As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

The size of the Government Cloud market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 17.13% during the forecast period. Recent developments include: October 2022 - Oracle announced multi-cloud offerings for the Indian government as it doubles down on modernizing its infrastructure in the digital era; there is an excellent demand for multi-Cloud offerings among the government stakeholders, and by introducing the multi-cloud users can migrate or build new applications on Azure and then connect to high-performance and high-availability managed Oracle Database services such as Autonomous Database running on Oracle Cloud Infrastructure (OCI)., August 2022 - Google Cloud announced its collaboration (MoU) with Singapore's National AI to build artificial intelligence applications, train public sector officers on AI, and create test and scale AI applications in key areas such as finance, sustainability, and healthcare.. Key drivers for this market are: 6.1 Need for Greater Storage Capabilities is Driving the Market Demand6.2 Need for Data Transparency are Expanding the Market. Potential restraints include: 7.1 Cloud Computing Skills Gap is Hindering the Market Growth. Notable trends are: Need for Greater Cloud Storage Capabilities to witness growth.

The Government Cloud market, valued at $35.48 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 17.13% from 2025 to 2033. This significant expansion is driven by several key factors. Increased government initiatives to digitize services and enhance citizen engagement are fueling demand for secure and scalable cloud solutions. The need for improved data management, enhanced cybersecurity, and efficient resource allocation further propels market growth. Furthermore, the transition from legacy IT infrastructure to cloud-based systems offers significant cost savings and operational efficiencies for government agencies. The market is segmented by deployment model (public, private, hybrid cloud), delivery mode (IaaS, PaaS, SaaS), and application (server & storage, disaster recovery, security & compliance, analytics, content management). Major players like Amazon Web Services, Microsoft Azure, Google Cloud Platform, and others are actively competing to capture market share, offering specialized solutions tailored to the unique needs and security requirements of government organizations. This competitive landscape fosters innovation and drives down costs, benefiting governments worldwide. The regional distribution of the Government Cloud market reveals a strong presence in North America, likely driven by advanced technological adoption and robust government digitalization initiatives. Europe and Asia-Pacific are also expected to witness substantial growth, reflecting increasing investments in digital infrastructure and cloud adoption across various government departments. While specific regional market share data is unavailable, it's plausible to anticipate a significant concentration in North America, followed by Europe and Asia-Pacific, with Latin America and the Middle East & Africa exhibiting moderate growth as their digital infrastructure matures. The adoption of cloud-based solutions in government functions is expected to continue its upward trajectory, driven by ongoing digital transformation efforts, improved security features, and the need for flexible and scalable IT infrastructure. The continued evolution of cloud technology and the development of specialized government cloud services will shape the market's future trajectory. Recent developments include: October 2022 - Oracle announced multi-cloud offerings for the Indian government as it doubles down on modernizing its infrastructure in the digital era; there is an excellent demand for multi-Cloud offerings among the government stakeholders, and by introducing the multi-cloud users can migrate or build new applications on Azure and then connect to high-performance and high-availability managed Oracle Database services such as Autonomous Database running on Oracle Cloud Infrastructure (OCI)., August 2022 - Google Cloud announced its collaboration (MoU) with Singapore's National AI to build artificial intelligence applications, train public sector officers on AI, and create test and scale AI applications in key areas such as finance, sustainability, and healthcare.. Key drivers for this market are: 6.1 Need for Greater Storage Capabilities is Driving the Market Demand6.2 Need for Data Transparency are Expanding the Market. Potential restraints include: 6.1 Need for Greater Storage Capabilities is Driving the Market Demand6.2 Need for Data Transparency are Expanding the Market. Notable trends are: Need for Greater Cloud Storage Capabilities to witness growth.