Kenneth Chiu, Ph.D. - US grants

This proposal researches a Common Instrument Middleware Architecture (CIMA)
to improve accessibility of instruments and to facilitate their integration into the Grid. The proposed middleware will be based on current Grid implementation standards and accessible through platform independent standards such as the Open Grid Services Architecture (OGSA) and the Common Component Architecture (CCA). Emphasis will be placed on supporting a variety of instrument and controller types including creating a small implementation that can be used with tiny wireless controllers such as the Berkeley Mote sensor package as well as embedded PC-104- and VME-based controller systems. The proposed CIMA implementation will be evaluated in three settings representing a spectrum of shared instrument applications: X-ray crystallography at a synchrotron source, real-time acquisition of network performance data with embedded monitors, and small sensor network nodes using Berkeley Mote wireless sensors. The end product will be a consistent and reusable framework for including shared instrument resources in geographically distributed Grids.

Broad impact and intellectual merit. The proposed work will have important ramifications in the development of many instrument-driven Grid computing projects, and by extension, to many science education programs. Further implications exist for the development of industrial standards for networking instruments and international e-Science collaborations, as well as developing and evaluating new ubiquitous computing methodologies. The co-PI's are active in the Global Grid Forum, the DoE CCA Forum, and other Grid and HPC standards efforts, and together with industrial and DoD projects these will be used to evolve and promulgate the results of the proposed work. The co- PI's also have a track record of mentoring students from underrepresented groups, and have education projects at all levels from K-12 to postdoctoral.

This three-year award is a collaborative project by the University of California-San Diego, University of Wisconsin-Madison, and Indiana University to automate the extensibility and scalability of data-generating networks. Rapid advances and deployment of cyberinfrastructure and sensor networks have created opportunities for new knowledge of ecological systems and their role in global environmental processes. Expanding current networks and developing new networks capable of addressing the spatial and temporal variability of important ecological processes such as lake metabolism at regional to global scales will require novel technical improvements in an architecture that transports data from sensors to databases, allows dynamic control of sensors and reconfiguration of the network, addresses data quality assurance, provides data access and query to distributed data in the network as well as to other relevant datasets, and provides tools for analysis. Specifically this project has two major technical goals:
1. Develop new methods and tools to help automate the updating of data flows from dynamically deployed sensors to publicly accessible biological databases.
2. Develop a suite of new algorithms and software for analysis of biological information to automate detection (real-time) of events based on data from sensors and databases, with applications to classification of signals to biological or physical events or to sensor failure, allowing rapid response.
The dissemination of analytical tools and framework developed will be useful to existing research projects, evolving environmental observing systems such as National Ecological Observatory Network, networks, agencies, international partners, and K-16 teachers. Graduate and under graduate students will participate in the project. Both software tools and basic research data from this project will be incorporated into existing and ongoing professional development resources for teachers and instructional resources for students in grades 6-12.

The CrystalGrid Framework (CGF) project will research the acquisition, transport, and curation of data over the entire data space of the field of X-ray crystallography, addressing methods for managing wide heterogeneity in data representations, formats, data containers, administrative domains and diverse instruments and equipment. Until recently, individual labs have simply imposed local homogeneity of format and procedure, and not stored lab-dependent metadata. This ad hoc system is limited, however, as crystallographers begin to cross between labs to accomplish their research objectives, and as increasing numbers and sizes of output data streams leave less time for each investigation. Local workflow must be made explicit, procedures must be formally described, and the history and assemblages of data expressed in an open, shareable way. Creation and management of complete, accessible records for each experiment is critical, as well as heterogeneity in data acquisition and management across the field.

To meet that need, this project will develop a framework of web service interfaces and data and metadata systems addressing the whole spectrum of crystallography. Project participants and collaborators will leverage existing projects, such as Reciprocal Net and Common Instrument Middleware Architecture, that address narrower issues in the problem domain. The CGF will also draw on collaborating projects with overlapping areas of interest, such as the UK-based Comb-e-Chem project. The resulting framework will be a useful environment for crystallographic investigations and an extensible platform on which new web-based applications can be built.

The CGF project involves the classic problem of dealing with heterogeneity in data, procedures, and instruments in the crystallography application space, and another classic problem in integrating the entire data collection, transport, and curation requirements of the domain into a seamless beginning to end system. The challenge is to create a virtualization system that manages heterogeneity in more than a single aspect and to provide vertical integration using only open, extensible, and interoperable standards and methodologies.

