Town of Edgartown, MA GIS Viewer

The Maine Geoparcel Viewer Application allows users to search and view available digital parcel data for Organized Townships and Unorganized Territories in the State of Maine. The Maine GeoLibrary and the Maine Office of GIS do not maintain parcel data for communities, cannot verify parcel ownership, and are not responsible for individual parcel data verification or updating emergency records concerning parcel addresses. If you have questions about a specific parcel, please contact the appropriate Town Office or County Registry of Deeds for the most up-to-date information.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. The "Maine Parcels Organized Towns Feature" layer and "Maine Parcels Organized Towns ADB" table are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Please contact the appropriate Town Office or the County Registry of Deeds for more up-to-date parcel information. Organized Town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), Maine Revenue Services is the authoritative source for parcel data. The "Maine Parcels Unorganized Territory" layer is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services to compile up-to-date parcel ownership information.

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Town of West Tisbury, MA GIS Viewer

The Danvers, MA - GIS provides the general public and other interested parties local government property tax and assessment information.

These shapefiles includes surficial geology, contacts, fault, and marker bed layers providing the legend for the surficial geology layer. Original data from 1940's-1960's. This database was developed to create a usable dataset of Kansas counties where no new mapping has taken place. It shows locations of geologic outcrops, contacts, and geologic structures in Kansas counties. This geologic data is that of the original geologic map and is the interpretation of the map's author. New information not included in this data may prove the interpretation to be incorrect. In addition, stratigraphic nomenclature used on the original map may not agree with current usage.Data is from the Kansas Geological Survey - Cartographic Services and its predecessors. The surficial geology layers display attributed polygons representing intervals in the stratigraphic sequence identified and mapped at the surface of the county. In the contacts layers of the database, contacts corresponding to the boundaries between adjacent geologic polygons on the map are represented by attributed line features. Marker bed layers include distinctive beds of rock strata that are easily distinguishable and observable over large horizontal distances. The surface expression of structural geologic features such as faults or the axis of a fold, syncline, or anticline are represented by attributed line features in the faults layers. Not all counties will have layers for all these features. Counties included are: Allen, Barton, Brown, Cheyenne, Clay, Cloud, Cowley, Decatur, Ellsworth, Franklin, Gove, Graham, Grant, Greeley, Harper, Haskell, Jackson, Kingman, Kiowa, Lane, Lincoln, Linn, Logan, Marshall, Meade, Miami, Mitchell, Nemaha, Ottawa, Pratt, Rawlins, Reno, Rice, Rush, Scott, Seward, Sheridan, Sherman, Stanton, Stevens, Sumner, Thomas, Trego, Wallace, Wichita

Underlying dimensions of landscape value assessment using principal axis factoring extraction method and Promax Rotation with Kaiser Normalization (KMO = 0.855, the significance level of Bartlett’s Test of Sphericity is 0.000, total variance explained is 53.725%).

The starting point for creating these maps is to determine the axis of the fairway. This axis line is precisely plotted using a calculation in ArcGis. The bank lines as recorded in the current area GIS file were used. Given the irregular structure of the banks, this resulted in a rather angular midline, which is why this midline has been adjusted partly by generalisation and partly by hand.

On the basis of this axis line, the minimum fairway widths have been plotted, as they apply to that specific route. This is calculated by plotting half of the required width to either side (buffing) from the center line. Where this width crosses the banks, infrastructural bottlenecks may arise. Where the waterway is wider than the minimum requirement, possible policy space arises. This is the space to further determine which function is assigned to it when elaborating. Different requirements apply to works of art, so other widths have also been set for this.

To complete the whole, the maps have been supplemented with topography, kilometre measurement and nautical functions such as bollards, mooring chairs etc. On the basis of the ship dimensions, manual waiting and berth functions have been drawn up.

A map layer has been created for each function. The functions are inventoried, recorded on management cards and manually recorded in Auto-Cad. The obtained files have been converted to ArcGIS.

