The Memex Metadata for Student Portfolios (M 2 ) project is using mobile technology to augment student memory and improve student learning. We have constructed a student-targeted Context Awareness Framework (CAF) and we are developing a metadata scheme that integrates the CAF with a variety of mobile technologies. In particular, we are exploring the use of Microsoft SenseCams, which capture images and sensory data approximately every 90 seconds and can extend student memory, enabling for an enriched learning experience for undergraduate biology students.We are exploring the use of SenseCams along with other mobile devices (e.g., a GIS and Tablet PC) for biology students conducting scientific field work such as specimen identification. The development of our CAF and metadata scheme also support the development of e-portfolios that can extend student memory and maintain useful records of educational activities. This paper presents research and development activities underlying the M 2 project, including research methods, evaluation activities, and next steps.

We explore the use of microstructured semiconductor neutron detectors (MSNDs) to map the ratio between thermal neutrons and higher energy neutrons. The system consists of alternating layers of modular neutron detectors (MNDs), each comprising arrays of twentyfour MSNDs, and high-density polyethylene moderators (HDPE) with gadolinium shielding to filter between thermal neutrons and higher energy neutrons. We experimentally measured the performance of three different configurations and demonstrated that the sensor system prototypes detect and differentiate thermal and epithermal neutrons. We discuss future planetary exploration applications of this compact, semiconductor-based low-energy neutron detection system.

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Genomic research involves a considerable amount of intensive computational and data management challenges including varying demand for computing resources, large data staging, management of complex workflows, and managing data and metadata across thousands of experiments and datasets. To address these challenges, researchers are typically forced to acquire and maintain experienced IT staff and informatics infrastructure. Increasingly researchers have explored cloud technologies, yet these lack key capabilities for data and workflow management. We introduce work to address these issues through use of federated cloud infrastructure coupled with data and workflow management technology. We present preliminary work toward the integration of three major technologies: ExoGENI, integrated Rule Oriented Data System (iRODS), and Pegasus/HTCondor, to develop a software infrastructure that better supports dataand workflow-centric genomic analysis. ExoGENI; genomic analysis; data management; cloud computing;I.