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Dataset Title:  NOAA-Navy Sanctuary Soundscape Monitoring Project, Fin Whale Sound Producion,
Gray's Reef, SanctSound_GR01_01_finwhale_1d
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Institution:  NOAA   (Dataset ID: noaaSanctSound_GR01_01_finwhale_1d)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  start_time {
    Float64 actual_range 1.5446592e+9, 1.5566688e+9;
    String axis "T";
    String comment "Start time of detections. Corresponding end time for detection in end_time_var at same index value as start_time_var.";
    String ioos_category "Time";
    String long_name "Start Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00.000Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  finwhale_presence {
    String cell_methods "time: sum (comment: presence (1) or absence (0) over time interval)";
    String comment "Presence of Fin Whale (0 = not present; 1 = present)";
    String ioos_category "Statistics";
    String long_name "fin whale presence";
    String standard_name "finwhale_presence";
    String units "boolean";
  }
  end_time {
    Float64 actual_range 1.5447456e+12, 1.5567552e+12;
    String axis "T";
    String comment "End time of detections. Corresponding start time for detection in start_time_var at same index value as end_time_var.";
    String ioos_category "Time";
    String long_name "End Time";
    String standard_name "end_time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  NC_GLOBAL {
    String abstract "This record represents fin whale sound production detected from raw passive acoustic data. The Low Frequency Detection and Classification System (LFDCS) call library for fin whale 20-Hz pulses was built for the data sampled at 120 Hz. All fin whale detections with a Mahalanobis distance of 3.0 or less were manually verified for true detections. A logistic regression was applied to these results to facilitate reducing the size of the dataset that ultimately needed to be manually verified for confident species detection. This analysis revealed that a minimum number of 29 detections per time window used (hour or day) need to be detected to ensure that a fin whale was truly detected with a confidence of 90%. All days with at least 29 detections were manually verified for daily presence of fin whale 20-Hz pulses. From days with 29 or more detections, fin whales were considered present for that day if a true detection was found within a regular inter-pulse interval pattern of at least three other 20-Hz pulses. These data were recorded at SanctSound Site GR01_01 between December 13, 2018 and May 01, 2019.";
    String acknowledgement "This project received funding from the U.S. Navy.";
    String cdm_data_type "TimeSeries";
    String citation "Cite as: NOAA Office of National Marine Sanctuaries and U.S Navy. 2021. Fin Whale Sound Production Recorded at SanctSound Site GR01_01, SanctSound Data Products. NOAA National Centers for Environmental Information. Accessed [date]. DOI: https://doi.org/http://doi.org/10.25921/57z4-ph29";
    String comment "Data quality: Quality data were recorded for the duration of the deployment.";
    String contributor_name "Simone Baumann-Pickering, Scripps Institution of Oceanography; Leila Hatch, NOAA Stellwagen Bank National Marine Sanctuary; John Joseph, U.S. Naval Postgraduate School; Anke Kuegler, Hawai'i Institute of Marine Biology, University of Hawai'i at Manoa; Marc Lammers, NOAA Hawaiian Islands Humpback Whale National Marine Sanctuary; Tetyana Margolina, U.S. Naval Postgraduate School; Karlina Merkens, NOAA Pacific Islands Fisheries Science Center; Lindsey Peavey Reeves, NOAA Channel Islands National Marine Sanctuary; Timothy Rowell, NOAA Northeast Fisheries Science Center; Jenni Stanley, Woods Hole Oceanographic Institution; Alison Stimpert, Moss Landing Marine Laboratories; Sofie Van Parijs, NOAA Northeast Fisheries Science Center; Eden Zang,NOAA Hawaiian Islands Humpback Whale National Marine Sanctuary";
    String contributor_role "Principal Investigator";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "ncei.info@noaa.gov";
    String creator_name "NOAA NCEI";
    String creator_url "https://www.ncei.noaa.gov/";
    String date_created "2022-08-22";
    String date_issued "2022-08-22";
    String featureType "TimeSeries";
    String geospatial_bounds "POINT (31.396417 -80.8904)";
    String history 
