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Dataset Title:  "Deepwater CTD - 87g1126.bdo.nc - 26.33N, -95.5W - 1987-11-23" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_87g1126_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   11 options
    salinity ?  =  PSU   12 options
    oxygen ?  =  milliliters per liter   7 options
    nitrite ?  =  micromols per liter   6 options
    nitrate ?  =  micromols   12 options
    phosphate ?  =  micromols per liter   12 options
    silicate ?  =  micromols per liter   11 options
    salinity2 ?  =  PSU   1 option:
    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 PSU milliliters per liter micromols per liter micromols micromols per liter micromols per liter PSU
40.0 24.75 36.441001892089844 4.770999908447266 0.029999999329447746 0.0 0.10000000149011612 0.8600000143051147 0.0
59.0 24.75 36.439998626708984 4.77400016784668 0.029999999329447746 0.05000000074505806 0.11999999731779099 1.090000033378601 0.0
79.0 23.290000915527344 36.4379997253418 4.8420000076293945 0.07999999821186066 0.03999999910593033 0.15000000596046448 0.8100000023841858 0.0
100.0 22.06999969482422 36.426998138427734 4.423999786376953 0.1599999964237213 0.6100000143051147 0.18000000715255737 0.8100000023841858 0.0
120.0 20.84000015258789 36.41400146484375 3.944999933242798 0.05000000074505806 4.03000020980835 0.2800000011920929 1.5099999904632568 0.0
139.0 19.65999984741211 36.46500015258789 2.999000072479248 0.05000000074505806 10.5 0.6399999856948853 3.4100000858306885 0.0
159.0 18.520000457763672 36.409000396728516 0.029999999329447746 13.199999809265137 0.699999988079071 3.5899999141693115 0.0
199.0 16.479999542236328 36.16400146484375 0.019999999552965164 16.5 0.9399999976158142 4.940000057220459 0.0
398.0 10.609999656677246 35.29600143432617 0.019999999552965164 29.299999237060547 1.600000023841858 13.399999618530273 0.0
593.0 7.739999771118164 34.9630012512207 0.029999999329447746 32.29999923706055 1.8300000429153442 20.600000381469727 0.0
793.0 6.03000020980835 34.888999938964844 0.019999999552965164 30.5 1.7300000190734863 25.399999618530273 0.0
988.0 5.139999866485596 34.91600036621094 0.009999999776482582 28.899999618530273 1.590000033378601 26.899999618530273 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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