Timeline / Data.gov — Health Datasets
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A new raw object was archived. Both versions are preserved. 690 line(s) added, 791 line(s) removed.
Evidence
| Source | Data.gov — Health Datasets |
|---|---|
| Agency | Data.gov |
| URL | https://api.gsa.gov/technology/datagov/v4/search?q=health&sort=last_harvested_date&per_page=100&api_key=${DATAGOV_API_KEY} |
| Observed by | Civic Memory, directly, on 2026-09-27T06:29:57+00:00 |
| Content type | application/json |
| Current object |
e9518fa937136584a957d6d196bb3fd55e046176650c9579ead2398eaa26df2e
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metadata
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| Previous object |
8336187598a63d2d57ee707786b39b407111ac4ebcf4e4505596685a8f723504
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metadata
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What changed derived
This diff is not evidence. It was produced by
civic-memory.diff_engine 1.1.0 at
2026-09-27T06:29:57+00:00 by normalizing the two archived objects above. The
objects are authoritative; this reading of them can be regenerated or deleted
without loss. 690 line(s) added, 791 line(s) removed.
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We predicted that trees in warmer, lower latitude cities would be in poorer health at lower levels of urbanization than trees at cooler, higher latitudes due to the interaction of urbanization, latitudinal temperature, and herbivory. To evaluate our predictions, we surveyed the abundance of scale insect herbivores on a single, common tree species (Acer rubrum) in eight US cities spanning 10° of latitude. 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Integrated modeling approaches assessing the impact of changes in climate, land use, and water withdrawals on stream flows and the subsequent impact of changes in flow regime on aquatic biota at multiple spatial scales are necessary to insure an adequate supply of water for humans and healthy river ecosystems. This report inventories and then directly examines and compares a subset of hydrological models implemented in the Southeastern US that were used to estimate streamflow at a number of gaged basins across the region. This effort was designed to evaluate, quantify and compare the magnitude, and investigate the potential causes of error, associated with predicted streamflows from seven hydrologic models of varying complexity and calibration strategy. This was accomplished by computing and then comparing classical hydrologic model fit statistics (e.g., mean bias, coefficient of determination, root mean squared error, NSE), and understanding the bias in the prediction in these and a subset of ecologically relevant flow metrics (ERFM).This spreadsheet contains model fit statistics for the model comparison workshop across 195 USGS streamflow gauges in the southeast. 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This was accomplished by computing and then comparing classical hydrologic model fit statistics (e.g., mean bias, coefficient of determination, root mean squared error, NSE), and understanding the bias in the prediction in these and a subset of ecologically relevant flow metrics (ERFM).This spreadsheet contains model fit statistics for the model comparison workshop across 195 USGS streamflow gauges in the southeast. 