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Regrets
Item | Desired Outcome | Time | Who | Notes | Decisions | Actions | |
---|---|---|---|---|---|---|---|
1 | Assemble
| Note taker - Susan Boone | 5 | ||||
2 | Special Collections data | How is each campus counting special collections data | 10 | All | by title? by item? linear feet? campus-owned stat: reporting determined locally UCB and UCLA: title count by collection | ||
3 | Timeline for Stats | Discuss/confirm timeline for new eR NZ data
Non eR NZ data (campuses affirming those data); submitting non-ILS data
Overall timeline:
| 15 | Daisy/Danielle/All | eResources numbers drop difficult to analyze/verify because the numbers were run differently YOYrevised output tracks exclusions, includes export by Collection Name for data verification. UCSD: narrative very helpful; approved stats UCI: future consideration NZ activators/managers of eResources (CDL) have a. role in reviewing data and title duplication UCB: re-running data by Collection Name will help identify drops due to cancellations, for example. Re-run with more filters very helpful UCSC: can verify physical data; rerun for eRes is helpful. UCD: data acceptable, ready to move forward as is UCM: re-run will give more details to understand drop. UCLA: data re-run helpful; question about the logic of current exclusions (OA) UCSF: re-run for eRes is helpful. | ||
4 | Is this helpful for campuses? Is it unnecessary? Any edits to the design? What would campuses like to see/remove? | 20 | Daisy/Danielle | revised output tracks exclusions, includes export by Collection Name for data verification. Timing of running Alma Analytics appears to impact results. | Daisy will re-run data mid month and beginning of month for collection-level eRes numbers review; | ||
5 | Wrap up | Review actions and decisions | 5 | ||||
6 | Parking Lot | Capture important topics for future discussion | |||||
7 | Total | 55 |
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