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ERDDAP > tabledap > Subset ?

Dataset Title:  "Deepwater CTD - 53879.ctd.nc - 26.0N, -92.5W - 1992-05-20" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_53879_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: 195)
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   195 options
    temperature ?  =  degree_C   29 options
    salinity ?  =  PSU   28 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 ?  =  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
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 25.159000396728516 36.039100646972656 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 -990.0 -99.0 -99.0 -99.0 -99.0 -99.0 0.0
9.0 25.124000549316406 36.190101623535156 9.300000190734863 -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 24.739999771118164 36.21229934692383 15.800000190734863 -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 24.520999908447266 36.2244987487793 22.299999237060547 0.0
23.0 -990.0 0.0
24.0 -990.0 0.0
25.0 -990.0 0.0
26.0 -990.0 0.0
27.0 -990.0 0.0
28.0 -990.0 0.0
29.0 24.304000854492188 36.219398498535156 29.399999618530273 0.0
30.0 -990.0 0.0
31.0 -990.0 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 24.06100082397461 36.21540069580078 36.099998474121094 0.0
37.0 -990.0 0.0
38.0 -990.0 0.0
39.0 -990.0 0.0
40.0 -990.0 0.0
41.0 -990.0 0.0
42.0 -990.0 0.0
43.0 23.125999450683594 36.2244987487793 43.099998474121094 0.0
44.0 -990.0 0.0
45.0 -990.0 0.0
46.0 -990.0 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 22.820999145507812 36.29140090942383 51.29999923706055 0.0
52.0 -990.0 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 22.613000869750977 36.325801849365234 59.400001525878906 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 22.073999404907227 36.252899169921875 66.9000015258789 0.0
67.0 -990.0 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 -990.0 0.0
75.0 21.516000747680664 36.216400146484375 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 21.194000244140625 36.22959899902344 83.0999984741211 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 20.547000885009766 36.14350128173828 90.80000305175781 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 20.132999420166016 36.227500915527344 97.30000305175781 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 20.03499984741211 36.2781982421875 102.5999984741211 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 -990.0 0.0
108.0 19.885000228881836 36.340999603271484 108.80000305175781 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 19.611000061035156 36.4109001159668 115.5 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 19.094999313354492 36.434200286865234 121.69999694824219 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 -990.0 0.0
127.0 -990.0 0.0
128.0 18.742000579833984 36.40380096435547 128.8000030517578 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 -990.0 0.0
134.0 -990.0 0.0
135.0 18.375 36.38159942626953 135.8000030517578 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 -990.0 0.0
141.0 18.06999969482422 36.36429977416992 142.10000610351562 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 -990.0 0.0
149.0 17.7810001373291 36.26499938964844 150.39999389648438 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 -990.0 0.0
158.0 17.41699981689453 36.27619934082031 159.1999969482422 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 17.19300079345703 36.24169921875 165.89999389648438 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 16.974000930786133 36.20830154418945 173.39999389648438 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 -990.0 0.0
179.0 -990.0 0.0
180.0 16.615999221801758 36.15359878540039 181.0 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 -990.0 0.0
186.0 -990.0 0.0
187.0 -990.0 0.0
188.0 -990.0 0.0
189.0 16.253000259399414 36.070499420166016 190.10000610351562 1.0
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 15.906000137329102 36.03810119628906 196.39999389648438 0.0

In total, there are 195 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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