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This inclusive understanding of health opens the door to management options that are more creative than traditional techniques to prevent or mitigate pathogens. The public health field uses a determinants of health framework to understand the physical, social, and cultural systems that impact health at the individual and community levels (National Academies of Sciences, Engineering, and Medicine 2016). Applying a similar framework to wildlife can help managers focus on tangible actions to positively impact wildlife health in the absence of disease (Wittrock et al. 2019). In the south-central United States, changes in water availability and quality resulting from changing temperature, precipitation, and land-use patterns can have significant impacts on the health of migratory birds that depend on wildlife refuges as seasonal habitat. These data were collected in partnership with several U.S. Fish and Wildlife Service National Wildlife Refuges across Oklahoma (n=2), New Mexico (n=3), and Louisiana (n=1, 8 individual refuges administratively grouped within the Southeast Louisiana Refuge Complex) to examine how managers define determinants of health for migratory birds and assess how those determinants may be impacted by local changes in water regimes. \nThis data release contains nineteen (19) related datafiles and their associated metadata. For each participating refuge there are three files: an image file of the final conceptual diagram, a .csv file containing information about the elements in the diagram, and a .csv file containing information about the connections in the diagram. The conceptual diagram of migratory bird health was constructed using information collected through interviews with refuge personnel and supplemented (to gain additional context, when necessary) with refuge management documents and information on their public-facing websites. The diagrams are provided as .jpg files exported from the free system mapping and visualization program Kumu, in which they were created. Each diagram consists of nodes (referred to as elements) and relationships (referred to as connections). The elements and connections represent elements of the refuge system that were highlighted by personnel as playing an important role in migratory bird health or water availability. Detailed, narrative descriptions for each system component are provided in their respective .csv files. There is also a .csv file containing data from a pre-interview survey sent to each refuge to gather basic information about the high-priority migratory bird guilds on their refuge, their management objectives in relation to migratory birds, and the water-related threats they consider highest concern.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.5066/P9CDTM9S", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.65539ebfd34ee4b6e05c3dec.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_65539ebfd34ee4b6e05c3dec", + "keyword": [ + "Atchafalaya National Wildlife Refuge", + "Bayou Sauvage National Wildlife Refuge", + "Bayou Teche National Wildlife Refuge", + "Big Branch Marsh National Wildlife Refuge", + "Bogue Chitto National Wildlife Refuge", + "Bosque del Apache National Wildlife Refuge", + "Breton National Wildlife Refuge", + "Cat Island National Wildlife Refuge", + "Delta National Wildlife Refuge", + "Mandalay National Wildlife Refuge", + "National Wildlife Refuge", + "Salt Plains National Wildlife Refuge", + "San Andres National Wildlife Refuge", + "Sevilleta National Wildlife Refuge", + "Tishomingo National Wildlife Refuge", + "USGS:65539ebfd34ee4b6e05c3dec", + "biota", + "birds", + "health and disease", + "inlandWaters", + "water resources" + ], + "modified": "2026-09-23T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-108.5449, 28.3044, -88.3740, 36.9148", + "theme": [ + "geospatial" + ], + "title": "Data Release for Assessing the Impact of Changing Water Resources on Migratory Bird Health and Management in the Central Flyway of the United States" + }, + "description": "There is a growing movement within natural resource management to view wildlife health as a cumulative outcome of many different factors, rather than simply the absence of disease. This