91 datasets found
  1. F

    Field Data Collection Software Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Jan 25, 2025
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    Market Research Forecast (2025). Field Data Collection Software Report [Dataset]. https://www.marketresearchforecast.com/reports/field-data-collection-software-16606
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Jan 25, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Market Overview The global Field Data Collection Software market has witnessed tremendous growth in recent years, driven by the increasing demand for real-time data collection and analysis. The market size was estimated to be XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033. Key growth drivers include the rising adoption of mobile devices and cloud-based platforms, the need for improved safety and compliance, and the increasing complexity of field operations. Segmentation and Regional Analysis The market is segmented by deployment type (cloud-based and on-premises) and application (environmental, construction, oil and gas, transportation, mining, and others). The environmental segment held the largest market share in 2025, driven by the growing need for environmental monitoring and compliance. Geographically, North America and Europe are the dominant markets, followed by Asia Pacific and the Middle East & Africa. The market in Asia Pacific is expected to witness significant growth in the coming years due to the rapidly expanding construction and mining industries.

  2. m

    Data Collection Software Market Size and Projections

    • marketresearchintellect.com
    Updated Mar 15, 2025
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    Market Research Intellect (2025). Data Collection Software Market Size and Projections [Dataset]. https://www.marketresearchintellect.com/product/global-data-collection-software-market-size-forecast/
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    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Market Research Intellect
    License

    https://www.marketresearchintellect.com/privacy-policyhttps://www.marketresearchintellect.com/privacy-policy

    Area covered
    Global
    Description

    The size and share of the market is categorized based on Type (Survey software, Form builders, Data gathering apps, Feedback collection tools, Field data collection tools) and Application (Market research, Customer feedback, Academic research, Field surveys, Product testing) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

  3. u

    SSBSE Proceedings Data Collection

    • rdr.ucl.ac.uk
    xlsx
    Updated Jun 24, 2020
    + more versions
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    Thelma Elita Colanzi; Wesley K. G. Assunção; Silvia R. Vergilio; Paulo Roberto Farah; Giovani Guizzo (2020). SSBSE Proceedings Data Collection [Dataset]. http://doi.org/10.5522/04/12554366.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 24, 2020
    Dataset provided by
    University College London
    Authors
    Thelma Elita Colanzi; Wesley K. G. Assunção; Silvia R. Vergilio; Paulo Roberto Farah; Giovani Guizzo
    License

    https://www.apache.org/licenses/LICENSE-2.0.htmlhttps://www.apache.org/licenses/LICENSE-2.0.html

    Description

    This is the result of data collection over the proceedings of the Symposium on Search Based Software Engineering (SSBSE). It contains information about each paper ever published at SSBSE, including citation counts, field of application, and more.The data was used as source for our work "The Symposium on Search-Based Software Engineering: Past, Present and Future", accepted at the Information and Software Technology journal in 2020.

  4. i15 LandUse Marin2011

    • data.ca.gov
    • data.cnra.ca.gov
    • +4more
    Updated Feb 16, 2022
    + more versions
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    California Department of Water Resources (2022). i15 LandUse Marin2011 [Dataset]. https://data.ca.gov/dataset/i15-landuse-marin2011
    Explore at:
    html, arcgis geoservices rest apiAvailable download formats
    Dataset updated
    Feb 16, 2022
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2011 Marin County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Marin County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during June 2011 by staff visiting each field and noting what was grown. Land use field boundaries were digitized using ArcGIS 9.3 then ArcGIS 10.0 using 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. To facilitate digitizing, Marin was divided in 2 portions, the Point Reyes area and all other areas of Marin County. These two areas were recombined after each portion was finished. The outer boundary of this land use survey coincides with the county line revisions completed by the California Department of Forestry and Fire Protection in 2009. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field using ESRI ArcMAP software, version 10.0. Global positioning system (GPS) units connected to the laptops were used to confirm the field team's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. The field team used a customized menu program to facilitate the gathering of field data. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

  5. A

    ‘Marin County Land Use Survey 2011’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jun 7, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘Marin County Land Use Survey 2011’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-marin-county-land-use-survey-2011-3939/6cf809ba/?iid=017-758&v=presentation
    Explore at:
    Dataset updated
    Jun 7, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Marin County
    Description

