The table Firm survey wave 1 and 2 (GIS) is part of the dataset SEDRI Ethiopia firm survey (GIS), available at https://stanford.redivis.com/datasets/rxq3-9x047we25. It contains 1585 rows across 3377 variables.

Fayette County Ohio GIS Survey Drawings. The information provided is for reference only and subject to independent verification. User assumes all responsibility for its use.

The United States Public Land Survey (PLS) divided land into one square mile units, termed sections. Surveyors used trees to locate section corners and other locations of interest (witness trees). As a result, a systematic ecological dataset was produced with regular sampling over a large region of the United States, beginning in Ohio in 1786 and continuing westward.
We digitized and georeferenced archival hand drawn maps of these witness trees for 27 counties in Ohio. This dataset consists of a GIS point shapefile with 11,925 points located at section corners, recording 26,028 trees (up to four trees could be recorded at each corner). We retain species names given on each archival map key, resulting in 70 unique species common names. PLS records were obtained from hand-drawn archival maps of original witness trees produced by researchers at The Ohio State University in the 1960’s. Scans of these maps are archived as “The Edgar Nelson Transeau Ohio Vegetation Survey” at The Ohio State University: http://hdl.handle.net/1811/64106.
The 27 counties are: Adams, Allen, Auglaize, Belmont, Brown, Darke, Defiance, Gallia, Guernsey, Hancock, Lawrence, Lucas, Mercer, Miami, Monroe, Montgomery, Morgan, Noble, Ottawa, Paulding, Pike, Putnam, Scioto, Seneca, Shelby, Williams, Wyandot. Coordinate Reference System: North American Datum 1983 (NAD83). This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.

Usage

The prefixes f1_ and f2_ indicate that variables correspond to either wave 1 or wave 2, respectively.

Public Land Survey SystemThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the Bureau of Land Management data, displays the Public Land Survey System (PLSS) in the United States. Per BLM, "The BLM is required to perform cadastral surveys on all federal interest and Indian lands. As part of survey work, the BLM maintains an essential land grid, known as the rectangular survey system or Public Land Survey System (PLSS), which is the basis for identifying legal descriptions of land parcels."PLSS Township 7N 22EData downloaded: October 17, 2023Data source: BLM National Public Land Survey System PolygonsNGDAID: 10 (BLM National PLSS Public Land Survey System Polygons)OGC API Features Link: (Public_Land_Survey_System - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information: About the Public Land Survey SystemSupport documentation: BLM National PLSS Public Land Survey System PolygonsFor feedback please contact: ArcGIScomNationalMaps@esri.comNGDA Data SetThis data set is part of the NGDA Cadastre Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cadastre is defined as the "past, current, and future rights and interests in real property including the spatial information necessary to describe geographic extents. Rights and interests are benefits or enjoyment in real property that can be conveyed, transferred, or otherwise allocated to another for economic remuneration. Rights and interests are recorded in land record documents. The spatial information necessary to describe geographic extents includes surveys and legal description frameworks such as the Public Land Survey System, as well as parcel-by-parcel surveys and descriptions. Does not include federal government or military facilities."For other NGDA Content: Esri Federal Datasets

This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific 'production' or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys), and the Bureau of Census 2015 Cartographic State Boundaries. The Entity-Attribute section of this metadata describes these components in greater detail. Please note that the data on this site, although published at regular intervals, may not be the most current PLSS data that is available from the BLM. Updates to the PLSS data at the BLM State Offices may have occurred since this data was published. To ensure users have the most current data, please contact the BLM PLSS Data Set Manager.

The Digital Geologic-GIS Map of the Rhoda Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (rhod_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (rhod_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (rhod_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (rhod_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (rhod_geology_metadata.txt or rhod_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because

BLM NV PLSSSpecialSurvey: This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.

Stratagex Ltd was contracted by the AGS in 2001 to compile a catalogue of all existing ground and airborne geophysical survey data contained in the archived mining assessment reports of the AGS, supplemented where possible with information on non-exclusive and proprietary surveys from exploration industry and other sources. This data set shows the airborne survey locations and detailed information about the survey including: Type of survey flown [fixed wing or helicopter. barometric (constant elevation) or drape (topographic contour following), Year of data acquisition and contractor, Description of the system flown [any one or combination of magnetics, VLF-EM, radiometrics, time domain electromagnetics (TDEM), frequency domain electromagnetics (FEM)]., Survey specifications (flying height, line direction, line separation, tie line spacing and direction), Location of the survey (corner co-ordinates of the survey area in UTM and latitude and longitude), Outline of the actual survey coverage (plan map of survey block outline on planimetric base), Owner of the data at time of acquisition (and contact person if available), Assessment of data quality (where possible, based on the maps or profiles made available by the Contractor/Mining Company who holds the data), Availability of the data for use or acquisition by the AGS (for compilation, resale, in-house research), Media and format that data is available on (paper, digital images, raw digital data, etc.), Asking price for acquiring the data (if available) and the conditions under which it would be made available.

