The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

The datasets contain the subject ADAS-equipped vehicle’s trajectory collected in naturalistic traffic conditions in central Ohio. The instrumented subject vehicle was either a discreet or readily-identifiable ADAS-equipped vehicle with SAE L2 capabilities. The dataset also contains trajectories for adjacent vehicles in traffic (observed by the subject vehicle’s onboard sensors).

DEVONIAN SHALES OF OHIO AND THEIR EASTERN AND SOUTHERN EQUIVALENTS

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

The data is taken from three intersections and 24 buses over a six month period in Cleveland, Ohio. The systems at the intersections provided MAP and SPAT messages and the SPAT message contained pedestrian detections from a series of cameras at the intersection. The buses received these messages and used them to alert the vehicle driver when pedestrians were about to enter the crosswalks or was in the crosswalk. The buses also used basic safety messages from external vehicles to warn the driver when another vehicle had the potential of making a right hand turn in front of the vehicle. The data contains bus locations, bus state changes, pedestrian detections and user interface state changes.

Facts and data about Ohio hydrocarbon wells. Includes: -(From the site:) "Location and formation top information for 749 wells in northwestern Ohio used in preparation of Report of Investigations No. 143: Stratigraphy, structure, and production history of the Trenton Limestone (Ordovician) and adjacent strata in northwestern Ohio" -"Well information and records from the Risk-Based Data Management System (RBDMS), created and maintained by the ODNR Division of Oil and Gas Resources Management. DDF2 contains all recorded Knox or deeper wells in the eastern half of the state plus all of the wells contained in RBDMS for the western half of the state (5,381 as of July 2009)." -Ohio oil and gas pools/fields; Stratigraphic units codes; County names and API codes; Unique township names in Ohio; and USGS 7.5-minute quadrangles in Ohio. Plus downloadable data available for purchase.

From the site: "The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown here as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary."

From the site: "Cell maps for each oil and gas assessment unit were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in an assessment unit or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, dry, or the type of production of the wells located within the cell is unknown. The well information was initially retrieved from the IHS Energy Group, PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary PI/Dwights PLUS Well Data. No proprietary data are displayed or included in the cell maps. The data from PI/Dwights PLUS Well Data were current as of October 2001 when the cell maps were created in 2002."

From the site: "Increasingly, hydrologic studies require information on the isotopic composition of natural waters. This report presents stable hydrogen (δ2H) and oxygen isotope ratios (δ180) of precipitation samples from seven selected sites of the National Oceanic and Atmospheric Administration's Atmospheric Integrated Research Monitoring Network (AIRMoN) collected during the years 1992-1994."

This dataset represents data derived from the NLCD dataset and the National Hydrography Dataset version 2.1(NHDPlusV2) (see Data Sources for links to NHDPlusV2 data and NLCD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated upstream catchments riparian area to provide watershed-level metrics for imperviousness values within the NLCD. This data set is derived from the NLCD Impervious Surfaces raster(imp2001) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2001 Landsat satellite data.(see Data Structure and Attribute Information for a description of each metric).

This dataset represents data derived from the NLCD dataset and the National Hydrography Dataset version 2.1(NHDPlusV2) (see Data Sources for links to NHDPlusV2 data and NLCD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated upstream catchments riparian area to provide watershed-level metrics for imperviousness values within the NLCD. This data set is derived from the NLCD Impervious Surfaces raster(imp2006) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2006 Landsat satellite data.

This dataset represents the population and housing unit density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds riparian buffers based on 2010 US Census data. Densities are calculated for every block group and watershed averages are calculated for every local NHDPlusV2 catchment(see Data Sources for links to NHDPlusV2 data and Census Data). This data set is derived from The TIGER/Line Files and related database (.dbf) files for the conterminous USA. It was downloaded as Block Group-Level Census 2010 SF1 Data in File Geodatabase Format (ArcGIS version 10.0). The landscape raster (LR) was produced based on the data compiled from the questions asked of all people and about every housing unit. The (block-group population / block group area) and (block-group housing units / block group area) were summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents data derived from the NLCD dataset and the National Hydrography Dataset version 2.1(NHDPlusV2) (see Data Sources for links to NHDPlusV2 data and NLCD). Attributes were calculated for every local NHDPlusV2 catchment and accumulated upstream catchments riparian area to provide watershed-level metrics for imperviousness values within the NLCD. This data set is derived from the NLCD Impervious Surfaces raster(imp2011) which describes percent imperviousness (continuous data type). Values indicate the degree to which the area is composed of impervious anthropogenic materials (e.g., parking surfaces, roads, building roofs). This raster was produced based on a decision-tree classification of circa 2011 Landsat satellite data.(see Data Structure and Attribute Information for a description of each metric).