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The Master Data Management (MDM) market is experiencing robust growth, projected to reach $15.33 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.93% from 2025 to 2033. This expansion is driven by the increasing need for businesses to consolidate and manage data from disparate sources to improve operational efficiency, enhance customer experience, and gain valuable insights for strategic decision-making. The rising adoption of cloud-based MDM solutions, fueled by scalability and cost-effectiveness, is a significant trend shaping the market landscape. Furthermore, the expanding adoption of MDM across diverse industries, particularly in IT and telecommunications, BFSI (Banking, Financial Services, and Insurance), and healthcare, contributes to the market's growth. However, factors such as the complexity of MDM implementation and the high initial investment costs can act as restraints. The market is segmented by component (software and services), deployment model (on-premise and cloud), enterprise size (large enterprises and SMEs), application (supplier, product, customer, and others), and industry vertical (IT & Telecom, BFSI, Healthcare, Government, Retail, Manufacturing, Education, and others). Leading vendors like SAS Institute, Informatica, TIBCO, and Oracle are actively competing to capture market share by offering innovative solutions and expanding their service portfolios. The projected growth trajectory of the MDM market suggests substantial opportunities for technology providers and businesses alike. The increasing volume and variety of data generated necessitate effective MDM strategies to ensure data quality, consistency, and accessibility. This demand is further amplified by the rise of data-driven decision-making across industries. While challenges related to implementation and costs exist, the benefits of improved data governance, enhanced business intelligence, and reduced operational risks are compelling enough to drive further market adoption. The shift towards cloud-based solutions is expected to continue, offering greater flexibility and scalability to businesses of all sizes. The competitive landscape is dynamic, with established players and emerging companies constantly innovating to cater to the evolving needs of the market. Recent developments include: May 2022 - Informatica and Oracle announced a strategic relationship that will enable Oracle Cloud Infrastructure (OCI) to use Informatica's product data integration and governance technologies. Additionally, Oracle has designated Informatica as a preferred partner for data governance and enterprise cloud data connectivity for lakehouse and data warehouse solutions on OCI. Oracle and Informatica have joined forces through this project to connect their products: Oracle Object Storage, Oracle Exadata Cloud Customer, Oracle Autonomous Database, and Oracle Exadata Database Service. These services will be made accessible in the Oracle Cloud Marketplace by Informatica and Oracle., March 2022 - Cloud-native Intelligent Multi-domain Master Data Management (MDM), launched by Informatica, an enterprise cloud data management company, will give clients trustworthy views of business-critical master data across all domains and assets, enhancing every function with intelligent data.. Key drivers for this market are: Increasing Demand for Verification and Compliance, Growing Usage of Data Quality Tools for Data Management. Potential restraints include: Expensive Integration and Maintenance activities, Concerns on Data Security and Privacy; Stringent Data Regulations Imposed in Various Regions. Notable trends are: Cloud MDM Segment to Hold a Significant Share.

