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Dataset Title:  "NEGOM CTD - n5l07s00.nc - 29.74N, 85.42W - 1999-05-23" Subscribe RSS
Institution:  OCEAN.TAMU   (Dataset ID: negom_n5l07s00)
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: 16)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

    pressure ?  =  dBar   16 options
    depth ?  =  m   16 options
    temperature ?  =  degree_C   16 options
    potentialTemperature ?  =  degree_C   16 options
    conductivity ?  =  Siemens per meter   15 options
    salinity ?  =  PSU   16 options
    sigmat ?  =  kg m-3   16 options
    descentRate ?  =  meters per second   15 options
    transmission ?  =  percent   14 options
    par ?  =  umol m-2 s-1   16 options
    parv ?  =  volts   16 options
    fluorescence ?  =  volts   15 options
    backscattering ?  =  volts   15 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)  

pressure depth temperature potentialTemperature conductivity salinity sigmat descentRate transmission par parv fluorescence backscattering
dBar m degree_C degree_C Siemens per meter PSU kg m-3 meters per second percent umol m-2 s-1 volts volts volts
2.5 2.5 26.032899856567383 26.03230094909668 5.3471999168396 34.50450134277344 22.649499893188477 0.10400000214576721 4.324999809265137 2196.0 3.638000011444092 0.9729999899864197 2.0280001163482666
3.0 3.0 26.047800064086914 26.047100067138672 5.348700046539307 34.50419998168945 22.644699096679688 0.2160000056028366 4.322000026702881 1156.0 3.364000082015991 0.9950000047683716 2.0230000019073486
3.5 3.5 26.05190086364746 26.05109977722168 5.348999977111816 34.50320053100586 22.64259910583496 0.15600000321865082 4.322999954223633 1003.0 3.299999952316284 0.9769999980926514 2.0220000743865967
4.0 4.0 26.05270004272461 26.051799774169922 5.349100112915039 34.503299713134766 22.642499923706055 0.2160000056028366 4.323999881744385 1067.0 3.3269999027252197 0.9120000004768372 2.0199999809265137
4.5 4.5 26.051000595092773 26.049999237060547 5.348999977111816 34.50339889526367 22.643199920654297 0.19900000095367432 4.324999809265137 997.7000122070312 3.2939999103546143 1.0010000467300415 2.0299999713897705
5.0 5.0 26.06410026550293 26.062999725341797 5.350299835205078 34.50270080566406 22.638599395751953 0.16699999570846558 4.326000213623047 937.5999755859375 3.2669999599456787 0.9629999995231628 2.0179998874664307
5.5 5.5 26.062599182128906 26.061399459838867 5.350200176239014 34.50279998779297 22.63909912109375 0.23499999940395355 4.322999954223633 811.2999877929688 3.2079999446868896 0.9769999980926514 2.01200008392334
6.0 6.0 26.04599952697754 26.044700622558594 5.348599910736084 34.503700256347656 22.645000457763672 0.1469999998807907 4.320000171661377 722.5 3.1549999713897705 1.024999976158142 2.0169999599456787
6.5 6.5 25.998699188232422 25.99720001220703 5.344200134277344 34.50740051269531 22.662599563598633 0.20100000500679016 4.321000099182129 701.5 3.1440000534057617 0.9879999756813049 2.015000104904175
7.0 7.0 25.99220085144043 25.9906005859375 5.343599796295166 34.507301330566406 22.664600372314453 0.17900000512599945 4.318999767303467 623.9000244140625 3.0910000801086426 0.9860000014305115 2.0239999294281006
7.599999904632568 7.5 25.976699829101562 25.975000381469727 5.342700004577637 34.512699127197266 22.673500061035156 0.16599999368190765 4.2870001792907715 581.5999755859375 3.062000036239624 1.0750000476837158 2.0230000019073486
8.100000381469727 8.0 25.762500762939453 25.760700225830078 5.325099945068359 34.545799255371094 22.76490020751953 0.1770000010728836 4.158999919891357 559.2999877929688 3.0460000038146973 1.0859999656677246 2.0269999504089355
8.600000381469727 8.5 25.526500701904297 25.524599075317383 5.314499855041504 34.647499084472656 22.914499282836914 0.17800000309944153 3.5769999027252197 539.7000122070312 3.0290000438690186 1.2120000123977661 2.1689999103546143
9.100000381469727 9.0 25.380800247192383 25.378799438476562 5.31279993057251 34.74589920043945 23.033599853515625 0.1459999978542328 3.5969998836517334 557.5 3.0439999103546143 1.2740000486373901 2.4000000953674316
9.600000381469727 9.5 25.138700485229492 25.136600494384766 5.30679988861084 34.887298583984375 23.214500427246094 0.17599999904632568 3.3259999752044678 519.4000244140625 3.0139999389648438 1.3380000591278076 2.63100004196167
10.100000381469727 10.0 25.122299194335938 25.120100021362305 5.308599948883057 34.91299819946289 23.23889923095703 0.1420000046491623 2.1500000953674316 423.3999938964844 2.928999900817871 1.4950000047683716 2.811000108718872

In total, there are 16 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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