Data Issues & Recommendations

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. 1—Data facilitation arm of CN: staff, community facilitation committee • 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)

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

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.