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On February 9, 2022 at 1:57:52 PM CST, Casey Clair:
Added resource CANWIN Project Metadata - 2019 Churchill UAV Data to Churchill Beluga Boat Drone Imagery
              
    
          
          
        
        
            f 1 { f 1 {
            2   "Creator": "Creator", 2   "Creator": "Creator",
            3   "Date": "2019-08-09", 3   "Date": "2019-08-09",
            4   "IdentifierType": "DOI", 4   "IdentifierType": "DOI",
            5   "PublicationYear": "2022", 5   "PublicationYear": "2022",
            6   "Publisher": "CanWIN", 6   "Publisher": "CanWIN",
            7   "RelatedIdentifierType": "URL", 7   "RelatedIdentifierType": "URL",
            8   "RelationType": "IsSupplementTo", 8   "RelationType": "IsSupplementTo",
            9   "ResourceType": "imagery", 9   "ResourceType": "imagery",
            10   "Rights": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0  10   "Rights": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 
            11 International", 11 International",
            12   "Version": "1.0", 12   "Version": "1.0",
            13   "accessTerms": "CanWIN datasets are licensed individually, however  13   "accessTerms": "CanWIN datasets are licensed individually, however 
            14 most are licensed under the Creative Commons Attribution 4.0  14 most are licensed under the Creative Commons Attribution 4.0 
            15 International (CC BY 4.0) Public License. Details for the licence  15 International (CC BY 4.0) Public License. Details for the licence 
            16 applied can be found using the Licence URL link provided with each  16 applied can be found using the Licence URL link provided with each 
            17 dataset. \r\nBy using data and information provided on this site you  17 dataset. \r\nBy using data and information provided on this site you 
            18 accept the terms and conditions of the License. Unless otherwise  18 accept the terms and conditions of the License. Unless otherwise 
            19 specified, the license grants the rights to the public to use and  19 specified, the license grants the rights to the public to use and 
            20 share the data and results derived therefrom as long as the proper  20 share the data and results derived therefrom as long as the proper 
            21 acknowledgment is given to the data licensor (citation), that any  21 acknowledgment is given to the data licensor (citation), that any 
            22 alteration to the data is clearly indicated, and that a link to the  22 alteration to the data is clearly indicated, and that a link to the 
            23 original data and the license is made available.", 23 original data and the license is made available.",
            24   "activityCollectionType": "", 24   "activityCollectionType": "",
            25   "analyticalMethodName": "", 25   "analyticalMethodName": "",
            26   "author": null, 26   "author": null,
            27   "author_email": null, 27   "author_email": null,
            28   "campaignEndDate": "2019-08-09", 28   "campaignEndDate": "2019-08-09",
            29   "campaignStartDate": "2019-07-28", 29   "campaignStartDate": "2019-07-28",
            30   "comments": "", 30   "comments": "",
            31   "contributorName": "Chan, Wayne", 31   "contributorName": "Chan, Wayne",
            32   "contributorType": "DataCurator", 32   "contributorType": "DataCurator",
            33   "contributors": [ 33   "contributors": [
            34     { 34     {
            35       "affiliation": "Centre for Earth Observation Science -  35       "affiliation": "Centre for Earth Observation Science - 
            36 University of Manitoba", 36 University of Manitoba",
            37       "contributorName": "Harasyn, Madison", 37       "contributorName": "Harasyn, Madison",
            38       "contributorType": "DataCollector", 38       "contributorType": "DataCollector",
            39       "email": "Madison.harasyn@umanitoba.ca", 39       "email": "Madison.harasyn@umanitoba.ca",
            40       "nameIdentifier": "https://orcid.org/0000-0002-5741-6766", 40       "nameIdentifier": "https://orcid.org/0000-0002-5741-6766",
            41       "nameIdentifierScheme": "ORCID", 41       "nameIdentifierScheme": "ORCID",
            42       "schemeURI": "http://orcid.org/" 42       "schemeURI": "http://orcid.org/"
            43     } 43     }
            44   ], 44   ],
            45   "creatorName": [ 45   "creatorName": [
            46     { 46     {
            47       "author": "Barber, David", 47       "author": "Barber, David",