While the project constitutes research into pertinent computer science problems, the plan for performing the research is centered on producing a product (the CGF) that will immediately be useful in addressing emerging technical problems in the field of X-ray crystallography. Within crystallography, one of the specific goals is to make structural results accessible that might otherwise never be seen, and so the CGF will help increase the body of scientific knowledge and improve the return on federal investment in the large numbers of x-ray diffractometers and associated instruments nationwide. Although the project targets specifically a few hundreds of crystallography labs worldwide, the software and methods created in it are intended to be reusable for any science moving from individual lab practices to a shared, global collaboratory system. In sciences such as high-energy physics and astronomy, the scientists have long shared single, unique, large instruments and had to create shared data management and instrument metadata. CGF is likely to be useful in other scientific disciplines which still use widely-distributed lab-based instruments that now need to be linked in data grids.

This project will engage citizens new to cyberinfrastructure (CI) in the development of tools and data analysis; train computer science (CS) students to work with citizens, thereby transforming what they envision as possible roles and responsibilities in their future careers; and, provide lake data to the broader research, policy?]making, and educational communities, thereby broadening understanding of lake ecosystems by students, citizen scientists, and the lay community worldwide. By engaging citizens as full participants in a community committed to understanding and sharing information on lake processes, their involvement in the continued development and use of CI tools will be enhanced and sustained. The goal of this project is to bring a citizen scientist group (Lake Sunapee Protective Association) into the development and use of CI and, in the process, create and evaluate a model for engaging a broader citizen base in the articulation of needs, and the development and use of CI tools. This will be done by focusing on three objectives: (1) create CI with citizen scientists to present real?]time and archived lake buoy data to the lay public in accessible and useful forms, while simultaneously training and transferring skills to the lake association; (2) educate and train CS students to interact effectively with citizen scientists and lay communities to co-develop CI; and (3) develop a model that expands the reach and impact of CI and employ a method to assess its success. The project will also assess and document the suitability of products to K?]8 education. These objectives will be accomplished through a series of three workshops with increasingly wider participation regionally and nationally for citizen scientists and K?]8 educators. Between meetings, project students and researchers working with members of the lake association and their education staff will implement the desired functionality into a portal?]based system. Close linkages to the Global Lake Ecological Observatory Network (GLEON) will help ensure that the results are broadly disseminated worldwide.

This Nanotechnology Undergraduate Education (NUE) in Engineering program entitled NUE: Nanotechnology for Manufacturing Flexible Electronics under the direction of Dr. Howard (Hao) Wang, SUNY at Binghamton, will develop a systematic undergraduate nanotechnology educational program in the School of Engineering with a focused theme of flexible electronics manufacturing and applications. The FlexE-NUE will address the educational needs in the great Nano/FlexE adventure at the undergraduate level through a series of courses, design projects, seminars, research and cyberinfrastructure experiences, as well as domestic and international collaborations.

Ecosystems have changed more rapidly in the past 50 years than at any other time in human history. Lakes are exemplars of change, as a variety of social, landscape and climate factors drive globally pervasive degradation of lake water quality and quantity. National and international ecological networks play a valuable role in monitoring the world?s ecosystems through continuous automated sensing of key environmental variables. The Global Lake Ecological Observatory Network (GLEON) has been amassing data from lake-sensor networks around the world. In this project, GLEON scientists transform ecological sensor networks from data collectors to knowledge generators through integration of the people, data, and cyberinfrastructure of lake-sensor networks. Scientists use three-dimensional lake simulation and advanced signal processing algorithms to exploit information embedded in sensor network data, as well as data from non-traditional sources, such as Web sites that log observations of city and state employees who monitor the lakes. Automated coupling of diverse data sources with simulation models will provide near-real-time prediction of lake conditions. With new data and model integration, scientists will gain new understanding into socially relevant environmental issues, such as the development of harmful algal blooms and the roles lakes play in the global carbon cycle.

The collaborative efforts of lake ecologists, computer scientists, and information technologists will yield transformations for the scientific disciplines and benefits for the broader community. The use of non-traditional data from the Internet will reveal new pathways for multi-discipline collaborations that study how ecosystems and societies interact. The rapidly expanding field of environmental sensor networks will benefit by use of the data-model integration techniques developed here. The insights into data-model coupling gained by computer scientists will be of benefit to other disciplines, such as the social sciences and biological epidemiology, in which diverse data sources and complex models are applied to complex problems. Finally, the novel and advanced techniques developed will enable the next generation of scientists to study lakes in ways not previously possible. Teams of students from multiple disciplines will participate in the creation of these new technologies, fostering collaborations that will lead to exchange of ideas and the emergence of a new way of studying our natural systems.