(Link to Metadata) The EnvironPollution_ENVPTS2001 data layer is based on the U.S. EPA's Envirofacts point shapefile. The data was provided to VCGI by the Vermont Agency of Natural Resources (ANR). The Envirofacts (EF) point shapefile layer in the National Shapefile Repository provides locations of EPA-regulated facilities from the Envirofacts Oracle table LRT_EF_COVERAGE_SRC, which is located within the Locational Reference Tables (LRT) contained in the Envirofacts Oracle Database. The spatial extent for this point shapefile is the conterminous U.S., Alaska, Hawaii, Puerto Rico, and the U.S. Virgin Islands. Facility data from various EPA program system tables are loaded into the LRT_EF_COVERAGE_SRC table. Only coordinate pairs with the highest accuracy values will represent each facility. The Envirofacts point shapefile contains data from the following EPA program systems: AFS - Aerometric Information Retrieval System (AIRS) Facility Subsystem PCS - Permit Compliance System TRIS - Toxic Release Inventory System CERCLIS - Comprehensive Environmental Response, Compensation, and Liability Information System RCRIS - Resource Conservation and Recovery Information System BRS - Biennial Reporting System Note: You can download additional facility information from http://www.epa.gov/enviro.

Throughout the Pacific Northwest, stream habitat degradation has been cited as a factor contributing to the decline and ESA listing of of Pacific Salmon. Thus, stream habitat restoration is a major component of recovery plans as a method to increase salmon population productivity. Over 10 years after the majority of salmon listings we now have many datasets available to evaluate salmon habitat restoration placement, including our restoration projects database (Pacific Northwest Salmon Habitat Project Database), habitat assessments, salmon recovery plans, and spatial habitat mapping. By creating data dictionaries and metrics to standardize and analyze currently available datasets (e.g., restoration data, recovery plans, monitoring data, population abundance data) evaluations can be made to determine whether the right types of habitat actions are going in where they are most valuable. Further, a quantitative framework can be developed to link habitat conditions to fish population condition through stream restoration actions. This information can them be provided to local groups and agencies involved in salmon recovery to help inform the adaptive management process. Pacific Northwest Salmon Habitat Project Oracle Database.

The AIMS Bioresources Library contained almost 20,000 entities, including extracts from over 7,600 samples of marine micro-organisms, frozen material and over 9,000 cryopreserved marine-derived micro-organisms. Biodiscovery is the sourcing of native biological material including plants, animals, fungi and microorganisms to identify bioactive compounds genes, enzymes and other proteins that may be used for commercial purposes such as pharmaceuticals and insecticides. AIMS has been involved in biodiscovery for 15 years and has explored Australia's mega-marine biodiversity for attributes with commercial application. The cornerstone of AIMS' biodiscovery effort is its substantial marine Bioresources Library. This collection has been sourced from over 1,500 sites across Australia. An Oracle database for the AIMS Bioresources Library was developed to contain information of taxonomy, housekeeping (location and nature of samples including taxonomic vouchers, extracts, fractions, pure compounds, frozen cultures), and biodiscovery research history (e.g. screening and structure elucidation results, dispatches to various external parties, etc). The database includes images most organisms and records the results of an array of bioassay tests which have varied over time with different programs and collaborators, and include anti-cancer, AIDS, anti-biotic and enzyme inhibition assays. The taxonomic data is available for release as long as the master sample numbers are not used as unique sample identifiers, e.g. OBIS. Requests for selected data release will be considered on a case by case basis as some information is commercial in confidence and may be subject to contract conditions. The database aimed to: -collate taxonomic and biogeographic details -link taxonomy and biogeography with bioactivity, and facilitate data mining -track the use of samples in their various forms -ensure compliance with contracts and access/benefit sharing agreements and permits -generate reports to regulatory authorities and jurisdictions of origin, on the use of material A subset of the data has been provided to the Ocean Biogeographic Information System (OBIS, http://iobis.org/explore/#/dataset/123l).

World-wide Ocean Optics Database

A very large volume of ocean optics-related data exist in numerous data formats and at many institutions around the world. This project is collecting all of these data, converting them to a single data format, and storing them in an Oracle database that is accessible over the internet at "http://wood.jhuapl.edu". The homepage has a link to maps showing data locations (primarily of chlorophyll profiles). Where possible, the data sets are quality- checked. The majority of the data collected to date are chlorophyll pigment profiles and diffuse attenuation coefficient profiles. Other key variables include the beam attenuation coefficient (c), the backscattering coefficient, backscatter, temperature, and salinity. Other parameters of interest are listed in the Table below. The data have also been merged with the 5 nmi resolution ocean bathymetry database known as DBDB5 so that one can search for data within a user-defined depth range. The optics database includes pertinent information such as wavelength, bandwidth, sensor type, cruise identifier, and principal author.

The data are useful for a variety of applications, such as field test planning, model development and validation, and area characterizations. The database is also invaluable to the US Navy for applications such as detectability assessments, tactical guidance, and environmental predictions.

Much of the data come from Bedford Institute of Oceanography in Halifax, Canada, and from NOAA.

