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Dataset Title:  "LATEX CTD - d94h065.nc - 29.07N, 93.0W - 1994-04-30" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: latex_d94h065)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files | Make a graph

Select a subset:      (Current number of distinct combinations of matching data: 35)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

    pressure ?  =  dBar   35 options
    depth ?  =  m   35 options
    temperature ?  =  degree_C   34 options
    potentialTemperature ?  =  degree_C   34 options
    conductivity ?  =  Siemens per meter   30 options
    salinity ?  =  PSU   34 options
    sigmat ?  =  kg m-3   33 options
    descentRate ?  =  meters per second   34 options
    transmission ?  =  percent   33 options
    par ?  =  umol m-2 s-1   6 options
    parv ?  =  volts   7 options
    fluorescence ?  =  volts   33 options
    backscattering ?  =  volts   21 options

View:      Map of All Related Data ?      Distinct Data Counts ?     Distinct Data ?      Related Data Counts ?     Related Data ?

 
Map of All Related Data ?   (Refine the map and/or download the image)

To view the map, check View : Map of All Related Data above.

WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.
 


Distinct Data Counts ?

To view the counts of distinct combinations of the variables listed above,
check View : Distinct Data Counts above and select a value for one of the variables above.

 


Distinct Data ?   (Metadata)    (Refine the data subset and/or download the data)  

