1. Metadata Management and Cataloging Breakout Jim Myers, Line Pouchard Ann Chervenak, Richard Mount, Larry Rahn, Greg Riccardi, Sonja Tidemann, Steve Wiley

2. Wrong Title? “I could do more science if I could: Automate workflow, Search my data faster. …” “My paper is too short, I need more metadata…”

3. Drivers for Metadata Extracting more value from data –Data useful beyond grad student lifetime –Single-user efficiency Dealing with Moore’s Law, CS Advances –Managing more complex experiments – unique names is no longer sufficient –Componetization of Codes –(Decomposition of concerns (aspects))

4. Drivers for Metadata Changing Science – moving beyond an oral tradition –Need to share context-dependent data across community(ies) (data dissemination/discovery) –Support mapping between data models (across domains, over time) –Managing non-hierarchical data relationships / multiple hierarchies at once –Describing hypothesis/statements of trust /reification (statements about other statements)

5. Catch 22 Everybody says metadata is important, but few actually record it –Frog in the pot –Tragedy of the Commons –Paradigm shift What’s changing? –New Science drivers require it –New technologies will simplify capture and management

6. Uses –Provenance (original conditions, subsequent workflow-workflow by example), Reproducing experiments and analysis Virtual Data Workflow-by-example –Data Discovery Metadata-based search (features, subsets, …) –Data Quality evaluation/review endorsement Curation/records information –Annotation Data context Relation to other data –Discovery/Mining/Inference/Monitoring –Not discussed much – metadata applies not only to data but services, programs, machines, instruments

7. R&D Challenges –What to standardize, what to record? Infrastructure is general, some schema should be (workflow, experiment mgmt) but most are domain specific –Metadata Services scalable, distributed, schema-independent semantic federation/ontology mapping, derived indexes/info retrieval service, global ids, rich authorization models, data granularity, inference services, curation (tuning based on access, etc.) ) Usability - Metadata input/capture – automation, cultural change, rewards, Google precedent Maintainability - Automated quality management

8. Logical Naming Lifecycle Discovery (data, schema) Basic Management (ingest, storage, query, update, notification,...) Reasoning (mapping, inference, …) Records (signing, nonrepudiation) Migration (schema, formats, signatures,...) Archival/versioning (copies of external data, services, …) Policy enforcement (fit for purpose, adheres to common data model, …) Federation & aggregation Collection and/or Compounding Curation (e.g. conflict detection & resolution) Workflow “Proxy” Provenance: Conceptual Services From workshop 1

9. Program Scope –Research – metadata services (see above) –Pilot – use of rich metadata to support grand challenge projects –Develop/deploy: General metadata capture tools – capture from workflows, problem solving environments –Maintain – metadata management as cyberinfrastructure (requires research on scaling, maintainability,…)