inclusive understanding of health opens the door to management options that are more creative than traditional techniques to prevent or mitigate pathogens. The public health field uses a determinants of health framework to understand the physical, social, and cultural systems that impact health at the individual and community levels (National Academies of Sciences, Engineering, and Medicine 2016). Applying a similar framework to wildlife can help managers focus on tangible actions to positively impact wildlife health in the absence of disease (Wittrock et al. 2019). In the south-central United States, changes in water availability and quality resulting from changing temperature, precipitation, and land-use patterns can have significant impacts on the health of migratory birds that depend on wildlife refuges as seasonal habitat. These data were collected in partnership with several U.S. Fish and Wildlife Service National Wildlife Refuges across Oklahoma (n=2), New Mexico (n=3), and Louisiana (n=1, 8 individual refuges administratively grouped within the Southeast Louisiana Refuge Complex) to examine how managers define determinants of health for migratory birds and assess how those determinants may be impacted by local changes in water regimes. \nThis data release contains nineteen (19) related datafiles and their associated metadata. For each participating refuge there are three files: an image file of the final conceptual diagram, a .csv file containing information about the elements in the diagram, and a .csv file containing information about the connections in the diagram. The conceptual diagram of migratory bird health was constructed using information collected through interviews with refuge personnel and supplemented (to gain additional context, when necessary) with refuge management documents and information on their public-facing websites. The diagrams are provided as .jpg files exported from the free system mapping and visualization program Kumu, in which they were created. Each diagram consists of nodes (referred to as elements) and relationships (referred to as connections). The elements and connections represent elements of the refuge system that were highlighted by personnel as playing an important role in migratory bird health or water availability. Detailed, narrative descriptions for each system component are provided in their respective .csv files. There is also a .csv file containing data from a pre-interview survey sent to each refuge to gather basic information about the high-priority migratory bird guilds on their refuge, their management objectives in relation to migratory birds, and the water-related threats they consider highest concern.", + "distribution_titles": [ + "Digital Data", + "Original Metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/a68dde0d-25f3-4355-86d4-f189ea19e48b", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/a68dde0d-25f3-4355-86d4-f189ea19e48b/raw", + "has_download": true, + "has_spatial": true, + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_65539ebfd34ee4b6e05c3dec", + "keyword": [ + "Atchafalaya National Wildlife Refuge", + "Bayou Sauvage National Wildlife Refuge", + "Bayou Teche National Wildlife Refuge", + "Big Branch Marsh National Wildlife Refuge", + "Bogue Chitto National Wildlife Refuge", + "Bosque del Apache National Wildlife Refuge", + "Breton National Wildlife Refuge", + "Cat Island National Wildlife Refuge", + "Delta National Wildlife Refuge", + "Mandalay National Wildlife Refuge", + "National Wildlife Refuge", + "Salt Plains National Wildlife Refuge", + "San Andres National Wildlife Refuge", + "Sevilleta National Wildlife Refuge", + "Tishomingo National Wildlife Refuge", + "USGS:65539ebfd34ee4b6e05c3dec", + "biota", + "birds", + "health and disease", + "inlandWaters", + "water