Collaborative Crystallography, Instrument Monitoring, Data Sharing and Archiving
Using Common Instrument Middleware Architecture (CIMA)
The Instrument Middleware Project supported by the National Science Foundation Middleware Initiative is working on development of tools that ease the Grid-enabling of scientific instrumentation. With the Common Instrument Middleware Architecture (CIMA) we are targeting to improve accessibility to instruments and to facilitate their integration into the grid. The CIMA implementation is currently being evaluated in several settings representing a spectrum of shared instrument applications including X-ray crystallography, Mass Spectroscopy, robotic telescopes and wireless sensor network nodes. CIMA is a consistent and reusable framework for including shared instrument resources in computing and storage grids. Here we present the implementation of CIMA applied to the field of single crystal X-ray crystallography. To allow the researchers to easily view the current and past data streams from an instrument a Crystallography Portal and associated portlets are being developed.
A major goal of the Crystallography Portal is to develop a system that allows remote access to grid-enabled diffraction instruments, provides a collaboration environment that allows the remote user to interact with on-site researchers, and also provides near real-time access to the data as it is being collected. In addition to these functions the portal also facilitates post-processing and archiving of raw data and metadata. The portal is expected to have a significant impact on access to highly specialized facilities such as instruments located at synchrotron sites. It will also be possible for others to observe details of the experiment and thus significantly reduce training time and costs for new users unfamiliar with the instruments. An obvious use of the crystallography portal is to provide students and faculty located in smaller universities and colleges the opportunity to use remote facilities in a similar manner.
Support from National Science Foundation grants SCI-0330568 and MRI CDA-0116050 is gratefully acknowledged.
Contributors:
Randall Bramley, Tharaka Devadithya, and Yu Ma
Department of Computer Science, Indiana University
Kenneth Chiu
Department of Computer Science, State University of New York at Binghamton (SUNY)
Nisha Gupta, Charles Hart, and Donald F. McMullen
Pervasive Technology Labs at Indiana University
John C. Huffman and Kianosh Huffman
School of Informatics, Indiana University