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Dataset Title:  "NEGOM CTD - n5l05s01.nc - 30.37N, 86.58W - 1999-05-25" Subscribe RSS
Institution:  OCEAN.TAMU   (Dataset ID: negom_n5l05s01)
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   24 options
    salinity ?  =  PSU   22 options
    sigmat ?  =  kg m-3   24 options
    descentRate ?  =  meters per second   22 options
    transmission ?  =  percent   22 options
    par ?  =  umol m-2 s-1   25 options
    parv ?  =  volts   25 options
    fluorescence ?  =  volts   25 options
    backscattering ?  =  volts   22 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 22.76569938659668 22.765100479125977 5.031300067901611 34.659698486328125 23.742900848388672 0.2280000001192093 4.120999813079834 1070.0 2.634999990463257 1.2640000581741333 -9.0
3.5 3.5 22.76689910888672 22.766199111938477 5.031400203704834 34.659698486328125 23.742599487304688 0.3149999976158142 4.123000144958496 883.0999755859375 2.549999952316284 1.2769999504089355 -9.0
4.0 4.0 22.766199111938477 22.765399932861328 5.031300067901611 34.65950012207031 23.742599487304688 0.3070000112056732 4.123000144958496 788.7999877929688 2.502000093460083 1.3070000410079956 -9.0
4.5 4.5 22.764299392700195 22.763399124145508 5.031199932098389 34.659698486328125 23.74340057373047 0.28200000524520874 4.123000144958496 706.4000244140625 2.4570000171661377 1.3250000476837158 0.7850000262260437
5.0 5.0 22.763200759887695 22.76219940185547 5.031099796295166 34.659698486328125 23.74370002746582 0.23399999737739563 4.125 666.5 2.430999994277954 1.3489999771118164 0.7799999713897705
5.5 5.5 22.75909996032715 22.757999420166016 5.030799865722656 34.66080093383789 23.745800018310547 0.2840000092983246 4.122000217437744 579.0 2.36899995803833 1.3509999513626099 0.7860000133514404
6.099999904632568 6.0 22.760299682617188 22.75909996032715 5.030900001525879 34.660301208496094 23.745100021362305 0.3019999861717224 4.116000175476074 496.6000061035156 2.303999900817871 1.3519999980926514 0.7979999780654907
6.599999904632568 6.5 22.75200080871582 22.750699996948242 5.030399799346924 34.662899017333984 23.749500274658203 0.2630000114440918 4.11299991607666 451.3999938964844 2.263000011444092 1.3819999694824219 0.7990000247955322
7.099999904632568 7.0 22.745899200439453 22.74449920654297 5.030099868774414 34.66490173339844 23.75279998779297 0.24699999392032623 4.113999843597412 373.70001220703125 2.180999994277954 1.3960000276565552 0.7960000038146973
7.599999904632568 7.5 22.744199752807617 22.742700576782227 5.03000020980835 34.66559982299805 23.753799438476562 0.28200000524520874 4.117000102996826 321.1000061035156 2.115000009536743 1.3869999647140503 0.8009999990463257
8.100000381469727 8.0 22.740699768066406 22.739099502563477 5.029900074005127 34.66699981689453 23.75589942932129 0.2750000059604645 4.119999885559082 307.1000061035156 2.0959999561309814 1.3899999856948853 0.781000018119812
8.600000381469727 8.5 22.724300384521484 22.72260093688965 5.0289998054504395 34.673500061035156 23.765499114990234 0.2840000092983246 4.119999885559082 267.3999938964844 2.0360000133514404 1.3799999952316284 0.7820000052452087
9.100000381469727 9.0 22.71929931640625 22.717500686645508 5.028800010681152 34.67530059814453 23.768400192260742 0.25699999928474426 4.124000072479248 243.6999969482422 1.996000051498413 1.3839999437332153 0.7739999890327454
9.600000381469727 9.5 22.714500427246094 22.712600708007812 5.028500080108643 34.67679977416992 23.77090072631836 0.24699999392032623 4.129000186920166 217.10000610351562 1.9450000524520874 1.4049999713897705 0.7789999842643738
10.100000381469727 10.0 22.69099998474121 22.68899917602539 5.027400016784668 34.68619918823242 23.784799575805664 0.1850000023841858 4.136000156402588 193.6999969482422 1.8960000276565552 1.3990000486373901 0.7689999938011169
10.600000381469727 10.5 22.621299743652344 22.619199752807617 5.024199962615967 34.717201232910156 23.82819938659668 0.1889999955892563 4.138999938964844 172.10000610351562 1.8450000286102295 1.4019999504089355 0.7630000114440918
11.100000381469727 11.0 22.37529945373535 22.37310028076172 5.014500141143799 34.837501525878906 23.989299774169922 0.16099999845027924 4.165999889373779 157.1999969482422 1.8049999475479126 1.4259999990463257 0.7400000095367432
11.600000381469727 11.5 22.202600479125977 22.200300216674805 5.006800174713135 34.91590118408203 24.097700119018555 0.18199999630451202 4.202000141143799 141.0 1.7580000162124634 1.378999948501587 0.7519999742507935
12.100000381469727 12.0 22.126399993896484 22.124000549316406 5.0005998611450195 34.9281005859375 24.128400802612305 0.1679999977350235 4.224999904632568 128.3000030517578 1.7170000076293945 1.3370000123977661 0.6970000267028809
12.600000381469727 12.5 22.09040069580078 22.087900161743164 4.997000217437744 34.92900085449219 24.13920021057129 0.16599999368190765 4.232999801635742 117.9000015258789 1.6799999475479126 1.3530000448226929 0.6890000104904175
13.100000381469727 13.0 22.075700759887695 22.07309913635254 4.995699882507324 34.93009948730469 24.14419937133789 0.19900000095367432 4.234000205993652 109.0999984741211 1.6469999551773071 1.3589999675750732 0.6859999895095825
13.600000381469727 13.5 22.042200088500977 22.039499282836914 4.993500232696533 34.94020080566406 24.161300659179688 0.14399999380111694 4.230999946594238 102.30000305175781 1.61899995803833 1.371000051498413 0.6869999766349792
14.100000381469727 14.0 21.966299057006836 21.963499069213867 4.990699768066406 34.979400634765625 24.212400436401367 0.1509999930858612 4.203000068664551 94.20999908447266 1.5829999446868896 1.4320000410079956 0.6909999847412109
14.600000381469727 14.5 21.916400909423828 21.91349983215332 4.991799831390381 35.0276985168457 24.263099670410156 0.1599999964237213 4.1020002365112305 86.45999908447266 1.5460000038146973 1.6009999513626099 0.7289999723434448
15.100000381469727 15.0 21.895200729370117 21.892200469970703 4.99429988861084 35.06489944458008 24.297300338745117 0.1589999943971634 3.930999994277954 80.41999816894531 1.5149999856948853 1.7990000247955322 0.7979999780654907

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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