This database will only be as complete as you make it. Therefore, please contact Jeff Smart (Project PI) as indicated below if you can contribute data. The database project is funded by ONR Ocean Optics (Steve Ackleson).

Jeffrey H. Smart, Room 7-320, Applied Physics Laboratory/Johns Hopkins University Johns Hopkins Rd, Laurel, Md 20723-6099 ph 301-953-6000 x4331, e-mail: jeff_smart@jhuapl.edu, FAX: 301-953-5950

Candidate Parameters to be Stored in the Optics Database.

Absorption Coefficient (/m) Scattering Coefficient Beam Attenuation Coefficient Diffuse Attenuation Coefficient Backscatter Angular Backscattering Coefficient Downwelling Irradiance Upwelling Irradiance Scalar Irradiance Upwelling Radiance Radiance Reflectance Pigment Concentration Chlorophyll a Chlorophyll b Chlorophyll c Phaeophytin

Related Variables of Potential Interest: Nutrients Oxygen Temperature Salinity SigmaT ([=density-1.0]*1000) BV Frequency

Dissemination Considerations

All data have special keys that specify who gathered the data and what dissemination restrictions (if any) apply. For example, some data are restricted to dissemination to DOD and DOD contractors.

List of 144 tutorials of the Youtube channel TutorielGeo: https://www.youtube.com/user/tutorielgeo/featured

More than 200 free tutorial videos on Qgis, Postgis, Geoserver, Pentaho, Talend, Google Earth Pro... as well as webmapping technologies and database management: Oracle, Mysql, SQL Server. Here is the link to the store: https://play.google.com/store/apps/details?id=com.tutorielgeo.mobileapps Here is the link to the website: https://tutorielgeo.com Here is the link of the Youtube channel:https://www.youtube.com/user/tutorielgeo Here is the link to the facebook page: https://www.facebook.com/Tutorielgeo-Geomatic-Tutorial-GIS-Tutorial-Webmapping-Tutorial-325658277554574/ Here is the link to the Twitter account: https://twitter.com/TutorielGeo Here is the link to the Google Plus page: https://plus.google.com/b/117203987416263637144/+tutorielgeo/posts

The product includes: • A collection of digital files (photographs, documents, maps, cross sections, sketches, etc) stored in cloud-hosted repository. • Oracle database tables linking the files to geological features and samples described in GA’s scientific databases (including but not limited to boreholes, samples, field sites, geological provinces, stratigraphic units, samples, mines, mineral deposits, isotopes, and mineral occurrences) • WMS and WFS web services which deliver the link to other Geoscience Australia geological feature web services.

Biologically-relevant radiation has been recorded since February 1997 using a Bentham spectroradiometer at Rothera. The Bentham spectroradiometer is sited on the roof of the Bonner Laboratory at Rothera. It measures spectral global irradiance between 280 and 600 nm (wavelengths from below UV-B to the middle of the visible range) with a step size of 0.5 nm and a resolution of 1 nm. Scans are recorded at various time intervals depending on the time of day and season peaking at every 30 minutes while the sun is above the horizon from the beginning of September until the end of April. These scans can be used to measure the amount of solar radiation reaching the earth''s surface at Rothera. It provides particularly useful background data for studies on the effects of increased UV-B, due to the ozone hole, on the plants and microbes in regions around Rothera. Data is managed in the Biological Science Data and Resource Centre. The Data is stored in an Oracle database. Data are stored in four pairs of Oracle tables. One member of each pair holds the spectral data while the other member holds all the meta-data concerned with each scan. The four pairs hold reference data (calibration lamp irradiance values, action spectra, spectral response functions etc.), wavelength calibration data, irradiance calibration data and measurements of spectral irradiance. The tables are owned by u_hp and are called: spec_ref_data, spec_ref_info, spec_wlcal_data,spec_wlcal_info, spec_ircal_data, spec_ircal_info, spec_measurements, spec_measurements_info. In August 2001 the spectroradiometer was destroyed by a laboratory fire. A new instrument was installed in February 2003. These data are stored in the oracle tables new_spec_wlcal_data, new_spec_wlcal_info, new_spec_ircal_data, new_spec_ircal_info, new_spec_measurements, new_spec_measurements_info. As the exact day of the data collection was not provided, and the metadata standard requires a YYYY-MM-DD for mat, this dataset has been dated as 1st February.

