Did fieldwork inthe lastyearHas beenat sea for>1 weekKnows moreaboutpolarimetrythan owning thesunglassesDoes alot ofoutreachHas beento theArcticOceanPrefersto codein RHas beento theSouthernOceanWantstoteachClimberStartrek fanLives >2hoursdrive tothe oceanPlays amusicalinstrumentHas beento theAtlanticOceanDoes adistancesportHas aGitHubHasconsideredbecoming anastronautLoves toplayboardgamesHas neverbeen toMainebefore thisLoves topaddleboardSpeaks>2languagesLikesto knitPrefersto codein pythonFilters alot ofseawaterFlewhere ona planeWorkswithlidarHasa catPrefers tocode inMATLABWent to or isgoing to awedding thissummerHas doneairbornescienceHas workedin a cleanlab (on landor on a ship)Knows howto properlyeat a lobsterHas used asun-photometerHas beento thePacificOceanModelsScubadivesDogpersonHas useda secchidiskDoesNOT wantto teachDoeslakescienceStudiedLatin orGreekMovedhousewithin thelast yearWorkswithcitizenscientistsHas beento aconcert inlast monthStarwarsfanFliesdronesDrove acar >1hour toget hereHasinteractedwith a CTD-RosetteCan tell afield workdisasterstoryHas beento theIndianOceanDid fieldwork inthe lastyearHas beenat sea for>1 weekKnows moreaboutpolarimetrythan owning thesunglassesDoes alot ofoutreachHas beento theArcticOceanPrefersto codein RHas beento theSouthernOceanWantstoteachClimberStartrek fanLives >2hoursdrive tothe oceanPlays amusicalinstrumentHas beento theAtlanticOceanDoes adistancesportHas aGitHubHasconsideredbecoming anastronautLoves toplayboardgamesHas neverbeen toMainebefore thisLoves topaddleboardSpeaks>2languagesLikesto knitPrefersto codein pythonFilters alot ofseawaterFlewhere ona planeWorkswithlidarHasa catPrefers tocode inMATLABWent to or isgoing to awedding thissummerHas doneairbornescienceHas workedin a cleanlab (on landor on a ship)Knows howto properlyeat a lobsterHas used asun-photometerHas beento thePacificOceanModelsScubadivesDogpersonHas useda secchidiskDoesNOT wantto teachDoeslakescienceStudiedLatin orGreekMovedhousewithin thelast yearWorkswithcitizenscientistsHas beento aconcert inlast monthStarwarsfanFliesdronesDrove acar >1hour toget hereHasinteractedwith a CTD-RosetteCan tell afield workdisasterstoryHas beento theIndianOcean

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
  1. Did field work in the last year
  2. Has been at sea for >1 week
  3. Knows more about polarimetry than owning the sunglasses
  4. Does a lot of outreach
  5. Has been to the Arctic Ocean
  6. Prefers to code in R
  7. Has been to the Southern Ocean
  8. Wants to teach
  9. Climber
  10. Star trek fan
  11. Lives >2 hours drive to the ocean
  12. Plays a musical instrument
  13. Has been to the Atlantic Ocean
  14. Does a distance sport
  15. Has a GitHub
  16. Has considered becoming an astronaut
  17. Loves to play board games
  18. Has never been to Maine before this
  19. Loves to paddleboard
  20. Speaks >2 languages
  21. Likes to knit
  22. Prefers to code in python
  23. Filters a lot of seawater
  24. Flew here on a plane
  25. Works with lidar
  26. Has a cat
  27. Prefers to code in MATLAB
  28. Went to or is going to a wedding this summer
  29. Has done airborne science
  30. Has worked in a clean lab (on land or on a ship)
  31. Knows how to properly eat a lobster
  32. Has used a sun-photometer
  33. Has been to the Pacific Ocean
  34. Models
  35. Scuba dives
  36. Dog person
  37. Has used a secchi disk
  38. Does NOT want to teach
  39. Does lake science
  40. Studied Latin or Greek
  41. Moved house within the last year
  42. Works with citizen scientists
  43. Has been to a concert in last month
  44. Star wars fan
  45. Flies drones
  46. Drove a car >1 hour to get here
  47. Has interacted with a CTD-Rosette
  48. Can tell a field work disaster story
  49. Has been to the Indian Ocean