    Analysis of ‘Marin County Land Use Survey 2011’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2d2b915b-c6e9-48bb-bbbe-84cd71f7dc22 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2011 Marin County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Marin County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during June 2011 by staff visiting each field and noting what was grown. Land use field boundaries were digitized using ArcGIS 9.3 then ArcGIS 10.0 using 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. To facilitate digitizing, Marin was divided in 2 portions, the Point Reyes area and all other areas of Marin County. These two areas were recombined after each portion was finished. The outer boundary of this land use survey coincides with the county line revisions completed by the California Department of Forestry and Fire Protection in 2009. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field using ESRI ArcMAP software, version 10.0. Global positioning system (GPS) units connected to the laptops were used to confirm the field team's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. The field team used a customized menu program to facilitate the gathering of field data. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

    --- Original source retains full ownership of the source dataset ---

  6. i15 LandUse Stanislaus2004

    • data.cnra.ca.gov
    • data.ca.gov
    • +5more
    Updated Feb 16, 2022
    + more versions
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    California Department of Water Resources (2022). i15 LandUse Stanislaus2004 [Dataset]. https://data.cnra.ca.gov/dataset/i15-landuse-stanislaus2004
    Explore at:
    arcgis geoservices rest api, kml, html, zip, csv, geojsonAvailable download formats
    Dataset updated
    Feb 16, 2022
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisionaldata sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2004 Stanislaus County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use data was gathered and reviewed by DWR staff using extensive field visits, 2004 National Agriculture Imagery Program (NAIP) aerial photography and Landsat 5 imagery. NAIP imagery from 2004 was used for data review. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of central and eastern Stanislaus County. The northern, eastern and southern boundaries are defined by the Stanislaus County boundary. The western extent of the survey area extends to the western edges of the Solyo (U.S.G.S. No. 37121E3) and Howard Ranch (U.S.G.S. No. 37121B1) 7.5’ quadrangles and is also bounded by the western and southern borders of the Copper Mountain (U.S.G.S. No. 37121D3) and Orestimba Peak (U.S.G.S. No. 37121C2) quadrangles. Land use boundaries were developed by updating line work from DWR's 2004 land use survey of Stanislaus County. Boundaries were modified on a quadrangle by quadrangle basis. Roads were delineated using the U.S. Census Bureau's TIGER®(Topologically Integrated Geographic Encoding and Referencing) database as guidelines. Other land use boundaries were adjusted and new fields were added based upon 2009 NAIP imagery. Field boundaries were drawn to depict observable areas of the same crop or other land uses and are not intended to represent legal parcel (ownership) boundaries. In this survey, some areas of creeks and rivers were included within polygons of riparian areas and not delineated separately. The primary field data collection for this survey was conducted between July 2010 and February 2011 by DWR staff from the South Central Region Office who visited each field and noted what was grown at that time. Supplemental field visits took place from April 28 through June 14, 2010 and from July 12 through August 3, 2010 when randomly selected fields were visited by SIWM staff to collect data for mapping crops using Landsat imagery analysis. For field data collection, 2009 NAIP imagery and vector files of land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. Some surveyors also used Landsat 5 imagery for the field survey. GPS units connected to the laptops were used to confirm the surveyors’ locations with respect to the fields. Virtually all agricultural fields were visited to positively identify the land use. Land use codes were entered in the field on laptop computers using ESRI ArcMAP software, version 9.3. Some staff took printed aerial photos into the field and wrote directly onto these photo field sheets. Attribute data from photo field sheets were coded and entered back in the office. Any necessary field boundary changes were digitized at the same time. In addition to the identification of crops through the collection of data in the field, a supervised classification of Landsat 5 data was used to identify fields with winter crops. The Landsat images of a selection of fields mapped by surveyors as grain, spinach, lettuce or fallow were reviewed using a time series of Landsat 5 images to confirm that the pattern of vegetation over time was consistent with the expected pattern for these crops. The selected fields were then used to develop spectral signatures for the represented crop categories using ERDAS Imagine and eCognition Developer software. Two Landsat 5 images, March 16, 2010 and April 17, 2010, were selected for identifying winter crops using a maximum likelihood supervised classification. The classified images were used to calculate zonal attributes for fields mapped during the summer survey as field crops, truck crops or fallow. Fields mapped during the survey as winter truck crops or grains were also included. For the fields that were classified as winter crops, a time series of Landsat imagery was reviewed for consistency with the classification results. Fields for which the identified winter crops were confirmed by the review of time series data were added to the shapefile database using the special condition “U”, indicating that they were identified by a method other than having been mapped during the field survey. To identify fields with summer crops that were missed during the field survey, fields identified as fallow were reviewed using 2010 NAIP and Landsat 5 imagery. Where the imagery indicated that crops had been produced, the attributes of these fields were changed to identify them as cropped. They are also labeled with special condition "U". Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

