1. Data Fabric IG From Testing to Recommendations Beth Plale

2. Objectives of Session Inductive examination of fabric composition A particular fabric or composition grows one RDA recommendation at a time Linked to n=1, n=2,... n=m discussion Tuesdday Design strategy to expand core compositions Identify incentives for e-infrastructure providers to provision core compositions in experimentation mode Assess will of group to take on challenge and determine next steps

3. Dimensions of Testing: getting to Data Fabrics RDA produces RDA Recommendations (i.e., outputs) Some technically-oriented RDA Recommendations have reference software with it, these are clearly starting point for any data fabric Many technically oriented RDA recommendations do not have reference software, yet are machine actionable (a schema for instance). These also are of immediate interest to data fabric composition. Other recommendations play important but background roles in early composition RDA recommendation : Purely human consumption and action RDA recommend- ation: Reference software Machine actionable reference Human consumption needed before being actionable reference

4. Compositions of components Core Components & Services Specific Components & Services Composition (or Fabric) A Composition B

5. Compositions of components Composition (Fabric) B Given nature of data (can’t do much without understanding it), successful data fabric will likely: 1. run on possibly distributed e-infrastructure (EUDAT, NDS, …) 2. Serve scholarly domain as domain infrastructure 3. Support multiple projects within that domain 4. And eventually result in cross-domain research For 3 and 4 to be realized, CoCo of 2 must be sharable

6. Start with simple composition in single fabric (inductive approach) Set of RDA recomendations pulled into single configuration for a use RDA PIT WG Recommendation and RDA Data Type Registry Recommendation are starting point because both are among few current RDA ouputs that have reference software

9. Objectives of Session Inductive examination of fabric composition A particular fabric or composition grows one RDA recommendation at a time Linked to n=1, n=2,... n=m discussion Tuesdday Design strategy to expand core compositions Identify incentives for e-infrastructure providers to provision core compositions in experimentation mode Assess will of group to take on challenge and determine next steps

10. PID minimal metadata Let’s agree on types that are used to minimally define the metadata (attributes) associated with a PID

11. Objectives of Session Inductive examination of fabric composition A particular fabric or composition grows one RDA recommendation at a time Linked to n=1, n=2,... n=m discussion Tuesdday Design strategy to expand core compositions Start with minimal PID attributes Identify incentives for e-infrastructure providers to provision core compositions in experimentation mode Assess will of group to take on challenge and determine next steps

12. Bringing visibility to food security data results: harvests of PRAGMA and RDA Beth Plale, Indiana Univ, USA; Jason Haga, AIST, Japan Launch use of two RDA products in Asia by utilizing PRAGMA community and tools to work with new rice genome group in Philippines and implement software services at AIST (Japan) using outputs of PID Information Types and Data Type Registries Working Groups Software will be installed additionally at National Data Service in US to stimulate US adoption

13. PRAGMA Data Service PRAGMA/Rocks compute VMs Rice genome variant discovery Bringing visibility to food security data results: harvests of PRAGMA and RDA Beth Plale, Indiana University, USA; Jason Haga, AIST, Japan Future Uses Persistent ID Types (PIT) Data Type Registry (DTR)