“Good enough” Nelson Memo Budget reduction Big Data Asked to “do more with less” CARE Data repository Cross- institution working group Direct vs. indirect costs HPC Long-term data preservation “Data science” Data services workflow Reproducibility “Understaffed” DMS budgeting Institutional repository Data curation Persistent identifiers (PIDs) “Unfunded mandate” Data reuse Data sharing Institutional data retention policy FAIR Data ethics Research cycle “It depends” Sensitive data Compliance Data security Who is going to look at the data anyway? Funder requirements Research software DMSP Consultations Institutional data management policy Research data lifecycle NIH Policy Data storage costs IRB Burden Public access plans Public access to research data AI “Good enough” Nelson Memo Budget reduction Big Data Asked to “do more with less” CARE Data repository Cross- institution working group Direct vs. indirect costs HPC Long-term data preservation “Data science” Data services workflow Reproducibility “Understaffed” DMS budgeting Institutional repository Data curation Persistent identifiers (PIDs) “Unfunded mandate” Data reuse Data sharing Institutional data retention policy FAIR Data ethics Research cycle “It depends” Sensitive data Compliance Data security Who is going to look at the data anyway? Funder requirements Research software DMSP Consultations Institutional data management policy Research data lifecycle NIH Policy Data storage costs IRB Burden Public access plans Public access to research data AI
(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.
“Good enough”
Nelson Memo
Budget reduction
Big Data
Asked to “do more with less”
CARE
Data repository
Cross-institution working group
Direct vs. indirect costs
HPC
Long-term data preservation
“Data science”
Data services workflow
Reproducibility
“Understaffed”
DMS budgeting
Institutional repository
Data curation
Persistent identifiers (PIDs)
“Unfunded mandate”
Data reuse
Data sharing
Institutional data retention policy
FAIR
Data ethics
Research cycle
“It depends”
Sensitive data
Compliance
Data security
Who is going to look at the data anyway?
Funder requirements
Research software
DMSP Consultations
Institutional data management policy
Research data lifecycle
NIH Policy
Data storage costs
IRB
Burden
Public access plans
Public access to research data
AI