            48       "creatorAffiliation": "Centre for Earth Observation Science -  48       "creatorAffiliation": "Centre for Earth Observation Science - 
            49 University of Manitoba", 49 University of Manitoba",
            50       "creatorEmail": "david.barber@umanitoba.ca", 50       "creatorEmail": "david.barber@umanitoba.ca",
            51       "creatorNameIdentifier": "", 51       "creatorNameIdentifier": "",
            52       "nameType": "Personal" 52       "nameType": "Personal"
            53     } 53     }
            54   ], 54   ],
            55   "creator_user_id": "cde7b848-a882-4fc7-97c9-670417bd6b43", 55   "creator_user_id": "cde7b848-a882-4fc7-97c9-670417bd6b43",
            56   "dataCuratorAffiliation": "Centre for Earth Observation Science -  56   "dataCuratorAffiliation": "Centre for Earth Observation Science - 
            57 University of Manitoba", 57 University of Manitoba",
            58   "dataCuratorEmail": "wayne.chan@umanitoba.ca", 58   "dataCuratorEmail": "wayne.chan@umanitoba.ca",
            59   "datasetCitation": "Harasyn, Madison; Barber, David, May 2021,  59   "datasetCitation": "Harasyn, Madison; Barber, David, May 2021, 
            60 \u201c2019 Churchill UAV Data\u201d, (N/A)", 60 \u201c2019 Churchill UAV Data\u201d, (N/A)",
            61   "datasetLevel": "0", 61   "datasetLevel": "0",
            62   "datasetPublisher": "CanWIN", 62   "datasetPublisher": "CanWIN",
            63   "dateType": "Updated", 63   "dateType": "Updated",
            64   "descriptionType": "Abstract", 64   "descriptionType": "Abstract",
            65   "eastBoundLongitude": "-94.0706395086", 65   "eastBoundLongitude": "-94.0706395086",
            66   "embargoDate": "", 66   "embargoDate": "",
            67   "endDate": "2019-08-09", 67   "endDate": "2019-08-09",
            68   "endDateType": "Other", 68   "endDateType": "Other",
            69   "frequency": "Not planned", 69   "frequency": "Not planned",
            70   "funderIdentifierType": "", 70   "funderIdentifierType": "",
            71   "funderName": "", 71   "funderName": "",
            72   "funderSchemeURI": "", 72   "funderSchemeURI": "",
            73   "groups": [ 73   "groups": [
            74     { 74     {
            75       "description": "Inland water features, drainage systems and  75       "description": "Inland water features, drainage systems and 
            76 their characteristics. Examples of data you can find here include  76 their characteristics. Examples of data you can find here include 
            77 river and lake data, water quality data. \r\n\r\nIn CEOS, related  77 river and lake data, water quality data. \r\n\r\nIn CEOS, related 
            78 research themes include biogeochemistry, Inland lakes and waters,  78 research themes include biogeochemistry, Inland lakes and waters, 
            79 modelling, remote sensing and technology, trace metals and  79 modelling, remote sensing and technology, trace metals and 
            80 contaminants.", 80 contaminants.",
            81       "display_name": "Freshwater", 81       "display_name": "Freshwater",
            82       "id": "8f8cd877-b037-4b1a-b928-f86d9e093741", 82       "id": "8f8cd877-b037-4b1a-b928-f86d9e093741",
            83       "image_display_url":  83       "image_display_url": 
            84 /data/uploads/group/2021-10-31-211937.658599hyinspirehydrography.svg", 84 /data/uploads/group/2021-10-31-211937.658599hyinspirehydrography.svg",
            85       "name": "freshwater", 85       "name": "freshwater",
            86       "title": "Freshwater" 86       "title": "Freshwater"
            87     }, 87     },
            88     { 88     {
            89       "description": "Features and characteristics of salt water  89       "description": "Features and characteristics of salt water 
            90 bodies.\r\n\r\nIn CEOS, related research themes include  90 bodies.\r\n\r\nIn CEOS, related research themes include 
            91 biogeochemistry, modelling, marine mammals, oil spill response,  91 biogeochemistry, modelling, marine mammals, oil spill response, 
            92 physical oceanography, remote sensing and technology and trace metals  92 physical oceanography, remote sensing and technology and trace metals 
            93 and contaminants", 93 and contaminants",
            94       "display_name": "Marine", 94       "display_name": "Marine",
            95       "id": "98238b1c-5be8-41ad-8c6e-74cdc4f5f369", 95       "id": "98238b1c-5be8-41ad-8c6e-74cdc4f5f369",
            96       "image_display_url":  96       "image_display_url": 
