1. Data Issues & Recommendations

2. Data Issues AON meeting last week—18 AON PIs, plus collaborating groups & programs –CADIS, an AON project for cooperative Arctic data & information system [Jim ] Ideas similar to those at this meeting Prototype data archive, model for the next level, what SEARCH or ARCSS might go to in the long run –AON network is diverse, mix of real-time & delayed measurements

3. CADIS How to deal with real-time data coming in –Catalog and summaries of data, automatic & dynamic to get it out quickly –Visualization tools –Simplify the process of getting data in –Details not final –Several diverse types of data, but little wildlife, ecological, model data (yet?) –Also, not bringing in historical data

4. CADIS Working initially with data from the new AON network One of the biggest efforts out of AON is the metadata

5. Data issues Weave into report a list of data archiving centers that already exist Status of pre-electronic data, or other valuable data not available to us Link different data-holders together; a clearinghouse Including social science or other not- traditionally-quantified data

6. Data issues LTK “fuzzy data” are being archived under another project [Shari G/Mark Parsons] Other types of data could be (with customization) brought into the metadata framework How do we identify data we need to bring in? Community review? NSF enforcement of archiving/metadata

7. Data issues Data submission is necessary but insufficient to accomplish what we want ARCSS has had a mechanism for bringing data in for a decade, but that alone has not produced integration. We need not just carrots & sticks for data submission, but support What can we do to encourage citation in journal papers that use data?

8. Data issues Collaboration requires clever formatting, e.g. ASCII to Net CDF We may get data via a tool that does not pass through the citation info

9. Recommendations Making data available in a variety of formats with: –Authentification & communication re data quality –Citation/attribution (cultural & technical issue) –Monitoring who uses the data might be intrusive, “corporate” –How to use preliminary data, and be notified of updates

10. 1—Data facilitation arm of CN: staff, community facilitation committee Interventions are not mechanical, require an actual group of people with a budget whose job it is to do these functions –Invest in data description –Identify valuable data and gaps –Facilitate as-yet unthought-of uses for the data, put no roadblocks in the way Data community, permanent staff

11. People need assistance developing metadata “community data [oversight or facilitation or advisory] committee” The community needs to have some ownership, through the facilitation committee (rather than blaming the permanent staff) 1—Data facilitation arm of CN: staff, community facilitation committee

12. Rapid online analysis capability 1—Data facilitation arm of CN: staff, community facilitation committee

13. 2—Fund data rescue Staff center could be paid to do data rescue Small projects/proposals to identify & rescue particular, needed datasets Also larger or even semi-permanent data rescue teams ARCSS general call could invite data- rescue proposals (including small ones)

14. 2—Fund data rescue Accommodate and enhance value of historical, perishable, nonstandard formats, nonquantitative data Digital library Appropriate data from non-Arctic sources Model documentation, code, input, output etc.