  7. A

    ‘Marin County Land Use Survey 2011’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jun 7, 2020
    + more versions
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘Marin County Land Use Survey 2011’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-marin-county-land-use-survey-2011-37d3/latest
    Explore at:
    Dataset updated
    Jun 7, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Marin County
    Description

    Analysis of ‘Marin County Land Use Survey 2011’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/af572d1a-4313-43a5-9f1f-1d020093afb1 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2011 Marin County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Marin County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during June 2011 by staff visiting each field and noting what was grown. Land use field boundaries were digitized using ArcGIS 9.3 then ArcGIS 10.0 using 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. To facilitate digitizing, Marin was divided in 2 portions, the Point Reyes area and all other areas of Marin County. These two areas were recombined after each portion was finished. The outer boundary of this land use survey coincides with the county line revisions completed by the California Department of Forestry and Fire Protection in 2009. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field using ESRI ArcMAP software, version 10.0. Global positioning system (GPS) units connected to the laptops were used to confirm the field team's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land uses. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. The field team used a customized menu program to facilitate the gathering of field data. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

    --- Original source retains full ownership of the source dataset ---

  8. a

    Geographic Response Plan (GRP) Staging Areas

    • hub.arcgis.com
    • geodata.myfwc.com
    • +1more
    Updated Jan 15, 2015
    + more versions
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    Florida Fish and Wildlife Conservation Commission (2015). Geographic Response Plan (GRP) Staging Areas [Dataset]. https://hub.arcgis.com/datasets/aa154cffcc004ee8ba4e02865b910bb3
    Explore at:
    Dataset updated
    Jan 15, 2015
    Dataset authored and provided by
    Florida Fish and Wildlife Conservation Commission
    Area covered
    Description

    For full FGDC metadata record, please click here.These data represent Staging and Response Locations collected by GPS for Mississippi, Alabama, and the Florida Panhandle prior to the Deepwater Horizon Oil Spill. The locations for the Peninsular portion of Florida, Georgia, South Carolina, Puerto Rico, and the US Virgin Islands have been compiled from numerous sources into this database schema and will at some later date (after Nov. 2010) be verified and validated by GPS. Staging and response locations were identified first by defining the types of locations that fit these descriptions. The broad categories were defined as Boat Ramp, Marina, Staging Area, or any combination of these. A marina may contain a boat ramp as well as a large parking lot with a seawall suitable for deploying equipment into the water. A staging area may contain just a waterfront park with access to the water, but no boat ramp or marina, but perhaps a dock or pier. These categories and attributes were used to design a specific database schema to collect information on these geographic features that could be used on a GPS-enabled field data collection device. Once the categories of information to be collected and the specifics of what types of information to be collected within each category were determined (the database schema), mobile devices were programmed to accomplish this task and area committee volunteers were used to conduct the field surveys. Field crews were given training on the devices. Guided by base maps identifying potential locations, they then traveled into the field to validate and collect specific GPS and attribute data on those locations. This was a cooperative effort between many federal, state, and local entities guided by FWC-FWRI that resulted in detailed and location-specific information on 366 staging area locations within Sector Mobile and a comprehensive GIS data set that is available on the DVD ROM and website as well a being used in the Geographic Response Plan Map Atlas production. Cyber-Tracker was the software used for this field data collection. Cyber-Tracker is a "shareware" software package developed as a data-capture tool designed for use in Environmental Conservation, Wildlife Biology and Disaster Relief. The software runs on numerous types of mobile devices and designing custom data capture processes for these devices requires no programming experience. Funded in large part by the European Commission and patroned by Harvard University, Cyber-Tracker Software has been a very valuable tool in the data collection efforts of this project. Cyber-Tracker Software can be found on the Internet at: http://www.cybertracker.co.za/.

