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ERDDAP > tabledap > Subset ?

Dataset Title:  "Deepwater CTD - 53871.ctd.nc - 27.5N, -91.0W - 1992-05-19" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_53871_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: 206)
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   206 options
    temperature ?  =  degree_C   30 options
    salinity ?  =  PSU   30 options
    oxygen ?  =  milligrams per liter   1 option:
    pressure ?  =  decibars   30 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 ?  =  2 options

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
2.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
3.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
4.0 -990.0 -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 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
9.0 24.586000442504883 36.019798278808594 9.5 -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 -990.0 -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 23.329999923706055 36.046199798583984 17.399999618530273 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
18.0 -990.0 0.0
19.0 -990.0 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 23.139999389648438 36.13029861450195 24.700000762939453 0.0
26.0 -990.0 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 23.023000717163086 36.22050094604492 32.20000076293945 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 21.756999969482422 36.09579849243164 39.099998474121094 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 21.344999313354492 36.1505012512207 46.70000076293945 0.0
47.0 -990.0 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 21.17799949645996 36.170799255371094 53.5 0.0
54.0 -990.0 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 20.84600067138672 36.168800354003906 59.900001525878906 0.0
60.0 -990.0 0.0
61.0 -990.0 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 20.652000427246094 36.18299865722656 67.19999694824219 0.0
68.0 -990.0 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 20.41900062561035 36.183998107910156 74.69999694824219 0.0
75.0 -990.0 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 20.277000427246094 36.17689895629883 81.69999694824219 0.0
82.0 -990.0 0.0
83.0 -990.0 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 20.035999298095703 36.22959899902344 88.30000305175781 0.0
89.0 -990.0 0.0
90.0 -990.0 0.0
91.0 -990.0 0.0
92.0 -990.0 0.0
93.0 -990.0 0.0
94.0 19.961999893188477 36.31269836425781 95.0 0.0
95.0 -990.0 0.0
96.0 -990.0 0.0
97.0 -990.0 0.0
98.0 -990.0 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 19.63599967956543 36.351200103759766 103.5 0.0
104.0 -990.0 0.0
105.0 -990.0 0.0
106.0 -990.0 0.0
107.0 -990.0 0.0
108.0 -990.0 0.0
109.0 19.19300079345703 36.38050079345703 110.0 0.0
110.0 -990.0 0.0
111.0 -990.0 0.0
112.0 -990.0 0.0
113.0 -990.0 0.0
114.0 -990.0 0.0
115.0 -990.0 0.0
116.0 18.97100067138672 36.41699981689453 116.5 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 18.929000854492188 36.4463996887207 123.30000305175781 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 18.724000930786133 36.43119812011719 129.5 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 18.238000869750977 36.4099006652832 136.6999969482422 0.0
137.0 -990.0 0.0
138.0 -990.0 0.0
139.0 -990.0 0.0
140.0 -990.0 0.0
141.0 -990.0 0.0
142.0 -990.0 0.0
143.0 17.60700035095215 36.3390007019043 143.6999969482422 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 -990.0 0.0
149.0 -990.0 0.0
150.0 17.30699920654297 36.28020095825195 151.10000610351562 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 16.985000610351562 36.23970031738281 158.60000610351562 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 16.666000366210938 36.21440124511719 165.3000030517578 0.0
165.0 -990.0 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 16.392000198364258 36.13529968261719 172.5 0.0
172.0 -990.0 0.0
173.0 -990.0 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 16.172000885009766 36.09479904174805 179.39999389648438 0.0
179.0 -990.0 0.0
180.0 -990.0 0.0
181.0 -990.0 0.0
182.0 -990.0 0.0
183.0 -990.0 0.0
184.0 -990.0 0.0
185.0 16.018999099731445 36.07149887084961 186.5 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 -990.0 0.0
192.0 15.711000442504883 36.034000396728516 193.6999969482422 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 -990.0 0.0
200.0 15.300999641418457 35.94279861450195 201.3000030517578 1.0
201.0 -990.0 0.0
202.0 -990.0 0.0
203.0 -990.0 0.0
204.0 -990.0 0.0
205.0 -990.0 0.0
206.0 -990.0 0.0
207.0 15.036999702453613 35.90840148925781 208.5 1.0

In total, there are 206 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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