Data to knowledge: How to get meaning from your result

Helen M. Berman; Margaret J. Gabanyi; Colin R. Groom; John E. Johnson; Garib N. Murshudov; Robert A. Nicholls; Vijay Reddy; Torsten Schwede; Matthew D. Zimmerman; John Westbrook; Wladek Minor

doi:10.1107/S2052252514023306

Data to knowledge: How to get meaning from your result

Helen M. Berman, Margaret J. Gabanyi, Colin R. Groom, John E. Johnson, Garib N. Murshudov, Robert A. Nicholls, Vijay Reddy, Torsten Schwede, Matthew D. Zimmerman, John Westbrook, Wladek Minor

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Structural and functional studies require the development of sophisticated 'Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB 'super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

Original language	English (US)
Pages (from-to)	45-58
Number of pages	14
Journal	IUCrJ
Volume	2
DOIs	https://doi.org/10.1107/S2052252514023306
State	Published - Jan 1 2015
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2015.

Keywords

big data
data deposition
databases
knowledge bases
meaning from data

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10.1107/S2052252514023306

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doi = "10.1107/S2052252514023306",

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AU - Berman, Helen M.

AU - Gabanyi, Margaret J.

AU - Groom, Colin R.

AU - Johnson, John E.

AU - Murshudov, Garib N.

AU - Nicholls, Robert A.

AU - Reddy, Vijay

AU - Schwede, Torsten

AU - Zimmerman, Matthew D.

AU - Westbrook, John

AU - Minor, Wladek

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AB - Structural and functional studies require the development of sophisticated 'Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB 'super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

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Data to knowledge: How to get meaning from your result

Abstract

Bibliographical note

Keywords

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