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Dataset Title:  "LATEX CTD - d94i102.nc - 28.98N, 94.74W - 1994-08-03" Subscribe RSS
Institution:  Texas A&M University, Department of Oceanography   (Dataset ID: latex_d94i102)
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: 26)
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   26 options
    depth ?  =  m   26 options
    temperature ?  =  degree_C   25 options
    potentialTemperature ?  =  degree_C   23 options
    conductivity ?  =  Siemens per meter   17 options
    salinity ?  =  PSU   8 options
    sigmat ?  =  kg m-3   19 options
    descentRate ?  =  meters per second   25 options
    transmission ?  =  percent   24 options
    par ?  =  umol m-2 s-1   3 options
    parv ?  =  volts   3 options
    fluorescence ?  =  volts   25 options
    backscattering ?  =  volts   23 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
1.5 1.5 28.626300811767578 28.625999450683594 5.734000205993652 35.314998626708984 22.427499771118164 0.0 2.950000047683716 0.7200000286102295 -9.0 1.7510000467300415 0.5019999742507935
2.0 2.0 28.624900817871094 28.624399185180664 5.73390007019043 35.314998626708984 22.42799949645996 -1.3300000429153442 2.986999988555908 0.7400000095367432 0.00800000037997961 1.7289999723434448 0.48500001430511475
2.5 2.5 28.625200271606445 28.62459945678711 5.73390007019043 35.31480026245117 22.427799224853516 0.07900000363588333 2.9719998836517334 0.7200000286102295 -9.0 1.7949999570846558 0.4970000088214874
3.0 3.0 28.625600814819336 28.624900817871094 5.734000205993652 35.314998626708984 22.427900314331055 -0.2939999997615814 2.9630000591278076 0.7300000190734863 0.0 1.7699999809265137 0.47699999809265137
3.5 3.5 28.625699996948242 28.624900817871094 5.734000205993652 35.315101623535156 22.42799949645996 -0.2240000069141388 2.9809999465942383 0.7300000190734863 0.0 1.7419999837875366 0.4779999852180481
4.0 4.0 28.62660026550293 28.625600814819336 5.734099864959717 35.314998626708984 22.42770004272461 0.09300000220537186 3.0260000228881836 0.7300000190734863 0.0 1.7389999628067017 0.4620000123977661
4.5 4.5 28.62689971923828 28.62579917907715 5.7342000007629395 35.315101623535156 22.42770004272461 0.2939999997615814 3.0179998874664307 0.7300000190734863 0.0 1.7660000324249268 0.46700000762939453
5.0 5.0 28.628000259399414 28.626800537109375 5.734300136566162 35.315101623535156 22.42729949951172 0.035999998450279236 3.005000114440918 0.7300000190734863 0.0 1.7730000019073486 0.4620000123977661
5.5 5.5 28.62820053100586 28.62689971923828 5.734399795532227 35.315101623535156 22.42729949951172 0.0729999989271164 3.0160000324249268 0.7300000190734863 0.0 1.7630000114440918 0.4569999873638153
6.0 6.0 28.627399444580078 28.625999450683594 5.734300136566162 35.315101623535156 22.427600860595703 0.12600000202655792 3.015000104904175 0.7300000190734863 0.0 1.753999948501587 0.4659999907016754
6.5 6.5 28.62849998474121 28.62700080871582 5.734499931335449 35.315101623535156 22.42729949951172 1.4179999828338623 3.01200008392334 0.7300000190734863 0.0 1.7710000276565552 0.4659999907016754
7.0 7.0 28.628799438476562 28.627099990844727 5.734499931335449 35.31520080566406 22.42729949951172 0.0820000022649765 3.01200008392334 0.7300000190734863 0.0 1.7799999713897705 0.45899999141693115
7.599999904632568 7.5 28.6299991607666 28.62820053100586 5.7347002029418945 35.31520080566406 22.427000045776367 -0.7440000176429749 3.0169999599456787 0.7300000190734863 0.0 1.7649999856948853 0.4560000002384186
8.100000381469727 8.0 28.630699157714844 28.628799438476562 5.734799861907959 35.315101623535156 22.426700592041016 0.09600000083446503 3.0260000228881836 0.7300000190734863 0.0 1.7660000324249268 0.4519999921321869
8.600000381469727 8.5 28.631799697875977 28.62980079650879 5.734899997711182 35.31520080566406 22.42639923095703 -0.007000000216066837 2.9739999771118164 0.7300000190734863 0.0 1.7640000581741333 0.47600001096725464
9.100000381469727 9.0 28.633100509643555 28.631000518798828 5.735099792480469 35.31520080566406 22.426000595092773 -0.004000000189989805 2.9489998817443848 0.7300000190734863 0.0 1.7769999504089355 0.4790000021457672
9.600000381469727 9.5 28.63409996032715 28.631799697875977 5.735199928283691 35.314998626708984 22.425600051879883 0.19499999284744263 2.890000104904175 0.7300000190734863 0.0 1.7680000066757202 0.4970000088214874
10.100000381469727 10.0 28.63290023803711 28.63050079345703 5.735099792480469 35.315101623535156 22.42609977722168 -0.453000009059906 2.9670000076293945 0.7300000190734863 0.0 1.7829999923706055 0.515999972820282
10.600000381469727 10.5 28.633399963378906 28.63089942932129 5.735099792480469 35.315101623535156 22.426000595092773 -0.45399999618530273 2.9839999675750732 0.7300000190734863 0.0 1.8990000486373901 0.49900001287460327
11.100000381469727 11.0 28.632299423217773 28.62969970703125 5.735099792480469 35.315101623535156 22.42639923095703 0.46299999952316284 2.930999994277954 0.7300000190734863 0.0 1.8509999513626099 0.5490000247955322
11.600000381469727 11.5 28.631799697875977 28.629100799560547 5.735000133514404 35.31520080566406 22.426599502563477 -3.8519999980926514 2.553999900817871 0.7300000190734863 0.0 1.8079999685287476 0.5830000042915344
12.100000381469727 12.0 28.632099151611328 28.629199981689453 5.735099792480469 35.315101623535156 22.42650032043457 -0.052000001072883606 2.9820001125335693 0.7300000190734863 0.0 1.8140000104904175 0.5320000052452087
12.600000381469727 12.5 28.63610076904297 28.633100509643555 5.735499858856201 35.31489944458008 22.425100326538086 -1.1679999828338623 2.7179999351501465 0.7300000190734863 0.0 1.8459999561309814 0.5929999947547913
13.100000381469727 13.0 28.64550018310547 28.64229965209961 5.736400127410889 35.31380081176758 22.421199798583984 -0.010999999940395355 1.7059999704360962 0.7300000190734863 0.0 1.809000015258789 1.2740000486373901
13.600000381469727 13.5 28.645700454711914 28.64240074157715 5.736299991607666 35.31340026855469 22.420900344848633 0.19499999284744263 1.3309999704360962 0.7300000190734863 0.0 1.8040000200271606 1.1690000295639038
14.100000381469727 14.0 28.64579963684082 28.64240074157715 5.736499786376953 35.31399917602539 22.421300888061523 0.3540000021457672 1.4199999570846558 0.7300000190734863 0.0 1.812000036239624 1.534999966621399

In total, there are 26 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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