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Dataset Title:  "Deepwater CTD - 87g1105.ctd.nc - 26.33N, -94.0W - 1987-11-19" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_87g1105_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: 369)
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   369 options
    temperature ?  =  degree_C   291 options
    salinity ?  =  PSU   171 options
    oxygen ?  =  milliliters per liter   7 options
    nitrite ?  =  micromols per liter   6 options
    nitrate ?  =  micromols   11 options
    phosphate ?  =  micromols per liter   10 options
    silicate ?  =  micromols per liter   13 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 nitrite nitrate phosphate silicate salinity2 qualityFlag
m degree_C PSU milliliters per liter micromols per liter micromols micromols per liter micromols per liter PSU
4.0 24.107999801635742 36.316001892089844 4.800000190734863 0.03999999910593033 0.05999999865889549 0.009999999776482582 3.4000000953674316 -99.0 0.0
7.0 24.11199951171875 36.31700134277344 4.797999858856201 0.029999999329447746 0.07000000029802322 0.009999999776482582 2.619999885559082 -99.0 0.0
13.0 24.113000869750977 36.31700134277344 4.791999816894531 0.029999999329447746 0.07000000029802322 0.009999999776482582 2.5399999618530273 -99.0 0.0
18.0 24.124000549316406 36.321998596191406 -99.0 0.09000000357627869 0.05000000074505806 0.07999999821186066 2.7100000381469727 -99.0 0.0
22.0 24.131999969482422 36.32600021362305 4.756999969482422 0.05999999865889549 0.07999999821186066 0.05000000074505806 2.4200000762939453 -99.0 0.0
27.0 24.135000228881836 36.32699966430664 4.793000221252441 0.03999999910593033 0.12999999523162842 0.05999999865889549 2.3399999141693115 -99.0 0.0
32.0 24.135000228881836 36.32600021362305 -99.0 0.05999999865889549 11.100000381469727 0.6100000143051147 5.5 -99.0 0.0
38.0 24.139999389648438 36.32899856567383 -99.0 0.05999999865889549 11.199999809265137 0.6100000143051147 5.539999961853027 -99.0 0.0
42.0 24.150999069213867 36.3380012512207 -99.0 0.03999999910593033 15.300000190734863 0.8100000023841858 6.650000095367432 -99.0 0.0
47.0 24.156999588012695 36.340999603271484 -99.0 0.05000000074505806 15.300000190734863 0.8399999737739563 7.019999980926514 -99.0 0.0
53.0 24.17099952697754 36.349998474121094 -99.0 0.029999999329447746 16.5 0.8799999952316284 7.510000228881836 -99.0 0.0
57.0 24.18600082397461 36.361000061035156 -99.0 0.05000000074505806 19.200000762939453 1.0499999523162842 9.989999771118164 -99.0 0.0
62.0 24.18899917602539 36.3650016784668 0.0
67.0 24.180999755859375 36.36600112915039 0.0
72.0 24.165000915527344 36.367000579833984 0.0
78.0 22.59000015258789 36.356998443603516 0.0
83.0 21.763999938964844 36.391998291015625 0.0
87.0 21.388999938964844 36.3849983215332 0.0
93.0 20.750999450683594 36.38999938964844 0.0
98.0 20.32900047302246 36.38399887084961 0.0
103.0 19.81100082397461 36.39099884033203 0.0
107.0 19.402999877929688 36.37300109863281 0.0
112.0 18.90399932861328 36.35100173950195 0.0
118.0 18.246999740600586 36.34000015258789 0.0
122.0 17.91200065612793 -99.9000015258789 0.0
128.0 16.961999893188477 36.17100143432617 1.0
132.0 16.381000518798828 36.082000732421875 1.0
137.0 15.60200023651123 36.005001068115234 1.0
142.0 15.288999557495117 35.983001708984375 0.0
147.0 15.088000297546387 35.948001861572266 0.0
152.0 14.861000061035156 35.928001403808594 0.0
158.0 14.541000366210938 35.862998962402344 0.0
163.0 14.100000381469727 35.80699920654297 0.0
168.0 13.824999809265137 35.76900100708008 0.0
172.0 13.633000373840332 35.742000579833984 0.0
177.0 13.366999626159668 35.70600128173828 0.0
