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Dataset Title:  "Deepwater CTD - 90g10015.ctd.nc - 28.19N, -93.73W - 1990-07-13" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_90g10015_ctd)
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: 62)
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   62 options
    temperature ?  =  degree_C   62 options
    salinity ?  =  PSU   62 options
    oxygen ?  =  milliliters per liter   8 options
    nitrite ?  =  micromols per liter   4 options
    nitrate ?  =  micromols   7 options
    phosphate ?  =  micromols per liter   7 options
    silicate ?  =  micromols per liter   5 options
    salinity2 ?  =  PSU   2 options
    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
2.0 29.81570053100586 30.340700149536133 5.076000213623047 0.009999999776482582 0.05000000074505806 0.009999999776482582 0.10000000149011612 -99.0 0.0
3.0 29.87380027770996 30.603500366210938 5.22599983215332 0.009999999776482582 0.05000000074505806 0.009999999776482582 0.10000000149011612 -99.0 0.0
4.0 29.831100463867188 30.608400344848633 4.989999771118164 0.3199999928474426 0.5 0.019999999552965164 0.10000000149011612 -99.0 0.0
5.0 29.775800704956055 30.64310073852539 4.980999946594238 0.3199999928474426 0.6499999761581421 0.05000000074505806 0.10000000149011612 -99.0 0.0
6.0 29.760400772094727 30.645999908447266 4.520999908447266 0.05000000074505806 3.2799999713897705 0.3100000023841858 0.30000001192092896 -99.0 0.0
7.0 29.71109962463379 30.679800033569336 3.509000062942505 0.009999999776482582 9.75 0.49000000953674316 2.299999952316284 -99.0 0.0
8.0 29.764400482177734 30.64349937438965 2.625 0.009999999776482582 30.68000030517578 1.909999966621399 18.600000381469727 -99.0 0.0
9.0 29.78619956970215 30.638900756835938 0.0
10.0 29.74799919128418 30.667200088500977 0.0
11.0 29.683500289916992 30.691099166870117 0.0
12.0 29.659400939941406 30.730899810791016 0.0
13.0 29.6658992767334 30.797399520874023 0.0
14.0 29.43720054626465 32.437599182128906 0.0
15.0 29.619300842285156 33.036399841308594 0.0
16.0 29.65329933166504 33.662498474121094 0.0
17.0 29.527799606323242 34.03770065307617 0.0
18.0 29.374399185180664 34.06999969482422 0.0
19.0 29.332599639892578 34.383201599121094 0.0
20.0 29.142799377441406 34.525299072265625 0.0
21.0 28.93589973449707 34.580501556396484 0.0
22.0 28.60300064086914 34.72079849243164 0.0
23.0 28.242000579833984 34.879600524902344 0.0
24.0 27.811399459838867 35.14139938354492 0.0
25.0 27.655000686645508 35.12699890136719 0.0
26.0 26.759700775146484 35.18330001831055 0.0
27.0 25.84819984436035 35.31340026855469 0.0
28.0 25.315399169921875 35.4640998840332 0.0
29.0 25.16469955444336 35.44309997558594 0.0
30.0 25.044700622558594 35.51390075683594 0.0
31.0 24.93790054321289 35.604000091552734 0.0
32.0 24.738399505615234 35.63479995727539 0.0
33.0 24.463899612426758 35.760101318359375 0.0
34.0 24.297800064086914 35.814300537109375 0.0
35.0 24.211200714111328 35.83700180053711 0.0
36.0 24.04490089416504 35.870399475097656 0.0
37.0 23.947500228881836 35.9202995300293 0.0
38.0 23.920299530029297 35.94969940185547 0.0
39.0 23.851499557495117 35.97460174560547 0.0
40.0 23.701499938964844 36.008399963378906 0.0
41.0 23.53230094909668 36.05659866333008 0.0
42.0 23.42449951171875 36.07339859008789 0.0
43.0 23.201900482177734 36.09980010986328 0.0
44.0 23.042299270629883 36.14350128173828 0.0
45.0 22.97920036315918 36.13719940185547 0.0
46.0 22.66710090637207 36.19139862060547 0.0
47.0 22.429399490356445 36.22380065917969 0.0
48.0 21.989700317382812 36.27539825439453 0.0
49.0 21.67300033569336 36.312801361083984 0.0
50.0 21.497900009155273 36.335201263427734 0.0
51.0 21.478900909423828 36.33689880371094 0.0
52.0 21.273300170898438 36.344600677490234 0.0
53.0 21.097900390625 36.358001708984375 0.0
54.0 21.03689956665039 36.35850143432617 0.0
55.0 20.940900802612305 36.36439895629883 0.0
56.0 20.878000259399414 36.3650016784668 0.0
57.0 20.845500946044922 36.36650085449219 0.0
58.0 20.824899673461914 36.367000579833984 0.0
59.0 20.775299072265625 36.367698669433594 0.0
60.0 20.749799728393555 36.36830139160156 0.0
61.0 20.734899520874023 36.368499755859375 0.0
62.0 20.722900390625 36.36819839477539 0.0
63.0 20.711000442504883 36.36859893798828 0.0

In total, there are 62 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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