Details for Harvest Job Id: a683ec07-c5ff-496b-8376-5e59d582fe03
Job Info
| Harvest Source: | DOI USGS DCAT-US |
| status: | complete |
| job_type: | harvest |
| date_created: | 2025-11-27 01:05:40.048064 |
| date_finished: | 2025-11-27 02:07:05.296323 |
| records_total: | 35630 |
| records_added: | 0 |
| records_updated: | 168 |
| records_deleted: | 0 |
| records_errored: | 2706 |
| records_unchanged: | 32756 |
| records_validated: | 168 |
| id: | a683ec07-c5ff-496b-8376-5e59d582fe03 |
Job Error Table
No job errors foundRecord Error Details
| Error type | Number of errors |
| ValidationError | 2708 |
90ee08e2-d659-425f-b834-eafa9d4f3847
Identifier: http://datainventory.doi.gov/id/dataset/usgs-586ee259e4b01a71ba0bc81d
Title: CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 flood-hazard projections: 1-year storm in Los Angeles County
Harvest Record ID: 3020970c-2050-4d4c-8e90-02c2892348b1
Error Message:
- <ValidationError: "$.description, 'Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated.\\nModel Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\\nPhase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented.\\nDetails: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM).\\nTier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. \\nWater-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). \\nA total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. \\nWave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. \\nTier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. \\nXBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). \\nResulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. \\nEvents: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. \\nDigital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber et al., 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11.\\nOutputs include: Areas of projected flood hazards: The area vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the storm simulation, based on the maximum elevation of still-water level (inundation for several minutes) at each CST profile. Enclosed areas illustrate the projected water surface and is shown extending from offshore to the extent of coastal flooding for different SLR scenarios between 0 - 2.0 m (0.25 m increments), and at 5.0 m. Low-lying vulnerable areas depict locations where projections indicate flood potential but are not connected to the primary flood surface. Flood potential indicates the maximum and minimum areas of flooding extent considering accuracy of the DEM, hydrodynamic model accuracy, and vertical land motion (Howell et al., 2016).\\nReferences Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. \\nLimber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245\\nRoelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006.\\nTolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333.\\nVitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.' does not match any of the acceptable formats: max string length requirement">
Type: ValidationError
Date Created: 2025-11-27 01:52:15.039533
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-675b170dd34edfeb8710ee3f
Title: Tailwater time series and telemetry receiver range testing data for Kentucky Dam between 2021 and 2022
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-661ed6a2d34e7eb9eb7e3f0d
Title: Code to assemble and generate output for an integrated population model of the endangered Cape Sable Seaside Sparrow
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Date Created: 2025-11-27 01:42:18.751939
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-680a93cfd4be0258eebab582
Title: Stream Health and Habitat Assessments for Mixed Agricultural Streams in Pennsylvania and Maryland (2023)
Harvest Record ID: 5f391208-2e16-4710-af28-8a24f5c7969c
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Date Created: 2025-11-27 01:47:39.676211
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-68b87d03d4be0247d9626375
Title: 2020 Level 2 Aquatic Areas - Upper Mississippi River System - Pool 4
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Date Created: 2025-11-27 01:38:08.883376
6c196c5d-579e-4a3c-8bef-3319ea303465
Identifier: http://datainventory.doi.gov/id/dataset/usgs-64e67107d34eeb681137dbd1
Title: Station CP26, Magnetotelluric sounding data, Rio Grande rift, New Mexico, 1997-2004
Harvest Record ID: 10211209-b902-4fd5-aae0-7bf1ca41f2fc
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Type: ValidationError
Date Created: 2025-11-27 02:04:02.967890
58d3bc17-e992-4214-93a6-ed1c61e3ead6
Identifier: http://datainventory.doi.gov/id/dataset/usgs-57f69380e4b0bc0bec06f597
Title: CoSMoS (Coastal Storm Modeling System) Southern California v3.0 Phase 2 water-level projections: 20-year storm in San Diego County
Harvest Record ID: 532b12ec-7780-4a7a-83f0-0187e8e6e24a
Error Message:
- <ValidationError: "$.description, 'Projected Hazard: Model-derived water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario.\\nModel Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings.\\nPhase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented.\\nDetails: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, San Diego, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM).\\nTier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. \\nWater-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). \\nA total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. \\nWave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. \\nTier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. \\nXBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). \\nResulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. \\nEvents: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. \\nDigital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. \\nOutputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. \\nReferences Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. \\nLimber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245\\nRoelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006.\\nTolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333.\\nVitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.' does not match any of the acceptable formats: max string length requirement">
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Date Created: 2025-11-27 01:50:03.416655
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-6838c7abd4be025925747123
Title: Geochemical, Geochronologic, and Isotopic Data for Proterozoic rocks from Arizona, California, Colorado, New Mexico, and Utah
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Date Created: 2025-11-27 01:46:53.961127
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-689f4888d4be027e7351d922
Title: Chemical and biological data from chronic bioassays with native Spea multiplicata exposed to field-collected water or sediment from the Pinyon Plain Mine containment pond in Arizona, USA, or single metals in water-only tests
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Date Created: 2025-11-27 01:53:42.035391
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Identifier: http://datainventory.doi.gov/id/dataset/usgs-5e18d8a3e4b0ecf25c5af15d
Title: Ground control points collected during unmanned aerial systems (UAS) flights over historic Ellicott City, Maryland in June 2018
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Type: ValidationError
Date Created: 2025-11-27 01:45:52.349722