This dataset represents the population and housing unit density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds based on 2010 US Census data. Densities are calculated for every block group and watershed averages are calculated for every local NHDPlusV2 catchment. This data set is derived from The TIGER/Line Files and related database (.dbf) files for the conterminous USA. It was downloaded as Block Group-Level Census 2010 SF1 Data in File Geodatabase Format (ArcGIS version 10.0). The landscape raster (LR) was produced based on the data compiled from the questions asked of all people and about every housing unit. The (block-group population / block group area) and (block-group housing units / block group area) were summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents total fresh surface-water withdrawals in agricultural land within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Measured as L/day as described in DOI: 10.1016/j.scitotenv.2020.137661

This dataset represents net anthropogenic Nitrogen within AOI: farm fertilizer + urban fertilizer + NOx deposition + CBNF + Human and Livestock food - crop N content - livestock N content within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents mean hillslope percent within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents deposition estimates of nutrients within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the National Atmospheric Deposition Program. The National Trends Network provides long-term records of precipitation chemistry across the United States. Individual rasters describe ammonium, nitrate, inorganic nitrogen, and average sulfur/nitrogen deposition per year. See Source Info for links to NADP. The nitrogen and sulfur characteristics (kg N/ha/yr) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

N from rock weathering (kg/ km2) within AOI

This dataset represents nitrogen surplus as kg N / yr, excluding biological N Fixation, within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents the density of road and stream crossings within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and then accumulated to provide watershed-level metrics. The landscape layer (raster) was developed by James Falcone of the USGS. US Census TIGER 2000 line files of roads and the NHDPlusV1 line files of all streams were converted to 30-meter grids where the presence of a street or stream was a 1 and everything else a 0. These were intersected and anything that was a 1 in both grids is the result. The density of road and stream crossings (crossings / square kilometer) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents percent area consisting of sandstone aquifers within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents percent area consisting of semiconsolidated sand aquifers within individual, local NHDPlusV2 catchments and upstream, contributing watersheds.

This dataset represents mean percent are burned from wildfires within individual, local NHDPlusV2 catchments and upstream, contributing watersheds for each year for 1984-2018.

This dataset represents the characterization of global forest extent and change by year from 2001 through 2013 within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Global Forest Change 2000–2013. These data are based on global tree cover loss for the period from 2001 to 2013 at a spatial resolution of 30m. The analysis used to create the landscape layer is based on Landsat data. Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. This landscape layer is a disaggregation of total forest loss to annual time scales. Encoded as either 0 (no loss) or else a value in the range 1–13, representing loss detected primarily in the year 2001–2013, respectively. The forest loss by year characteristics (%) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the Index of Watershed Integrity / Index of Catchment Integrity (IWI/ICI) within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on 23 other StreamCat metrics. The Index of Watershed Integrity (IWI) is based on first order approximations of relationships between stressors and six watershed functions: hydrologic regulation, regulation of water chemistry, sediment regulation, hydrologic connectivity, temperature regulation, and habitat provision. Link to paper: https://doi.org/10.1016/j.ecolind.2017.10.070 The Index of Watershed Integrity / Index of Catchment Integrity (IWI/ICI) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

This dataset represents the wetness index within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Composite Topographic Index (See Supplementary Info for Glossary of Terms). The Composite Topographic Index (CTI) is based on contributing area, slope, and overland flow and has been developed internally at the EPA for the EnviroAtls (http://edg.epa.gov/data/Public/ORD/EnviroAtlas/National/). As defined for use in EnviroAtlas datasets and as used here, “wet areas are typically created by runoff from natural land cover when rain falls on saturated soil. Surface and rill (or small channel) runoff carries excess water to lowland depressions or wet areas. Runoff collects in wet areas until they fill and overflow downstream. In this way, stream networks can be extended into new areas that would not be hydrologically connected during drier times. Wet area expansion and watershed hydrological connectivity differ between humid temperate and semi-arid and arid climates (where drought and soil crusts limit infiltration and produce flashier runoff)” (from https://enviroatlas.epa.gov/enviroatlas/datafactsheets/pdf/ESN/PercentForestonWetAreas.pdf). The Mean Composite Topographic Index (CTI)[Wetness Index] were summarized to produce local catchment-level and watershed-level metrics as a continuous data type.

A paper describing the results of two hydraulic fracturing events. From the paper: "The current natural gas supply problems in the United States arc encouraging research into new or underdeveloped gas sources. One such source is the Devonian Shale sequence of Kentucky, West Virginia, Pennsylvania, and Ohio. These thick shale deposits contain large gas resources, but have not been fully exploited because of the severely restricted gas well flow rates. Shale wells must be artificially stimulated to improve gas deliverability to a commercial level, and research is underway to develop a stimulation method suited to the Devonian Shale. This report describes a performance comparison of two wells completed in the Shale near Youngstown, Ohio: one stimulated conventionally with hydraulic fracturing, the other with a nitrogen/water foam treatment. Both wells have comparable reservoir sections and received equivalently sized stimulation treatments. Drilling and fracture programs for both wells are discussed along with short-term gas production tests conducted to evaluate the fracture treatments. Early indications show that the foam fracturing method is better suited to the reservoir conditions found in the Devonian Shale, resulting in much faster fracturing fluid clean-up and higher initial gas production than the conventional water method."

From the site: "A cells polygon feature class was created by the U.S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Ohio. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. The well information was acquired from the Ohio Department of Natural Resources, Division of Geological Survey in a Geographic Information System (GIS) data layer that contains all of the locatable oil and gas wells in Ohio. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2004."

From the site: "A GIS database of geologic units and structural features in Ohio, with lithology, age, data structure, and format written and arranged just like the other states."

From the site: "Geologic map of the Higginsport quadrangle, Ohio-Kentucky and part of the Russellville quadrangle, Mason County, Kentucky"