This dataset spatially represents the OZROX Field Geology Database.The OZROX database contains location and field description information. Field descriptions include, information on lithology, stratigraphic unit, alteration, magnetic susceptibility, hand-held radiometric spectrometer response and structural measurements.OZROX has over 100 000 field sites derived mainly from AGSO - Geoscience Australia's mapping, with additional contributions from Universities and State Surveys. Many of Geoscience Australia's laboratory databases link to OZROX in the corporate Oracle relational database system.

Benthic Habitats Video Image Archive is an on-line repository of .avi files and still images (.jpg or .tiff) collected during a series of surveys by MNF and other vessels. The associated BHIMAGE Oracle data base records associated geo-location data and image annotations.

Image data (video and stills) in this collection are from deep continental shelf and upper slope benthic habitats. Image collection was enabled by the development of deep towed video systems since the late 1990's (Bax & Williams 1999; Shortis et al. 2008). An evolving but nonetheless standardized annotation methodology annotation physical structures (substrate and geomorphology) and biota has been used for surveys since 2000 (Kloser et al 2004). The data-base retains annotations in the original scoring schemes and translations between schemes including to the CATAMI classification scheme are documented.

Video and still-image data can be accessed via the AODN Portal (refer to link below), select "Observation Data", then "CSIRO Oceans and Atmosphere", click on the "CMAR Instruments" and lastly: "CMAR Video Tows - all voyages", the image below illustrates how to reach this dataset. Note that this is subject to change when the CMAR tag is replaced by the new O&A.

CAUTION: NVIS datasets are large and computationally intensive! Please allow sufficient time to plan and execute any geoprocessing of NVIS datasets.

This dataset is a vector layer delineating the Pre-1750 ('pre-clearing', 'pre-European') native vegetation types across Australia made available for release under CC BY. This dataset is part of a larger compilation of Australian states and territories (with the exception of most of NSW and TAS which do not have pre-1750 data for this version) for the latest release (Version 5.1) of the National Vegetation Information System (NVIS). Each state/territory is available for separate download, due to size limitations.

This dataset comprises part of NVIS Version 5.0. This dataset was also used to develop previous Major Vegetation Groups and Subgroup products (version 3.0 to present). Note that the interpretation of vegetation descriptions (i.e. in the lookup table) may differ between versions.

The comparable estimated extant ('present') vegetation are in the following separate dataset(s): NVIS5_1_AUST_EXT_

Feature classes are named - NVIS5_1_AUST_EXT_ or NVIS5_1_AUST_PRE_ .

To use this dataset and display the Level 1-6 hierarchy the field "NVISDSC1" must be joined to the field "NVIS_ID" in the lookup table, available for separate: NVIS5_1_LUT_AUST_DETAILn > where n is an integer incremented with minor updates - see metadata for this table for instructions.

Once this table has been joined, a simple display option is to use the field "MVG_NAME" which includes the names of the NVIS Major Vegetation Groups (MVGs) as described at: http://www.environment.gov.au/land/publications/factsheets-vegetation-profiles-major-vegetation-groups. The most detailed account of the pre-clearing vegetation is in the field L6_SUB_ASSOCIATION following the NVIS scheme. [A legend or 'shadesets' for the MVGs and MVSs can be found

For internal users - N:\Veg\NVIS_V5_1_\MVG_MVS\Symbology\Vector

For external users - in this package: - MVG5_1_BY_NAME.lyr - MVG5_1_BY_NUMBER.lyr - MVS5_1_BY_NAME.lyr - MVS5_1_BY_NUMBER.lyr Use the field “MVG_NUMBER/MVS_NUMBER” or “MVG_NAME/MVS_NAME” for the symbology.]