resources" + ], + "last_harvested_date": "2026-09-27T01:26:04.687178", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", + "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", + "name": "Department of the Interior", + "organization_type": "Federal Government", + "slug": "doi" + }, + "parent_identifier": null, + "popularity": 2, + "publisher": "U.S. Geological Survey", + "slug": "data-release-for-assessing-the-impact-of-changing-water-resources-on-migratory-bird-health", + "spatial_centroid": { + "lat": 31.748559999999998, + "lon": -100.47654 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -108.5449, + 28.3044 + ], + [ + -108.5449, + 36.9148 + ], + [ + -88.374, + 36.9148 + ], + [ + -88.374, + 28.3044 + ], + [ + -108.5449, + 28.3044 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "Data Release for Assessing the Impact of Changing Water Resources on Migratory Bird Health and Management in the Central Flyway of the United States", + "type": "dataset" + }, + { + "_score": 13.323019, + "_sort": [ + 1790469337021, + 13.323019, + 8, + "8cd021d3-2ddd-423b-a774-a007bfc76322" + ], + "dcat": { + "accessLevel": "public", + "bureauCode": [ + "010:12" + ], + "contactPoint": { + "@type": "vcard:Contact", + "fn": "Joe Heffron", + "hasEmail": "mailto:joseph.heffron@usda.gov" + }, + "description": "This repository contains data supporting the publication, \"Association of runoff risk with private well contamination in a fractured dolostone aquifer.\" In this study, a cohort of private wells in the Silurian dolomite aquifer of northeast Wisconsin, USA, was repeatedly sampled to determine an association between modeled runoff risk using the Runoff Risk Advisory Forecast (RRAF, v. 2.1; https://runoffriskadvisory.wi.gov/) and well water contamination. The dataset comprises water quality data for private well samples (nitrate concentration, total coliforms, and bovine microbial source tracking [MST] markers), modeled surface runoff risk for each well over the duration of the study, and well-specific risk factors related to well construction and location.", + "distribution": [ + { + "@type": "dcat:Distribution", + "accessURL": "https://doi.org/10.15482/USDA.ADC/31114333", + "description": "Landing page for access to the data", + "format": "XML", + "mediaType": "application/http", + "title": "Digital Data" + }, + { + "@type": "dcat:Distribution", + "description": "The metadata original format", + "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.dcde8656-023e-4381-b5a5-bae9a3aefdd1.xml", + "format": "XML", + "mediaType": "text/xml", + "title": "Original Metadata" + } + ], + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_dcde8656-023e-4381-b5a5-bae9a3aefdd1", + "keyword": [ + "USGS:dcde8656-023e-4381-b5a5-bae9a3aefdd1", + "environment", + "health", + "inlandWaters" + ], + "modified": "2026-08-02T00:00:00Z", + "publisher": { + "@type": "org:Organization", + "name": "U.S. Geological Survey" + }, + "spatial": "-88.4619, 43.4689, -87.4292, 45.0270", + "theme": [ + "geospatial" + ], + "title": "Data from: Association of runoff risk with private well contamination in a fractured dolostone aquifer" + }, + "description": "This repository contains data supporting the publication, \"Association of runoff risk with private well contamination in a fractured dolostone aquifer.