The dataset consists of tiled orthogonal imagery produced from nadir images captured by Pictometry International in January/February of 2015. This dataset consists of two combined mosaic datasets- one with 9 inch resolution imagery and one with 4 inch resolution imagery. This dataset is a referenced mosaic dataset with a slightly different projected coordinate system than the original. The original coordinate system was NAD83_North_Carolina_ft while the referenced projection is NAD_1983_StatePlane_North_Carolina_FIPS_3200_Feet. However, the projection and geographic coordinate systems are the same between datasets and are listed below.Projection: Lambert Conformal ConicFalse_Easting: 2000000.002616666000000000False_Northing: 0.000000000000000000Central_Meridian: -79.000000000000000000Standard_Parallel_1: 34.333333333333336000Standard_Parallel_2: 36.166666666666664000Latitude_Of_Origin: 33.750000000000000000Linear Unit: Foot_USMeters per unit: 0.304800609601219Name: GCS_North_American_1983Angular Unit: Degree (0.0174532925199433)Prime Meridian: Greenwich (0.0)Datum: D_North_American_1983Spheroid: GRS_1980Semimajor Axis: 6378137.0Semiminor Axis: 6356752.314140356Inverse Flattening: 298.257222101

City of Pittsfield, MA GIS Viewer

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has …Show full descriptionAbstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details. The main objectives of the Clarence-Moreton *SEEBASETM and GIS Project study were to provide the NSW Department of Primary Industries with an integrated regional interpretation of the basement composition, lithology, structure and depth of the Clarence-Moreton Basin in New South Wales. This included the construction of a depth to basement image (SEEBASE™) for the area. The effects of the basement geology on the evolution of the Clarence-Moreton Basin (both onshore and offshore) and of its precursors, the Esk Trough and the Ipswich Basin have been investigated. Attention was focused on the formation and reactivation of the basin controlling structures. The evolution of these structures has been evaluated in the light of the different tectonic events that have affected the area. Maturity and fluid flow migration maps derived from SEEBASETM grids indicate that the central axis of the onshore depocentres and parts of the offshore basin are mature for present-day oil generation. However, these maps probably underestimate the maturity along the eastern margin of the basin, due to significant uplift and erosion that occurred during the Cenomanian. Such areas are likely to be mature for hydrocarbon generation provided adequate source rocks are present at depth. Available gravity and magnetic data have been reprocessed and enhanced with an extensive set of wavelength and amplitude filters. An ArcMap 9.0 GIS product has been constructed that includes all structural interpretations, as well as the potential field data. The revised and expanded interpretation of the structure and basin architecture in the area of the Clarence-Moreton Basin provides an improved understanding of basin evolution in the region, which will contribute to the reduction of exploration risks in the area. [Taken from executive summary of report cited in History] This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Department of Industry. http://www.industry.nsw.gov.au/ Dataset History No specific metadata file or history statement provided. See MR707_report.pdf in 'seebase&strucuralGisProject_cla-mor_nsw-dpi\Report' directory. Dataset Citation NSW Department of Primary Industries (2014) Clarence-Moreton SEEBASE & Structural GIS Project data.. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/b1690f8b-4025-45d2-96a0-6feb03ff3e52.

Road surfaces were compiled by Axis Geospatial, LLC using aerial photography and LiDAR data captured by Pictometry Corp in 2013. Final delivery was April 2015. Road edges were compiled by surface type of concrete, asphalt, gravel, dirt, or under construction. The road surface was compiled to have all open intersections unless the surface type changed. Areas within the road network where surface type changes, a pavement change line connects the edge of the road at the change to the centerline at the change to the other side of the road edge at the change. Roads have precedence over parking areas and can be used for sides of parking areas. Ongoing updates to planimetrics are done based on Construction Plans, Building Permits, and updated aerial photography.

This is a GIS point feature shapefile representing wells, and their temperatures, that are located in the general Utah FORGE area near Milford, Utah. There are also fields that represent interpolated temperature values at depths of 200 m, 1000 m, 2000 m, 3000 m, and 4000 m. in degrees Fahrenheit.

The temperature values at specific depths as mentioned above were derived as follows. In cases where the well reached a given depth (200 m and 1, 2, 3, or 4 km), the temperature is the measured temperature. For the shallower wells (and at deeper depths in the wells reaching one or more of the target depths), temperatures were extrapolated from the temperature-depth profiles that appeared to have stable (re-equilibrated after drilling) and linear profiles within the conductive regime (i.e. below the water table or other convective influences such as shallow hydrothermal outflow from the Roosevelt Hydrothermal System). Measured temperatures/gradients from deeper wells (when available and reasonably close to a given well) were used to help constrain the extrapolation to greater depths.

Most of the field names in the attribute table are intuitive, however HF = heat flow, intercept = the temperature at the surface (x-axis of the temperature-depth plots) based on the linear segment of the plot that was used to extrapolate the temperature profiles to greater depths, and depth_m is the total well depth. This information is also present in the shapefile metadata.

Town of Monterey, MA GIS Viewer

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in July 2013. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to understand better the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source interacts with and affects the morphologic evolution of the barrier-island system. Data collected from this study can be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from prior sampling efforts can provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in July 2013 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from https://pubs.usgs.gov/ds/894/downloads.html.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

This website is provided by the Town of West Tisbury. The information is updated periodically by the town assessor. The public may search for parcel boundaries and view all the associated assessing data for that parcel. The MVC does not maintain this parcel look-up website.

