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Dataset Title:  Hakai Institute British Columbia Central Coast Fixed Platform Acoustic Doppler
Current Profiler Time Series Provisional
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Institution:  Hakai Institute   (Dataset ID: HakaiADCPTimeSeriesProfileProvisional)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background | Data Access Form | Files | Make a graph

Select a subset:      (Current number of distinct combinations of matching data: 12)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

    filename ?  =  12 options
    instrument_serial_number ?  =  2 options
    instrument_model ?  =  2 options
    station ?  =  8 options
    latitude ?  =  degrees_north   12 options
    longitude ?  =  degrees_east   12 options

View:      Map of Distinct Data ?      Distinct Data Counts ?     Distinct Data ?      Related Data Counts ?     Related Data ?

 
Map of Distinct Data ?   (Refine the map and/or download the image)
Optional: Click on the map to select the closest data.
Map of Distinct 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)  

filename instrument_serial_number instrument_model station latitude longitude
degrees_north degrees_east
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-Starfish_20200709.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) Starfish 51.68261666666667 -128.12121666666667
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-Starfish_20200712.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) Starfish 51.68265 -128.12126666666666
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-Surf Pass_20200703-20200703.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) Surf Pass 51.6648 -128.14386666666667
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-Triquet_20200707.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) Triquet 51.804116666666665 -128.2548
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-Triquetta_20200705.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) Triquetta 51.79875 -128.23761666666667
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-West Beach_20200704.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) West Beach 51.64875 -128.15118333333334
Hakai_RDI-ADCP-Workhorse-SN18834_Calvert-Womanley_20200706.adcp.L1 WH18834 RDI WH Long Ranger ADCP 1200kHz (WH18834) Womanley 51.803 -128.19856666666666
Hakai_RDI-ADCP-Workhorse-SN24621_Calvert-KC6M_20190514-20190719.adcp.L1 WH24621 RDI WH Long Ranger ADCP 300kHz (WH24621) KC6M 51.65498490867293 -128.08130689237439
Hakai_RDI-ADCP-Workhorse-SN24621_Calvert-KC6M_20190721-20190930.adcp.L1 WH24621 RDI WH Long Ranger ADCP 300kHz (WH24621) KC6M 51.654467847589764 -128.0810750334744
Hakai_RDI-ADCP-Workhorse-SN24621_Calvert-KC6M_20191005-20200208.adcp.L1 WH24621 RDI WH Long Ranger ADCP 300kHz (WH24621) KC6M 51.65457237760079 -128.0811795243457
Hakai_RDI-ADCP-Workhorse-SN24621_Calvert-KC6M_20200212-20200828.adcp.L1 WH24621 RDI WH Long Ranger ADCP 300kHz (WH24621) KC6M 51.65450184493861 -128.0792607721594
Hakai_RDI-ADCP-Workhorse-SN24621_Quadra-QuadraADCP_20190222-20190408.adcp.L1 WH24621 RDI WH Long Ranger ADCP 300kHz (WH24621) QuadraADCP 50.11795 -125.21538333333334

In total, there are 12 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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