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Dataset Title:	"Deepwater CTD - 88g0530d.ctd.nc - 27.67N, -94.74W - 1988-10-20"
Institution:	Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_88g0530d_ctd)
Information:	Summary | License | FGDC | ISO 19115 | Metadata | Background | Data Access Form | Files | Make a graph

Select a subset:      (Current number of distinct combinations of matching data: 71)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

depth	= (ANY) 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0 65.0 70.0 75.0 80.0 85.0 90.0 95.0 100.0 105.0 110.0 115.0 120.0 125.0 130.0 135.0 140.0 145.0 150.0 155.0 160.0 165.0 170.0 175.0 180.0 185.0 190.0 195.0 200.0 205.0 210.0 215.0 220.0 225.0 230.0 235.0 240.0 245.0 250.0 255.0 260.0 265.0 270.0 275.0 280.0 285.0 290.0 295.0 300.0 305.0 310.0 315.0 320.0 325.0 330.0 335.0 340.0 345.0 350.0 355.0  m	71 options
temperature	= (ANY) 10.387700080871582 10.597700119018555 10.698399543762207 10.756999969482422 10.832900047302246 10.877599716186523 10.988100051879883 11.15410041809082 11.352499961853027 11.477100372314453 11.60420036315918 11.736100196838379 11.906100273132324 11.997099876403809 12.100899696350098 12.2391996383667 12.430899620056152 12.517900466918945 12.587300300598145 12.753800392150879 12.872599601745605 12.965700149536133 13.034600257873535 13.092499732971191 13.236800193786621 13.416099548339844 13.618599891662598 13.793600082397461 14.04699993133545 14.223799705505371 14.345199584960938 14.452099800109863 14.575599670410156 14.673800468444824 14.82610034942627 15.029999732971191 15.195699691772461 15.348199844360352 15.490599632263184 15.661100387573242 15.824899673461914 16.06570053100586 16.291200637817383 16.494800567626953 16.912700653076172 17.332700729370117 17.79520034790039 18.02050018310547 18.243900299072266 18.320199966430664 18.609800338745117 19.17799949645996 19.43670082092285 19.819299697875977 20.20509910583496 20.71109962463379 21.34239959716797 22.689599990844727 23.79159927368164 24.529800415039062 25.131000518798828 25.877399444580078 26.424299240112305 26.63629913330078 26.75279998779297 26.792200088500977 26.798799514770508 26.813100814819336 26.81679916381836 26.832300186157227 26.851900100708008  degree_C	71 options
salinity	= (ANY) 35.267601013183594 35.294700622558594 35.30929946899414 35.31850051879883 35.32600021362305 35.33679962158203 35.35200119018555 35.37379837036133 35.40299987792969 35.42179870605469 35.434600830078125 35.45650100708008 35.481201171875 35.48320007324219 35.498600006103516 35.51179885864258 35.53609848022461 35.562599182128906 35.5723991394043 35.582000732421875 35.607601165771484 35.627899169921875 35.640499114990234 35.650901794433594 35.660099029541016 35.68330001831055 35.70199966430664 35.71189880371094 35.74259948730469 35.77000045776367 35.78369903564453 35.80229949951172 35.83039855957031 35.85279846191406 35.8661994934082 35.88869857788086 35.89030075073242 35.903499603271484 35.918701171875 35.95399856567383 35.97990036010742 36.005699157714844 36.02579879760742 36.0531005859375 36.06779861450195 36.11970138549805 36.15010070800781 36.1505012512207 36.16910171508789 36.19110107421875 36.27320098876953 36.27410125732422 36.31650161743164 36.31919860839844 36.33089828491211 36.351898193359375 36.35879898071289 36.364498138427734 36.3931999206543 36.410099029541016 36.41320037841797 36.417598724365234 36.41889953613281 36.42610168457031 36.42829895019531 36.4297981262207 36.444698333740234 36.44580078125 36.45330047607422 36.48310089111328 36.50120162963867  PSU	71 options
oxygen	= (ANY) -99.0 2.4760000705718994 2.4769999980926514 2.4790000915527344 2.484999895095825 2.5329999923706055 2.568000078201294 2.7070000171661377 2.7809998989105225 3.053999900817871 4.173999786376953 4.505000114440918 4.513999938964844 4.574999809265137 4.5920000076293945 4.684999942779541 4.7179999351501465  milliliters per liter	18 options
nitrite	= (ANY) 0.0 0.009999999776482582 0.019999999552965164 0.09000000357627869 0.20999999344348907 0.2800000011920929  micromols per liter	7 options
nitrate	= (ANY) 0.10000000149011612 0.20000000298023224 0.4000000059604645 0.800000011920929 13.5 17.200000762939453 18.799999237060547 20.100000381469727 24.799999237060547 27.0 27.100000381469727 27.200000762939453 28.700000762939453  micromols	14 options
phosphate	= (ANY) 0.029999999329447746 0.03999999910593033 0.05999999865889549 0.07000000029802322 0.10000000149011612 0.699999988079071 0.9200000166893005 1.0199999809265137 1.100000023841858 1.399999976158142 1.5399999618530273 1.5499999523162842 1.6299999952316284 1.6399999856948853  micromols per liter	15 options
silicate	= (ANY) 0.8999999761581421 1.100000023841858 1.899999976158142 2.0999999046325684 2.4000000953674316 2.700000047683716 3.700000047683716 5.300000190734863 6.400000095367432 6.800000190734863 10.199999809265137 12.199999809265137 12.399999618530273 12.600000381469727 12.899999618530273 14.199999809265137 14.300000190734863  micromols per liter	18 options
salinity2	= (ANY) -99.0  PSU	2 options
qualityFlag	= (ANY) 0.0	1 option: 0.0