\" In this study, a cohort of private wells in the Silurian dolomite aquifer of northeast Wisconsin, USA, was repeatedly sampled to determine an association between modeled runoff risk using the Runoff Risk Advisory Forecast (RRAF, v. 2.1; https://runoffriskadvisory.wi.gov/) and well water contamination. The dataset comprises water quality data for private well samples (nitrate concentration, total coliforms, and bovine microbial source tracking [MST] markers), modeled surface runoff risk for each well over the duration of the study, and well-specific risk factors related to well construction and location.", + "distribution_titles": [ + "Digital Data", + "Original Metadata" + ], + "harvest_record": "https://catalog.data.gov/harvest_record/638013b2-0595-4c8a-8303-65e5fc8d1c8c", + "harvest_record_raw": "https://catalog.data.gov/harvest_record/638013b2-0595-4c8a-8303-65e5fc8d1c8c/raw", + "has_download": true, + "has_spatial": true, + "identifier": "http://datainventory.doi.gov/id/dataset/USGS_dcde8656-023e-4381-b5a5-bae9a3aefdd1", + "keyword": [ + "USGS:dcde8656-023e-4381-b5a5-bae9a3aefdd1", + "environment", + "health", + "inlandWaters" + ], + "last_harvested_date": "2026-09-27T00:35:37.021880", + "organization": { + "aliases": [ + "dept" + ], + "code_repo_exempt": false, + "code_repo_url": null, + "description": null, + "id": "143529f7-2eef-4a07-b227-93ac9e84fad8", + "logo": "https://raw.githubusercontent.com/GSA/logo/master/doi.png", + "name": "Department of the Interior", + "organization_type": "Federal Government", + "slug": "doi" + }, + "parent_identifier": null, + "popularity": 8, + "publisher": "U.S. Geological Survey", + "slug": "data-from-association-of-runoff-risk-with-private-well-contamination-in-a-fractured-dolost", + "spatial_centroid": { + "lat": 44.09214, + "lon": -88.04882 + }, + "spatial_shape": { + "coordinates": [ + [ + [ + -88.4619, + 43.4689 + ], + [ + -88.4619, + 45.027 + ], + [ + -87.4292, + 45.027 + ], + [ + -87.4292, + 43.4689 + ], + [ + -88.4619, + 43.4689 + ] + ] + ], + "type": "Polygon" + }, + "theme": [ + "geospatial" + ], + "title": "Data from: Association of runoff risk with private well contamination in a fractured dolostone aquifer", + "type": "dataset" + }, + { + "_score": 11.90819, "_sort": [ 1790468011762, - 11.909242, + 11.90819, 0, "854ed57c-c130-4d62-bc97-73a0394f197a" ], @@ -144,10 +814,10 @@ "type": "dataset" }, { - "_score": 7.436758, + "_score": 7.417881, "_sort": [ 1790467960290, - 7.436758, + 7.417881, 2, "7f483797-31c7-4306-9f86-513c8b4fc723" ], @@ -509,10 +1179,10 @@ "type": "dataset" }, { - "_score": 13.14366, + "_score": 13.138929, "_sort": [ 1790467928081, - 13.14366, + 13.138929, 3, "14161756-4efe-4e27-8b6f-f7f1fab02865" ], @@ -720,10 +1390,10 @@ "type": "dataset" }, { - "_score": 12.021953, + "_score": 12.002105, "_sort": [ 1790467927213, - 12.021953, + 12.002105, 1, "05efb358-9bb3-427b-8418-5a364200d2e9" ], @@ -1068,10 +1738,10 @@ "type": "dataset" }, { - "_score": 2.1930828, + "_score": 2.1908073, "_sort": [ 1790467925140, - 2.1930828, + 2.1908073, 11, "35b8a0a3-e688-4171-91f2-3b80daa9b6eb" ], @@ -2069,10 +2739,10 @@ "type": "dataset" }, { - "_score": 0.75564766, + "_score": 0.7562084, "_sort": [ 1790467915030, - 0.75564766, + 0.7562084, 7, "adf16a74-2c83-45e5-92d1-1bc3a24e332d" ], @@ -3154,10 +3824,10 @@ "type": "dataset" }, { - "_score": 1.317028, + "_score": 1.3161373, "_sort": [ 1790467913687, - 1.317028, + 1.3161373, 10, "10c075fb-66b6-4ac7-b871-f7e36cab8b8f" ], @@ -4213,10 +4883,10 @@ "type": "dataset" }, { - "_score": 12.705593, + "_score": 12.684143, "_sort": [ 1790467807836, - 12.705593, + 12.684143, 40, "259f1516-86e3-46a2-9131-eac155e40b2f" ], @@ -5820,10 +6490,10 @@ "type": "dataset" }, { - "_score": 7.3919907, + "_score": 7.3548336, "_sort": [ 1790467786460, - 7.3919907, + 7.3548336, 13, "a31eacee-17c9-4ca2-8ff6-1d81941d84bf" ], @@ -6271,775 +6941,4 @@ ], "title": "Comprehensive Dataset and Reports of the Mamala Bay Study during 1993-1995 on Oahu, Hawaii" }, - "description": "This collection contains comprehensive datasets and reports collected in support of the Mamala Bay Study during 1993-1995. Physical, chemical, biological, and geological datasets and reports are included.\n\nReports included in this collection include the following:\n\nMB-1 Mamala Bay Study Management Mamala Bay Study Commission: R. R. Colwell, G. T. Orlob, and J. R. Schubel, Commissioners.