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Computational Infrastructure Strategic vision Support high performance computing end to end computing services data as infrastructure Content curation trust provenance peer review publishing Life cycle of data from conception to curation Issues 1 Data as infrastructure Competitiveness funding agencies requiring data mgt plans Local capacity to use reuse data User interavces Computational support Desktop palmtop svcs Value added svcs eg 24 7 helpdesk Access discovery Issues 2 Who owns the problem Data unites and divides scholars Funding organized by administrative entity not by function or problem Continuum of capacity Excel to exoscale Short term approaches threaten long term solution Local leadership vs distributed access Local people distant servers Visibility UCLA UC UCLA leadership capacity in data curation Coordination with eScholarship repository Critical mass of content Position UC and UCLA in DC for competitive advantage


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