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Dataset Title:  "Deepwater CTD - d92c098.bdo.nc - 27.66N, -93.0W - 1992-11-12" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_d92c098_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   6 options
    nitrate ?  =  micromols   9 options
    phosphate ?  =  micromols   8 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
2.0 25.221599578857422 36.2849006652832 -9.0 0.0 0.07000000029802322 0.03999999910593033 3.700000047683716 -9.0 0.0
9.0 25.222000122070312 36.28779983520508 -9.0 0.0 0.07000000029802322 0.03999999910593033 3.509999990463257 -9.0 0.0
28.0 25.097000122070312 36.28960037231445 -9.0 0.0 0.07000000029802322 0.03999999910593033 3.2300000190734863 -9.0 0.0
52.0 25.0939998626709 36.298500061035156 -9.0 0.0 0.07000000029802322 0.05000000074505806 2.859999895095825 -9.0 0.0
73.0 24.889699935913086 36.30500030517578 -9.0 0.019999999552965164 0.03999999910593033 0.05000000074505806 2.619999885559082 -9.0 0.0
82.0 22.910400390625 36.394798278808594 -9.0 0.12999999523162842 0.07999999821186066 0.05000000074505806 2.2899999618530273 -9.0 0.0
98.0 22.189199447631836 36.378501892089844 -9.0 0.15000000596046448 0.4300000071525574 0.05999999865889549 2.3399999141693115 -9.0 0.0
128.0 19.869400024414062 36.42539978027344 -9.0 0.029999999329447746 7.949999809265137 0.41999998688697815 4.53000020980835 -9.0 0.0
166.0 18.14859962463379 36.415401458740234 -9.0 0.009999999776482582 12.0 0.6600000262260437 5.25 -9.0 0.0
227.0 14.82349967956543 35.94660186767578 -9.0 0.019999999552965164 17.739999771118164 1.0499999523162842 8.359999656677246 -9.0 0.0
266.0 11.748299598693848 35.47489929199219 -9.0 0.009999999776482582 24.31999969482422 1.4900000095367432 13.100000381469727 -9.0 0.0
316.0 10.792699813842773 35.33570098876953 -9.0 0.019999999552965164 25.90999984741211 1.6100000143051147 15.350000381469727 -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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