\nMB-2 Mamala Bay Database: W. J. Kimmerer, Garcia and Associates (formerly with BioSystems, Inc.), Tiburon, CA.\nMB-3 Pollutant Source Identification: M. Stevenson, and J. M. O'Connor, Kinnetic Laboratories, Inc., Lahaina, HI.\nMB-4 Plume Modeling: P. J. W. Roberts, Georgia Institute of Technology, Atlanta, GA.\nMB-5 Modeling Transport and Fate of Pathogenic Organisms in Mamala Bay: A. F. Blumberg and J. P. Connolly, HydroQual, Inc., Mahwah, NJ.\nMB-6 Ocean Current Measurements: P. Hamilton, J. Singer and E. Waddell, Science Application International Corporation, Raleigh, NC.\nMB-7 Characterization of the Microbiological Quality of Water in Mamala Bay: R. S. Fujioka, University of Hawaii, Honolulu, HI.\nMB-7 Overall Impact of Sand Island Outfall on the Incidence of Pathogens in Mamala Bay: C. P. Gerba and I. L. Pepper, University of Arizona, Tucson, AZ.\nMB-7 Risk of Swimming-Acquired Illnesses in Mamala Bay: C. P. Gerba, University of Arizona, Tucson, AZ; C. N. Haas, Drexel University, Philadelphia, PA; J. B. Rose, University of South Florida, St. Petersburg, FL.\nMB-7 Molecular Investigation of the Effects of Pollution on Pathogenic and Indigenous Bacteria in Mamala Bay: R. T. Hill, University of Maryland, Baltimore, MD.\nMB-7 Molecular Detection of Staphylococcus aureus in Waters of Mamala Bay, Hawaii: R. T. Hill, University of Maryland, Baltimore, MD.\nMB-7 Microbiological Aspects of Point and Non-Point Source Pollution in Mamala Bay: M. R. Landry, University of Hawaii, Honolulu, Hawaii; F. C. Dobbs, Old Dominion University, Norfolk, VA.\nMB-7 Coliphage and Indigenous Phage in Mamala Bay: J. H. Paul and J. B. Rose, University of South Florida, St. Petersburg, FL.\nMB-7 Viability of cryptosporidium parvum in Marine Waters: J. B. Rose, University of South Florida, St. Petersburg, FL.\nMB-9 Definition of Indicator Species for Pollution Monitoring in Mamala Bay, Oahu, Hawaii: J. H. Bailey-Brock, University of Hawaii, Honolulu, HI.\nMB-9 Impact of Point and Non-Point Source Pollution on Coral Reef Ecosystems in Mamala Bay: R. W. Grigg, University of Hawaii, Honolulu, HI.\nMB-9 Shallow Marine Community Response to Point and Non-Point Sources of Pollution in Mamala Bay, Oahu, Part A: Fish and Coral Communities: E. A. Kay, J. H. Bailey-Brock, R. E. Brock, University of Hawaii, Honolulu, HI.\nMB-9 Shallow Marine Community Response to Point and Non-Point Sources of Pollution in Mamala Bay, Oahu, Part B: Micromolluscan Assemblages and Algal Biomass: E. A. Kay, J. H. Bailey-Brock, R. E. Brock, University of Hawaii, Honolulu, HI.\nMB-9 Effects of Sewage Discharges and Stream Runoff on Phytoplankton Communities and Water Quality in Mamala Bay: E. A. Laws and D. Ziemann, University of Hawaii, Honolulu, HI.\nMB-9 Temporal Variability in Macrobenthic Community Structure and the Effect of Freshwater Runoff: S. A. McCarthy, E. A. Kay and J. H. Bailey-Brock, University of Hawaii, Honolulu, HI.\nMB-9 Recruitment Patterns of Marine Benthic Invertebrates in Mamala Bay: A Process-Oriented Measure of Ecosystem Response to Pollution: C. R. Smith and P. E. Parnell, University of Hawaii, Honolulu, HI.\nMB-10 Part I: Management Alternatives and Management Measures for Waste Discharges to the Mamala Bay Ecosystem: K. Courtney, PRC Environmental Management, Inc., Honolulu, HI; J. M. O'Connor, Kinnetic Laboratories, Inc., Santa Cruz, CA.\nMB-10 Part II: Identification of Stressors of Concern in the Mamala Bay Ecosystem: J. M. O'Connor, Kinnetic Laboratories, Inc., Santa Cruz, CA.\nMB-10 Part III: Effects of Effluent from the Barbers Point and Sand Island Outfalls on the Mamala Bay Ecosystem: W. J. Kimmerer, Romberg Tiburon Center, Tiburon, CA; J. M. O'Connor, Kinnetic Laboratories, Inc., Santa Cruz, CA.