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Digital Preservation in Data-Driven Science

Digital Preservation in Data-Driven Science

Digital Preservation in Data-Driven Science. Outline. Data-driven Science From Data to Processes Process Capture Contractual solution: ESCROW How to evaluate?. The Fourth Paradigm.

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436 views • 42 slides
Digital Immortality

Digital Immortality

Digital Immortality. OR. Keeping Digital Data for Ever. Dr David Holdsworth <ecldh@leeds.ac.uk>. http://www.leeds.ac.uk/cedars/. Obsolete(?) Data. 1 Things that must be kept by law 2 Things that must be destroyed by law 3 Things that we choose to keep

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651 views • 34 slides
PREMIS & Geospatial Resources

PREMIS & Geospatial Resources

Stanford Digital Repository. PREMIS & Geospatial Resources. Nancy J. Hoebelheinrich Knowledge Motifs San Mateo, CA. Outline. Brief descriptions: High Resolution orthoimagery (HRO) GIS dataset Complexities using PREMIS for these Geospatial Resources Further investigations. Description :

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351 views • 17 slides
Characterisation

Characterisation

Characterisation. Adrian Brown The National Archives, UK. Develop tools and services to characterise the significant properties of digital objects, to support: Development of preservation plans Validation of preservation actions (evaluating change) The subproject considers:

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293 views • 15 slides
Introducing Significant Properties

Introducing Significant Properties

Introducing Significant Properties. Stephen Grace and Gareth Knight Centre for e-Research. Overview of KeepIt session. Introduction to Significant Properties InSPECT methodology Practical exercise in object analysis Practical exercise in stakeholder analysis Implementing SPs in an archive

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379 views • 13 slides
Modeling Host Load

Modeling Host Load

Modeling Host Load. Peter A. Dinda Thesis Seminar 2/9/98. Outline. Why model host load? Desirable properties Modeling techniques Results of Markov modeling. Why Model Host Load?. [t min ,t max ]. ?. Load partially determines exec time Benchmarks for trace-based simulation

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119 views • 11 slides

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