Visualizing Collaborative Time-Varying Scientific Datasets

  • J. M. Sharif
  • M. S. S. Omar
  • M. S. A. Latiff
  • M. A. Ngadi
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 15)


Our perceptive of the scientific datasets has largely relied on numerical and statistical analysis of data from experimental dimension and computer simulation result [4][14][11][12][13]. In particular, we consider a simulated 3D time-varying model of scientific datasets and examine the temporal correlation among datasets. Our goal is to contrive effective visual representations to assist scientists in ascertaining temporal correlation among intricate and apparently chaotic scientific datasets. We propose a hybrid application with combination of streamline, global and local color scale and opacity scheme for spatio-temporal collaborative depiction. We illustrated also few images that can offer an effective tool for visually mining 3D time-varying scientific datasets.


scientific data visualization hybrid scheme coding theory color scale time-varying data spatio-temporal visualization molecular dynamics visual representation 


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  1. [1]
    D. M. Beazley and P. S. Lomdahl. Lightweight computational steering of very large scale molecular dynamics simulations. In Conference on High Performance Networking and Computing. IEEE, 1996.Google Scholar
  2. [2]
    C. Best and H.-C. Hege. Visualizing and identifying conformational ensembles in molecular dynamics trajectories. In Computing in Science and Engineering, pages 68–74, 2002.Google Scholar
  3. [3]
    V. L. Bulatov and R. W. Grimes. Visualization of molecular dynamics simulations. In Eurographics UK Chapter, 1996.Google Scholar
  4. [4]
    K. Funke. Jump relaxation in solid electrolytes. Progr. Solid State Chem, 22:111–195, 1993.CrossRefGoogle Scholar
  5. [5]
    H.L. Gordon and R.J. Somorjai. Fuzzy cluster analysis of molecular dynamics trajectories. volume 14, pages 249–264, Oct 1992.Google Scholar
  6. [6]
    T. Horiuchi and N. Go. Peojection of monte carlo and molecular dynamics trajectories onto the normal axes: human lysozyme. In Proteins, volume 10, pages 106–116, 1991.CrossRefPubMedGoogle Scholar
  7. [7]
    H. Huitema and R. V. Liere. Interactive visualization of protein dynamics. In Proceedings of Conference on Computer Graphics (VISUALIZATION 2000), pages 465–468. IEEE, Oct 2000.Google Scholar
  8. [8]
    J. Imada, P. Chapman, and S.M. Rothstein. Recognizing patterns in high-dimensional data: automated histogram filtering for protein structure elucidation. In 19th International Symposium on High Performance Computing Systems and Applications, 2005. HPCS 2005, pages 238–243. IEEE, May 2005.Google Scholar
  9. [9]
    M.E. Karpen, D.J. Tobias, and C.L. Brooks. Statistical clustering techniques for the analysis molecular dynamics: Analysis of 2.2-ns trajectories of ypgdv. volume 32, pages 412–420, Jan 1993.Google Scholar
  10. [10]
    S.L. Kazmirski, A. Li, and V. Daggett. Analysis methods for comparison of multiple molecular dynamics trajectories: Applications to protein unfolding pathways and denatured ensembles. volume 290, pages 283–304, Jan 1999.Google Scholar
  11. [11]
    K. L. Ngai. Analysis of nmr and conductivity-relaxation measurements in glassy li 2 s-sis 2 fast-ion conductors. Physical Review B, 48, 1993.Google Scholar
  12. [12]
    K. L. Ngai, G. N. Greaves, and C. T. Moynihan. Correlation between the activation energies for ionic conductivity for short and long time scales and the kohlrausch stretchting parameter b for ionically conducting solid and melts. Phys. Rev. Lett, 80:1018–1021, 1998.CrossRefGoogle Scholar
  13. [13]
    K. L. Ngai, Y. Wang, and C. T. Moynihan. The mixed alkali effect revisited: importance of ion interactions. Journal of Non-Crystalline, 307–310:999–1011, 2002.CrossRefGoogle Scholar
  14. [14]
    W. Smith, G. N. Greaves, and M. J. Gillan. Computer simulation of sodium disilicate glass. Journal Chemical Physics, 103, 1995.Google Scholar
  15. [15]
    Adrian P. Wiley, Martin T. Swain, Stephen C. Phillips, Jonathan W. Essex, and Colin M. Edge. Parametrization of reversible digitally filtered molecular dynamics simulations. In Journal of Chemical Theory and Computation, volume 1, pages 24–35, Feb 2005.CrossRefPubMedGoogle Scholar
  16. [16]
    Huabing Zhu, Tony Kai Yun Chan, Lizhe Wang, Wentong Cai, and Simon See. A prototype of distributed molecular visualization on computational grids. In Future Generation Comp. Syst, volume 20, pages 727–737, 2004.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • J. M. Sharif
    • 2
  • M. S. S. Omar
    • 1
  • M. S. A. Latiff
    • 2
  • M. A. Ngadi
    • 2
  1. 1.Bioinformatics Research Lab, Faculty of ScienceUniversity Technology of MalaysiaJohorMalaysia
  2. 2.Department of Computer System & CommunicationUniversity Technology of MalaysiaSkudaiMalaysia

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