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Dataset Title:	"Deepwater CTD - d94i052.bdo.nc - 27.56N, -93.0W - 1994-07-31"
Institution:	Texas A&M University, Department of Oceanography   (Dataset ID: deepwater_d94i052_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 19.0 34.0 54.0 68.0 84.0 108.0 134.0 209.0 274.0 347.0 644.0  m	12 options
temperature	= (ANY) 6.363999843597412 9.870800018310547 11.862799644470215 13.809100151062012 17.109699249267578 18.841400146484375 19.937299728393555 21.459999084472656 23.677600860595703 26.511199951171875 29.256999969482422 29.314599990844727  degree_C (potential temperature from ctd)	12 options
salinity	= (ANY) 34.90549850463867 35.2135009765625 35.498199462890625 35.79209899902344 36.21039962768555 36.2244987487793 36.24760055541992 36.27370071411133 36.28089904785156 36.282798767089844 36.35240173339844 36.4463005065918  PSU	12 options
oxygen	= (ANY) 2.6600000858306885 2.6700000762939453 2.7699999809265137 2.880000114440918 3.0299999713897705 3.2699999809265137 3.930000066757202 4.630000114440918 4.639999866485596 4.739999771118164 5.190000057220459 5.300000190734863  milliliters per liter	12 options
nitrite	= (ANY) 0.0 0.009999999776482582 0.019999999552965164 0.029999999329447746 0.03999999910593033 0.05999999865889549 0.09000000357627869  micromols	7 options
nitrate	= (ANY) 0.11999999731779099 0.15000000596046448 0.28999999165534973 0.3100000023841858 5.059999942779541 10.90999984741211 14.329999923706055 20.239999771118164 24.100000381469727 27.950000762939453 30.309999465942383  micromols	11 options
phosphate	= (ANY) 0.0 0.009999999776482582 0.019999999552965164 0.029999999329447746 0.23999999463558197 0.5600000023841858 0.7699999809265137 1.1799999475479126 1.4500000476837158 1.7300000190734863 1.9600000381469727  micromols	11 options
silicate	= (ANY) 0.699999988079071 1.0199999809265137 1.0399999618530273 1.659999966621399 1.8300000429153442 2.690000057220459 3.380000114440918 4.349999904632568 7.659999847412109 10.75 14.520000457763672 23.100000381469727  micromols	12 options
salinity2	= (ANY) 34.90800094604492 35.2130012512207 35.50199890136719 35.797000885009766 36.215999603271484 36.21699905395508 36.275001525878906 36.279998779296875 36.28900146484375 36.316001892089844 36.35499954223633 36.439998626708984  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	29.256999969482422	36.21039962768555	4.639999866485596	0.009999999776482582	0.3100000023841858	0.029999999329447746	1.8300000429153442	36.21699905395508	0.0
19.0	29.314599990844727	36.35240173339844	4.630000114440918	0.009999999776482582	0.15000000596046448	0.019999999552965164	1.659999966621399	36.35499954223633	0.0
34.0	26.511199951171875	36.28089904785156	5.190000057220459	0.029999999329447746	0.11999999731779099	0.009999999776482582	1.0399999618530273	36.316001892089844	0.0
54.0	23.677600860595703	36.24760055541992	5.300000190734863	0.009999999776482582	0.15000000596046448	0.0	0.699999988079071	36.275001525878906	0.0
68.0	21.459999084472656	36.2244987487793	4.739999771118164	0.05999999865889549	0.28999999165534973	0.019999999552965164	1.0199999809265137	36.215999603271484	0.0
84.0	19.937299728393555	36.282798767089844	3.930000066757202	0.09000000357627869	5.059999942779541	0.23999999463558197	2.690000057220459	36.28900146484375	0.0
108.0	18.841400146484375	36.4463005065918	3.0299999713897705	0.03999999910593033	10.90999984741211	0.5600000023841858	3.380000114440918	36.439998626708984	0.0
134.0	17.109699249267578	36.27370071411133	2.880000114440918	0.019999999552965164	14.329999923706055	0.7699999809265137	4.349999904632568	36.279998779296875	0.0
209.0	13.809100151062012	35.79209899902344	2.7699999809265137	0.0	20.239999771118164	1.1799999475479126	7.659999847412109	35.797000885009766	0.0
274.0	11.862799644470215	35.498199462890625	2.6600000858306885	0.019999999552965164	24.100000381469727	1.4500000476837158	10.75	35.50199890136719	1.0
347.0	9.870800018310547	35.2135009765625	2.6700000762939453	0.019999999552965164	27.950000762939453	1.7300000190734863	14.520000457763672	35.2130012512207	0.0
644.0	6.363999843597412	34.90549850463867	3.2699999809265137	0.029999999329447746	30.309999465942383	1.9600000381469727	23.100000381469727	34.90800094604492	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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