            97 ata/uploads/group/2021-10-31-211516.365746ofinspireoceanographic.svg", 97 ata/uploads/group/2021-10-31-211516.365746ofinspireoceanographic.svg",
            98       "name": "marine", 98       "name": "marine",
            99       "title": "Marine" 99       "title": "Marine"
            100     } 100     }
            101   ], 101   ],
            102   "id": "b5f259b4-3ace-4750-bfb0-47c4e794082f", 102   "id": "b5f259b4-3ace-4750-bfb0-47c4e794082f",
            103   "isopen": false, 103   "isopen": false,
            104   "keywords": "Churchill estuary,UAV,aerial  104   "keywords": "Churchill estuary,UAV,aerial 
            105 photos,beluga,boat,kayak,video", 105 photos,beluga,boat,kayak,video",
            106   "kvSchemeURI": "", 106   "kvSchemeURI": "",
            107   "laboratory": "", 107   "laboratory": "",
            108   "licenceShemeURI": "https://spdx.org/licenses", 108   "licenceShemeURI": "https://spdx.org/licenses",
            109   "licenceType": "Open", 109   "licenceType": "Open",
            110   "license_id": null, 110   "license_id": null,
            111   "license_title": null, 111   "license_title": null,
            112   "maintainer": null, 112   "maintainer": null,
            113   "maintainer_email": null, 113   "maintainer_email": null,
            114   "metadata_created": "2022-02-04T19:17:36.940301", 114   "metadata_created": "2022-02-04T19:17:36.940301",
            n 115   "metadata_modified": "2022-02-09T19:46:27.800455", n 115   "metadata_modified": "2022-02-09T19:57:51.735773",
            116   "methodCitation": "", 116   "methodCitation": "",
            117   "methodDescrioption": "", 117   "methodDescrioption": "",
            118   "methodDescriptionType": "Methods", 118   "methodDescriptionType": "Methods",
            119   "methodLink": "", 119   "methodLink": "",
            120   "methodSummary": "", 120   "methodSummary": "",
            121   "methodUrl": "", 121   "methodUrl": "",
            122   "name": "churchill-beluga-boat-drone-imagery", 122   "name": "churchill-beluga-boat-drone-imagery",
            123   "northBoundLatitude": "58.8195795207", 123   "northBoundLatitude": "58.8195795207",
            124   "notes": "Aerial imagery surveys are commonly used in marine mammal  124   "notes": "Aerial imagery surveys are commonly used in marine mammal 
            125 research to determine population size, habitat distribution and  125 research to determine population size, habitat distribution and 
            126 habitat use. Analysis of aerial photos involves hours of manually  126 habitat use. Analysis of aerial photos involves hours of manually 
            127 identifying individuals present in each image and converting raw  127 identifying individuals present in each image and converting raw 
            128 counts into useable biological statistics. Our research proposes the  128 counts into useable biological statistics. Our research proposes the 
            129 use of deep learning algorithms as an assistive technology to increase  129 use of deep learning algorithms as an assistive technology to increase 
            130 the efficiency of the marine mammal research workflow. To test the  130 the efficiency of the marine mammal research workflow. To test the 
            131 feasibility of this proposal, the existing YOLOv4 convolutional neural  131 feasibility of this proposal, the existing YOLOv4 convolutional neural 
            132 network model was trained to detect belugas, kayaks and motorized  132 network model was trained to detect belugas, kayaks and motorized 
            133 boats in unmanned aerial vehicle (UAV) imagery. Computer-based object  133 boats in unmanned aerial vehicle (UAV) imagery. Computer-based object 
            134 detection achieved an average precision of 61.17% for belugas, 98.58%  134 detection achieved an average precision of 61.17% for belugas, 98.58% 
            135 for boats, and 95.97% for kayaks. We then tested the performance of  135 for boats, and 95.97% for kayaks. We then tested the performance of 
            136 computer vision tracking of belugas and manned watercraft in UAV  136 computer vision tracking of belugas and manned watercraft in UAV 
            137 videos using the DeepSORT tracking algorithm, achieving a multiple  137 videos using the DeepSORT tracking algorithm, achieving a multiple 
            138 object tracking accuracy (MOTA) ranging from 37% \u2013 88% and  138 object tracking accuracy (MOTA) ranging from 37% \u2013 88% and 
            139 multiple object tracking precision (MOTP) between 63% \u2013 86%.  139 multiple object tracking precision (MOTP) between 63% \u2013 86%. 
