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Dataset Title:  "Deepwater CTD - 91g0248.ctd.nc - 27.71N, -94.97W - 1991-03-00" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_91g0248_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: 126)
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   125 options
    temperature ?  =  degree_C   124 options
    salinity ?  =  PSU   114 options
    oxygen ?  =  milliliters per liter   24 options
    nitrite ?  =  micromols per liter   6 options
    nitrate ?  =  micromols   19 options
    phosphate ?  =  micromols per liter   22 options
    silicate ?  =  micromols per liter   19 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
1.0 20.48509979248047 36.13140106201172 -99.0 0.0 0.0 0.11999999731779099 1.2000000476837158 -99.0 0.0
2.0 20.49049949645996 36.13589859008789 5.427999973297119 0.0 0.0 0.11999999731779099 1.2999999523162842 -99.0 0.0
3.0 20.49169921875 36.20140075683594 5.4029998779296875 0.0 0.0 0.10999999940395355 1.2999999523162842 -99.0 0.0
4.0 20.490999221801758 36.197200775146484 5.418000221252441 0.0 0.0 0.14000000059604645 1.2999999523162842 -99.0 0.0
5.0 20.498699188232422 36.19770050048828 5.502999782562256 0.0 0.0 0.12999999523162842 1.100000023841858 -99.0 0.0
6.0 20.49799919128418 36.1974983215332 5.425000190734863 0.0 0.0 0.15000000596046448 1.0 -99.0 0.0
7.0 20.49799919128418 36.19710159301758 5.427000045776367 0.0 0.0 0.17000000178813934 1.100000023841858 -99.0 0.0
8.0 20.497499465942383 36.196800231933594 5.4079999923706055 0.019999999552965164 0.10000000149011612 0.15000000596046448 1.0 -99.0 0.0
9.0 20.497900009155273 36.196998596191406 5.413000106811523 0.029999999329447746 0.20000000298023224 0.1599999964237213 1.0 -99.0 0.0
10.0 20.49679946899414 36.1968994140625 5.3379998207092285 0.029999999329447746 0.5 0.1599999964237213 1.2000000476837158 -99.0 0.0
11.0 20.498899459838867 36.1963996887207 3.4660000801086426 0.03999999910593033 10.0 0.6200000047683716 4.0 -99.0 0.0
12.0 20.502700805664062 36.196800231933594 2.9100000858306885 0.009999999776482582 12.300000190734863 0.7599999904632568 4.5 -99.0 0.0
13.0 20.50819969177246 36.1963996887207 2.990000009536743 0.0 14.399999618530273 0.8999999761581421 5.599999904632568 -99.0 0.0
15.0 20.509300231933594 36.19649887084961 3.003000020980835 0.0 16.100000381469727 0.9399999976158142 6.300000190734863 -99.0 0.0
16.0 20.5093994140625 36.19620132446289 3.260999917984009 0.0 17.0 1.0399999618530273 7.0 -99.0 0.0
17.0 20.510499954223633 36.19609832763672 3.186000108718872 0.0 18.200000762939453 1.0800000429153442 8.0 -99.0 0.0
18.0 20.513700485229492 36.196800231933594 2.8440001010894775 0.0 19.299999237060547 1.1699999570846558 8.800000190734863 -99.0 0.0
19.0 20.51300048828125 36.19670104980469 2.744999885559082 0.0 21.0 1.2999999523162842 9.899999618530273 -99.0 0.0
20.0 20.51180076599121 36.196800231933594 2.937999963760376 0.0 22.799999237060547 1.4299999475479126 11.399999618530273 -99.0 0.0
21.0 20.51140022277832 36.19670104980469 2.6710000038146973 0.0 24.5 1.5299999713897705 12.800000190734863 -99.0 0.0
22.0 20.512699127197266 36.196800231933594 2.9140000343322754 0.0 25.600000381469727 1.559999942779541 13.699999809265137 -99.0 0.0
23.0 20.514299392700195 36.19820022583008 2.936000108718872 0.0 28.100000381469727 1.7200000286102295 16.299999237060547 -99.0 0.0
24.0 20.513900756835938 36.19850158691406 2.9600000381469727 0.0 29.0 1.7899999618530273 17.600000381469727 -99.0 0.0
25.0 20.515300750732422 36.199100494384766 -99.0 0.0 28.399999618530273 1.7599999904632568 17.100000381469727 -99.0 0.0
26.0 20.523700714111328 36.20859909057617 0.0
27.0 20.524599075317383 36.2140998840332 0.0
28.0 20.52630043029785 36.21900177001953 0.0
29.0 20.528400421142578 36.224098205566406 0.0
30.0 20.535400390625 36.24300003051758 0.0
31.0 20.497400283813477 36.273399353027344 0.0
32.0 20.437599182128906 36.28820037841797 0.0
33.0 20.384899139404297 36.295501708984375 0.0
34.0 20.391599655151367 36.2942008972168 0.0
35.0 20.380199432373047 36.29600143432617 0.0
36.0 20.364999771118164 36.29840087890625 0.0
37.0 20.350400924682617 36.29999923706055 0.0
38.0 20.343299865722656 36.300899505615234 0.0
39.0 20.348600387573242 36.29650115966797 0.0
