Peter A. Meyer, Harvard University
Stephanie Socias, Harvard University
Jason Key, Harvard University
Elizabeth Ransey, Harvard University
Emily C. Tjon, Harvard University
Alejandro Buschiazzo, Institut Pasteur de Montevideo
Ming Lei, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences
Chris Botka, Harvard University
James Withrow, Cornell University
David Neau, Cornell University
Kanagalaghatta Rajashankar, Cornell University
Karen S. Anderson, Yale University
Chung-I Chang, Academia Sinica, Taiwan
Walter J. Chazin, Vanderbilt University
Kevin D. Corbett, University of California, San Diego
Michael S. Cosgrove, SUNY Upstate Medical University, Syracuse
Sean Crosson, University of Chicago
Sirano Dhe-Paganon, Department of Cancer Biology, Dana-Farber Cancer Institute
Enrico Di Cera, Saint Louis University School of Medicine
Catherine L. Drennan, Massachusetts Institute of Technology
Michael J. Eck, Harvard University
Brandt F. Eichman, Vanderbilt University
Qing R. Fan, Columbia University
Adrian R. Ferre-D’Amare, Laboratory of RNA Biophysics, National Heart, Lung and Blood Institute, NIH, Bethesda, Maryland
J. Christopher Fromme, Cornell University
K. Christopher Garcia, Stanford University
Rachelle Gaudet, Harvard University
Peng Gong, Wuhan Institute of Virology, Chinese Academy of Sciences
Stephen C. Harrison, Harvard University
Ekaterina E. Heldwein, Tufts University
Zongchao Jia, Queen’s University, Kingston, Ontario
Robert J. Keenan, University of Chicago
Andrew C. Kruse, Harvard University
Marc Kvansaku, La Trobe University
Jason S. McLellan, Dartmouth College

Document Type

Article

Publication Date

3-7-2016

Publication Title

Nature Communications

Department

Geisel School of Medicine

Abstract

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.

DOI

10.1038/ncomms10882

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