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ERDDAP > tabledap > Subset ?

Dataset Title:  "Deepwater CTD - d93g223.bdo.nc - 26.33N, -96.3W - 1993-11-20" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_d93g223_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)   10 options
    salinity ?  =  PSU   10 options
    oxygen ?  =  milliliters per liter   1 option: -9.0
    nitrite ?  =  micromols   5 options
    nitrate ?  =  micromols   12 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
1.0 24.103200912475586 35.92599868774414 -9.0 0.009999999776482582 0.12999999523162842 0.009999999776482582 3.700000047683716 -9.0 0.0
13.0 -9.0 -9.0 -9.0 0.009999999776482582 0.10000000149011612 0.009999999776482582 3.5299999713897705 -9.0 0.0
28.0 -9.0 -9.0 -9.0 0.0 0.15000000596046448 0.009999999776482582 3.359999895095825 -9.0 0.0
39.0 -9.0 -9.0 -9.0 0.0 0.11999999731779099 0.009999999776482582 3.190000057220459 -9.0 0.0
48.0 24.26059913635254 36.01919937133789 -9.0 0.009999999776482582 0.1899999976158142 0.009999999776482582 2.9800000190734863 -9.0 0.0
59.0 24.2012996673584 36.17020034790039 -9.0 0.09000000357627869 0.2199999988079071 0.019999999552965164 2.890000104904175 -9.0 0.0
69.0 22.756900787353516 36.25709915161133 -9.0 0.07000000029802322 2.4800000190734863 0.1899999976158142 5.440000057220459 -9.0 0.0
84.0 21.013399124145508 36.34410095214844 -9.0 0.019999999552965164 4.449999809265137 0.27000001072883606 5.03000020980835 -9.0 0.0
99.0 19.255199432373047 36.40650177001953 -9.0 0.009999999776482582 11.020000457763672 0.6600000262260437 7.110000133514404 -9.0 0.0
128.0 18.072799682617188 36.3578987121582 -9.0 0.009999999776482582 12.699999809265137 0.6700000166893005 5.46999979019165 -9.0 0.0
173.0 16.325199127197266 36.1693000793457 -9.0 0.009999999776482582 15.210000038146973 0.8500000238418579 6.690000057220459 -9.0 0.0
225.0 14.352999687194824 35.875099182128906 -9.0 0.009999999776482582 18.399999618530273 1.059999942779541 8.550000190734863 -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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