DOE/BC/14897-6

The bulk download facility provides the entire contents of each major API data set in a single ZIP file. A small JSON formatted manifest file lists the bulk files and the update date of each file. The manifest is generally updated daily and can be downloaded from http://api.eia.gov/bulk/manifest.txt. The manifest contains information about the bulk files, including all required common core attributes.

EAGLE-I, an interactive geographic information system (GIS) that allows users to view and map the nation's energy infrastructure and obtain near real-time informational updates concerning the electric, petroleum and natural gas sectors within one visualization platform.

This dataset contains a variety of data about the Fort Bliss geothermal area, part of the southern portion of the Tularosa Basin, New Mexico. The dataset contains schematic models for the McGregor Geothermal System, a shallow temperature survey of the Fort Bliss geothermal area. The dataset also contains Century OH logs, a full temperature profile, and complete logs from well RMI 56-5, including resistivity and porosity data, drill logs with drill rate, depth, lithology, mineralogy, fractures, temperature, pit total, gases, and descriptions among other measurements as well as CDL, CNL, DIL, GR Caliper and Temperature files. A shallow (2 meter depth) temperature survey of the Fort Bliss geothermal area with 63 data points is also included. Two cross sections through the Fort Bliss area, also included, show well position and depth. The surface map included shows faults and well spatial distribution. Inferred and observed fault distributions from gravity surveys around the Fort Bliss geothermal area.

This submission contains raster files associated with several datasets that include earthquake density, Na/K geothermometers, fault density, heat flow, and gravity. Integrated together using spatial modeler tools in ArcGIS, these files can be used for play fairway analysis in regard to geothermal exploration.

EPAct TRS

This data set includes the magnetotelluric (MT) data collected from October 21 to November 9, 2016 over the San Emidio geothermal field in Nevada by Quantec Geoscience USA Inc. on behalf of US Geothermal Inc. as part of a project entitled "A Novel Approach to Map Permeability Using Passive Seismic Emission Tomography". This data set includes descriptions of the instrumentation, data acquisition and processing procedures, as well as the final processed data and digital archive formats. A total of 81 MT locations were surveyed (52 profile sites, and 29 MT sites). Data were processed and inspected for quality assurance on site, and reviewed daily by the geophysicist in charge of the project.

This dataset contains the major event bus routes to and from Sydney Olympic Park.

Contains the latest Metro / Outer Metro Bus contract boundaries in a shapefile format This dataset is the shapefile equivalent to the pdf maps in the [Opal Trips - Bus](https://opendata.transport.nsw.gov.au/dataset/opal-trips-bus) dataset

NMFWRI represents the state’s only dedicated capability for supporting the spatial data analysis needs of external stakeholders in the natural resources sector, as well as the GIS/GPS capacity for Highlands University and for most of northern New Mexico. NMFWRI’s GIS work also provides help with maps and other geographic information to New Mexico groups engaged in forest restoration and land management, but who are too small to maintain their own GIS capability. These groups include soil and water conservation districts, municipalities, private groups and individuals, and tribal organizations.

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

The utility of passive seismic emission tomography for mapping geothermal permeability has been tested at San Emidio in Nevada. The San Emidio study area overlaps a geothermal field in production since 1987 and another resource to the south of the production field. Passive seismic data collections were completed at San Emidio in late 2016 by Microseismic Inc as part of a DOE project. The PSET results are being analyzed as part of the WHOLESCALE project. This submission includes P-wave velocity model data, and the passive seismic data with more information on each bellow.