The 3D Land Surveying System market is experiencing robust growth, projected to reach a market size of $1752.7 million in 2025. While the provided CAGR is missing, considering the technological advancements driving automation in surveying and the increasing demand for precise data in infrastructure development and construction, a conservative estimate of a 7% CAGR from 2025 to 2033 is reasonable. This would indicate a significant expansion of the market, driven by factors such as the increasing adoption of advanced technologies like LiDAR and photogrammetry, rising infrastructure investments globally, and the need for efficient and accurate land data for urban planning and environmental monitoring. The market segmentation, encompassing fixed and mobile surveying systems and applications across surveying & mapping, construction, and other sectors, reveals diverse growth opportunities. The preference for mobile systems is likely to increase due to their portability and ease of use, while the construction sector is expected to be a major driver of market growth due to the rising number of construction projects globally. The regional distribution shows substantial potential across North America, Europe, and Asia-Pacific, reflecting the concentration of developed economies and significant infrastructure investments. However, developing regions in the Middle East & Africa and South America are also showing promising growth potential as infrastructure development and urbanization accelerate. Competitive dynamics involve a mix of established surveying firms and emerging technology providers, emphasizing both service-based and technology-driven solutions. The continued integration of AI and machine learning into surveying systems is likely to further enhance the efficiency and accuracy of land surveying, fueling market expansion in the coming years. This combination of technological innovation and growing infrastructural needs ensures a sustained upward trajectory for the 3D Land Surveying System market.

In support of new permitting workflows associated with anticipated WellSTAR needs, the CalGEM GIS unit extended the existing BLM PLSS Township & Range grid to cover offshore areas with the 3-mile limit of California jurisdiction. The PLSS grid as currently used by CalGEM is a composite of a BLM download (the majority of the data), additions by the DPR, and polygons created by CalGEM to fill in missing areas (the Ranchos, and Offshore areas within the 3-mile limit of California jurisdiction).CalGEM is the Geologic Energy Management Division of the California Department of Conservation, formerly the Division of Oil, Gas, and Geothermal Resources (as of January 1, 2020).Update Frequency: As Needed

Student -Vanuatu National University

These are the cadastral reference features that provide the basis and framework for parcel mapping and for other mapping. This feature data set contains PLSS and Other Survey System data. The other survey systems include subdivision plats and those types of survey reference systems. This feature data set also include feature classes to support the special conditions in Ohio. This data set represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.

This data release contains the GIS data supporting U.S. Geological Survey Open-File Report (OFR) 2005-1252, "The Geologic Map of Seattle—A Progress Report," published in 2005 by Kathy Goetz Troost, Derek B. Booth, Aaron P. Wisher, and Scott A. Shimel (https://doi.org/10.3133/ofr20051252). The OFR was prepared for the 2005 Washington Hydrogeology Symposium and describes the status of geologic mapping for Seattle, Washington, at the time. The map is the result of field mapping and compilation of subsurface geologic data during the years 1999–2004 and was funded by the City of Seattle and the U.S. Geological Survey. Data from more than 36,000 exploration points, geotechnical borings, monitoring wells, excavations, and outcrops were used in making the map. The northern part of the 2005 OFR and the supporting GIS data were subsequently published as two geologic maps: Booth, D.B., Troost, K.G., and Shimel, S.A., 2005, Geologic map of northwestern Seattle (part of the Seattle North 7.5’ X 15’ Quadrangle), King County, Washington: U.S. Geological Survey Scientific Investigations Map 2903, https://doi.org/10.3133/sim2903. Booth, D.B., Troost, K.G., and Shimel, S.A., 2009, Geologic map of northeastern Seattle (part of the Seattle North 7.5' x 15' quadrangle), King County, Washington: U.S. Geological Survey Scientific Investigations Map 3065, https://doi.org/10.3133/sim3065. The southern part of the 2005 OFR and the supporting GIS data were not subsequently published for various reasons. With the original authors' permission, the GIS data used to create the map shown in OFR 2005-1252 are being released here to best meet modern open-data standards and to allow for use in future studies and mapping. The data included in this data release are only those components necessary to create the map shown in OFR 2005-1252. The following map features were not available and are not included in this data release: bedding point data, faults, anticlines, and contact lines. OFR_2005-1252.gdb is an Esri geodatabase containing the following feature classes: ofr_2005_1252_geology_poly (1,068 features); ofr_2005_1252_fill_poly (424 features); ofr_2005_1252_seattle_fault_zone_poly (1 feature); ofr_2005_1252_wastage_landslide_deposits_poly (188 features); ofr_2005_1252_beds_line (6 features); and ofr_2005_1252_scarp_line (351 features). Metadata records associated with each of these elements contain more detailed descriptions of their purposes, constituent entities, and attributes. A shapefile (non-geodatabase) version of the dataset is also included, although due to character limits, some field names and text cells in the attribute tables were truncated relative to the equivalent values in the geodatabase. The authors ask that users of the geologic map data cite both the open-file report and the GIS data release: Open-File Report: Troost, K.G., Booth, D.B., Wisher, A.P., and Shimel, S.A., 2005, The geologic map of Seattle—a progress report: U.S. Geological Survey Open-File Report 2005-1252, https://doi.org/10.3133/ofr20051252. GIS data: Troost, K.G., Booth, D.B., Wisher, A.P., and Shimel, S.A., 2024, GIS data for U.S. Geological Survey OFR 2005-1252, The geologic map of Seattle—a progress report: U.S. Geological Survey data release, https://doi.org/10.5066/P93L6SPS.