  9. Global Forestry Software Market Size By Type (On-Premise Forestry Software,...

    • verifiedmarketresearch.com
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    VERIFIED MARKET RESEARCH, Global Forestry Software Market Size By Type (On-Premise Forestry Software, Cloud-Based Forestry Software), By Competitive Landscape, By Geographic Scope And Forecast [Dataset]. https://www.verifiedmarketresearch.com/product/forestry-software-market/
    Explore at:
    Dataset provided by
    Verified Market Researchhttps://www.verifiedmarketresearch.com/
    Authors
    VERIFIED MARKET RESEARCH
    License

    https://www.verifiedmarketresearch.com/privacy-policy/https://www.verifiedmarketresearch.com/privacy-policy/

    Time period covered
    2024 - 2031
    Area covered
    Global
    Description

    Forestry Software Market size was valued at USD 1.29 Billion in 2024 and is projected to reach USD 7.95 Billion by 2031, growing at a CAGR of 22.39% during the forecasted period 2024 to 2031.

    The Forestry Software Market is experiencing significant growth driven by several factors. Firstly, increasing global concerns regarding deforestation, environmental conservation, and sustainable forestry practices are compelling forestry organizations to adopt digital solutions for efficient management of resources. Secondly, technological advancements, such as Geographic Information System (GIS) integration, remote sensing, and cloud computing, are enhancing the capabilities of forestry software, enabling better decision-making processes and resource optimization. Thirdly, the rising demand for timber, coupled with the need for improved operational efficiency and cost reduction in forestry operations, is driving the adoption of software solutions for inventory management, harvest planning, and logistics optimization. Moreover, regulatory requirements for compliance with environmental standards and certification programs are further incentivizing the adoption of forestry software solutions. Additionally, the emergence of mobile-based applications and field data collection tools is facilitating real-time monitoring and data analysis, contributing to market growth.

  10. Type of medical domain attacked (n = 200).

    • figshare.com
    xls
    Updated Jun 1, 2023
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    Rohini J. Haar; Casey B. Risko; Sonal Singh; Diana Rayes; Ahmad Albaik; Mohammed Alnajar; Mazen Kewara; Emily Clouse; Elise Baker; Leonard S. Rubenstein (2023). Type of medical domain attacked (n = 200). [Dataset]. http://doi.org/10.1371/journal.pmed.1002559.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Rohini J. Haar; Casey B. Risko; Sonal Singh; Diana Rayes; Ahmad Albaik; Mohammed Alnajar; Mazen Kewara; Emily Clouse; Elise Baker; Leonard S. Rubenstein
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Type of medical domain attacked (n = 200).

  11. c

    Archive of Digital Boomer Seismic Reflection Data Collected During USGS...

    • s.cnmilf.com
    • data.usgs.gov
    • +3more
    Updated Jul 6, 2024
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    U.S. Geological Survey (2024). Archive of Digital Boomer Seismic Reflection Data Collected During USGS Field Activity 08LCA01 in 10 Central Florida Lakes, March 2008 [Dataset]. https://s.cnmilf.com/user74170196/https/catalog.data.gov/dataset/archive-of-digital-boomer-seismic-reflection-data-collected-during-usgs-field-activity-08l-8b128
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Central Florida
    Description

    In March of 2008, the U.S. Geological Survey and St. Johns River Water Management District (SJRWMD) conducted geophysical surveys in Lakes Avalon, Big, Colby, Helen, Johns, Prevatt, Searcy, Saunders, Three Island, and Trout, located in central Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, FACS logs, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public _domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/j/j108fl/html/j-1-08-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

  12. A

    ‘Tulare County Land Use Survey 2007’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Sep 15, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘Tulare County Land Use Survey 2007’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-tulare-county-land-use-survey-2007-f352/e91176bd/?iid=027-493&v=presentation
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    Dataset updated
    Sep 15, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Tulare County
    Description

    Analysis of ‘Tulare County Land Use Survey 2007’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/24d8af24-fd48-4b6b-bd2a-f9aaed5bd880 on 12 February 2022.

