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ERDDAP > tabledap > Subset ?

Dataset Title:  "Deepwater CTD - 53890.ctd.nc - 26.0N, -94.0W - 1992-05-21" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_53890_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: 200)
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   200 options
    temperature ?  =  degree_C   29 options
    salinity ?  =  PSU   29 options
    oxygen ?  =  milligrams per liter   1 option:
    pressure ?  =  decibars   29 options
    nitrite ?  =  not-measured   2 options
    nitrate ?  =  not-measured   2 options
    phosphate ?  =  not-measured   2 options
    silicate ?  =  not-measured   2 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 pressure nitrite nitrate phosphate silicate salinity2 qualityFlag
m degree_C PSU milligrams per liter decibars not-measured not-measured not-measured not-measured PSU
4.0 25.645000457763672 35.653099060058594 3.799999952316284 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
5.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
6.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
7.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
8.0 25.636999130249023 35.65409851074219 8.100000381469727 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
9.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
10.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
11.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
12.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
13.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
14.0 25.30699920654297 35.7411994934082 14.100000381469727 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
15.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
16.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
17.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
18.0 25.2549991607666 35.754398345947266 18.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
19.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
20.0 -990.0 0.0
21.0 -990.0 0.0
22.0 -990.0 0.0
23.0 -990.0 0.0
24.0 -990.0 0.0
25.0 -990.0 0.0
26.0 24.97800064086914 35.78070068359375 25.899999618530273 0.0
27.0 -990.0 0.0
28.0 -990.0 0.0
29.0 -990.0 0.0
30.0 -990.0 0.0
31.0 -990.0 0.0
32.0 24.895000457763672 35.791900634765625 32.29999923706055 0.0
33.0 -990.0 0.0
34.0 -990.0 0.0
35.0 -990.0 0.0
36.0 -990.0 0.0
37.0 -990.0 0.0
38.0 -990.0 0.0
39.0 24.711000442504883 35.82939910888672 39.400001525878906 0.0
40.0 -990.0 0.0
41.0 -990.0 0.0
42.0 -990.0 0.0
43.0 -990.0 0.0
44.0 -990.0 0.0
45.0 -990.0 0.0
46.0 -990.0 0.0
47.0 23.53499984741211 35.92259979248047 46.900001525878906 0.0
48.0 -990.0 0.0
49.0 -990.0 0.0
50.0 -990.0 0.0
51.0 -990.0 0.0
52.0 -990.0 0.0
53.0 -990.0 0.0
54.0 23.045000076293945 35.94279861450195 54.5 0.0
55.0 -990.0 0.0
56.0 -990.0 0.0
57.0 -990.0 0.0
58.0 -990.0 0.0
59.0 -990.0 0.0
60.0 -990.0 0.0
61.0 23.062000274658203 36.07659912109375 61.900001525878906 0.0
62.0 -990.0 0.0
63.0 -990.0 0.0
64.0 -990.0 0.0
65.0 -990.0 0.0
66.0 -990.0 0.0
67.0 -990.0 0.0
68.0 23.14299964904785 36.160701751708984 68.9000015258789 0.0
69.0 -990.0 0.0
70.0 -990.0 0.0