The Portland and Tualatin basins are part of the Puget-Willamette Lowland in the Cascadia forearc of Oregon and Washington. The Coast Range to the west has undergone Paleogene transtension and Neogene transpression, which is reflected in basin stratigraphy. To better understand the tectonic evolution of the region, Darby Scanlon modeled three key stratigraphic horizons and their associated depocenters (areas of maximum sediment accumulation) through space and time using well log, seismic, outcrop, aeromagnetic, and gravity data. Three isochore maps were created to constrain the location of Portland and Tualatin basin depocenters during 1) Pleistocene to mid-Miocene (0-15 Ma), 2) eruption of the Columbia River Basalt Group (CRBG, 15.5-16.5 Ma), and 3) MidMiocene to late Eocene time (~17-35 Ma). Results show that the two basins each have distinct mid-Miocene to Pleistocene depocenters. The depth to CRBG in the Portland basin reaches a maximum of ~1,640 ft, 160 ft deeper than the Tualatin basin. Although the Portland basin is separated from the Tualatin basin by the Portland Hills, inversion of gravity data suggests that the two were connected as one continuous basin prior to CRBG deposition. Local thickening of CRBG flows over a gravity low coincident with the Portland Hills suggests that Neogene transpression in the forearc reactivated the SylvanOatfield and Portland Hills faults as high angle reverse faults. This structural inversion separated the once continuous Portland and Tualatin basins in the mid-late Miocene. A change in the stress regime at that time marks the transition from Paleogene forearc extension to deformation dominated by north-south shortening due to collision of the forearc against the Canadian Coast Mountains. An eastward shift of the forearc basin ii depocenter over the Neogene likely reflects uplift of the Coast Range to the west. A change in regional stress in the mid to late-Miocene, along with uplift of the Oregon Coast Range, caused a 10-fold decrease in sediment accumulation rates across the Portland and Tualatin basins. Transpressional oblique-slip faulting continues to deform the region as the forearc undergoes clockwise rotation and collides with the rigid Canadian Coast Mountains to the north.

A collection of open and public data that contains REE and Coal data that covers the US coal basins.

The REE and Coal Open Database is an online collection of subsurface contextual data from publicly available geological, geochemical, and geospatial resources. These data align to and support execution of NETL’s REE coal assessment method. The database includes basin- and national-level spatial datasets, in addition to other non-spatial data that support the assessment approach. Data in this collection are sourced from a range of authoritative, public sources, including NETL, U.S. Geological Survey (USGS), Energy Information Administration (EIA), and state geological surveys.

There have been a number of requests for "State Roads" data. This data is currently available via the Data.NSW Spatial Collaboration Portal. To access Road Segment Data please follow the instructions below; 1. From [https://portal.spatial.nsw.gov.au/](https://portal.spatial.nsw.gov.au/) 2. Click the *Browse Data* tile 3. Click *NSW Data themes* tile 4. Click the *Transport* icon 5. Click the link [NSW Transport Theme - Road Name Extent](https://portal.spatial.nsw.gov.au/portal/apps/sites/#/home/items/d05fb8d70c4c42bd859e8b8cae60a387)  6. Click on Export Data (highlighted above)  7. **Do you want to select which layers to export?** You can select *Yes* and choose from the layers provided OR you can select *No Thanks. I want to export data from all layers*. This will download all data sets. In the example below RoadNameExtent is selected.  8. Click *Next* > 9. **Do you want to specify an extent**? Select No or *Yes, by drawing the extent on a map*. If you select ‘No’ all the data will be extracted. If you wanted to specify an extent of data extraction, e.g. around Sydney in the screenshot below, use the square icon labelled ‘Draw a rectangle’. This will draw a square centred on the point where you clicked on the map. You can change the shape from the icon labelled ‘Reshape’ to get the required area of extraction.  10. Click on *Next* > 11. Select your preferred Export format. Please note the limitation of ESRI Shape files truncating attribute names to 10 characters. 12. Select your preferred Export datum: Please note the current preference of GDA2020, although that depends on your objective. 13. Select your preferred Export coordinate system: ‘Geographic’ will export the geometries in latitude/longitude. MGAxx coordinate systems will export the geometries in metres. 14. Type your email address  15. Click *Export* 16. You should see the screen below  17. Check your inbox for an email from Customer Service with a link to download the data.

In December 2016, 1301 vertical-component seismic instruments were deployed at the San Emidio Geothermal field in Nevada. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in one-minute increments in SEGD and MSEED formats. See the metadata in GDR submission (linked below as "Seismic Survey 2016 Metadata at San Emidio Nevada") for details about the seismic station locations, seismic data logger specifications, instrumentation specifications, descriptions of data, a fracture finding summary, and the final report for the 2016 seismic survey done in San Emidio, Nevada.

1301 Vertical Component seismic instruments were deployed at San Emidio Geothermal field in Nevada in December 2016. The first record starts at 2016-12-05T02:00:00.000000Z (UTC) and the last record ends at 2016-12-11T14:00:59.998000Z (UTC). Data are stored in individual files in one-minute increments. Data includes seismic station locations, seismic data logger specifications, instrumentation specifications, descriptions of data, a fracture finding summary and the final report for the 2016 WHOLESCALE seismic survey done in San Emidio, Nevada.

A software code to retrieve the uncertainty from lineralized hard X-ray chemcial images. The code is written in R and uses a Markov Chain implemented through the package rjags.

