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Dataset Title:	"Deepwater CTD - d93e201.bdo.nc - 26.53N, -96.33W - 1993-05-00"
Institution:	Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_d93e201_bdo)
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: 12)
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) 0.0 8.0 19.0 28.0 36.0 48.0 57.0 73.0 98.0 146.0 197.0 283.0  m	12 options
temperature	= (ANY) 12.17240047454834 14.21969985961914 16.653200149536133 17.9330997467041 19.158199310302734 20.408599853515625 21.393800735473633 22.470800399780273 23.367300033569336 23.489299774169922 23.614599227905273 23.82309913635254  degree_C (potential temperature from ctd)	12 options
salinity	= (ANY) 31.07990074157715 32.05950164794922 35.54800033569336 35.85850143432617 35.8849983215332 36.08430099487305 36.13249969482422 36.21500015258789 36.30839920043945 36.343101501464844 36.3671989440918 36.42250061035156  PSU	12 options
oxygen	= (ANY) 2.619999885559082 2.680000066757202 2.7699999809265137 2.809999942779541 2.9100000858306885 3.5 4.090000152587891 4.46999979019165 4.730000019073486 4.889999866485596 4.980000019073486 5.039999961853027  milliliters per liter	12 options
nitrite	= (ANY) 0.05999999865889549 0.07000000029802322 0.07999999821186066 0.11999999731779099 0.12999999523162842 0.14000000059604645 0.20999999344348907 0.28999999165534973  micromols	8 options
nitrate	= (ANY) 0.12999999523162842 0.18000000715255737 0.20999999344348907 0.699999988079071 3.509999990463257 6.949999809265137 11.0 13.350000381469727 15.300000190734863 17.989999771118164 23.6299991607666  micromols	11 options
phosphate	= (ANY) 0.029999999329447746 0.03999999910593033 0.07000000029802322 0.20000000298023224 0.36000001430511475 0.5699999928474426 0.7200000286102295 0.8500000238418579 1.1699999570846558 1.4600000381469727  micromols	10 options
silicate	= (ANY) 1.6200000047683716 1.8300000429153442 1.850000023841858 2.5299999713897705 2.569999933242798 3.0799999237060547 3.4000000953674316 4.300000190734863 4.369999885559082 5.019999980926514 8.399999618530273 11.430000305175781  micromols	12 options
salinity2	= (ANY) 31.086000442504883 32.553001403808594 35.55500030517578 35.86000061035156 35.917999267578125 36.09199905395508 36.17599868774414 36.21799850463867 36.32099914550781 36.34600067138672 36.382999420166016 36.41999816894531  PSU	12 options
qualityFlag	= (ANY) 0.0 1.0	2 options

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 (potential temperature from ctd)	PSU	milliliters per liter	micromols	micromols	micromols	micromols	PSU
0.0	23.614599227905273	31.07990074157715	5.039999961853027	0.07000000029802322	0.12999999523162842	0.03999999910593033	2.5299999713897705	31.086000442504883	0.0
8.0	23.489299774169922	32.05950164794922	4.980000019073486	0.07000000029802322	0.12999999523162842	0.029999999329447746	3.0799999237060547	32.553001403808594	0.0
19.0	23.82309913635254	35.8849983215332	4.889999866485596	0.05999999865889549	0.20999999344348907	0.029999999329447746	1.8300000429153442	35.917999267578125	0.0
28.0	23.367300033569336	36.08430099487305	4.730000019073486	0.07999999821186066	0.18000000715255737	0.029999999329447746	1.6200000047683716	36.09199905395508	0.0
36.0	22.470800399780273	36.13249969482422	4.46999979019165	0.28999999165534973	0.699999988079071	0.07000000029802322	1.850000023841858	36.17599868774414	0.0
48.0	21.393800735473633	36.30839920043945	4.090000152587891	0.28999999165534973	3.509999990463257	0.20000000298023224	2.569999933242798	36.32099914550781	0.0
57.0	20.408599853515625	36.3671989440918	3.5	0.20999999344348907	6.949999809265137	0.36000001430511475	3.4000000953674316	36.382999420166016	0.0
73.0	19.158199310302734	36.42250061035156	2.9100000858306885	0.14000000059604645	11.0	0.5699999928474426	4.369999885559082	36.41999816894531	0.0
98.0	17.9330997467041	36.343101501464844	2.809999942779541	0.11999999731779099	13.350000381469727	0.7200000286102295	5.019999980926514	36.34600067138672	0.0
146.0	16.653200149536133	36.21500015258789	2.7699999809265137	0.12999999523162842	15.300000190734863	0.8500000238418579	4.300000190734863	36.21799850463867	0.0
197.0	14.21969985961914	35.85850143432617	2.680000066757202	0.12999999523162842	17.989999771118164	1.1699999570846558	8.399999618530273	35.86000061035156	0.0
283.0	12.17240047454834	35.54800033569336	2.619999885559082	0.14000000059604645	23.6299991607666	1.4600000381469727	11.430000305175781	35.55500030517578	1.0

In total, there are 12 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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