71.0 -990.0 0.0
72.0 -990.0 0.0
73.0 -990.0 0.0
74.0 -990.0 0.0
75.0 22.844999313354492 36.11410140991211 75.69999694824219 0.0
76.0 -990.0 0.0
77.0 -990.0 0.0
78.0 -990.0 0.0
79.0 -990.0 0.0
80.0 -990.0 0.0
81.0 -990.0 0.0
82.0 -990.0 0.0
83.0 22.464000701904297 36.03200149536133 83.69999694824219 0.0
84.0 -990.0 0.0
85.0 -990.0 0.0
86.0 -990.0 0.0
87.0 -990.0 0.0
88.0 -990.0 0.0
89.0 -990.0 0.0
90.0 22.4689998626709 36.0724983215332 90.9000015258789 0.0
91.0 -990.0 0.0
92.0 -990.0 0.0
93.0 -990.0 0.0
94.0 -990.0 0.0
95.0 -990.0 0.0
96.0 -990.0 0.0
97.0 -990.0 0.0
98.0 22.8799991607666 36.346099853515625 98.69999694824219 0.0
99.0 -990.0 0.0
100.0 -990.0 0.0
101.0 -990.0 0.0
102.0 -990.0 0.0
103.0 -990.0 0.0
104.0 -990.0 0.0
105.0 22.753999710083008 36.36640167236328 105.4000015258789 0.0
106.0 -990.0 0.0
107.0 -990.0 0.0
108.0 -990.0 0.0
109.0 -990.0 0.0
110.0 -990.0 0.0
111.0 -990.0 0.0
112.0 -990.0 0.0
113.0 22.493999481201172 36.3390007019043 113.5999984741211 0.0
114.0 -990.0 0.0
115.0 -990.0 0.0
116.0 -990.0 0.0
117.0 -990.0 0.0
118.0 -990.0 0.0
119.0 -990.0 0.0
120.0 -990.0 0.0
121.0 -990.0 0.0
122.0 22.341999053955078 36.324798583984375 122.69999694824219 0.0
123.0 -990.0 0.0
124.0 -990.0 0.0
125.0 -990.0 0.0
126.0 -990.0 0.0
127.0 -990.0 0.0
128.0 -990.0 0.0
129.0 22.042999267578125 36.27009963989258 130.39999389648438 0.0
130.0 -990.0 0.0
131.0 -990.0 0.0
132.0 -990.0 0.0
133.0 -990.0 0.0
134.0 -990.0 0.0
135.0 -990.0 0.0
136.0 -990.0 0.0
137.0 -990.0 0.0
138.0 -990.0 0.0
139.0 21.187999725341797 36.23059844970703 139.5 0.0
140.0 -990.0 0.0
141.0 -990.0 0.0
142.0 -990.0 0.0
143.0 -990.0 0.0
144.0 -990.0 0.0
145.0 -990.0 0.0
146.0 -990.0 0.0
147.0 -990.0 0.0
148.0 20.964000701904297 36.319698333740234 148.8000030517578 0.0
149.0 -990.0 0.0
150.0 -990.0 0.0
151.0 -990.0 0.0
152.0 -990.0 0.0
153.0 -990.0 0.0
154.0 -990.0 0.0
155.0 -990.0 0.0
156.0 -990.0 0.0
157.0 20.5310001373291 36.382598876953125 157.8000030517578 0.0
158.0 -990.0 0.0
159.0 -990.0 0.0
160.0 -990.0 0.0
161.0 -990.0 0.0
162.0 -990.0 0.0
163.0 -990.0 0.0
164.0 -990.0 0.0
165.0 20.239999771118164 36.42610168457031 166.1999969482422 0.0
166.0 -990.0 0.0
167.0 -990.0 0.0
168.0 -990.0 0.0
169.0 -990.0 0.0
170.0 -990.0 0.0
171.0 -990.0 0.0
172.0 -990.0 0.0
173.0 19.552000045776367 36.4900016784668 174.0 0.0
174.0 -990.0 0.0
175.0 -990.0 0.0
176.0 -990.0 0.0
177.0 -990.0 0.0
178.0 -990.0 0.0
179.0 -990.0 0.0
180.0 -990.0 0.0
181.0 -990.0 0.0
182.0 18.881999969482422 36.37950134277344 183.10000610351562 0.0
183.0 -990.0 0.0
184.0 -990.0 0.0
185.0 -990.0 0.0
186.0 -990.0 0.0
187.0 -990.0 0.0
188.0 -990.0 0.0
189.0 -990.0 0.0
190.0 -990.0 0.0
191.0 18.468000411987305 36.39680099487305 192.1999969482422 0.0
192.0 -990.0 0.0
193.0 -990.0 0.0
194.0 -990.0 0.0
195.0 -990.0 0.0
196.0 -990.0 0.0
197.0 -990.0 0.0
198.0 -990.0 0.0
199.0 17.96500015258789 36.358299255371094 200.6999969482422 0.0
200.0 -990.0 0.0
201.0 -990.0 0.0
202.0 -990.0 0.0
203.0 17.83300018310547 36.34000015258789 204.39999389648438 0.0

In total, there are 200 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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