\nMB-10 Part IV: Proposed Monitoring Plan to Assess the Efficacy of Waste Treatment Alternatives Applied in the Mamala Bay Watershed: J. M. O'Connor, Kinnetic Laboratories, Inc., Santa Cruz, CA.\nMB-10 Infectious Disease Public Health Risk Assessment: A. W. Oliveri and R. C. Cooper, Eisenberg, Oliveri, and Associates, Oakland, CA.\nMB-11 Water Quality Management in Mamala Bay: Mamala Bay Study Commissioners and project PIs.\nMB-11A Wastewater Management Strategies in an Integrated Coastal Management Plan for Mamala Bay: D. R. F. Harleman, Consulting Engineers, Cambridge, MA.\nMB-SP2 Plume Dynamics and Dispersion in Mamala Bay, Hawaii: B. H. Jones and T. D. Dickey, University of Southern California, Los Angeles, CA.", - "distribution_titles": [ - "NCEI Dataset Landing Page", - "Browse granules", - "Search for granules", - "https://www.wrrc.hawaii.edu/publications/project-reports/", - "https://www.ncei.noaa.gov/archive/accession/0189890", - "GCMD Keyword Forum Page", - "NCEI Contact Information" - ], - "harvest_record": "https://catalog.data.gov/harvest_record/c6934292-31cf-40e6-b6fe-8b148b2c857b", - "harvest_record_raw": "https://catalog.data.gov/harvest_record/c6934292-31cf-40e6-b6fe-8b148b2c857b/raw", - "harvest_record_transformed": "https://catalog.data.gov/harvest_record/c6934292-31cf-40e6-b6fe-8b148b2c857b/transformed", - "has_download": false, - "has_spatial": true, - "identifier": "https://data.noaa.gov/waf/NOAA/NESDIS/ncei/accessions/iso/xml/Mamala_Bay_Study_1993-1995.xml", - "keyword": [ - "0000030", - "0000624", - "0001172", - "0001188", - "9600024", - "9600041", - "9800089", - "9800147", - "9800149", - "9800151", - "9800152", - "9800169", - "9800170", - "9800174", - "9900064", - "9900112", - "9900120", - "9900121", - "9900151", - "AIR TEMPERATURE - DRY BULB", - "AIR TEMPERATURE - WET BULB", - "AMMONIA (NH3)", - "AMMONIUM (NH4)", - "ANIMALS - INDIVIDUAL", - "ANIMALS - INDIVIDUAL - COUNTS", - "bacteria", - "BACTERIA - BACTERIAL DENSITY", - "BAROMETRIC PRESSURE", - "BEACH TAR - WATERLINE TO SAMPLE DISTANCE", - "BENTHIC COMMUNITIES", - "BENTHIC SPECIES", - "BENTHIC SPECIES - TAXA COUNTS", - "BENTHIC SPECIES - TAXA IDENTITIES", - "BENTHIC SPECIES - TAXA WET WEIGHTS", - "biological data", - "BIOMASS", - "CAS (CHEMICAL ABSTRACTS SERVICE) PARAMETER CODES", - "CHEMISTRY - SEDIMENT", - "CHLOROPHYLL A", - "CHLOROPHYLL B", - "CHLOROPHYLL C", - "cloud amount/frequency", - "cloud type", - "CONDUCTIVITY", - "CORAL", - "CORE - BOTTOM", - "CRUSTACEANS", - "CURRENT METER - EAST-WEST COMPONENT (U)", - "CURRENT METER - NORTH-SOUTH COMPONENT (V)", - "CURRENT METER - SPEED", - "CURRENT SPEED", - "CURRENT SPEED - UP/DOWN COMPONENT (W)", - "DEPTH - OBSERVATION", - "DEPTH - SENSOR", - "DISSOLVED ORGANIC CARBON", - "DISSOLVED ORGANIC MATTER", - "DISSOLVED OXYGEN", - "EFFLUENT - DISCHARGE DESCRIPTION", - "EFFLUENT - DISCHARGE IDENTIFIER", - "EFFLUENT - DISCHARGE POINT", - "EFFLUENT - DISCHARGE PRIMARY INDUSTRY", - "EFFLUENT - DISCHARGE QUANTITY", - "EFFLUENT - ZONE OF INITIAL DILUTION", - "FISH SPECIES", - "FLUORESCENCE", - "HABITAT - BENTHIC", - "HABITAT - SEDIMENT SIZE", - "HYDROCARBONS - TOTAL RESOLVED", - "HYDROSTATIC PRESSURE", - "INVERTEBRATE SPECIES", - "LIGHT ATTENUATION", - "LIGHT TRANSMISSION", - "MACROFAUNA", - "MICROBIOLOGY", - "MOLLUSKS", - "NITRATE", - "nitrate + nitrite content (concentration)", - "NITRITE", - "NITROGEN", - "NITROGEN - PARTICULATE", - "NUTRIENTS", - "Ocean pollution and marine debris", - "OPTICAL PROPERTIES", - "OXYGEN", - "PARTICULATE ORGANIC CARBON", - "PARTICULATE ORGANIC NITROGEN (PON)", - "pH", - "PHAEOPHYTIN", - "PHAEOPIGMENTS", - "phosphate", - "PHOSPHATE - TOTAL FILTER PASSING", - "PHOTOSYNTHETIC ACTIVE RADIATION (PAR)", - "PLANKTON", - "POLYCHLORINATED BIPHENYLS", - "PRIMARY PRODUCTIVITY", - "PRIMARY PRODUCTIVITY - PHYTOPLANKTON", - "SALINITY", - "SEA STATE", - "Secchi