Point feature class and related table containing the Precise Surveys measurement time series. Measurements include elevations, Northings and Eastings, distances, and point-to-point measurements. Northing and Easting measurements are in CA State Plane Coordinate systems, Elevations measurements are provided in NAVD88 or NGVD29. This dataset is for data exploration only. These measurements and point locations are not considered survey-grade since there may be nuances such as epochs, adjustments, and measurement methods that are not fully reflected in the GIS data. These values are not considered authoritative values and should not be used in-lieu of actual surveyed values provided by a licensed land surveyor. Related data and time series are stored in a table connected to the point feature class via a relationship class. There may be multiple table entries and time series associated to a single mark. Data was assembled through an import of Excel tables and import of mark locations in ArcGIS Pro. Records were edited by DOE, Geomatics, GDSS to resolve any non-unique mark names. This dataset was last updated 4/2024.

This GIS dataset includes onshore seismic surveys undertaken by Geoscience Australia and its predecessors in collaboration with the State and Territory Geological Surveys, National Research Facility for Earth Sounding (ANSIR), AuScope Earth Imaging, Universities and other industry and research partners from 1976 to present. This Shapefile enables users to display seismic lines on a map and contains links to data packages available for free download. The data are also available as a web service (see Related Links).

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

In support of new permitting workflows associated with anticipated WellSTAR needs, the CalGEM GIS unit extended the existing BLM PLSS Township & Range grid to cover offshore areas with the 3-mile limit of California jurisdiction. The PLSS grid as currently used by CalGEM is a composite of a BLM download (the majority of the data), additions by the DPR, and polygons created by CalGEM to fill in missing areas (the Ranchos, and Offshore areas within the 3-mile limit of California jurisdiction).

Stratagex Ltd was contracted by the AGS in 2001 to compile a catalogue of all existing ground and airborne geophysical survey data contained in the archived mining assessment reports of the AGS, supplemented where possible with information on non-exclusive and proprietary surveys from exploration industry and other sources. This data set shows the airborne survey locations and detailed information about the survey including: Type of survey flown [fixed wing or helicopter. barometric (constant elevation) or drape (topographic contour following), Year of data acquisition and contractor, Description of the system flown [any one or combination of magnetics, VLF-EM, radiometrics, time domain electromagnetics (TDEM), frequency domain electromagnetics (FEM)]., Survey specifications (flying height, line direction, line separation, tie line spacing and direction), Location of the survey (corner co-ordinates of the survey area in UTM and latitude and longitude), Outline of the actual survey coverage (plan map of survey block outline on planimetric base), Owner of the data at time of acquisition (and contact person if available), Assessment of data quality (where possible, based on the maps or profiles made available by the Contractor/Mining Company who holds the data), Availability of the data for use or acquisition by the AGS (for compilation, resale, in-house research), Media and format that data is available on (paper, digital images, raw digital data, etc.), Asking price for acquiring the data (if available) and the conditions under which it would be made available.