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Dataset Title:  "NEGOM CTD - n2l01s01.nc - 29.06N, 89.03W - 1998-05-14" Subscribe RSS
Institution:  OCEAN.TAMU   (Dataset ID: negom_n2l01s01)
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: 25)
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   25 options
    depth ?  =  m   25 options
    temperature ?  =  degree_C   25 options
    potentialTemperature ?  =  degree_C   25 options
    conductivity ?  =  Siemens per meter   22 options
    salinity ?  =  PSU   25 options
    sigmat ?  =  kg m-3   25 options
    descentRate ?  =  meters per second   23 options
    transmission ?  =  percent   25 options
    par ?  =  umol m-2 s-1   23 options
    parv ?  =  volts   23 options
    fluorescence ?  =  volts   25 options
    backscattering ?  =  volts   25 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 26.022899627685547 26.02239990234375 3.7237000465393066 23.050800323486328 14.039899826049805 -0.1120000034570694 0.8460000157356262 580.7999877929688 2.934999942779541 2.618000030517578 3.51200008392334
3.0 3.0 25.948299407958984 25.94770050048828 3.746799945831299 23.24690055847168 14.209199905395508 0.13600000739097595 0.8730000257492065 413.6000061035156 2.760999917984009 2.621000051498413 3.309999942779541
3.5 3.5 25.837400436401367 25.836599349975586 3.7757999897003174 23.502700805664062 14.433899879455566 0.25699999928474426 0.8880000114440918 234.3000030517578 2.509999990463257 2.624000072479248 3.265000104904175
4.0 4.0 25.177099227905273 25.176300048828125 3.9502999782562256 25.068599700927734 15.803999900817871 0.2800000011920929 0.8090000152587891 125.80000305175781 2.249000072479248 2.61299991607666 2.947999954223633
4.5 4.5 22.60460090637207 22.603700637817383 4.558899879455566 31.148799896240234 21.127199172973633 0.2759999930858612 1.5449999570846558 77.66999816894531 2.0360000133514404 2.4210000038146973 2.63100004196167
5.0 5.0 22.044700622558594 22.043699264526367 4.660799980163574 32.34120178222656 22.186899185180664 0.20000000298023224 2.124000072479248 42.11000061035156 1.7799999713897705 2.2090001106262207 1.909000039100647
5.5 5.5 21.484699249267578 21.483600616455078 4.762700080871582 33.56639862060547 23.271400451660156 0.2879999876022339 2.4790000915527344 25.93000030517578 1.5640000104904175 2.0280001163482666 1.9329999685287476
6.0 6.0 21.002199172973633 21.000999450683594 4.830599784851074 34.4900016784668 24.10540008544922 0.2409999966621399 2.736999988555908 16.59000015258789 1.3680000305175781 1.8760000467300415 1.8229999542236328
6.5 6.5 20.98349952697754 20.9822998046875 4.842400074005127 34.5994987487793 24.193899154663086 0.24500000476837158 2.8559999465942383 11.600000381469727 1.2139999866485596 1.8229999542236328 1.8450000286102295
7.0 7.0 20.96489906311035 20.963600158691406 4.8541998863220215 34.70920181274414 24.282400131225586 0.21199999749660492 2.75 7.960000038146973 1.0549999475479126 1.6339999437332153 1.7960000038146973
7.599999904632568 7.5 20.770299911499023 20.76889991760254 4.93120002746582 35.48970031738281 24.92959976196289 0.2930000126361847 2.936000108718872 7.079999923706055 1.0 1.4270000457763672 1.621000051498413
8.100000381469727 8.0 20.79439926147461 20.79290008544922 4.933199882507324 35.485198974609375 24.919700622558594 0.22200000286102295 3.055999994277954 5.010000228881836 0.8519999980926514 1.2960000038146973 1.4459999799728394
8.600000381469727 8.5 20.752700805664062 20.751100540161133 4.940899848937988 35.58250045776367 25.00510025024414 0.23600000143051147 2.9230000972747803 4.019999980926514 0.7570000290870667 1.24399995803833 1.3329999446868896
9.100000381469727 9.0 20.65880012512207 20.657100677490234 4.943299770355225 35.68080139160156 25.10540008544922 0.2460000067949295 2.7009999752044678 3.440000057220459 0.6899999976158142 1.2309999465942383 1.6299999952316284
9.600000381469727 9.5 20.642799377441406 20.641000747680664 4.941500186920166 35.67900085449219 25.108400344848633 0.17100000381469727 2.6459999084472656 2.7300000190734863 0.5889999866485596 1.2109999656677246 1.8109999895095825
10.100000381469727 10.0 20.626800537109375 20.624900817871094 4.939599990844727 35.677101135253906 25.111299514770508 0.15199999511241913 2.6029999256134033 2.369999885559082 0.5260000228881836 1.2120000123977661 1.8680000305175781
10.600000381469727 10.5 20.524099349975586 20.5221004486084 4.940899848937988 35.77399826049805 25.212900161743164 0.20399999618530273 2.572999954223633 1.7899999618530273 0.4059999883174896 1.2059999704360962 1.9240000247955322
11.100000381469727 11.0 20.476200103759766 20.47410011291504 4.940800189971924 35.81330108642578 25.255800247192383 0.18299999833106995 2.559999942779541 1.5199999809265137 0.33500000834465027 1.2079999446868896 1.9850000143051147
11.600000381469727 11.5 20.44890022277832 20.446699142456055 4.940299987792969 35.832401275634766 25.277700424194336 0.15700000524520874 2.5420000553131104 1.2699999809265137 0.2590000033378601 1.215999960899353 2.003999948501587
12.100000381469727 12.0 20.419300079345703 20.41699981689453 4.939799785614014 35.85300064086914 25.30139923095703 0.16899999976158142 2.4690001010894775 1.0800000429153442 0.18299999833106995 1.218000054359436 2.062000036239624
12.600000381469727 12.5 20.423599243164062 20.421199798583984 4.939799785614014 35.84939956665039 25.297500610351562 0.15299999713897705 2.4509999752044678 0.9100000262260437 0.11100000143051147 1.225000023841858 2.1470000743865967
13.100000381469727 13.0 20.409700393676758 20.40719985961914 4.939599990844727 35.85940170288086 25.308900833129883 0.21199999749660492 2.436000108718872 0.7200000286102295 0.014000000432133675 1.2139999866485596 2.2880001068115234
13.600000381469727 13.5 20.405399322509766 20.402799606323242 4.939499855041504 35.861900329589844 25.312000274658203 0.18299999833106995 2.4089999198913574 0.699999988079071 0.0 1.2410000562667847 2.24399995803833
14.100000381469727 14.0 20.40570068359375 20.403099060058594 4.939300060272217 35.859798431396484 25.310300827026367 0.12700000405311584 2.3529999256134033 0.699999988079071 0.0 1.2549999952316284 2.378999948501587
14.600000381469727 14.5 20.40130043029785 20.39859962463379 4.938799858093262 35.85960006713867 25.311399459838867 -0.12099999934434891 2.2079999446868896 0.699999988079071 0.0 1.305999994277954 3.2330000400543213

In total, there are 25 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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