depth", - "SEDIMENT - GRAIN SIZE", - "SEDIMENT PROPERTIES", - "SEDIMENTS", - "SEDIMENTS - DRY WEIGHT", - "SEDIMENTS - PARTICLE SIZE FRACTIONS", - "SIGMA-T", - "silicate", - "SOUND VELOCITY", - "SPECIES IDENTIFICATION", - "SPECIES IDENTIFICATION - COUNT", - "SPECIES IDENTIFICATION - LIFE STAGE", - "SPECIES IDENTIFICATION - SEX", - "SPECIES IDENTIFICATION - WET WEIGHT", - "suspended solids", - "TOTAL PHOSPHATE", - "TOTAL PHOSPHORUS", - "TOXIN", - "TRANSMISSIVITY", - "turbidity", - "UPWELLING IRRADIANCE", - "VERTEBRATES", - "VISIBILITY", - "WATER DENSITY", - "water depth", - "WATER TEMPERATURE", - "WEATHER", - "WIND DIRECTION", - "WIND SPEED", - "ZOOPLANKTON ABUNDANCE", - "ZOOPLANKTON BIOMASS", - "ZOOPLANKTON IDENTITIES", - "ZOOPLANKTON SPECIES IDENTITIES", - "ZOOPLANKTON SPECIES NUMBER PER SAMPLE", - "ADCP", - "bathythermograph - BT", - "bottle", - "conductivity sensor", - "corer", - "CTD", - "current meter", - "fluorometer", - "laboratory analysis", - "mass spectrometer", - "meteorological sensor", - "mooring", - "multi-parameter water quality sensor", - "net - zooplankton net", - "pH sensor", - "photograph", - "pressure gauge", - "pump sampler", - "salinometer", - "Secchi disk", - "sediment sampler - corer", - "sediment sampler - grab", - "sediment sieve", - "swimmer/diver", - "thermistor", - "thermograph", - "thermometer", - "tide gauge", - "transmissometer", - "trap - sediment", - "visual analysis", - "visual observation", - "benthic", - "biological", - "chemical", - "continuous", - "current measurements", - "laboratory analyses", - "optical", - "physical", - "profile", - "sediment analysis", - "survey", - "survey - biological", - "survey - coral reef", - "survey - swimmer/diver", - "time series", - "FIXED PLATFORM OF UNITED STATES", - "RV Nathaniel B. Palmer", - "SWIMMER/DIVER", - "UNKNOWN PLATFORMS OF UNITED STATES", - "Chesapeake Biological Laboratory", - "Hawaii State Department of Health", - "Oceanic Institute of Hawai'i Pacific University", - "Oceanit Laboratories, Inc.", - "Old Dominion University", - "Pacific International Center for High Technology Research", - "Science Applications International Corporation - Raleigh, NC", - "The University of Arizona", - "University of Hawaiʻi at Mānoa", - "University of South Florida", - "Chesapeake Biological Laboratory", - "Hawaii State Department of Health", - "Kinnetic Laboratories, Inc.", - "Oceanic Institute of Hawai'i Pacific University", - "Oceanit Laboratories, Inc.", - "Pacific International Center for High Technology Research", - "Science Applications International Corporation - Raleigh, NC", - "The University of Arizona", - "University of Hawaiʻi at Mānoa", - "COASTAL", - "Coral Reef Information System (CoRIS)", - "CORAL REEF STUDIES", - "Mamala Bay Study", - "Coastal Waters of Hawaii", - "Hawaiian Islands Humpback Whale National Marine Sanctuary", - "North Pacific Ocean", - "Northeast Pacific Ocean (limit-180)", - "oceanography", - "DOC/NOAA/NESDIS/NODC > National Oceanographic Data Center, NESDIS, NOAA, U.S. Department of Commerce", - "HI/DOH > State of Hawaii Department of Health", - "SAIC > Science Applications International Corporation", - "EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC PRESSURE", - "EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE", - "EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND DIRECTION", - "EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS > SURFACE WINDS > WIND SPEED", - "EARTH SCIENCE > ATMOSPHERE > CLOUDS > CLOUD PROPERTIES > CLOUD FREQUENCY", - "EARTH SCIENCE > ATMOSPHERE > CLOUDS > CLOUD TYPES", - "EARTH SCIENCE > BIOLOGICAL CLASSIFICATION", - "EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES", - "EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES > ARTHROPODS > CRUSTACEANS", - 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