Integrated research data management in the Structural Sciences
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ManjulaPatelFolgen
19. May 2011•0 gefällt mir•896 views
Integrated research data management in the Structural Sciences
19. May 2011•0 gefällt mir•896 views
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A presentation given by Manjula Patel (UKOLN, University of Bath) at the I2S2 workshop "Scaling Up to Integrated Research Data Management", IDCC 2010, 6th December 2010, Chicago. http://www.ukoln.ac.uk/projects/I2S2/events/IDCC-2010-ScalingUp-Wksp/
ManjulaPatelFolgen
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Integrated research data management in the Structural Sciences
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- 4. Objectives Scale and complexity : small laboratory to institutional installation to large scale facilities e.g. DLS & ISIS, STFC Interdisciplinary issues : research across domain boundaries Data lifecycle : data flows and data transformations over time University of Cambridge (Earth Sciences) DLS & ISIS, STFC EPSRC National Crystallography Service University of Cambridge (Chemistry)
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- 7. Earth Sciences, Cambridge: Typical workflow Martin Dove & Erica Yang, July 2010
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- 13. An Idealised Scientific Research Activity Lifecycle Model
- 14. An Idealised Scientific Research Activity Lifecycle Model Research Management (CERIF) Data Management and Provenance (CSMD, OPM) Software descriptions Bibliographic records (FRBR, SWAP) Curation (OAIS, PREMIS) Dublin Core, Ontologies DRM, Creative Commons
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- 19. Data analysis workflow Data analysis folders A rchive B rowse R estore Derived Data Scientific software: Gudrun
- 20. I2S2-IM
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Hinweis der Redaktion
- Non-intrusive soln, so researchers can concentrate on research Try not to Demand users to change their established practice Change their programs Change deployment platform Change their programs’ interface Change how their programs interact with their data Try to Adapt to their existing practice Allow them to use their favourites (program, language, platform, OS, format, file system, …) Make their life easier Ask them to do as little as possible
- Could drop this slide if too many.
- Software study: two fortran programs, one Java gui Understanding the data: correction data, sample data, calibration data Learn about the software from the scientists