knows 3TaylorSwiftsongsLives >2hoursdrive tothe oceanPlays amusicalinstrumentSpeaks>2languagesMovedhousewithin thelast yearHas beento thePacificOceanHas beenat sea for>1 weekDoeslakescienceHas beento theAtlanticOceanDoesnot likepizzaHas beento theArcticOceanPrefersto codein RKnows moreaboutpolarimetrythan owningthe sunglassesFilters alot ofseawaterClimberHas workedin a cleanlab (on landor on a ship)Has beenat sea for>1 weekHasconsideredbecomingan astronautPlays amusicalinstrumentDid fieldwork inthe lastyearHas doneairbornescienceScubadivesHasa dogDogpersonHas aGitHubFilters alot ofseawaterHas used asun-photometerKnows amagictrickHas beento theSouthernOceanWriteswikipediaarticlesHasinteractedwith a CTD-RosettePrefers tocode inMATLABHasa catCan tell afield workdisasterstoryWorkswithradarStarwarsfanFlewhere ona planeIs anatmosphericscientistIshungryright nowHas beento aconcert inlast monthLovesaerosolsWorkswithlidarHas useda weatherballoonDoes adistancesportPrefersto codein pythonWorkswithcitizenscientistsKnowswhat asundog isFliesdronesDoesNOT liketo teachbut has toDrove acar >1hour toget hereDoes alot ofoutreachModels(data orfashion)Has called intoa PACEScience &ApplicationsTeam (SAT)Meetingknows 3TaylorSwiftsongsLives >2hoursdrive tothe oceanPlays amusicalinstrumentSpeaks>2languagesMovedhousewithin thelast yearHas beento thePacificOceanHas beenat sea for>1 weekDoeslakescienceHas beento theAtlanticOceanDoesnot likepizzaHas beento theArcticOceanPrefersto codein RKnows moreaboutpolarimetrythan owningthe sunglassesFilters alot ofseawaterClimberHas workedin a cleanlab (on landor on a ship)Has beenat sea for>1 weekHasconsideredbecomingan astronautPlays amusicalinstrumentDid fieldwork inthe lastyearHas doneairbornescienceScubadivesHasa dogDogpersonHas aGitHubFilters alot ofseawaterHas used asun-photometerKnows amagictrickHas beento theSouthernOceanWriteswikipediaarticlesHasinteractedwith a CTD-RosettePrefers tocode inMATLABHasa catCan tell afield workdisasterstoryWorkswithradarStarwarsfanFlewhere ona planeIs anatmosphericscientistIshungryright nowHas beento aconcert inlast monthLovesaerosolsWorkswithlidarHas useda weatherballoonDoes adistancesportPrefersto codein pythonWorkswithcitizenscientistsKnowswhat asundog isFliesdronesDoesNOT liketo teachbut has toDrove acar >1hour toget hereDoes alot ofoutreachModels(data orfashion)Has called intoa PACEScience &ApplicationsTeam (SAT)Meeting

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