40.0 20.337400436401367 36.300201416015625 0.0
41.0 20.332000732421875 36.30059814453125 0.0
42.0 20.320499420166016 36.30080032348633 0.0
43.0 20.3169002532959 36.30059814453125 0.0
44.0 20.305599212646484 36.29999923706055 0.0
46.0 20.292699813842773 36.30149841308594 0.0
47.0 20.28860092163086 36.30440139770508 0.0
48.0 20.27720069885254 36.307498931884766 0.0
49.0 20.261499404907227 36.30630111694336 0.0
50.0 20.250699996948242 36.30820083618164 0.0
51.0 20.25909996032715 36.31480026245117 0.0
52.0 20.268699645996094 36.31650161743164 0.0
53.0 20.269100189208984 36.31959915161133 0.0
54.0 20.269699096679688 36.31959915161133 0.0
55.0 20.27050018310547 36.31999969482422 0.0
56.0 20.27519989013672 36.32210159301758 0.0
57.0 20.278099060058594 36.32590103149414 0.0
61.0 20.279199600219727 36.32440185546875 0.0
62.0 20.278900146484375 36.32400131225586 0.0
63.0 20.27869987487793 36.32490158081055 0.0
64.0 20.273799896240234 36.325599670410156 0.0
65.0 20.282800674438477 36.326499938964844 0.0
67.0 20.283599853515625 36.327999114990234 0.0
68.0 20.283700942993164 36.3286018371582 0.0
69.0 20.284299850463867 36.32789993286133 0.0
70.0 20.284099578857422 36.327999114990234 0.0
71.0 20.285200119018555 36.32749938964844 0.0
72.0 20.284500122070312 36.32789993286133 0.0
73.0 20.283899307250977 36.33689880371094 0.0
74.0 20.284299850463867 36.3286018371582 0.0
75.0 20.28420066833496 36.32929992675781 0.0
76.0 20.284000396728516 36.329200744628906 0.0
77.0 20.282100677490234 36.3297004699707 0.0
78.0 20.281200408935547 36.329898834228516 0.0
80.0 20.28019905090332 36.33169937133789 0.0
81.0 20.277000427246094 36.3317985534668 0.0
82.0 20.273300170898438 36.33150100708008 0.0
83.0 20.268400192260742 36.33209991455078 0.0
84.0 20.265499114990234 36.33470153808594 0.0
85.0 20.256200790405273 36.34109878540039 0.0
85.0 20.26300048828125 36.33769989013672 0.0
87.0 20.254100799560547 36.34349822998047 0.0
88.0 20.24020004272461 36.34640121459961 0.0
91.0 19.23579978942871 36.445899963378906 0.0
92.0 19.105899810791016 36.45560073852539 0.0
93.0 19.238800048828125 36.471900939941406 0.0
94.0 18.953899383544922 36.450801849365234 0.0
95.0 18.936599731445312 36.450599670410156 0.0
96.0 18.93160057067871 36.44089889526367 0.0
97.0 18.912599563598633 36.448001861572266 0.0
98.0 18.867799758911133 36.442298889160156 0.0
99.0 18.70199966430664 36.43450164794922 0.0
100.0 18.649999618530273 36.43519973754883 0.0
101.0 18.496999740600586 36.40650177001953 0.0
102.0 18.34119987487793 36.40169906616211 0.0
103.0 18.251300811767578 36.394500732421875 0.0
104.0 18.321500778198242 36.39939880371094 0.0
105.0 18.069000244140625 36.3760986328125 0.0
106.0 17.991100311279297 36.37030029296875 0.0
108.0 17.829599380493164 36.351898193359375 0.0
109.0 17.778099060058594 36.34709930419922 0.0
110.0 17.704700469970703 36.33700180053711 0.0
111.0 17.530000686645508 36.3203010559082 0.0
112.0 17.435400009155273 36.312400817871094 0.0
114.0 17.28030014038086 36.284698486328125 0.0
115.0 17.175800323486328 36.278099060058594 0.0
116.0 17.099899291992188 36.2692985534668 0.0
117.0 17.01759910583496 36.25910186767578 0.0
118.0 17.076000213623047 36.266700744628906 0.0
119.0 16.870800018310547 36.24720001220703 0.0
120.0 16.966400146484375 36.244598388671875 0.0
121.0 16.730199813842773 36.2223014831543 0.0
122.0 16.635400772094727 36.20800018310547 0.0
123.0 16.520999908447266 36.195098876953125 0.0
124.0 16.452199935913086 36.186500549316406 0.0
125.0 16.338600158691406 36.172401428222656 0.0
126.0 16.355199813842773 36.178001403808594 0.0
127.0 16.370399475097656 36.1692008972168 0.0
129.0 16.158700942993164 36.14910125732422 0.0
130.0 16.062299728393555 36.131900787353516 0.0
131.0 16.014999389648438 36.12689971923828 0.0
132.0 15.970499992370605 36.11920166015625 0.0
133.0 16.05620002746582 36.12779998779297 0.0
134.0 15.871500015258789 36.10340118408203 0.0
135.0 15.84119987487793 36.096500396728516 0.0
136.0 15.769800186157227 36.08879852294922 0.0
137.0 15.73639965057373 36.08409881591797 0.0
138.0 15.774100303649902 36.08660125732422 0.0

In total, there are 126 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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