The NVIS vegetation attributes contain information on vegetation structure (growth form, height and cover) and floristics (genus and species) as documented in the Australian Native Vegetation Assessment (NLWRA, 2001) and the Australian Vegetation Attribute Manual Version 7.0 (NVIS Technical Working Group, 2017). Levels 1 to 6 of the NVIS Information Hierarchy are present in this dataset, where provided by the custodian.

Attribute data for the above datasets marked "Level 6" were supplied in the XML Transfer System, which checks for consistency throughout the NVIS Information Hierarchy (NVIS Technical Working Group, 2017). Attribute data was loaded into an Oracle database at ERIN, where each vegetation type in the source data was allocated a unique NVIS identifier (NVIS_ID). Some attribute data, principally that coming from the Native Vegetation Mapping Program (NVMP), met the standard NVIS quality assurance tests.

The spatial data were supplied with SOURCE_CODEs, which were converted to NVIS identifiers (NVISDSC1-6). Please note that vegetation mapping can be quite complex and a single polygon may contain a "mosaic" of up to six vegetation associations. Other NVIS attributes were also updated on the basis of the supplied data.

For this update, Version 5.1, the extant dataset for Tasmania has been updated, with gapfilling work being completed for the NSW extant dataset. Some of the rulesets underpinning the assignment of MVGs and MVSs have also been updated to improve consistency for their allocation. Version 5.0 substantially standardised the lookup tables (NVIS5_1_LUT_DETAILxxxx and NVIS5_1_LUT_AUST_FLATxxxx). For more detail refer to the associate lookup tables. Previously, Version 4.2 updated NSW. For version 4.1 most agencies supplied data to the update.

This dataset is not comparable with earlier versions of NVIS, such as the 2001 Native Vegetation Assessment (which used a different structure and vegetation typology).

Reference: NVIS Technical Working Group (2017) Australian Vegetation Attribute Manual: National Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., de Lacey, C. and Bossard, K.B. (Eds)CC - Attribution (CC BY) This data has been licensed under the Creative Commons Attribution 4.0 International Licence. More information can be found at https://creativecommons.org/licenses/by/4.0/

You are free to: Share - copy and redistribute the material in any medium or format Adapt - remix, transform, and build upon the material

for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

The Victorian EVCs (source data for Victorian NVIS) are currently under redevelopment. This data should be USED WITH CAUTION until the new data is incorporated into a future version of NVIS. Once available, it may be worth obtaining the new source data from the State Custodian in order to have the most up to date and relevant data.This may be the case with other states also, given the timing of NVIS releases, and thus considered when using any NVIS product. © Commonwealth of Australia (Department of the Environment and Energy) 2018

CAUTION: NVIS datasets are large and computationally intensive! Please allow sufficient time to plan and execute any geoprocessing of NVIS datasets.

This dataset is a vector layer delineating the extant ('present') native vegetation types across Australia made available for release under CC BY. This dataset is part of a larger compilation of Australian states and territories for the latest release (Version 5.1) of the National Vegetation Information System (NVIS). Each state/territory is available for separate download, due to size limitations.

This dataset comprises part of NVIS Version 5.0. This dataset was also used to develop previous Major Vegetation Groups and Subgroup products (version 3.0 to present). Note that the interpretation of vegetation descriptions (i.e. in the lookup table) may differ between versions.

The comparable estimated Pre-1750 ('pre-clearing', 'pre-European') vegetation are in the following separate dataset(s): NVIS5_1_AUST_PRE_

Feature classes are named - NVIS5_1_AUST_EXT_ or NVIS5_1_AUST_PRE_ .