            140 Results from this research indicate that deep learning technology can  140 Results from this research indicate that deep learning technology can 
            141 perform at a similar caliber as human annotators in beluga and  141 perform at a similar caliber as human annotators in beluga and 
            142 watercraft detection and tracking, allowing for larger datasets to be  142 watercraft detection and tracking, allowing for larger datasets to be 
            143 processed within a fraction of the time. ", 143 processed within a fraction of the time. ",
            n 144   "num_resources": 15, n 144   "num_resources": 16,
            145   "num_tags": 7, 145   "num_tags": 7,
            146   "organization": { 146   "organization": {
            147     "approval_status": "approved", 147     "approval_status": "approved",
            148     "created": "2017-07-21T13:15:49.935872", 148     "created": "2017-07-21T13:15:49.935872",
            149     "description": "The Centre for Earth Observation Science (CEOS)  149     "description": "The Centre for Earth Observation Science (CEOS) 
            150 was established in 1994 with a mandate to research, preserve and  150 was established in 1994 with a mandate to research, preserve and 
            151 communicate knowledge of Earth system processes using the technologies  151 communicate knowledge of Earth system processes using the technologies 
            152 of Earth Observation Science. Research is multidisciplinary and  152 of Earth Observation Science. Research is multidisciplinary and 
            153 collaborative seeking to understand the complex interrelationships  153 collaborative seeking to understand the complex interrelationships 
            154 between elements of Earth systems, and how these systems will likely  154 between elements of Earth systems, and how these systems will likely 
            155 respond to climate change. Although researchers have worked in many  155 respond to climate change. Although researchers have worked in many 
            156 regions, the Arctic marine system has always been a unifying focus of  156 regions, the Arctic marine system has always been a unifying focus of 
            157 activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate  157 activity.\r\n\r\nIn 2012, CEOS, along with the Greenland Climate 
            158 Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre  158 Research Centre (GCRC, Nuuk, Greenland) and the Arctic Research Centre 
            159 (ARC, Aarhus, Denmark) established the Arctic Science Partnership,  159 (ARC, Aarhus, Denmark) established the Arctic Science Partnership, 
            160 thereby integrating academic and research initiatives.\r\n\r\nAreas of  160 thereby integrating academic and research initiatives.\r\n\r\nAreas of 
            161 existing research activity are divided among key themes:\r\n\r\nArctic  161 existing research activity are divided among key themes:\r\n\r\nArctic 
            162 Anthropology/Paleoclimatology: LiDAR scanning and digital site  162 Anthropology/Paleoclimatology: LiDAR scanning and digital site 
            163 preservation, archaeo-geophysics, permafrost degredation, lithic  163 preservation, archaeo-geophysics, permafrost degredation, lithic 
            164 morphometrics, zooarchaeology, proxy studies, paleodistribution of sea  164 morphometrics, zooarchaeology, proxy studies, paleodistribution of sea 
            165 ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture,  165 ice, landscape learning, Paleo-Eskimo culture, Thule Inuit culture, 
            166 ethnographic analogy, traditional knowledge, climate change and  166 ethnographic analogy, traditional knowledge, climate change and 
            167 northern heritage resource management.\r\n\r\nAtmospheric  167 northern heritage resource management.\r\n\r\nAtmospheric 
            168 Studies/Meteorology: Boundary layer, precipitation, clouds, storms and  168 Studies/Meteorology: Boundary layer, precipitation, clouds, storms and 
            169 extreme weather, circulation, eddy correlations, polar vortex,  169 extreme weather, circulation, eddy correlations, polar vortex, 
            170 climate, teleconnections, geophysical fluid dynamics, flux and energy  170 climate, teleconnections, geophysical fluid dynamics, flux and energy 
            171 budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice  171 budgets, ocean-sea ice-atmosphere interface, radiative transfer, ice 
            172 albedo feedback, cloud radiative forcing, pCO2.  172 albedo feedback, cloud radiative forcing, pCO2. 