"All acoustic data were processed using the Low Frequency Detection and Classification System (LFDCS; Baumgartner and Mussoline, 2011), which creates conditioned spectrograms using a short-time Fourier transform with a data frame of 512 samples and 75% overlap (80% overlap for the 120 Hz decimated data (blue and fin whales)), resulting in a time step of 64 ms and frequency resolution of 3.9 Hz (for 120 Hz data: 853 ms time step and 0.23 Hz frequency resolution). After tracing contour lines, or “pitch tracks”, through tonal sounds, the program uses multivariate discriminant function analysis to classify the pitch tracks into species-specific call types based on a call library. Each detection is assigned a Mahalanobis distance (MD), which measures the deviation of a sound’s pitch track from the assigned call type (see Baumgartner and Mussoline (2011) for a more complete description). A lower MD indicates a closer match to the assigned call type.  For a well-developed call type in the LFDCS (i.e., the seven attributes used in the discriminant function analysis are multivariate normal), 75% of pitch-tracks for the call type will have a MD of 3.0 or less (Baumgartner et al., 2013). Setting a MD threshold is necessary to minimize the false detection rates, but in doing so causes some true detections to be missed in the analysis. The MD threshold of 3.0 was chosen for all vocalizations detected and classified in the humpback, sei, and fin whale call library.  However, for blue whales, false detection rates were lower than any of the other species, thus a MD of 5.0 was chosen to decrease the probability of missing true detections. All LFDCS detections were manually reviewed by trained acoustic analysts to determine daily presence of each of the four baleen whale species.  A true detection was defined as a pitch track that correctly classified a call or song unit to the species that produced it (Bonnell et al., 2016).  Given the variability of each species' call type, the specific methodology to determine daily acoustic presence was different for each species.  The LFDCS call library for fin whale 20-Hz pulses was built for the data sampled at 120 Hz. All fin whale detections with a MD of 3.0 or less were manually verified for true detections. A logistic regression was applied to these results to facilitate reducing the size of the dataset that ultimately needed to be manually verified for confident species detection. This analysis revealed that a minimum number of 29 detections per time window used (hour or day) need to be detected to ensure that a fin whale was truly detected with a confidence of 90%. All days with at least 29 detections were manually verified for daily presence of fin whale 20-Hz pulses. From days with 29 or more detections, fin whales were considered present for that day if a true detection was found within a regular inter-pulse interval pattern of at least three other 20-Hz pulses.  Data were processed with LFDCS
2024-03-28T12:54:10Z (local files)
2024-03-28T12:54:10Z http://coastwatch.pfeg.noaa.gov/griddap/noaaSanctSound_GR01_01_finwhale_1d.das";
    String id "http://doi.org/10.25921/57z4-ph29";
    String infoUrl "https://ncei.noaa.gov";
    String institution "NOAA";
    String instrument "SoundTrap ST500";
    String keywords "acoustic attenuation/transmission, acoustics, ambient noise, aquatic ecosystems, cetacean, environmental, fish, frequency, intensity, marine environment monitoring, marine habitat, national centers for environmental information, Navy, NOAA, ocean acoustics, oceans, office of national marine sanctuaries, passive acoustic recorder, pressure, sound_intensity_level_in_water, soundscapes";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "The data may be used and redistributed for free but are not intended for legal use, since it may contain inaccuracies. Neither the data creator, NOAA, nor the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information.";
    String naming_authority "NOAA-Navy";
    String project "NOAA-Navy Sanctuary Soundscape Monitoring Project";
    String publisher_email "erd.data@noaa.gov";
    String publisher_name "NOAA NMFS SWFSC ERD";
    String publisher_type "institution";
    String publisher_url "https://www.pfeg.noaa.gov";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "NOAA and the U.S. Navy are working to better understand underwater sound within the U.S. National Marine Sanctuary System. From 2018 to 2021, these agencies will work with numerous scientific partners to study sound within seven national marine sanctuaries and one marine national monument, which includes waters off Hawai'i and the east and west coasts. Standardized measurements will assess sounds produced by marine animals, physical processes (e.g., wind and waves), and human activities. Collectively, this information will help NOAA and the Navy measure sound levels and baseline acoustic conditions in sanctuaries. This work is a continuation of ongoing Navy and NOAA research, including efforts by NOAA's Office of National Marine Sanctuaries This dataset represents the derived products from the raw acoustic data that are archived at NOAA National Centers for Environmental Information.";
    String title "NOAA-Navy Sanctuary Soundscape Monitoring Project, Fin Whale Sound Producion, Gray's Reef, SanctSound_GR01_01_finwhale_1d";
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.


 
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