To use this dataset and display the Level 1-6 hierarchy the field "NVISDSC1" must be joined to the field "NVIS_ID" in the lookup table, available for separate: NVIS5_1_LUT_AUST_DETAILn > where n is an integer incremented with minor updates - see metadata for this table for instructions.

Once this table has been joined, a simple display option is to use the field "MVG_NAME" which includes the names of the NVIS Major Vegetation Groups (MVGs) as described at: http://www.environment.gov.au/land/publications/factsheets-vegetation-profiles-major-vegetation-groups. The most detailed account of the pre-clearing vegetation is in the field L6_SUB_ASSOCIATION following the NVIS scheme. [A legend or 'shadesets' for the MVGs and MVSs can be found

For internal users - N:\Veg\NVIS_V5_1_\MVG_MVS\Symbology\Vector

For external users - in this package: - MVG5_1_BY_NAME.lyr - MVG5_1_BY_NUMBER.lyr - MVS5_1_BY_NAME.lyr - MVS5_1_BY_NUMBER.lyr Use the field “MVG_NUMBER/MVS_NUMBER” or “MVG_NAME/MVS_NAME” for the symbology.]

The NVIS vegetation attributes contain information on vegetation structure (growth form, height and cover) and floristics (genus and species) as documented in the Australian Native Vegetation Assessment (NLWRA, 2001) and the Australian Vegetation Attribute Manual Version 7.0 (NVIS Technical Working Group, 2017). Levels 1 to 6 of the NVIS Information Hierarchy are present in this dataset, where provided by the custodian.

Attribute data for the above datasets marked "Level 6" were supplied in the XML Transfer System, which checks for consistency throughout the NVIS Information Hierarchy (NVIS Technical Working Group, 2017). Attribute data was loaded into an Oracle database at ERIN, where each vegetation type in the source data was allocated a unique NVIS identifier (NVIS_ID). Some attribute data, principally that coming from the Native Vegetation Mapping Program (NVMP), met the standard NVIS quality assurance tests.

The spatial data were supplied with SOURCE_CODEs, which were converted to NVIS identifiers (NVISDSC1-6). Please note that vegetation mapping can be quite complex and a single polygon may contain a "mosaic" of up to six vegetation associations. Other NVIS attributes were also updated on the basis of the supplied data.

For this update, Version 5.1, the extant dataset for Tasmania has been updated, with gapfilling work being completed for the NSW extant dataset. Some of the rulesets underpinning the assignment of MVGs and MVSs have also been updated to improve consistency for their allocation. Version 5.0 substantially standardised the lookup tables (NVIS5_1_LUT_DETAILxxxx and NVIS5_1_LUT_AUST_FLATxxxx). For more detail refer to the associate lookup tables. Previously, Version 4.2 updated NSW. For version 4.1 most agencies supplied data to the update.

This dataset is not comparable with earlier versions of NVIS, such as the 2001 Native Vegetation Assessment (which used a different structure and vegetation typology).

Reference: NVIS Technical Working Group (2017) Australian Vegetation Attribute Manual: National Vegetation Information System, Version 7.0. Department of the Environment and Energy, Canberra. Prep by Bolton, M.P., de Lacey, C. and Bossard, K.B. (Eds)CC - Attribution (CC BY) This data has been licensed under the Creative Commons Attribution 4.0 International Licence. More information can be found at https://creativecommons.org/licenses/by/4.0/

You are free to: Share - copy and redistribute the material in any medium or format Adapt - remix, transform, and build upon the material

for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

The Victorian EVCs (source data for Victorian NVIS) are currently under redevelopment. This data should be USED WITH CAUTION until the new data is incorporated into a future version of NVIS. Once available, it may be worth obtaining the new source data from the State Custodian in order to have the most up to date and relevant data. This may be the case with other states also, given the timing of NVIS releases, and thus considered when using any NVIS product. © Commonwealth of Australia (Department of the Environment and Energy) 2018