This group offers a collection of key spatial data resources housed or linked wtihin EDX

A carriageway is width of roadway for the movement of vehicles. There are single carriageways and dual carriageways represented as single or dual lines in the spatial representation. This dataset contains the the number of through lanes, the width of the through lanes and the surfaced width for State Roads. There are also non-state roads in the dataset, which commonly only contain lengths and no widths.

TPA has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. 1991 – 2006 – Australian Standard Geographical Classification (ASGC), with Statistical Local Areas (SLA), Census Collection District (CCD) and Mesh Blocks (MB). Prior to 2006, TZs aligned to Statistical Local Areas (SLA). GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

TPA has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. Geographic coverage – Sydney Greater Metropolitan Area (GMA) In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. 1991 – 2006 – Australian Standard Geographical Classification (ASGC), with Statistical Local Areas (SLA), Census Collection District (CCD) and Mesh Blocks (MB). Prior to 2006, TZs aligned to Statistical Local Areas (SLA). GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

Transport Performance and Analytics (TPA) has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. 1991-2006 - Australian Standard Geographical Classification (ASGC), with Statistical Local Areas (SLA), Census Collection District (CCD) and Mesh Blocks (MB). Prior to 2006, TZs aligned to Statistical Local Areas (SLA) Note that GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

Transport Performance and Analytics (TPA) has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. 1991-2006 - Australian Standard Geographical Classification (ASGC), with Statistical Local Areas (SLA), Census Collection District (CCD) and Mesh Blocks (MB). Prior to 2006, TZs aligned to Statistical Local Areas (SLA) Note that GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

Transport Performance and Analytics (TPA) has a range of location information products which include digitised boundaries. Travel Zones (TZs) are the geographic units of TPA’s data collection, transport modelling and analysis. They are designed to align as much as possible with current Australian Bureau of Statistics (ABS) geographic classification for that year, to allow for comparison of different data sources at standard geographies. In order to provide for a similar level of trip generation across zones, TZs are designed to be small in areas with high land use densities and larger in areas of lower density. The key land uses of interest in defining TZs are employment, housing and transport infrastructure. As urban areas change over time, TPA creates a new zoning system in line with each 5-yearly population Census. Digitised boundaries for these zoning systems and equivalences between them, are available to users to undertake spatial analysis. Travel Zones fit within ABS SA2. Note that GMA may change slightly at the boundary to align with ABS geographic classifications. This will not usually impact on the comparison of data over time.

Travel Zones (TZs) are the spatial unit of geography defined by Transport Performance and Analytics (TPA), a business unit within Transport for NSW (TfNSW). The TZ spatial layer is applied to data sources used by TfNSW for transport modelling and analysis, including the Household Travel Survey and the Census 2016 Journey to Work data. The Australian Bureau of Statistics (ABS) Statistical Area boundaries form the foundation of the TZ. Generally, a TZ is larger than a Statistical Area Level 1 or Mesh Block, both ABS geography definitions. The ABS Statistical Areas are based on population counts whereas TZ boundaries are defined using population, employment, housing and transport infrastructure. TZs are designed to have standardised trip generation levels across all zones. This causes zones to be different sizes across the metropolitan area. As with many other spatial boundaries, TZs tend to be small in areas with high land-use densities and larger in areas of lower density. This dataset now includes a CSV file mapping the Transit Stop Number (TSN) to the Travel Zone (TZ16). It captures the stop name, suburb and coordinates. [Travel Zone Explorer](https://www.transport.nsw.gov.au/data-and-research/reference-information/travel-zone-explorer-visualisation) is an interactive map where you can search for Travel Zones (TZ) and find out the current and future population in occupied private dwellings by age and sex.

These ESRI shapefiles show spatial data, points on a map. In addition, shapefiles provide attribute data for each point. Shapefile’s attribute data include spatial information such as latitude and longitude, the address, and obligation amount.

This submission contains information on thirty-six rock samples collected from San Emidio, Nevada during January, 2021 for Subtask 2.3 of the WHOLESCALE project. The following resources include a .zip of rock sample photos taken in the field, a .zip of rock sample photos taken in the laboratory at UW-Madison, and an excel catalog of rock samples with information on sample name, rock type, coordinates of sample location, structural measurements, field notes, observations for plug preparation (e.g., weathering, ability to be cut and cored), and rock descriptions. It should be noted that not every sample was photographed in the field. Names and descriptions of rock formation units are taken from Rhodes et al. (2011). The README.txt file is a description of this submission.