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ERDDAP > tabledap > Subset ?

Dataset Title:  "Deepwater CTD - 53893.ctd.nc - 26.5N, -95.0W - 1992-05-22" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_53893_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: 208)
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   208 options
    temperature ?  =  degree_C   27 options
    salinity ?  =  PSU   26 options
    oxygen ?  =  milligrams per liter   1 option:
    pressure ?  =  decibars   28 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
1.0 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
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 25.64900016784668 36.054298400878906 6.300000190734863 -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 -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 25.648000717163086 36.05329895019531 12.300000190734863 -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 -990.0 0.0
18.0 -990.0 0.0
19.0 -990.0 0.0
20.0 25.648000717163086 36.050201416015625 20.100000381469727 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 25.160999298095703 36.077598571777344 26.399999618530273 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 -990.0 0.0
33.0 25.082000732421875 36.15869903564453 33.099998474121094 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 25.033000946044922 36.18299865722656 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 24.249000549316406 36.147499084472656 46.20000076293945 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 -990.0 0.0
54.0 23.429000854492188 36.155601501464844 54.20000076293945 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 23.385000228881836 36.2963981628418 61.70000076293945 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 23.15399932861328 36.30350112915039 68.30000305175781 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 23.076000213623047 36.3390007019043 75.5999984741211 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 -990.0 0.0
84.0 22.906999588012695 36.33489990234375 84.19999694824219 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 -990.0 0.0
91.0 22.733999252319336 36.34410095214844 91.5999984741211 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 -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 -990.0 0.0
104.0 -990.0 0.0
105.0 -990.0 0.0
106.0 -990.0 0.0
107.0 22.33300018310547 36.30659866333008 108.0999984741211 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 -990.0 0.0
114.0 -990.0 0.0
115.0 22.05500030517578 36.27519989013672 116.19999694824219 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 -990.0 0.0
123.0 -990.0 0.0
124.0 21.708999633789062 36.256900787353516 124.5 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 -990.0 0.0
130.0 -990.0 0.0
131.0 21.454999923706055 36.2599983215332 131.60000610351562 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 20.979999542236328 36.26300048828125 140.0 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 20.632999420166016 36.36640167236328 149.5 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 20.209999084472656 36.46160125732422 157.10000610351562 0.0
157.0 -990.0 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 19.64299964904785 36.43830108642578 165.60000610351562 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 -990.0 0.0
172.0 -990.0 0.0
173.0 19.291000366210938 36.4099006652832 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 18.840999603271484 36.391700744628906 181.10000610351562 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 -990.0 0.0
186.0 -990.0 0.0
187.0 -990.0 0.0
188.0 18.475000381469727 36.391700744628906 189.39999389648438 0.0
189.0 -990.0 0.0
190.0 -990.0 0.0
191.0 -990.0 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 17.90399932861328 36.36640167236328 197.1999969482422 0.0
197.0 -990.0 0.0
198.0 -990.0 0.0
199.0 -990.0 0.0
200.0 -990.0 0.0
201.0 -990.0 0.0
202.0 17.63599967956543 36.30149841308594 203.89999389648438 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 -990.0 0.0
208.0 17.45400047302246 36.273101806640625 209.1999969482422 0.0

In total, there are 208 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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