    --- Dataset description provided by original source is as follows ---

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisionaldata sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2007 Tulare County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM), Water Use Efficiency Branch (WUE). Digitized land use boundaries and associated attributes were gathered by staff from DWR’s South Central Region (SCRO), using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Prior to the summer field survey by SCRO, WUE staff analyzed Landsat 5 imagery to identify fields likely to have winter crops. The combined land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s WUE Land Use Unit and SCRO. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of western Madera County conducted by DWR, South Central Regional Office staff, under the leadership of Steve Ewert, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2011. SCRO staff physically visited each delineated field, noting the crops grown at each location. Land use field boundaries were digitized using 2006 National Agriculture Imagery Program (NAIP) imagery as the base reference. Roads and waterways were delineated from a countywide shapefile using the U.S. Census Bureau's TIGER® (Topologically Integrated Geographic Encoding and Referencing) database and then clipped to match the USGS quadrangle boundaries. Digitized field boundaries were created on a quadrangle by quadrangle basis. Digitizing was completed at 1:4000 scale for the entire survey area. Field boundaries were delineated to depict observable areas of the same (homogeneous) land use type. Field boundaries do not represent legal parcel (ownership) boundaries, and are not meant to be used as formal parcel boundaries. Field work for DWR land use surveys typically occur during the summer and early fall agricultural seasons, so it can be difficult to identify fields where winter crops have been produced earlier during the survey year. To improve the mapping of winter crops, Landsat 5 imagery was analyzed to identify fields with high vegetative cover in late winter/early spring. Visual inspection of the Landsat scene displayed in false color infrared was used to select fields with both high and low vegetative cover as training data sets. These fields were used to develop spectral signatures using ERDAS Imagine and eCognition Developer software. The Landsat image was classified using a maximum likelihood supervised classification to label each pixel as vegetated or not vegetated. Then, the zonal attributes of polygons representing agricultural fields were summarized to identify fields vegetated during the winter. Polygons representing potential winter crops were used as an additional reference during field visits, and closely checked for winter crop residue. Site visits occurred from July through October 2007. Images and land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. GPS units connected to the laptops were used to confirm the surveyor's location with respect to each field. Some staff took printed copies of aerial photos into the field and wrote directly onto these photo field sheets. The data from the photo field sheets were digitized and entered back in the office. Land use codes associated with each polygon were entered in the field on laptop computers using ESRI ArcGIS software, version 9.3. Virtually all delineated fields were visited to positively observe and identify the land use type. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR's South Central Region, and at DSIWM headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

    --- Original source retains full ownership of the source dataset ---

  13. m

    Mobile Forms Automation Software Market Size and Projections

    • marketresearchintellect.com
    Updated Mar 15, 2025
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    Market Research Intellect (2025). Mobile Forms Automation Software Market Size and Projections [Dataset]. https://www.marketresearchintellect.com/product/global-mobile-forms-automation-software-market-size-and-forecast/
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    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Market Research Intellect
    License

    https://www.marketresearchintellect.com/privacy-policyhttps://www.marketresearchintellect.com/privacy-policy

    Area covered
    Global
    Description

    The size and share of the market is categorized based on Application (Field service technicians, Sales representatives, Inspectors and auditors, Maintenance workers, Logistics and delivery personnel, Healthcare professionals) and Product (Offline forms software, Mobile data collection software, Field service automation software, Inspection forms software, Enterprise mobility software) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

  14. A

    ‘Alpine County Land Use Survey 2013’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jun 7, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘Alpine County Land Use Survey 2013’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-alpine-county-land-use-survey-2013-1fa1/latest
    Explore at:
    Dataset updated
    Jun 7, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Alpine County
    Description

    Analysis of ‘Alpine County Land Use Survey 2013’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b01b91a9-6731-4e52-a898-edc07fb2213c on 12 February 2022.

    --- Dataset description provided by original source is as follows ---

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2013 Alpine County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. The land uses that were mapped were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.

    This data represents a land use survey of Alpine County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during September 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

    --- Original source retains full ownership of the source dataset ---

  15. d

    Data from: Multibeam bathymetric data collected in the vicinity of Woods...

