DataservicesworkflowPersistentidentifiers(PIDs)BurdenInstitutionalrepositoryDataethicsSensitivedataFAIR“Goodenough”Asked to“do morewith less”BudgetreductionPublicaccessplansReproducibilityCompliance“Unfundedmandate”Direct vs.indirectcostsDMSPConsultationsPublicaccess toresearchdataDatacurationNelsonMemoDMSbudgeting“Itdepends”CAREWho is goingto look at thedataanyway?Datasharing“Understaffed”BigDataAIFunderrequirementsCross-institutionworkinggroupNIHPolicyLong-termdatapreservationDatareuseInstitutionaldataretentionpolicyDatarepositoryInstitutionaldatamanagementpolicy“Datascience”ResearchcycleDatasecurityIRBDatastoragecostsHPCResearchsoftwareResearchdatalifecycleDataservicesworkflowPersistentidentifiers(PIDs)BurdenInstitutionalrepositoryDataethicsSensitivedataFAIR“Goodenough”Asked to“do morewith less”BudgetreductionPublicaccessplansReproducibilityCompliance“Unfundedmandate”Direct vs.indirectcostsDMSPConsultationsPublicaccess toresearchdataDatacurationNelsonMemoDMSbudgeting“Itdepends”CAREWho is goingto look at thedataanyway?Datasharing“Understaffed”BigDataAIFunderrequirementsCross-institutionworkinggroupNIHPolicyLong-termdatapreservationDatareuseInstitutionaldataretentionpolicyDatarepositoryInstitutionaldatamanagementpolicy“Datascience”ResearchcycleDatasecurityIRBDatastoragecostsHPCResearchsoftwareResearchdatalifecycle

RADS 2 Kick Off Bingo! - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Data services workflow
  2. Persistent identifiers (PIDs)
  3. Burden
  4. Institutional repository
  5. Data ethics
  6. Sensitive data
  7. FAIR
  8. “Good enough”
  9. Asked to “do more with less”
  10. Budget reduction
  11. Public access plans
  12. Reproducibility
  13. Compliance
  14. “Unfunded mandate”
  15. Direct vs. indirect costs
  16. DMSP Consultations
  17. Public access to research data
  18. Data curation
  19. Nelson Memo
  20. DMS budgeting
  21. “It depends”
  22. CARE
  23. Who is going to look at the data anyway?
  24. Data sharing
  25. “Understaffed”
  26. Big Data
  27. AI
  28. Funder requirements
  29. Cross-institution working group
  30. NIH Policy
  31. Long-term data preservation
  32. Data reuse
  33. Institutional data retention policy
  34. Data repository
  35. Institutional data management policy
  36. “Data science”
  37. Research cycle
  38. Data security
  39. IRB
  40. Data storage costs
  41. HPC
  42. Research software
  43. Research data lifecycle