Speaks>2languagesLovesaerosolsWorkswithcitizenscientistsIshungryright nowHas beento theAtlanticOceanPrefersto codein pythonClimberHas beenat sea for>1 weekStarwarsfanDoesnot likepizzaLives >2hoursdrive tothe oceanHas beenat sea for>1 weekWorkswithlidarHas useda weatherballoonHasa dogDrove acar >1hour toget hereDoeslakescienceHas workedin a cleanlab (on landor on a ship)WorkswithradarDoes adistancesportHasconsideredbecomingan astronautFliesdronesModels(data orfashion)Has doneairbornescienceHas beento theSouthernOceanPlays amusicalinstrumentKnows moreaboutpolarimetrythan owningthe sunglassesHas aGitHubDogpersonWriteswikipediaarticlesHas beento thePacificOceanDoesNOT liketo teachbut has toKnowswhat asundog isMovedhousewithin thelast yearHas called intoa PACEScience &ApplicationsTeam (SAT)MeetingKnows amagictrickIs anatmosphericscientistKnowswhat asundog isHas beento aconcert inlast monthHasa catHasinteractedwith a CTD-RosetteDoes alot ofoutreachHas used asun-photometerPrefersto codein RFlewhere ona planeHas beento theArcticOceanPlays amusicalinstrumentDid fieldwork inthe lastyearFilters alot ofseawaterCan tell afield workdisasterstoryknows 3TaylorSwiftsongsScubadivesPrefers tocode inMATLABSpeaks>2languagesLovesaerosolsWorkswithcitizenscientistsIshungryright nowHas beento theAtlanticOceanPrefersto codein pythonClimberHas beenat sea for>1 weekStarwarsfanDoesnot likepizzaLives >2hoursdrive tothe oceanHas beenat sea for>1 weekWorkswithlidarHas useda weatherballoonHasa dogDrove acar >1hour toget hereDoeslakescienceHas workedin a cleanlab (on landor on a ship)WorkswithradarDoes adistancesportHasconsideredbecomingan astronautFliesdronesModels(data orfashion)Has doneairbornescienceHas beento theSouthernOceanPlays amusicalinstrumentKnows moreaboutpolarimetrythan owningthe sunglassesHas aGitHubDogpersonWriteswikipediaarticlesHas beento thePacificOceanDoesNOT liketo teachbut has toKnowswhat asundog isMovedhousewithin thelast yearHas called intoa PACEScience &ApplicationsTeam (SAT)MeetingKnows amagictrickIs anatmosphericscientistKnowswhat asundog isHas beento aconcert inlast monthHasa catHasinteractedwith a CTD-RosetteDoes alot ofoutreachHas used asun-photometerPrefersto codein RFlewhere ona planeHas beento theArcticOceanPlays amusicalinstrumentDid fieldwork inthe lastyearFilters alot ofseawaterCan tell afield workdisasterstoryknows 3TaylorSwiftsongsScubadivesPrefers tocode inMATLAB

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.


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