Subset

Dataset Title:	PacIOOS Nearshore Sensor 012: Kalama Beach Park, Maui, Hawaii
Institution:	Pacific Islands Ocean Observing System (PacIOOS) (Dataset ID: nss_012)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Make a graph

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Select a subset: (Current number of distinct combinations of matching data: 15)
Make as many selections as you want, in any order. Each selection changes the other options (and the map and data below) accordingly.

temperature_dm_qd	= 1	2 options
salinity_dm_qd	= 1	2 options
chlorophyll_dm_qd	= 1	2 options
turbidity_dm_qd	= 1	2 options
pressure_dm_qd	= 1	2 options
sensor_depth_dm_qd	= 1	2 options
ph_dm_qd	= 1	2 options
oxygen_dm_qd	= 1	2 options
oxygen_saturation_dm_qd	= 1	2 options
station_name	=	2 options
platform1	=	2 options
instrument1	=	2 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)

temperature_dm_qd	salinity_dm_qd	chlorophyll_dm_qd	turbidity_dm_qd	pressure_dm_qd	sensor_depth_dm_qd	ph_dm_qd	oxygen_dm_qd	oxygen_saturation_dm_qd	station_name	platform1	instrument1
1	1	1	1	1	1	1	1	1
-9	-9	-9	-9	-9	-9	-9	0	-9	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	-9	-9	-9	-9	-9	-9	-9	-9	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	-9	-9	-9	0	-9	-9	-9	-9	nss_012_002	nss_012_002	Sea-Bird Scientific SBE 16plus V2 SeaCAT CTD coupled with a Sea-Bird Scientific ECO FLNTU optical sensor
0	0	-9	-9	-9	-9	-9	-9	-9	nss_012_002	nss_012_002	Sea-Bird Scientific SBE 16plus V2 SeaCAT CTD coupled with a Sea-Bird Scientific ECO FLNTU optical sensor
0	0	-9	-9	-9	0	-9	-9	-9	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	-9	-9	-9	0	-9	0	0	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	-9	-9	-9	0	0	-9	-9	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	-9	-9	-9	0	0	0	0	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	-9	-9	0	-9	-9	-9	-9	nss_012_002	nss_012_002	Sea-Bird Scientific SBE 16plus V2 SeaCAT CTD coupled with a Sea-Bird Scientific ECO FLNTU optical sensor
0	0	-9	0	-9	0	0	0	0	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	-9	0	0	-9	-9	-9	-9	nss_012_002	nss_012_002	Sea-Bird Scientific SBE 16plus V2 SeaCAT CTD coupled with a Sea-Bird Scientific ECO FLNTU optical sensor
0	0	0	-9	-9	0	0	0	0	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	0	-9	0	-9	-9	-9	-9	nss_012_002	nss_012_002	Sea-Bird Scientific SBE 16plus V2 SeaCAT CTD coupled with a Sea-Bird Scientific ECO FLNTU optical sensor
0	0	0	0	-9	0	0	0	0	nss_012_001	nss_012_001	YSI 6600 V2-4 Multiparameter Water Quality Sonde
0	0	0	0	0	-9	-9	-9	-9	nss_012_002	nss_012_002	Sea-Bird Scientific SBE 16plus V2 SeaCAT CTD coupled with a Sea-Bird Scientific ECO FLNTU optical sensor

In total, there are 15 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.

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.