CivicMemory

Timeline / Data.gov — Health Datasets

changed Source changed

A new raw object was archived. Both versions are preserved. 2301 line(s) added, 2191 line(s) removed.

Evidence

SourceData.gov — Health Datasets
AgencyData.gov
URLhttps://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 2026-10-04T00:30:01+00:00
Content typeapplication/json
Current object 15311b50c426c129e717936fb542ddbef29596d3751a27726546a02c7fd5417f download raw metadata
Previous object b315295a38ece3461e762941f5122d7b82cecef86ce9d39b053c9cb11d9cc417 download raw metadata

What changed derived

This diff is not evidence. It was produced by civic-memory.diff_engine 1.1.0 at 2026-10-04T00:30:01+00:00 by normalizing the two archived objects above. The objects are authoritative; this reading of them can be regenerated or deleted without loss. 2301 line(s) added, 2191 line(s) removed.

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- },
- "description": "In streams located throughout temperate regions of the United States, salinity and chloride concentrations have been increasing over time, which can threaten stream health and drinking water quality. During snow melt events, chloride concentrations can rapidly increase and exceed ecological thresholds, making it important to frequently determine chloride concentrations. However, traditional sampling poses difficulties to rapidly determining chloride concentrations due to timing constraints. To supplement traditional sampling, specific conductance, which is monitored remotely and at 15-minute intervals in many streams, has been used as a primary predictor of chloride concentrations in surrogate regression models. Prior to developing surrogate models, geochemical modeling can be used to calculate the percent contribution of chloride to specific conductance to determine if site-specific regression models are appropriate. In Chester County, Pennsylvania, chloride surrogate models have been previously published for three stream sites using streamflow and specific conductance as explanatory variables. The previously published models use data collected through late 2022/early 2023. However, these models were recalculated to include data through 2024. This data release contains supporting information for the publication titled \"Method to Rapidly Track Salinization in Surface Waters\", and included in the data release is code to complete the geochemical analysis, compare the published models both visually and statistically to newly calculated models that incorporate data collected through 2024, and to create figures evaluating the data. \nModeling data and code are included for 3 stream sites:\nValley Creek at PA Turnpike Bridge near Valley Forge, PA (USGS station 01473169) with discrete water-quality data from nearby downstream site Valley Creek at Wilson Road near Valley Forge, PA (USGS station 01473170);\nWhite Clay Creek near Strickersville, PA (USGS station 01478245); and \nBrandywine Creek at Chadds Ford, PA (USGS station 01481000).\nThis data release contains 11 files: \n1. \"READ_ME_SuppInfo_MethodTrackSalinization.txt\": This file contains important information about the data release files and should be read prior to using the data and code. \n2. \"GEOREF_SuppInfo_MethodTrackSalinization.txt\": This file contains information about the sites used in the analysis. \n3. \"PHREEQC_salinitycalcs.R\": This is the code to complete the geochemical analysis using PHREEQC. \n4-6. \"phreeq_xxxxxxxx.csv\": These are the input files for the \"PHREEQC_salinitycalcs.R\" script. These input files contain discrete water-quality data. xxxxxxxx refers to the three stream sites: USGS-01473170, USGS-01478245, USGS-01481000. \n7. \"models_ancova.R\": This is the code to develop the published and newly calculated chloride surrogate regression models. There is also code to complete an Analysis of Covariance to statistically compare the surrogate models. \n8. \"Prediction_Intervals_Plots_Flow_Duration_Curves.R\": This is the code to visually compare the published and newly calculated surrogate regression models, as well as to create flow duration curves. \n9-11. \"combined_data_xxxxxxxx.csv\": Three input files for the \"models_ancova.R\" script and the \"Prediction_Intervals_Plots_Flow_Duration_Curves.R\" script. These files contain chloride, specific conductance, and streamflow data. xxxxxxxx refers to the three stream sites: USGS-01473169, USGS-01478245, USGS-01481000.",
+ "fn": "NPS IRMA Help",
+ "hasEmail": "mailto:NRSS_DataStore@nps.gov"
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- "inlandWaters"
- ],
- "modified": "2026-09-30T00:00:00Z",
+ "description": "CWD data for ERMN Forest Health 2007-2025",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763355?Reference=2320459",
+ "format": "csv",
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+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763356?Reference=2320459",
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+ "mediaType": "text/csv",
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+ "description": "groundstory data for ERMN Forest Health 2007-2025",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763357?Reference=2320459",
+ "format": "csv",
+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_Groundstory.csv"
+ },
+ {
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+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763360?Reference=2320459",
+ "format": "csv",
+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_Plots.csv"
+ },
+ {
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+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763361?Reference=2320459",
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+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_Saplings.csv"
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+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763362?Reference=2320459",
+ "format": "csv",
+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_Seedlings.csv"
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+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763359?Reference=2320459",
+ "format": "csv",
+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_NPSpecies.csv"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "stand data for ERMN Forest Health 2007-2025",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763363?Reference=2320459",
