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    Single-shot diffraction data from the Mimivirus particle using an X-ray free-electron laser

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    Author
    Ekeberg, T; Svenda, M; Seibert, MM; Abergel, C; Maia, FRNC; Seltzer, V; DePonte, DP; Aquila, A; Andreasson, J; Iwan, B; ...
    Date
    2016-08-01
    Source Title
    Scientific Data
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    Martin, Andrew
    Affiliation
    School of Physics
    Metadata
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    Document Type
    Journal Article
    Citations
    Ekeberg, T., Svenda, M., Seibert, M. M., Abergel, C., Maia, F. R. N. C., Seltzer, V., DePonte, D. P., Aquila, A., Andreasson, J., Iwan, B., Jonsson, O., Westphal, D., Odic, D., Andersson, I., Barty, A., Liang, M., Martin, A. V., Gumprecht, L., Fleckenstein, H. ,... Hajdu, J. (2016). Single-shot diffraction data from the Mimivirus particle using an X-ray free-electron laser. SCIENTIFIC DATA, 3 (1), https://doi.org/10.1038/sdata.2016.60.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256407
    DOI
    10.1038/sdata.2016.60
    Abstract
    Free-electron lasers (FEL) hold the potential to revolutionize structural biology by producing X-ray pules short enough to outrun radiation damage, thus allowing imaging of biological samples without the limitation from radiation damage. Thus, a major part of the scientific case for the first FELs was three-dimensional (3D) reconstruction of non-crystalline biological objects. In a recent publication we demonstrated the first 3D reconstruction of a biological object from an X-ray FEL using this technique. The sample was the giant Mimivirus, which is one of the largest known viruses with a diameter of 450 nm. Here we present the dataset used for this successful reconstruction. Data-analysis methods for single-particle imaging at FELs are undergoing heavy development but data collection relies on very limited time available through a highly competitive proposal process. This dataset provides experimental data to the entire community and could boost algorithm development and provide a benchmark dataset for new algorithms.

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