1. GeoData 2011 Workshop Data Life Cycle Break Out #3 Wednesday, 2 March 2011 Moderator: Mohan Ramamurthy, Unidata

2. Acknowledgements Excellent note-taking by: Katie Dunn, RPI – Thank you, Katie! And many thanks to all those who participated in and contributed to a highly productive breakout!

3. Topic: Communications Strategy What do you see as the important elements of a communications strategy to meet the needs of the research and agency communities? – Do these include demographically underserved communities (including aspects of management and data infrastructures)?

4. What is a communications strategy in the context of lifecycle? what does this mean? Communicating with people who may not think of data as having a life cycle Various stakeholders and users (management, producers, primary and secondary users) Strategic product development

5. Data Life Cycle Definition and Context for data Planning for data collection Collection (Data and metadata) Selection and Appraisal Processing (Quality control, (re)formatting, adding additional metadata, etc.) Archiving, including discovery and access services, integrity checks Use metrics Migration & Replication Accommodation of new information, updating, etc. Disposal & Retention decision (evaluation)

6. Important Elements Audience Marketing Use experiences & related metrics (who used what and how?) Data policy Education & Training Central discovery (Search different stages - Provenance) ROI and Value proposition Metadata gathering (lowering barrier) User mental models Library for data and all related materials Multi-dimensional data mining Networking of data, systems, projects, & people (Facebook for data?) Framing, points of view, rhetorical aspects, and style Local community building Look through the lens of OAIS reference model?

7. Meaning of Underserved Communities NSF has traditionally decided who's underserved based on demographics. There is a need for a different segmentation. – academic researchers – application researchers – by agency vs. academic – by discipline (e.g., biology/ecology) – for profit vs. non-profit?

8. What is the one most important thing you need to make progress in your field/job/project, re: data life cycle? metadata gathering (how we get the data description from the PI into the system. codifying and encoding it.) making the bar as low as possible, making it an ongoing process, institutionalizing requirements. electronic equivalent of libraries. how to provide access/preservation to things in a multitude of formats? Not just PDFs - products, data sets, etc. Online AND permanent. multidimensional data mining. need something that allows users to go upstream and downstream in related datasets and data products. networking and connecting of technology and society local community building of interest groups theory of data genesis - ontogenesis (piaget) - what makes data meaningful? minimum metadata for optimum use - needs to be enough to work, but not so much that people won't contribute.

9. What is the one most important thing you're working on right now? design of a public system to support spatial data sharing metadata collection issues data citation granularity, persistence, etc. finishing 3 vol. handbook of earth science data management integration of data sets converting database into a print book because not sure that the database will persist. semantics data publication system for geodesy datasets fundamental research on how scientific practice relates to curation system development - understanding where systems requirements can be generalized across disciplines and where they can't. (example: embargoes (general) vs. things that are different for different subdisciplines) workforce development different strategy for data integration dimensions and neighborhoods, science communication.

10. What three things would you like to get out of the meeting? understand cross-disciplinary data integration issues enable interactivity/interoperability of data gain stimulation of thinking, new ideas, advancement of perspective. hear from other people who are working on data publication systems & related issues meet, bond & network with other people, learn from them have face to face communication influence NSF's thinking about data-related CI

11. What three things do you expect to contribute to the meeting? perspectives and experiences - lack of knowledge how to talk to people who don't know what you know? broadening notion of the future of research communication - getting beyond the article. publishing and supporting individual assertions (e.g. nano-publications) building communities of "data-doers", relationships with others. – (we will not really have time to understand each others' dialects, mental models, etc. commonalities, differences. it's time consuming to make sure we understand each other. how can we establish an ongoing conversation and/or intellectual commons? – ESIP Federation - all the different stakeholders formed different units to be sure there was a balance. communication with IT tool development community