            173 \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles,  173 \r\n\r\nBiogeochemistry: Organic carbon, greenhouse gases, bubbles, 
            174 Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace  174 Ikaite, carbonate chemistry, CO2 fluxes, mercury and other trace 
            175 metals, minerals, hydrocarbons, brine processes, otolith  175 metals, minerals, hydrocarbons, brine processes, otolith 
            176 microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury,  176 microchemistry, sediments, biomarkers. \r\n\r\nContaminants: Mercury, 
            177 trace metals, PAHs, source, transport, transformation, pathways,  177 trace metals, PAHs, source, transport, transformation, pathways, 
            178 bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth  178 bioaccumulations, marine ecosystems, marine chemistry. \r\nEarth 
            179 Observation Science: Active and passive microwave, LiDAR, EM  179 Observation Science: Active and passive microwave, LiDAR, EM 
            180 induction, spatial-temporal analysis, forward and inverse scattering  180 induction, spatial-temporal analysis, forward and inverse scattering 
            181 models, complex permittivity, ocean colour, ocean surface roughness,  181 models, complex permittivity, ocean colour, ocean surface roughness, 
            182 NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology:  182 NIR, TIR, satellite telemetry, GPS. Ice-Associated Biology: 
            183 Biophysical processes, primary production; ice algae, ice  183 Biophysical processes, primary production; ice algae, ice 
            184 microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland  184 microbiology, bio-optics, under-ice phytoplankton. \r\n\r\nInland 
            185 Lakes and Waters: Hydrologic connectivity, watershed systems, sediment  185 Lakes and Waters: Hydrologic connectivity, watershed systems, sediment 
            186 transport, nutrient transport, contaminants, landscape processes,  186 transport, nutrient transport, contaminants, landscape processes, 
            187 remote sensing, freshwater-marine coupling. Marine Mammals: Seals,  187 remote sensing, freshwater-marine coupling. Marine Mammals: Seals, 
            188 whales, habitat, conservation, satellite telemetry, distribution,  188 whales, habitat, conservation, satellite telemetry, distribution, 
            189 population studies, prey behaviour, bioacoustics.\r\n\r\nModelling:  189 population studies, prey behaviour, bioacoustics.\r\n\r\nModelling: 
            190 Simulation of sea ice and oceanic regional processes, Nucleus for  190 Simulation of sea ice and oceanic regional processes, Nucleus for 
            191 European Modelling of the Ocean (NEMO), ice-ocean modelling and  191 European Modelling of the Ocean (NEMO), ice-ocean modelling and 
            192 interactions, hind cast simulations and projections for sea ice state  192 interactions, hind cast simulations and projections for sea ice state 
            193 and ocean variables based on CMIP5 scenarios and MIROC5 forcing,  193 and ocean variables based on CMIP5 scenarios and MIROC5 forcing, 
            194 validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and  194 validation.\r\n\r\nOceanography: Circulation, temperature, in-flow and 
            195 out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy  195 out-flow shelves, water dynamics, microturbulence, Beaufort Gyre, eddy 
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            593       "description": "Drone footage of beluga whales and boats in the  593       "description": "Drone footage of beluga whales and boats in the 
            594 Churchill Estuary as part of the Churchill Beluga Boat Study.", 594 Churchill Estuary as part of the Churchill Beluga Boat Study.",
            595       "format": "MP4", 595       "format": "MP4",
            596       "hash": "", 596       "hash": "",
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            598       "last_modified": null, 598       "last_modified": null,
            n 599       "metadata_modified": "2022-02-09T19:46:27.805552", n 599       "metadata_modified": "2022-02-09T19:57:51.743935",