    • catalog.data.gov
    • data.usgs.gov
    Updated Feb 22, 2025
    + more versions
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    U.S. Geological Survey (2025). Multibeam bathymetric data collected in the vicinity of Woods Hole, Massachusetts, during USGS Field Activity 2021-037-FA using a dual-head Teledyne Seabat T20-R multibeam echo sounder (32-bit GeoTIFF, UTM Zone 19N, WGS 84, GEOID 18 (MSL) Vertical Datum, 50cm resolution) [Dataset]. https://catalog.data.gov/dataset/multibeam-bathymetric-data-collected-in-the-vicinity-of-woods-hole-massachusetts-during-us
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    Dataset updated
    Feb 22, 2025
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Woods Hole, Massachusetts
    Description

    In November 2021, the U.S. Geological Survey collected high-resolution multibeam sonar data in the vicinity of Eel Pond, in Woods Hole, Massachusetts using a dual-head Teledyne Seabat T20-R multibeam echo sounder (MBES). The main objective of this survey was to evaluate new sonar system features prior to their use in future field activities. In addition to bathymetry and relative acoustic backscatter data, normalized acoustic backscatter data were also collected. Unlike relative backscatter data, normalized backscatter data compensate for adjustments made to sonar power, gain, absorption, spreading, and frequency parameters made during acquisition. In order for backscatter intensity levels to remain consistent along survey lines, and from line to line, relative backscatter data require that minimal adjustments are made to these parameters during acquisition, which can degrade the sonar performance for a given survey site. However, the ability to allow the sonar acquisition software to change sonar parameters based on variations in bathymetry and the survey environment during acquisition allows these parameters to be optimized. Having these parameters optimized for this survey allowed the USGS to evaluate this new normalized backscatter capability to ensure the collected backscatter intensity levels were referenced to a factory calibrated level. Eel Pond in Woods Hole, MA was chosen as a test area for its proximity to the USGS Coastal and Marine Science Center. It provides a variety of substrates on which to evaluate the performance of the sonar, and bathymetric/backscatter data of this area may prove useful to other projects and institutions in the area.

  16. i15 LandUse Alpine2013

    • data.ca.gov
    • data.cnra.ca.gov
    • +6more
    Updated Feb 16, 2022
    + more versions
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    California Department of Water Resources (2022). i15 LandUse Alpine2013 [Dataset]. https://data.ca.gov/dataset/i15-landuse-alpine2013
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    kml, geojson, csv, arcgis geoservices rest api, zip, htmlAvailable download formats
    Dataset updated
    Feb 16, 2022
    Dataset authored and provided by
    California Department of Water Resourceshttp://www.water.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This map is designated as Final.

    Land-Use Data Quality Control

    Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.

    Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.

    Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.

    The 2013 Alpine County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. The land uses that were mapped were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.

    This data represents a land use survey of Alpine County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during September 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

  17. I

    Integrated Hydrological Survey Software System Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Feb 12, 2025
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    Data Insights Market (2025). Integrated Hydrological Survey Software System Report [Dataset]. https://www.datainsightsmarket.com/reports/integrated-hydrological-survey-software-system-1929819
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    ppt, pdf, docAvailable download formats
    Dataset updated
    Feb 12, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global integrated hydrological survey software system market is projected to grow from an estimated USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX% during the forecast period 2025-2033. This growth can be attributed to the increasing need for accurate and timely hydrological data for various applications such as flood forecasting, water resources management, and water quality monitoring. Key drivers of the market include the rising demand for real-time hydrological data, the growing adoption of advanced technologies such as remote sensing and GIS, and the increasing emphasis on environmental conservation. Additionally, various government initiatives and regulations aimed at promoting water resource management are expected to further boost the market growth. The market is segmented into application, type, and region. Based on application, the business development segment is expected to hold the largest market share due to the growing need for hydrological data for infrastructure planning and development. By type, the data acquisition software segment is expected to dominate the market due to its wide adoption in field data collection. Geographically, North America is expected to hold the largest market share, followed by Europe and Asia-Pacific.

  18. Z

    Data from: Data Management and Sharing: Practices and Perceptions of...

