No CrossRef data available.
Article contents
Informatics for Combinatorial Experiments: Accelerating Data Interpretation
Published online by Cambridge University Press: 26 February 2011
Abstract
Combinatorial experiments provide a means of generating large amounts of experimental data; however that does not necessarily lead to high throughput interpretation of that data. In this paper we provide a brief summary of how one can use informatics techniques to accelerate data interpretation from high throughput experiments. We provide examples from high throughput nanoindentation and diffraction experiments.
Keywords
- Type
- Research Article
- Information
- Copyright
- Copyright © Materials Research Society 2006
References
REFERENCES
1.
Rajan, K., Suh, C., Rajagopalan, A., Li, X., Mat. Res. Soc. Symp. Proc., 700 (2002).Google Scholar
3.
Potyrailo, R. A., Wroczynski, R. J., Lemmon, J. P., Flanagan, W. P., Siclovan, O. P., J.Comb. Chem.
5, 8 (2003).Google Scholar
5.
Benson, M. L., Saleh, T. A., Liaw, P. K., Choo, H., Wang, X.-L., Stoica, A. D., Daymond, M. R., Brown, D. W., Buchanan, R. A., and Klarstrom, D. L., Denver X-ray Conference 2004.Google Scholar
6.
Jiang, L., Brooks, C. R., Liaw, P. K., Dunlap, J., Rawn, C. J., Peascoe, R. A., and Klarstrom, D. L., Met. Trans. A
35, 785 (2004).Google Scholar