            600       "mimetype": "video/mp4", 600       "mimetype": "video/mp4",
            601       "mimetype_inner": null, 601       "mimetype_inner": null,
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            603       "package_id": "b5f259b4-3ace-4750-bfb0-47c4e794082f", 603       "package_id": "b5f259b4-3ace-4750-bfb0-47c4e794082f",
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            609       "url":  609       "url": 
            610 .ad.umanitoba.ca/erddap/files/Drone_Data_588f_21fe_8ff6/beluga11.mp4", 610 .ad.umanitoba.ca/erddap/files/Drone_Data_588f_21fe_8ff6/beluga11.mp4",
            611       "url_type": null 611       "url_type": null
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            618       "datastore_contains_all_records_of_source_file": false,
            619       "description": "CANWIN metadata templates are filled out for 
            620 instruments, deployments, datasets, and projects. This template is for 
            621 UAV (unmanned aerial vehicle) videos and photos collected during the 
            622 2019 field season over the Churchill Estuary.",
            623       "format": "DOCX",
            624       "hash": "",
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            638 /download/canwin_project_metadata_template2019churchill_harasyn.docx",
            639       "url_type": "upload"
            612     } 640     }
            613   ], 641   ],
            614   "rightsIdentifier": "CC-BY-NC-SA-4.0", 642   "rightsIdentifier": "CC-BY-NC-SA-4.0",
            615   "rightsIdentifierScheme": "SPDX", 643   "rightsIdentifierScheme": "SPDX",
            616   "rightsURI": "https://spdx.org/licenses/CC-BY-NC-SA-4.0.html", 644   "rightsURI": "https://spdx.org/licenses/CC-BY-NC-SA-4.0.html",
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            618   "spatial":  646   "spatial": 
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            620   "spatial_regions": "churchill", 648   "spatial_regions": "churchill",
            621   "startDate": "2019-07-28", 649   "startDate": "2019-07-28",
            622   "startDateType": "Collected", 650   "startDateType": "Collected",
            623   "state": "active", 651   "state": "active",
            624   "status": "Complete", 652   "status": "Complete",
            625   "subjectScheme": "", 653   "subjectScheme": "",
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            632       "vocabulary_id": null 660       "vocabulary_id": null
            633     }, 661     },
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            639       "vocabulary_id": null 667       "vocabulary_id": null
            640     }, 668     },
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            646       "vocabulary_id": null 674       "vocabulary_id": null
            647     }, 675     },
            648     { 676     {
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            653       "vocabulary_id": null 681       "vocabulary_id": null
            654     }, 682     },
            655     { 683     {
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            660       "vocabulary_id": null 688       "vocabulary_id": null
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            662     { 690     {
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            667       "vocabulary_id": null 695       "vocabulary_id": null
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            680   ], 708   ],
            681   "title": "Churchill Beluga Boat Drone Imagery", 709   "title": "Churchill Beluga Boat Drone Imagery",
            682   "titleType": "Alternative Title", 710   "titleType": "Alternative Title",
            683   "type": "dataset", 711   "type": "dataset",
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            686 anwin-data-statement/resource/5b942a87-ef4e-466e-8319-f588844e89c0).", 714 anwin-data-statement/resource/5b942a87-ef4e-466e-8319-f588844e89c0).",
            687   "variablesMeasured": "", 715   "variablesMeasured": "",
            688   "version": null, 716   "version": null,
            689   "westBoundLongitude": "-94.3257662171" 717   "westBoundLongitude": "-94.3257662171"
            690 } 718 }