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Dataset Title:  "NEGOM CTD - n1l09s01.nc - 29.5N, 83.6W - 1997-11-20" Subscribe RSS
Institution:  OCEAN.TAMU   (Dataset ID: negom_n1l09s01)
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   15 options
    potentialTemperature ?  =  degree_C   16 options
    conductivity ?  =  Siemens per meter   14 options
    salinity ?  =  PSU   12 options
    sigmat ?  =  kg m-3   15 options
    descentRate ?  =  meters per second   13 options
    transmission ?  =  percent   11 options
    par ?  =  umol m-2 s-1   16 options
    parv ?  =  volts   16 options
    fluorescence ?  =  volts   16 options
    backscattering ?  =  volts   13 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
3.0 3.0 17.01219940185547 17.011699676513672 4.408299922943115 34.27799987792969 24.957300186157227 0.1599999964237213 4.089000225067139 1185.0 3.2200000286102295 1.38100004196167 0.2070000022649765
3.5 3.5 17.01300048828125 17.012399673461914 4.408400058746338 34.27790069580078 24.95709991455078 0.1860000044107437 4.086999893188477 1040.0 3.1679999828338623 1.4010000228881836 0.19499999284744263
4.0 4.0 17.013999938964844 17.0132999420166 4.4085001945495605 34.27790069580078 24.956899642944336 0.19200000166893005 4.085999965667725 899.7999877929688 3.1029999256134033 1.3990000486373901 0.18199999630451202
4.5 4.5 17.016399383544922 17.01569938659668 4.40880012512207 34.277801513671875 24.956199645996094 0.16300000250339508 4.085999965667725 805.2999877929688 3.053999900817871 1.4160000085830688 0.18299999833106995
5.0 5.0 17.019500732421875 17.018699645996094 4.40910005569458 34.27750015258789 24.955299377441406 0.19099999964237213 4.090000152587891 720.2999877929688 3.00600004196167 1.4299999475479126 0.18299999833106995
5.5 5.5 17.01889991760254 17.01799964904785 4.40910005569458 34.277801513671875 24.955699920654297 0.1599999964237213 4.085000038146973 615.4000244140625 2.938999891281128 1.440999984741211 0.19099999964237213
6.0 6.0 17.01420021057129 17.013200759887695 4.408599853515625 34.27790069580078 24.956899642944336 0.19599999487400055 4.0920000076293945 531.4000244140625 2.878000020980835 1.4579999446868896 0.1809999942779541
6.5 6.5 17.01300048828125 17.011899948120117 4.408599853515625 34.27790069580078 24.957199096679688 0.164000004529953 4.090000152587891 484.3999938964844 2.8369998931884766 1.4769999980926514 0.19200000166893005
7.0 7.0 16.999099731445312 16.997900009155273 4.407700061798096 34.282100677490234 24.96380043029785 0.19099999964237213 4.085999965667725 438.5 2.7939999103546143 1.5180000066757202 0.20100000500679016
7.599999904632568 7.5 16.98740005493164 16.9862003326416 4.407299995422363 34.288299560546875 24.97130012512207 0.16699999570846558 4.083000183105469 367.3999938964844 2.7170000076293945 1.5269999504089355 0.19499999284744263
8.100000381469727 8.0 16.980899810791016 16.97960090637207 4.408100128173828 34.30080032348633 24.982500076293945 0.18799999356269836 4.085000038146973 344.29998779296875 2.688999891281128 1.5420000553131104 0.1889999955892563
8.600000381469727 8.5 16.98550033569336 16.984100341796875 4.410699844360352 34.31949996948242 24.99570083618164 0.15800000727176666 4.085999965667725 304.79998779296875 2.635999917984009 1.5670000314712524 0.19300000369548798
9.100000381469727 9.0 17.00830078125 17.006799697875977 4.417300224304199 34.357398986816406 25.019399642944336 0.20399999618530273 4.0970001220703125 287.29998779296875 2.609999895095825 1.565999984741211 0.19699999690055847
9.600000381469727 9.5 17.078899383544922 17.077299118041992 4.431399822235107 34.41910171508789 25.050100326538086 0.15700000524520874 4.126999855041504 242.89999389648438 2.5369999408721924 1.531999945640564 0.1889999955892563
10.100000381469727 10.0 17.21489906311035 17.213199615478516 4.456900119781494 34.524200439453125 25.09830093383789 0.17499999701976776 4.15500020980835 230.39999389648438 2.5139999389648438 1.4780000448226929 0.18000000715255737
10.600000381469727 10.5 17.301700592041016 17.29990005493164 4.473199844360352 34.591400146484375 25.129100799560547 0.19599999487400055 4.168000221252441 180.89999389648438 2.4149999618530273 1.4869999885559082 0.18400000035762787

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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