View:      Map of All Related Data      Distinct Data Counts     Distinct Data  0 10 100 1000 10000 100000      Related Data Counts     Related Data  0 10 100 1000 10000 100000

 

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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.
 

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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.
 

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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
5.0	26.813100814819336	35.48320007324219	4.5920000076293945	0.0	0.10000000149011612	0.05999999865889549	2.700000047683716	-99.0	0.0
10.0	26.851900100708008	35.70199966430664	4.7179999351501465	0.0	0.10000000149011612	0.03999999910593033	2.4000000953674316	-99.0	0.0
15.0	26.81679916381836	35.78369903564453	4.684999942779541	0.009999999776482582	0.10000000149011612	0.029999999329447746	1.899999976158142	-99.0	0.0
20.0	26.798799514770508	35.89030075073242	4.505000114440918	0.09000000357627869	0.20000000298023224	0.03999999910593033	2.0999999046325684	-99.0	0.0
25.0	26.832300186157227	36.15010070800781	4.173999786376953	0.20999999344348907	0.800000011920929	0.10000000149011612	2.4000000953674316	-99.0	0.0
30.0	26.792200088500977	36.27320098876953	4.513999938964844	0.2800000011920929	0.4000000059604645	0.07000000029802322	1.100000023841858	-99.0	0.0
35.0	26.75279998779297	36.31650161743164	4.574999809265137	0.2800000011920929	0.4000000059604645	0.07000000029802322	0.8999999761581421	-99.0	0.0
40.0	26.63629913330078	36.351898193359375	3.053999900817871	0.009999999776482582	13.5	0.699999988079071	3.700000047683716	-99.0	0.0
45.0	26.424299240112305	36.410099029541016	2.7809998989105225	0.009999999776482582	17.200000762939453	0.9200000166893005	5.300000190734863	-99.0	0.0
50.0	25.877399444580078	36.4297981262207	2.7070000171661377	0.009999999776482582	18.799999237060547	1.0199999809265137	6.400000095367432	-99.0	0.0
55.0	25.131000518798828	36.45330047607422	-99.0	0.009999999776482582	20.100000381469727	1.100000023841858	6.800000190734863	-99.0	0.0
60.0	24.529800415039062	36.48310089111328	2.568000078201294	0.009999999776482582	24.799999237060547	1.399999976158142	10.199999809265137	-99.0	0.0
65.0	23.79159927368164	36.50120162963867	2.5329999923706055	0.009999999776482582	24.799999237060547	1.399999976158142	10.199999809265137	-99.0	0.0
70.0	22.689599990844727	36.417598724365234	2.4790000915527344	0.009999999776482582	27.100000381469727	1.5399999618530273	12.199999809265137	-99.0	0.0
75.0	21.34239959716797	36.42610168457031	2.4760000705718994	0.009999999776482582	27.0	1.5499999523162842	12.399999618530273	-99.0	0.0
80.0	20.71109962463379	36.41889953613281	2.4760000705718994	0.009999999776482582	27.0	1.5499999523162842	12.600000381469727	-99.0	0.0
85.0	20.20509910583496	36.44580078125	2.4760000705718994	0.009999999776482582	27.200000762939453	1.5499999523162842	12.899999618530273	-99.0	0.0
90.0	19.819299697875977	36.444698333740234	2.484999895095825	0.009999999776482582	28.700000762939453	1.6399999856948853	14.199999809265137	-99.0	0.0
95.0	19.43670082092285	36.42829895019531	2.4769999980926514	0.019999999552965164	28.700000762939453	1.6299999952316284	14.300000190734863	-99.0	0.0
