Ishungryright nowHasa dogKnows moreaboutpolarimetrythan owningthe sunglassesHas called intoa PACEScience &ApplicationsTeam (SAT)MeetingMovedhousewithin thelast yearDrove acar >1hour toget herePlays amusicalinstrumentHas beento theSouthernOceanFlewhere ona planePlays amusicalinstrumentPrefersto codein RHas beenat sea for>1 weekHas beento theArcticOceanHas workedin a cleanlab (on landor on a ship)Filters alot ofseawaterPrefers tocode inMATLABHas beenat sea for>1 weekHasa catHas used asun-photometerDoes adistancesportHasconsideredbecomingan astronautPrefersto codein pythonDoesnot likepizzaDogpersonHas beento thePacificOceanKnowswhat asundog isFliesdronesHas beento aconcert inlast monthLives >2hoursdrive tothe oceanDoeslakescienceStarwarsfanDid fieldwork inthe lastyearWriteswikipediaarticlesClimberDoes alot ofoutreachFilters alot ofseawaterHas doneairbornescienceDoesNOT liketo teachbut has toSpeaks>2languagesHas useda weatherballoonIs anatmosphericscientistCan tell afield workdisasterstoryHas aGitHubScubadivesKnows amagictrickWorkswithlidarModels(data orfashion)Has beento theAtlanticOceanWorkswithradarLovesaerosolsWorkswithcitizenscientistsHasinteractedwith a CTD-Rosetteknows 3TaylorSwiftsongsIshungryright nowHasa dogKnows moreaboutpolarimetrythan owningthe sunglassesHas called intoa PACEScience &ApplicationsTeam (SAT)MeetingMovedhousewithin thelast yearDrove acar >1hour toget herePlays amusicalinstrumentHas beento theSouthernOceanFlewhere ona planePlays amusicalinstrumentPrefersto codein RHas beenat sea for>1 weekHas beento theArcticOceanHas workedin a cleanlab (on landor on a ship)Filters alot ofseawaterPrefers tocode inMATLABHas beenat sea for>1 weekHasa catHas used asun-photometerDoes adistancesportHasconsideredbecomingan astronautPrefersto codein pythonDoesnot likepizzaDogpersonHas beento thePacificOceanKnowswhat asundog isFliesdronesHas beento aconcert inlast monthLives >2hoursdrive tothe oceanDoeslakescienceStarwarsfanDid fieldwork inthe lastyearWriteswikipediaarticlesClimberDoes alot ofoutreachFilters alot ofseawaterHas doneairbornescienceDoesNOT liketo teachbut has toSpeaks>2languagesHas useda weatherballoonIs anatmosphericscientistCan tell afield workdisasterstoryHas aGitHubScubadivesKnows amagictrickWorkswithlidarModels(data orfashion)Has beento theAtlanticOceanWorkswithradarLovesaerosolsWorkswithcitizenscientistsHasinteractedwith a CTD-Rosetteknows 3TaylorSwiftsongs

Human Bingo: Find someone who... - 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.


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
  1. Is hungry right now
  2. Has a dog
  3. Knows more about polarimetry than owning the sunglasses
  4. Has called into a PACE Science & Applications Team (SAT) Meeting
  5. Moved house within the last year
  6. Drove a car >1 hour to get here
  7. Plays a musical instrument
  8. Has been to the Southern Ocean
  9. Flew here on a plane
  10. Plays a musical instrument
  11. Prefers to code in R
  12. Has been at sea for >1 week
  13. Has been to the Arctic Ocean
  14. Has worked in a clean lab (on land or on a ship)
  15. Filters a lot of seawater
  16. Prefers to code in MATLAB
  17. Has been at sea for >1 week
  18. Has a cat
  19. Has used a sun-photometer
  20. Does a distance sport
  21. Has considered becoming an astronaut
  22. Prefers to code in python
  23. Does not like pizza
  24. Dog person
  25. Has been to the Pacific Ocean
  26. Knows what a sundog is
  27. Flies drones
  28. Has been to a concert in last month
  29. Lives >2 hours drive to the ocean
  30. Does lake science
  31. Star wars fan
  32. Did field work in the last year
  33. Writes wikipedia articles
  34. Climber
  35. Does a lot of outreach
  36. Filters a lot of seawater
  37. Has done airborne science
  38. Does NOT like to teach but has to
  39. Speaks >2 languages
  40. Has used a weather balloon
  41. Is an atmospheric scientist
  42. Can tell a field work disaster story
  43. Has a GitHub
  44. Scuba dives
  45. Knows a magic trick
  46. Works with lidar
  47. Models (data or fashion)
  48. Has been to the Atlantic Ocean
  49. Works with radar
  50. Loves aerosols
  51. Works with citizen scientists
  52. Has interacted with a CTD-Rosette
  53. knows 3 Taylor Swift songs