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Dataset Title:	"Deepwater CTD - d94j160.bdo.nc - 27.11N, -96.35W - 1994-11-12"
Institution:	Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_d94j160_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) 1.0 22.0 44.0 64.0 71.0 86.0 105.0 112.0 132.0 145.0 173.0 225.0  m	12 options
temperature	= (ANY) 14.440099716186523 15.846699714660645 16.808700561523438 18.610300064086914 19.811399459838867 20.670400619506836 22.11389923095703 24.35420036315918 26.35930061340332 26.374799728393555 26.382999420166016 26.387500762939453  degree_C (potential temperature from ctd)	12 options
salinity	= (ANY) 35.89830017089844 36.10810089111328 36.23210144042969 36.276100158691406 36.276798248291016 36.27830123901367 36.29710006713867 36.36309814453125 36.37419891357422 36.390098571777344 36.409400939941406 36.43090057373047  PSU	12 options
oxygen	= (ANY) 2.6600000858306885 2.740000009536743 2.759999990463257 2.8399999141693115 3.2699999809265137 3.619999885559082 4.230000019073486 4.610000133514404 4.630000114440918 4.639999866485596 4.659999847412109 4.840000152587891  milliliters per liter	12 options
nitrite	= (ANY) 0.0 0.019999999552965164 0.029999999329447746 0.03999999910593033 0.10999999940395355 0.11999999731779099  micromols	6 options
nitrate	= (ANY) 0.019999999552965164 0.07000000029802322 0.10999999940395355 0.14000000059604645 0.18000000715255737 1.7699999809265137 4.880000114440918 9.369999885559082 13.170000076293945 14.850000381469727 18.239999771118164  micromols	11 options
phosphate	= (ANY) 0.009999999776482582 0.019999999552965164 0.029999999329447746 0.03999999910593033 0.05000000074505806 0.10999999940395355 0.2199999988079071 0.3700000047683716 0.5899999737739563 0.8100000023841858 0.8999999761581421 1.1100000143051147  micromols	12 options
silicate	= (ANY) 0.6399999856948853 0.6800000071525574 0.7599999904632568 0.9200000166893005 1.3200000524520874 1.7200000286102295 2.0999999046325684 3.2300000190734863 4.829999923706055 5.460000038146973 7.539999961853027  micromols	11 options
salinity2	= (ANY) 35.891998291015625 36.106998443603516 36.242000579833984 36.26900100708008 36.27000045776367 36.27899932861328 36.29399871826172 36.35499954223633 36.3650016784668 36.38800048828125 36.40599822998047 36.426998138427734  PSU	12 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 (potential temperature from ctd)	PSU	milliliters per liter	micromols	micromols	micromols	micromols	PSU
1.0	26.382999420166016	36.27830123901367	4.610000133514404	0.0	0.18000000715255737	0.05000000074505806	0.9200000166893005	36.26900100708008	0.0
22.0	26.387500762939453	36.276798248291016	4.630000114440918	0.0	0.10999999940395355	0.029999999329447746	0.7599999904632568	36.27899932861328	0.0
44.0	26.374799728393555	36.276100158691406	4.659999847412109	0.0	0.07000000029802322	0.019999999552965164	0.7599999904632568	36.27000045776367	0.0
64.0	26.35930061340332	36.29710006713867	4.639999866485596	0.0	0.07000000029802322	0.009999999776482582	0.6800000071525574	36.29399871826172	0.0
71.0	24.35420036315918	36.37419891357422	4.840000152587891	0.11999999731779099	0.019999999552965164	0.03999999910593033	0.6399999856948853	36.3650016784668	0.0
86.0	22.11389923095703	36.36309814453125	4.230000019073486	0.10999999940395355	0.14000000059604645	0.10999999940395355	1.3200000524520874	36.35499954223633	0.0
105.0	20.670400619506836	36.390098571777344	3.619999885559082	0.03999999910593033	1.7699999809265137	0.2199999988079071	1.7200000286102295	36.38800048828125	0.0
112.0	19.811399459838867	36.43090057373047	3.2699999809265137	0.029999999329447746	4.880000114440918	0.3700000047683716	2.0999999046325684	36.426998138427734	0.0
132.0	18.610300064086914	36.409400939941406	2.8399999141693115	0.019999999552965164	9.369999885559082	0.5899999737739563	3.2300000190734863	36.40599822998047	0.0
145.0	16.808700561523438	36.23210144042969	2.740000009536743	0.019999999552965164	13.170000076293945	0.8100000023841858	4.829999923706055	36.242000579833984	0.0
173.0	15.846699714660645	36.10810089111328	2.759999990463257	0.019999999552965164	14.850000381469727	0.8999999761581421	5.460000038146973	36.106998443603516	0.0
225.0	14.440099716186523	35.89830017089844	2.6600000858306885	0.029999999329447746	18.239999771118164	1.1100000143051147	7.539999961853027	35.891998291015625	0.0

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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