The global high-accuracy electronic digital theodolite market is experiencing robust growth, driven by increasing infrastructure development, surging demand for precise surveying and mapping solutions in construction, and the rising adoption of advanced technologies like GPS and GIS. The market, estimated at $850 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key trends, including the miniaturization and improved accuracy of theodolites, leading to wider adoption across diverse applications. Furthermore, the integration of digital technologies enhances data processing and analysis speeds, increasing efficiency and reducing project timelines. While the market faces certain restraints such as high initial investment costs and the availability of skilled labor to operate these sophisticated instruments, the overall market outlook remains positive due to the continuous advancements in the technology and the growing demand for precise measurements across various industries. The market segmentation reveals a strong preference for dual-axis theodolites over single-axis models, reflecting the need for comprehensive data acquisition in complex projects. Online sales channels are gaining traction, supplementing traditional offline sales, aided by increased e-commerce penetration. Major players like Hexagon, Trimble, Nikon, and Leica Geosystems are actively investing in research and development, expanding their product portfolios, and enhancing their global distribution networks to maintain their competitive edge. Regional analysis indicates strong growth in Asia Pacific, driven by rapid urbanization and infrastructure development in countries like China and India. North America and Europe also contribute significantly to the market, owing to robust construction and surveying activities. The forecast period (2025-2033) is expected to witness considerable growth, fueled by sustained investments in infrastructure and the evolving needs of various industries. This in-depth report provides a comprehensive analysis of the global high accuracy electronic digital theodolite market, projecting a market value exceeding $2 billion by 2030. It delves into market dynamics, competitive landscapes, and future growth trajectories, leveraging millions of data points to offer actionable insights for businesses, investors, and researchers. This report is optimized for high search volume keywords such as "electronic digital theodolite market," "high accuracy theodolite," "theodolite sales," and "geospatial surveying equipment."

(a) Seven main steps from 3D data acquisition and processing, feature extraction, data mining procedures (classifiers and feature selection) to possible applications of the method. (b–d) illustrations of feature extraction methods. (b) Section areas and section convolutions. (i) Tooth (upper molar of Rhinolophus blasii) is divided into 10 equal sections perpendicular to the z-axis. Upper and lower bounds (relative to the zApex) for every second section are given along the z-axis. (ii) Occlusal view with every second section highlighted in red, with areas and convolutions for each section. (c) Orientation patch count (OPC). (i) Surface of the tooth (upper molar of Felis silvestris) is grouped into surface vertices according to their orientation in the xy-plane (ii). (iii) Vertices are further grouped according to their 4-cell connectivity followed by (iv) exclusion of small patches. (v) The resulting OPC value is the final number of patches. (d) The effect of surface folding and elongation on surface relief. Relief is calculated by dividing the 3D surface area by its 2D projected area. A flat, unspecialized surface (i) has a relief of 1. If the surface is folded (ii), such as in Otomys irroratus, or elongated (iii), such as in Felis silvestris, its relief increases.

PhotoMOB is a GIS toolbox automating the tasks to perform a spatial-temporal quantification of grain size distribution, bed stability and fractional mobility from photos of gravel bed river after hydrological events. Grain are classified as identical (i.e., stationary) or different (i.e., mobile/turned out).

The grain size distributions from the photos can be extracted in the form Area-by-Number or Grid-by-Number and are compatible with measurements obtained via other methods as pebble-count.

This folder contains data related to both papers in following subfolders:

• 1_PhotoMOB_part1_Data: data used for error assessment of grains characterisation
  • Grain dataset collected in the field (1_Real),with measurement of the axes (a, b and c) measured using the Pebble-Box, measurement of the weight, estimation of the position of the grains on the surface of the bed.
  • Grain dataset digitised manually and identified automatically by the supervised and automated PhotoMOB procedures as well as for Basegrain free stand-alone tool from Detert and Weitbrecht (2013) and Sedimetrics Digital Gravelometer from Graham (2005a, 2005b).
• 2_PhotoMOB_part2_Data: data used for error assessment of grain dynamics
• 3_Example_Data_ready_to_PhotoMOB_Extractor_App:
  • This corresponds to a dataset of 3 hydrological events processed by the PhotoMOB GIS protocol (Part 1 +Part 2). These data are available as an example to be used in the PhotoMOB Extractor application (https://shiny.fannyville.com/photomob_extractor/) to obtain an overview of the output available from these data.