pressure depth temperature potentialTemperature conductivity salinity sigmat descentRate transmission par parv fluorescence backscattering
dBar m degree_C degree_C Siemens per meter PSU kg m-3 meters per second percent umol m-2 s-1 volts volts volts
3.0 3.0 23.126100540161133 23.125499725341797 5.138299942016602 35.20370101928711 24.05340003967285 0.6359999775886536 4.230999946594238 0.8700000047683716 0.2720000147819519 0.5239999890327454 0.03700000047683716
3.5 3.5 23.126399993896484 23.125699996948242 5.138400077819824 35.204200744628906 24.053699493408203 0.671999990940094 4.236999988555908 0.7200000286102295 0.18799999356269836 0.5220000147819519 0.035999998450279236
4.0 4.0 23.12649917602539 23.125699996948242 5.138400077819824 35.20370101928711 24.05340003967285 0.4050000011920929 4.23799991607666 0.6800000071525574 0.16599999368190765 0.5239999890327454 0.035999998450279236
4.5 4.5 23.12689971923828 23.125999450683594 5.138400077819824 35.20320129394531 24.052900314331055 0.45100000500679016 4.23799991607666 0.5400000214576721 0.05999999865889549 0.5289999842643738 0.03700000047683716
5.0 5.0 23.126800537109375 23.12579917907715 5.138400077819824 35.202999114990234 24.052900314331055 0.6830000281333923 4.238999843597412 0.47999998927116394 0.014000000432133675 0.5479999780654907 0.03500000014901161
5.5 5.5 23.126699447631836 23.125600814819336 5.138400077819824 35.20280075073242 24.05270004272461 0.7480000257492065 4.241000175476074 0.4699999988079071 0.0020000000949949026 0.546999990940094 0.035999998450279236
6.0 6.0 23.126699447631836 23.12540054321289 5.138500213623047 35.203800201416016 24.053499221801758 0.671999990940094 4.236000061035156 0.4699999988079071 0.0 0.5350000262260437 0.03500000014901161
6.5 6.5 23.124799728393555 23.12350082397461 5.138899803161621 35.207801818847656 24.057199478149414 0.4909999966621399 4.235000133514404 0.4699999988079071 0.0 0.546999990940094 0.03500000014901161
7.0 7.0 23.119199752807617 23.117700576782227 5.139599800109863 35.21739959716797 24.066099166870117 0.8859999775886536 4.230999946594238 0.4699999988079071 0.0 0.5630000233650208 0.03500000014901161
7.599999904632568 7.5 23.114099502563477 23.112600326538086 5.140100002288818 35.225799560546875 24.07390022277832 0.9639999866485596 4.223999977111816 0.4699999988079071 0.0 0.5600000023841858 0.05400000140070915
8.100000381469727 8.0 23.111299514770508 23.109600067138672 5.140699863433838 35.232398986816406 24.07979965209961 0.8889999985694885 4.2179999351501465 0.4699999988079071 0.0 0.5659999847412109 0.04800000041723251
8.600000381469727 8.5 23.104700088500977 23.10300064086914 5.14109992980957 35.24020004272461 24.087600708007812 0.597000002861023 4.202000141143799 0.4699999988079071 0.0 0.5849999785423279 0.039000000804662704
9.100000381469727 9.0 23.06439971923828 23.0625 5.142399787902832 35.282798767089844 24.13170051574707 0.8080000281333923 4.193999767303467 0.4699999988079071 0.0 0.6299999952316284 0.03700000047683716
9.600000381469727 9.5 23.010799407958984 23.008800506591797 5.140699863433838 35.31230163574219 24.16950035095215 0.9380000233650208 4.1570000648498535 0.4699999988079071 0.0 0.6330000162124634 0.03700000047683716
10.100000381469727 10.0 22.9060001373291 22.903900146484375 5.134399890899658 35.347900390625 24.2268009185791 0.8920000195503235 4.077000141143799 0.4699999988079071 0.0 0.7089999914169312 0.04500000178813934
10.600000381469727 10.5 22.87849998474121 22.876300811767578 5.135900020599365 35.381500244140625 24.26020050048828 0.6330000162124634 4.066999912261963 0.4699999988079071 0.0 1.246999979019165 0.057999998331069946
11.100000381469727 11.0 22.83530044555664 22.833099365234375 5.133299827575684 35.39609909057617 24.283700942993164 0.5519999861717224 4.084000110626221 0.4699999988079071 0.0 1.031999945640564 0.041999999433755875
11.600000381469727 11.5 22.78380012512207 22.781400680541992 5.127299785614014 35.39139938354492 24.295000076293945 0.8629999756813049 4.085000038146973 0.4699999988079071 0.0 1.0119999647140503 0.04899999871850014
12.100000381469727 12.0 22.695199966430664 22.692800521850586 5.11460018157959 35.363399505615234 24.2992000579834 0.9309999942779541 4.001999855041504 0.4699999988079071 0.0 1.0240000486373901 0.054999999701976776
12.600000381469727 12.5 22.503700256347656 22.50119972229004 5.09060001373291 35.331600189208984 24.329700469970703 0.8090000152587891 3.984999895095825 0.4699999988079071 0.0 1.0759999752044678 0.05900000035762787
13.100000381469727 13.0 22.37070083618164 22.368099212646484 5.087399959564209 35.41490173339844 24.430700302124023 0.5550000071525574 4.017000198364258 0.4699999988079071 0.0 1.2359999418258667 0.06800000369548798
13.600000381469727 13.5 22.25779914855957 22.25510025024414 5.089600086212158 35.52360153198242 24.545299530029297 0.800000011920929 4.122000217437744 0.4699999988079071 0.0 1.25600004196167 0.054999999701976776
14.100000381469727 14.0 22.260000228881836 22.257200241088867 5.1072001457214355 35.660499572753906 24.648700714111328 0.7820000052452087 4.169000148773193 0.4699999988079071 0.0 1.1690000295639038 0.06599999964237213
14.600000381469727 14.5 22.29170036315918 22.288799285888672 5.114099979400635 35.688201904296875 24.660900115966797 0.5770000219345093 4.190000057220459 0.4699999988079071 0.0 1.0670000314712524 0.05700000002980232
15.100000381469727 15.0 22.271400451660156 22.268400192260742 5.114299774169922 35.70650100708008 24.680500030517578 0.3179999887943268 4.184999942779541 0.4699999988079071 0.0 0.9649999737739563 0.05900000035762787
15.600000381469727 15.5 22.222900390625 22.21980094909668 5.114200115203857 35.74549865722656 24.723899841308594 0.46000000834465027 4.160999774932861 0.4699999988079071 0.0 1.027999997138977 0.05000000074505806
16.100000381469727 16.0 22.097400665283203 22.094200134277344 5.101500034332275 35.7484016418457 24.761600494384766 0.6669999957084656 4.129000186920166 0.4699999988079071 0.0 1.1799999475479126 0.04899999871850014
16.600000381469727 16.5 21.994800567626953 21.991500854492188 5.091700077056885 35.75600051879883 24.796300888061523 0.6909999847412109 4.111999988555908 0.4699999988079071 0.0 1.312000036239624 0.050999999046325684
17.100000381469727 17.0 21.95560073852539 21.952199935913086 5.087800025939941 35.756900787353516 24.808000564575195 0.42899999022483826 4.089000225067139 0.4699999988079071 0.0 1.5069999694824219 0.054999999701976776
17.600000381469727 17.5 21.835599899291992 21.83209991455078 5.074900150299072 35.75410079956055 24.839500427246094 0.20399999618530273 4.060999870300293 0.4699999988079071 0.0 1.805999994277954 0.054999999701976776
18.100000381469727 18.0 21.837600708007812 21.833999633789062 5.0746002197265625 35.750301361083984 24.83609962463379 0.34700000286102295 4.039000034332275 0.4699999988079071 0.0 1.7619999647140503 0.06400000303983688
18.600000381469727 18.5 21.78190040588379 21.778200149536133 5.066999912261963 35.735801696777344 24.840599060058594 0.33799999952316284 4.0 0.4699999988079071 0.0 1.7300000190734863 0.09000000357627869
19.100000381469727 19.0 21.50670051574707 21.503000259399414 5.030300140380859 35.67179870605469 24.868499755859375 0.29899999499320984 3.8489999771118164 0.4699999988079071 0.0 1.7719999551773071 0.06400000303983688
19.600000381469727 19.5 21.31329917907715 21.309499740600586 5.007699966430664 35.651798248291016 24.90679931640625 0.25600001215934753 3.734999895095825 0.4699999988079071 0.0 1.6970000267028809 0.09600000083446503
20.100000381469727 20.0 21.287099838256836 21.283199310302734 5.006400108337402 35.66299819946289 24.92259979248047 0.22200000286102295 3.7260000705718994 0.4699999988079071 0.0 1.815000057220459 0.13300000131130219

In total, there are 35 rows of distinct combinations of the variables listed above. All of the rows are shown above.
To change the maximum number of rows displayed, change View : Distinct Data above.
 


Related Data Counts ?

To view the related data counts,
check View : Related Data Counts above and select a value for one of the variables above.

WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.
 


Related Data ?   (Metadata)    (Refine the data subset and/or download the data)

To view the related data, change View : Related Data above.

WARNING: This may involve lots of data. For some datasets, this may be slow. Consider using this only when you need it and have selected a small subset of the data.


 
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