182.0 13.265999794006348 35.68600082397461 0.0
187.0 13.11400032043457 35.66299819946289 0.0
192.0 12.909000396728516 35.63600158691406 0.0
197.0 12.788999557495117 35.61899948120117 0.0
202.0 12.706999778747559 35.60200119018555 0.0
207.0 12.583999633789062 35.58100128173828 0.0
212.0 12.47599983215332 35.566001892089844 0.0
217.0 12.196000099182129 35.518001556396484 0.0
222.0 12.036999702453613 35.505001068115234 0.0
227.0 11.920999526977539 35.474998474121094 0.0
232.0 11.803999900817871 35.46699905395508 0.0
237.0 11.699000358581543 35.45399856567383 0.0
242.0 11.531999588012695 35.428001403808594 0.0
248.0 11.430000305175781 35.417999267578125 0.0
252.0 11.347999572753906 35.4010009765625 0.0
257.0 11.222999572753906 35.38199996948242 0.0
262.0 11.130000114440918 35.36600112915039 0.0
267.0 10.942000389099121 35.34199905395508 0.0
272.0 10.875 35.33599853515625 0.0
277.0 10.822999954223633 35.32500076293945 0.0
283.0 10.682999610900879 35.30799865722656 0.0
287.0 10.602999687194824 35.30099868774414 0.0
292.0 10.519000053405762 35.279998779296875 0.0
297.0 10.454000473022461 35.277000427246094 0.0
302.0 10.338000297546387 35.25699996948242 0.0
307.0 10.20300006866455 35.23899841308594 0.0
312.0 10.133999824523926 35.23099899291992 0.0
317.0 10.039999961853027 35.215999603271484 0.0
322.0 9.923999786376953 35.20100021362305 0.0
327.0 9.795000076293945 35.180999755859375 0.0
332.0 9.668999671936035 35.16600036621094 0.0
337.0 9.57699966430664 35.15599822998047 0.0
342.0 9.458000183105469 35.141998291015625 0.0
347.0 9.380000114440918 35.13800048828125 0.0
352.0 9.310999870300293 35.12799835205078 0.0
356.0 9.25 35.124000549316406 0.0
362.0 9.1850004196167 35.11000061035156 0.0
367.0 9.093000411987305 35.097999572753906 0.0
373.0 8.977999687194824 35.0890007019043 0.0
377.0 8.935999870300293 35.08300018310547 0.0
382.0 8.892000198364258 35.082000732421875 0.0
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392.0 8.77299976348877 35.066001892089844 0.0
397.0 8.682000160217285 35.055999755859375 0.0
402.0 8.645999908447266 35.05400085449219 0.0
408.0 8.586000442504883 35.04899978637695 0.0
412.0 8.532999992370605 35.03799819946289 0.0
417.0 8.430000305175781 35.020999908447266 0.0
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427.0 8.243000030517578 35.00600051879883 0.0
432.0 8.190999984741211 35.005001068115234 0.0
437.0 8.145999908447266 34.99800109863281 0.0
442.0 8.060999870300293 34.98699951171875 0.0
446.0 7.998000144958496 34.983001708984375 0.0
452.0 7.940000057220459 34.97800064086914 0.0
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482.0 7.551000118255615 34.948001861572266 0.0
487.0 7.486999988555908 34.94200134277344 0.0
492.0 7.445000171661377 34.939998626708984 0.0
497.0 7.36299991607666 34.933998107910156 0.0
502.0 7.315999984741211 34.93199920654297 0.0
507.0 7.2829999923706055 34.930999755859375 0.0
512.0 7.215000152587891 34.92399978637695 0.0
517.0 7.171000003814697 34.92399978637695 0.0
522.0 7.131999969482422 34.91899871826172 0.0
527.0 7.057000160217285 34.91400146484375 0.0
532.0 6.991000175476074 34.9109992980957 0.0
537.0 6.925000190734863 34.90999984741211 0.0
543.0 6.8460001945495605 34.90299987792969 0.0
547.0 6.7870001792907715 34.90299987792969 0.0
552.0 6.730999946594238 34.900001525878906 0.0
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657.0 5.980999946594238 34.88999938964844 0.0
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672.0 5.925000190734863 34.891998291015625 0.0
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1457.0 4.203999996185303 34.965999603271484 0.0
1462.0 4.203999996185303 34.965999603271484 0.0
1467.0 4.203999996185303 34.965999603271484 0.0