    • data.niaid.nih.gov
    • zenodo.org
    • +1more
    Updated Jun 3, 2022
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    Borghi, John (2022). Data Management and Sharing: Practices and Perceptions of Psychology Researchers [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3961956
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    Dataset updated
    Jun 3, 2022
    Dataset provided by
    Borghi, John
    Van Gulick, Ana
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

  19. d

    usSEABED FACies data for the New York-New Jersey Region (NYNJ_FAC).

    • datadiscoverystudio.org
    • data.usgs.gov
    • +3more
    htm, txt, zip
    Updated May 19, 2018
    + more versions
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    (2018). usSEABED FACies data for the New York-New Jersey Region (NYNJ_FAC). [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/af9f7ee2d9a242a3a18985c85b41fa8b/html
    Explore at:
    zip, txt, htmAvailable download formats
    Dataset updated
    May 19, 2018
    Description

    description: The facies data layer (FAC) is a point coverage of known sediment samplings, inspections, and probings from the usSEABED data collection and integrated using the software system dbSEABED. The facies data layer (FAC) represents concatenated information about components (minerals and rock type), genesis (igneous, metamorphic, carbonate, terrigenous), and other appropriate groupings of information about the seafloor. The facies data are parsed from written descriptions from cores, grabs, photographs, and videos, and may apply only to a subsample as denoted by the Top, Bottom, and SamplePhase fields. Lack of values in a defined facies field does not necessarily imply lack of the components defining that field, but may imply a lack of data for that field.; abstract: The facies data layer (FAC) is a point coverage of known sediment samplings, inspections, and probings from the usSEABED data collection and integrated using the software system dbSEABED. The facies data layer (FAC) represents concatenated information about components (minerals and rock type), genesis (igneous, metamorphic, carbonate, terrigenous), and other appropriate groupings of information about the seafloor. The facies data are parsed from written descriptions from cores, grabs, photographs, and videos, and may apply only to a subsample as denoted by the Top, Bottom, and SamplePhase fields. Lack of values in a defined facies field does not necessarily imply lack of the components defining that field, but may imply a lack of data for that field.

  20. U

    Urban Forestry Software Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Feb 11, 2025
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    Archive Market Research (2025). Urban Forestry Software Report [Dataset]. https://www.archivemarketresearch.com/reports/urban-forestry-software-18244
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Feb 11, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global urban forestry software market is projected to reach a value of USD 121 million by 2033, expanding at a CAGR of XX% during the forecast period (2025-2033). This growth is primarily driven by the increasing demand for efficient and cost-effective urban forestry management solutions. Urban forestry software streamlines data collection, analysis, and planning processes, enabling municipalities and other stakeholders to optimize tree inventory management, tree maintenance, and emergency response. Key trends in the urban forestry software market include the adoption of cloud-based solutions, the integration of mobile technologies, and the growing emphasis on data analytics. Cloud-based software offers flexibility, scalability, and remote access, making it an attractive option for organizations. Mobile applications enhance field data collection and provide real-time updates, increasing efficiency and accuracy. Data analytics enables organizations to gain valuable insights into tree health, canopy cover, and other aspects of urban forestry management, allowing them to make data-driven decisions. The market is segmented based on type (cloud-based and on-premise) and application (government, forestry companies, and others).

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Market Research Forecast (2025). Field Data Collection Software Report [Dataset]. https://www.marketresearchforecast.com/reports/field-data-collection-software-16606

Field Data Collection Software Report

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Dataset updated
Jan 25, 2025
Dataset authored and provided by
Market Research Forecast
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https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

Time period covered
2025 - 2033
Area covered
Global
Variables measured
Market Size
Description

Market Overview The global Field Data Collection Software market has witnessed tremendous growth in recent years, driven by the increasing demand for real-time data collection and analysis. The market size was estimated to be XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033. Key growth drivers include the rising adoption of mobile devices and cloud-based platforms, the need for improved safety and compliance, and the increasing complexity of field operations. Segmentation and Regional Analysis The market is segmented by deployment type (cloud-based and on-premises) and application (environmental, construction, oil and gas, transportation, mining, and others). The environmental segment held the largest market share in 2025, driven by the growing need for environmental monitoring and compliance. Geographically, North America and Europe are the dominant markets, followed by Asia Pacific and the Middle East & Africa. The market in Asia Pacific is expected to witness significant growth in the coming years due to the rapidly expanding construction and mining industries.

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