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ERDDAP > tabledap > Subset ?

Dataset Title:  "Deepwater CTD - 53892.ctd.nc - 26.0N, -95.0W - 1992-05-22" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_53892_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   30 options
    salinity ?  =  PSU   27 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 ?  =  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 -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 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
10.0 25.51300048828125 36.03099822998047 10.0 -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 25.51099967956543 36.03099822998047 16.399999618530273 -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 -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 24.608999252319336 36.055301666259766 24.899999618530273 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 24.250999450683594 36.102901458740234 31.299999237060547 0.0
32.0 -990.0 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 24.204999923706055 36.10900115966797 38.900001525878906 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 24.11400032043457 36.11199951171875 46.0 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 23.645000457763672 36.25389862060547 52.400001525878906 0.0
53.0 -990.0 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 -990.0 0.0
60.0 23.350000381469727 36.319698333740234 60.0 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 23.165000915527344 36.340999603271484 67.0 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 23.051000595092773 36.343101501464844 74.9000015258789 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 22.976999282836914 36.34709930419922 80.5 0.0
81.0 -990.0 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 22.823999404907227 36.35319900512695 87.9000015258789 0.0
88.0 -990.0 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 22.7549991607666 36.343101501464844 94.5999984741211 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 22.60099983215332 36.35319900512695 106.5999984741211 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 22.49799919128418 36.32789993286133 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 22.256000518798828 36.31060028076172 120.69999694824219 0.0
121.0 -990.0 0.0
122.0 -990.0 0.0
123.0 -990.0 0.0
124.0 -990.0 0.0
125.0 -990.0 0.0
126.0 22.048999786376953 36.27920150756836 126.80000305175781 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 -990.0 0.0
132.0 -990.0 0.0
133.0 21.820999145507812 36.28329849243164 134.39999389648438 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 -990.0 0.0
140.0 21.35300064086914 36.29750061035156 141.3000030517578 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 20.940000534057617 36.21839904785156 148.3000030517578 0.0
148.0 -990.0 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 20.732999801635742 36.252899169921875 154.89999389648438 0.0
155.0 -990.0 0.0
156.0 -990.0 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 20.680999755859375 36.35419845581055 162.1999969482422 0.0
162.0 -990.0 0.0
163.0 -990.0 0.0
164.0 -990.0 0.0
165.0 -990.0 0.0
166.0 -990.0 0.0
167.0 20.493000030517578 36.42110061645508 168.3000030517578 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 20.16900062561035 36.45050048828125 174.3000030517578 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 19.777000427246094 36.47169876098633 181.39999389648438 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 19.305999755859375 36.47269821166992 188.10000610351562 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 -990.0 0.0
193.0 -990.0 0.0
194.0 -990.0 0.0
195.0 19.093000411987305 36.460601806640625 196.1999969482422 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 -990.0 0.0
201.0 -990.0 0.0
202.0 18.784000396728516 36.42409896850586 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 18.55699920654297 36.393699645996094 209.5 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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