100.0	19.17799949645996	36.41320037841797							0.0
105.0	18.609800338745117	36.3931999206543							0.0
110.0	18.320199966430664	36.364498138427734							0.0
115.0	18.243900299072266	36.35879898071289							0.0
120.0	18.02050018310547	36.33089828491211							0.0
125.0	17.79520034790039	36.31919860839844							0.0
130.0	17.332700729370117	36.27410125732422							0.0
135.0	16.912700653076172	36.19110107421875							0.0
140.0	16.494800567626953	36.16910171508789							0.0
145.0	16.291200637817383	36.1505012512207							0.0
150.0	16.06570053100586	36.11970138549805							0.0
155.0	15.824899673461914	36.06779861450195							0.0
160.0	15.661100387573242	36.0531005859375							0.0
165.0	15.490599632263184	36.02579879760742							0.0
170.0	15.348199844360352	36.005699157714844							0.0
175.0	15.195699691772461	35.97990036010742							0.0
180.0	15.029999732971191	35.95399856567383							0.0
185.0	14.82610034942627	35.918701171875							0.0
190.0	14.673800468444824	35.903499603271484							0.0
195.0	14.575599670410156	35.88869857788086							0.0
200.0	14.452099800109863	35.8661994934082							0.0
205.0	14.345199584960938	35.85279846191406							0.0
210.0	14.223799705505371	35.83039855957031							0.0
215.0	14.04699993133545	35.80229949951172							0.0
220.0	13.793600082397461	35.77000045776367							0.0
225.0	13.618599891662598	35.74259948730469							0.0
230.0	13.416099548339844	35.71189880371094							0.0
235.0	13.236800193786621	35.68330001831055							0.0
240.0	13.092499732971191	35.660099029541016							0.0
245.0	13.034600257873535	35.650901794433594							0.0
250.0	12.965700149536133	35.640499114990234							0.0
255.0	12.872599601745605	35.627899169921875							0.0
260.0	12.753800392150879	35.607601165771484							0.0
265.0	12.587300300598145	35.582000732421875							0.0
270.0	12.517900466918945	35.5723991394043							0.0
275.0	12.430899620056152	35.562599182128906							0.0
280.0	12.2391996383667	35.53609848022461							0.0
285.0	12.100899696350098	35.51179885864258							0.0
290.0	11.997099876403809	35.498600006103516							0.0
295.0	11.906100273132324	35.481201171875							0.0
300.0	11.736100196838379	35.45650100708008							0.0
305.0	11.60420036315918	35.434600830078125							0.0
310.0	11.477100372314453	35.42179870605469							0.0
315.0	11.352499961853027	35.40299987792969							0.0
320.0	11.15410041809082	35.37379837036133							0.0
325.0	10.988100051879883	35.35200119018555							0.0
330.0	10.877599716186523	35.33679962158203							0.0
335.0	10.832900047302246	35.32600021362305							0.0
340.0	10.756999969482422	35.31850051879883							0.0
345.0	10.698399543762207	35.30929946899414							0.0
350.0	10.597700119018555	35.294700622558594							0.0
355.0	10.387700080871582	35.267601013183594							0.0

In total, there are 71 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.
 

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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.
 

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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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