed significant learning about leaf structure and function, but not plant evolution. Also, students performed better on short-answer questions on a post-test than on an essay that demanded they use conceptual knowledge to explain plant adaptation across environments. These results imply that a major design effort for CENS educational materials must be to help students integrate factual knowledge into coherent conceptual frameworks.

For users to trust and interpret the data in scientific digital libraries, they must be able to assess the integrity of those data. Criteria for data integrity vary by context, by scientific problem, by individual, and a variety of other factors. This paper compares technical approaches to data integrity with scientific practices, as a case study in the Center for Embedded Networked Sensing (CENS) in the use of wireless, in-situ sensing for the collection of large scientific data sets. The goal of this research is to identify functional requirements for digital libraries of scientific data that will serve to bridge the gap between current technical approaches to data integrity and existing scientific practices.