+ "format": "csv",
+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_Stand.csv"
+ },
+ {
+ "@type": "dcat:Distribution",
+ "description": "tree data for ERMN Forest Health 2007-2025",
+ "downloadURL": "https://irma.nps.gov/DataStore/DownloadFile/763364?Reference=2320459",
+ "format": "csv",
+ "mediaType": "text/csv",
+ "title": "ERMN_ForestHealth_2320459_Trees.csv"
+ }
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+ "Allegheny Portage Railroad National Historic Site",
+ "BLUE",
+ "Bluestone National Scenic River",
+ "DEWA",
+ "Delaware Water Gap National Recreation Area",
+ "ERMN",
+ "Eastern Rivers and Mountains Network",
+ "FLNI",
+ "FONE",
+ "FRHI",
+ "Flight 93 National Memorial",
+ "Fort Necessity National Battlefield",
+ "Friendship Hill National Historic Site",
+ "GARI",
+ "Gauley River National Recreation Area",
+ "IMD",
+ "Inventory and Monitoring Division",
+ "JOFL",
+ "Johnstown Flood National Memorial",
+ "NERI",
+ "NPS",
+ "National Park Service",
+ "New River Gorge National Park and Preserve",
+ "forest health monitoring",
+ "vegetation"
+ ],
+ "landingPage": "https://irma.nps.gov/DataStore/Reference/Profile/2320459",
+ "license": "http://www.usa.gov/publicdomain/label/1.0/",
+ "modified": "2026-09-28T00:00:00Z",
+ "programCode": [
+ "010:118",
+ "010:119"
+ ],
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- "name": "U.S. Geological Survey"
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- "theme": [
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- ],
- "title": "Supporting information for publication titled \"Method to Rapidly Track Salinization in Surface Waters\""
- },
- "description": "In streams located throughout temperate regions of the United States, salinity and chloride concentrations have been increasing over time, which can threaten stream health and drinking water quality. During snow melt events, chloride concentrations can rapidly increase and exceed ecological thresholds, making it important to frequently determine chloride concentrations. However, traditional sampling poses difficulties to rapidly determining chloride concentrations due to timing constraints. To supplement traditional sampling, specific conductance, which is monitored remotely and at 15-minute intervals in many streams, has been used as a primary predictor of chloride concentrations in surrogate regression models. Prior to developing surrogate models, geochemical modeling can be used to calculate the percent contribution of chloride to specific conductance to determine if site-specific regression models are appropriate. In Chester County, Pennsylvania, chloride surrogate models have been previously published for three stream sites using streamflow and specific conductance as explanatory variables. The previously published models use data collected through late 2022/early 2023. However, these models were recalculated to include data through 2024. This data release contains supporting information for the publication titled \"Method to Rapidly Track Salinization in Surface Waters\", and included in the data release is code to complete the geochemical analysis, compare the published models both visually and statistically to newly calculated models that incorporate data collected through 2024, and to create figures evaluating the data. \nModeling data and code are included for 3 stream sites:\nValley Creek at PA Turnpike Bridge near Valley Forge, PA (USGS station 01473169) with discrete water-quality data from nearby downstream site Valley Creek at Wilson Road near Valley Forge, PA (USGS station 01473170);\nWhite Clay Creek near Strickersville, PA (USGS station 01478245); and \nBrandywine Creek at Chadds Ford, PA (USGS station 01481000).\nThis data release contains 11 files: \n1. \"READ_ME_SuppInfo_MethodTrackSalinization.txt\": This file contains important information about the data release files and should be read prior to using the data and code. \n2. \"GEOREF_SuppInfo_MethodTrackSalinization.txt\": This file contains information about the sites used in the analysis. \n3. \"PHREEQC_salinitycalcs.R\": This is the code to complete the geochemical analysis using PHREEQC. \n4-6. \"phreeq_xxxxxxxx.csv\": These are the input files for the \"PHREEQC_salinitycalcs.R\" script. These input files contain discrete water-quality data. xxxxxxxx refers to the three stream sites: USGS-01473170, USGS-01478245, USGS-01481000. \n7. \"models_ancova.R\": This is the code to develop the published and newly calculated chloride surrogate regression models. There is also code to complete an Analysis of Covariance to statistically compare the surrogate models. \n8. \"Prediction_Intervals_Plots_Flow_Duration_Curves.R\": This is the code to visually compare the published and newly calculated surrogate regression models, as well as to create flow duration curves. \n9-11. \"combined_data_xxxxxxxx.csv\": Three input files for the \"models_ancova.R\" script and the \"Prediction_Intervals_Plots_Flow_Duration_Curves.R\" script. These files contain chloride, specific conductance, and streamflow data. xxxxxxxx refers to the three stream sites: USGS-01473169, USGS-01478245, USGS-01481000.",
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- ],
- "license": "https://www.nist.gov/open/license",
- "modified": "2021-10-05 00:00:00",
- "programCode": [
- "006:045"
- ],
- "publisher": {
- "@type": "org:Organization",
- "name": "National Institute of Standards and Technology"
- },
- "replaces": "ark:/88434/mds2-2352",
- "theme": [
- "Bioscience:Bioprocessing",
- "Health:Pharmaceuticals",
- "Manufacturing:Biomanufacturing"
- ],
- "title": "Datasets from an interlaboratory comparison to characterize a multi-modal polydisperse sub-micrometer bead dispersion"
- },
- "description": "These four data files contain datasets from an interlaboratory comparison that characterized a polydisperse five-population bead dispersion in water. A more detailed version of this description is available in the ReadMe file (PdP-ILC_datasets_ReadMe_v1.txt), which also includes definitions of abbreviations used in the data files. Paired samples were evaluated, so the datasets are organized as pairs associated with a randomly assigned laboratory number. The datasets are organized in the files by instrument type: PTA (particle tracking analysis), RMM (resonant mass measurement), ESZ (electrical sensing zone), and OTH (other techniques not covered in the three largest grou
+