1472.0 4.203000068664551 34.965999603271484 0.0
1477.0 4.202000141143799 34.965999603271484 0.0
1482.0 4.203000068664551 34.965999603271484 0.0
1488.0 4.197999954223633 34.96699905395508 0.0
1492.0 4.197000026702881 34.96699905395508 0.0
1498.0 4.196000099182129 34.96699905395508 0.0
1502.0 4.196000099182129 34.96699905395508 0.0
1507.0 4.196000099182129 34.96699905395508 0.0
1512.0 4.196000099182129 34.96699905395508 0.0
1517.0 4.196000099182129 34.96699905395508 0.0
1522.0 4.197000026702881 34.96699905395508 0.0
1527.0 4.197000026702881 34.96699905395508 0.0
1532.0 4.196000099182129 34.96699905395508 0.0
1537.0 4.195000171661377 34.96699905395508 0.0
1542.0 4.193999767303467 34.96699905395508 0.0
1548.0 4.192999839782715 34.96699905395508 0.0
1552.0 4.192999839782715 34.96699905395508 0.0
1556.0 4.192999839782715 34.96799850463867 0.0
1562.0 4.191999912261963 34.96699905395508 0.0
1567.0 4.191999912261963 34.96799850463867 0.0
1572.0 4.191999912261963 34.96699905395508 0.0
1577.0 4.192999839782715 34.96799850463867 0.0
1583.0 4.192999839782715 34.96799850463867 0.0
1587.0 4.192999839782715 34.96799850463867 0.0
1592.0 4.192999839782715 34.96799850463867 0.0
1598.0 4.192999839782715 34.96699905395508 0.0
1602.0 4.192999839782715 34.96699905395508 0.0
1607.0 4.193999767303467 34.96699905395508 0.0
1612.0 4.193999767303467 34.96799850463867 0.0
1617.0 4.195000171661377 34.96799850463867 0.0
1622.0 4.195000171661377 34.96799850463867 0.0
1628.0 4.196000099182129 34.96799850463867 0.0
1632.0 4.196000099182129 34.96799850463867 0.0
1637.0 4.197000026702881 34.96799850463867 0.0
1642.0 4.197000026702881 34.96699905395508 0.0
1648.0 4.197000026702881 34.96799850463867 0.0
1652.0 4.197000026702881 34.96799850463867 0.0
1657.0 4.197999954223633 34.96799850463867 0.0
1662.0 4.197999954223633 34.96799850463867 0.0
1667.0 4.198999881744385 34.96799850463867 0.0
1672.0 4.198999881744385 34.96799850463867 0.0
1678.0 4.199999809265137 34.96799850463867 0.0
1682.0 4.199999809265137 34.96799850463867 0.0
1687.0 4.201000213623047 34.96699905395508 0.0
1692.0 4.201000213623047 34.96799850463867 0.0
1697.0 4.201000213623047 34.96799850463867 0.0
1703.0 4.202000141143799 34.96799850463867 0.0
1708.0 4.202000141143799 34.96799850463867 0.0
1712.0 4.203000068664551 34.96799850463867 0.0
1717.0 4.203000068664551 34.96799850463867 0.0
1722.0 4.203999996185303 34.96799850463867 0.0
1727.0 4.203999996185303 34.96699905395508 0.0
1732.0 4.203999996185303 34.96799850463867 0.0
1737.0 4.204999923706055 34.96699905395508 0.0
1742.0 4.204999923706055 34.96799850463867 0.0
1747.0 4.204999923706055 34.96799850463867 0.0
1752.0 4.205999851226807 34.96799850463867 0.0
1757.0 4.205999851226807 34.96799850463867 0.0
1762.0 4.206999778747559 34.96799850463867 0.0
1767.0 4.206999778747559 34.96799850463867 0.0
1772.0 4.206999778747559 34.96799850463867 0.0
1777.0 4.208000183105469 34.96799850463867 0.0
1782.0 4.208000183105469 34.96799850463867 0.0
1787.0 4.208000183105469 34.96799850463867 0.0
1792.0 4.209000110626221 34.96699905395508 0.0
1797.0 4.209000110626221 34.96799850463867 0.0
1802.0 4.209000110626221 34.96799850463867 0.0
1807.0 4.209000110626221 34.96799850463867 0.0
1812.0 4.209000110626221 34.96799850463867 0.0
1817.0 4.209000110626221 34.96799850463867 0.0
1822.0 4.209000110626221 34.96799850463867 0.0
1828.0 4.209000110626221 34.96799850463867 0.0
1832.0 4.209000110626221 34.96799850463867 0.0
1837.0 4.210000038146973 34.96799850463867 0.0
1843.0 4.210000038146973 34.96799850463867 0.0

In total, there are 369 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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