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Dataset Title:  "Deepwater CTD - d93g220.bdo.nc - 26.01N, -96.31W - 1993-11-20" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_d93g220_bdo)
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: 12)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

    depth ?  =  m   12 options
    temperature ?  =  degree_C (potential temperature from ctd)   12 options
    salinity ?  =  PSU   12 options
    oxygen ?  =  milliliters per liter   1 option: -9.0
    nitrite ?  =  micromols   7 options
    nitrate ?  =  micromols   11 options
    phosphate ?  =  micromols   10 options
    silicate ?  =  micromols   12 options
    salinity2 ?  =  PSU   1 option: -9.0
    qualityFlag ?  =  1 option: 0.0

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)  

depth temperature salinity oxygen nitrite nitrate phosphate silicate salinity2 qualityFlag
m degree_C (potential temperature from ctd) PSU milliliters per liter micromols micromols micromols micromols PSU
1.0 24.073999404907227 35.68239974975586 -9.0 0.0 0.14000000059604645 0.029999999329447746 2.509999990463257 -9.0 0.0
7.0 24.083900451660156 35.69070053100586 -9.0 0.0 0.07000000029802322 0.019999999552965164 2.3399999141693115 -9.0 0.0
15.0 24.092899322509766 35.69940185546875 -9.0 0.0 0.07000000029802322 0.019999999552965164 2.2899999618530273 -9.0 0.0
22.0 24.221900939941406 35.87950134277344 -9.0 0.009999999776482582 0.10000000149011612 0.019999999552965164 2.1700000762939453 -9.0 0.0
41.0 24.332399368286133 36.02159881591797 -9.0 0.009999999776482582 0.05999999865889549 0.0 2.0 -9.0 0.0
55.0 24.45560073852539 36.2322998046875 -9.0 0.10000000149011612 0.05000000074505806 0.009999999776482582 1.7400000095367432 -9.0 0.0
64.0 23.4375 36.25960159301758 -9.0 0.20000000298023224 0.44999998807907104 0.03999999910593033 1.9500000476837158 -9.0 0.0
68.0 21.958499908447266 36.30509948730469 -9.0 0.10999999940395355 1.9700000286102295 0.12999999523162842 2.9200000762939453 -9.0 0.0
89.0 19.647499084472656 36.38850021362305 -9.0 0.029999999329447746 9.289999961853027 0.5199999809265137 5.170000076293945 -9.0 0.0
95.0 19.11840057373047 36.40439987182617 -9.0 0.019999999552965164 10.779999732971191 0.6100000143051147 5.800000190734863 -9.0 0.0
148.0 17.196399688720703 36.27790069580078 -9.0 0.009999999776482582 14.1899995803833 0.7699999809265137 5.889999866485596 -9.0 0.0
204.0 15.187999725341797 36.006099700927734 -9.0 0.009999999776482582 17.059999465942383 0.9399999976158142 6.650000095367432 -9.0 0.0

In total, there are 12 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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