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 SDE Best Available Geographic Database (SBAGD) is a historic database comprising the GEODATA TOPO 250K Series 3 data and any updates that were made from 2008-2013. This vector data represents …Show full descriptionThe SDE Best Available Geographic Database (SBAGD) is a historic database comprising the GEODATA TOPO 250K Series 3 data and any updates that were made from 2008-2013. This vector data represents major topographic features and has been sourced through many programs such as the National Topographic Information Coordination Initiative (NTICI). The topographic data complies with the Topographic Data and Map Specifications for the National Topographic Database & NTMS Series 1:250 000 & 1:100 000 scale topographic map products version 6.0.

The Oregon Watershed Restoration Inventory database (OWRI) contains information about completed restoration projects that were implemented in Oregon beginning in 1995. The complete dataset consists of point, line, and polygon features. Data for projects not funded by the Oregon Watershed Enhancement Board (OWEB) are acquired through a voluntary "Annual Call for Data"; while reporting is required for projects funded by OWEB and Oregon Department of Fish and Wildlife R & E grant programs. Restoration practitioners submit a standardized reporting form and attach project location maps. Once acquired, data sheets and maps are each assigned a unique project identification number. This number links spatial project data with tabular project data that are stored in a relational database using Microsoft SQL software.

These are the ephemeral streams as defined in the 20.6.4.97 NMAC. Note that there are many other ephemeral waters as defined in the USGS National Hydrography Dataset.

This study presents the case of China’s Jiangsu Province. The spatial-temporal pattern evolution of different manufacturing sectors is discussed using spatial analysis technology (spatial autocorrelation and standard deviation ellipses). The Granger test is used to analyze the relationship between the change in the manufacturing industry spatial agglomeration and regional economic differences. The following conclusions are drawn: 1) The spatial agglomeration trend of most manufacturing sectors is weakening. Much of the manufacturing sector, like the rubber and plastic product industries, has been transferred from southern to northern Jiangsu. 2) From the scale, only a minority of these enterprises possess substantial registered capital. The capital injection scale of more manufacturing enterprises is insignificant. At the same time, manufacturing companies with substantial financial resources are increasingly inclined to choose less-concentrated areas when choosing new investment areas. 3) The reduction of regional economic differences is considered to be the Granger-cause for the decline of the spatial agglomeration degree of the manufacturing industry in Jiangsu Province. Analyzing the spatiotemporal pattern of the manufacturing industry in Jiangsu Province will provide specific policy reference values for the manufacturing industry and economic development of Jiangsu province. In addition, for companies of different sizes, the findings of this paper also provide valuable references on how they can choose suitable investment locations according to their size in the future.

NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.

Currently filtered for Storm Date is after 12/1/2023Purpose: This is a feature layer of tornado swaths for the NWS Damage Assessment Toolkit.The National Weather Service (NWS) Damage Assessment Toolkit (DAT) has been utilized experimentally since 2009 to assess damage following tornadoes and convective wind events. The DAT is a GIS-based framework for collecting, storing, and analyzing damage survey data, utilizing the Enhanced Fujita (EF) scale for the classification of damage. Data collected from individual locations via mobile device are transmitted to a central geospatial database where they are quality controlled and analyzed to assign the official EF rating. In addition to the individual point, the data are analyzed to generate track centerlines and damage swaths. High resolution satellite imagery and radar data, through partnership with the NASA Short-term Prediction Research and Transition Center, are also available to aid in the analysis. The subsequent dataset is then made available through a web-based graphical interface and GIS services.Here is the full REST service: https://services.dat.noaa.gov/arcgis/rest/services/nws_damageassessmenttoolkitGeoplatform website: https://communities.geoplatform.gov/disasters/noaa-damage-assessment-toolkit-dat/More InformationWelcome to the National Weather Service Damage Assessment Toolkit. Data on this interface is collected during NWS Post-Event Damage Assessments. While the data has been quality controlled, it is still considered preliminary. Official statistics for severe weather events can be found in the Storm Data publication, available from the National Centers for Environmental Information (NCEI) at: https://www.ncdc.noaa.gov/IPS/sd/sd.html Questions regarding this data can be addressed to: parks.camp@noaa.gov.

NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.The Utah Geochronology Database contains ages and related dating information of sampled geologic materials (soil and rock). Ages were obtained using argon (40Ar/39Ar), cosmogenic (10Be and 36Cl), fission track, luminescence (TL, IRSL, and OSL), radiocarbon (14C), rubidium-strontium (87Rb/87Sr), tephrochronology, tritium, or uranium-thorium-lead (238U-235U/206Pb-207Pb) dating methods and were analyzed for a variety of geologic-related projects by the UGS and others. These ages were used in geologic mapping projects, fault trench investigations to determine the timing of past earthquakes dating fault movement and to develop other paleoseismic parameters, in the dating of basalt flows for eruption histories, and similar projects.Since geochronologic methods have significantly evolved and improved through time, older data is often not as reliable or usable as more recently dated materials. The user should use caution in using this data, as significant knowledge and experience is often needed to interpret and apply geochronologic data to projects correctly.As the database is expanded in the future, age results from other geochronologic dating methods are anticipated to be added. Various geochronologic data from geologic mapping projects may be found here. Donations of geochronologic data in Utah are appreciated, so that these data can be permanently archived and made available to all users. Contact the UGS for more details.The Tephrochronology feature class contains metadata specific to each tephrochronology sample in the database. Not all fields will contain data, due to the lack of available information, or the field may not apply to the specific sample.

This dataset represents Rock Outcrop Critical Environmental Features (CEFs) identified during the development review process since 1995. Prior to 1995, data is either unavailable or lost. Rock Outcrop CEFs were digitized from construction plans, environmental assessments, and City of Austin staff field observations into a versioned SDE database using ArcMap.

NOTE: This dataset holds 'static' data that we don't expect to change. We have removed it from the SDE database and placed it in ArcGIS Online, but it is still considered part of the SGID and shared on opendata.gis.utah.gov.

This data layer was developed to support Denver's Food Retail Expansion to Support Health (FRESH) program. This layer will be stored on the City and County of Denver Spatial Database Engine (SDE) server. The data will be maintained on the SDE by the Office of Economic Development. It will be distributed through the SDE and the Denver Open Data Catalog. Data will be updated as time and resources permit, as data falls out of date, or through annual/bi-annual scheduled maintenance.Denver FRESH representatives will monitor Denver GIS requirements and submit current FRESH data layers to City SDE as applicable. Published data will comply with the City and County of Denver’s requirements and Denver Environmental Health’s GIS Cartographic Standards as described in the data layer’s metadata file. Data sources that originate outside the City must be cited. City data beyond normal base layers should have the sources stated, or if the layer is “unusual” or time sensitive then the source and year should be stated.

The iCARE SDE is a cloud-based, big data analytics platform sitting within Imperial College Healthcare NHS Trust (ICHT) NHS infrastructure. This, combined with the iCARE Team’s robust method of data de-identification, make the Environment an incredibly secure platform. The fact that it can be accessed remotely using the Trust’s Virtual Desktop Infrastructure means that researchers can perform their work remotely and are therefore not constrained by location. (imperial.dcs@nhs.net)

The iCARE SDE enables clinicians, researchers and data scientists to access large-scale, highly curated databases for the purposes of research, clinical audit and service evaluation. The iCARE SDE enables advanced data analytics through a scalable virtual infrastructure supporting Azure Machine Learning, Python, R and STATA and a large variety of snowflake SQL tooling.

The main iCARE data model is a HRA REC approved database covering all routinely captured information from Imperial College Healthcare Trust (ICHT) Electronic Health Record and 39 linked (at the patient-level) clinical and non-clinical systems. It contains data for all patients from 2015 onwards and is updated weekly as a minimum, and close to real-time when required. It includes inpatient, outpatient, A&E, pathology, cancer, imaging treatments, e-prescribing, procedures, clinical notes, Consent, clinical trials, tissue bank samples, Patient safety and incidents, Patient experience, Staffing and environment data.

Data can also be linked to primary care data for the 2.8million population in Northwest London, HRA REC approved, Whole Systems Integrated Care (WSIC) hosted database and other health and social care providers when approved.

On a project-by-project basis the model can be expanded to curate and include new data (including multi-modality data), that is either captured routinely or through approved research and clinical trials. There are streamlined processes to approve and curate new data (imperial.dataaccessrequest@nhs.net) and data will always remain hosted in the SDE.

This dataset contains the official listing of all public educational organizations in Connecticut. Data elements include district name, school name, organization type, organization code, address, open date, interdistrict magnet status and grades offered.

Included data are collected by the CT State Department of Education (CSDE) through the Directory Manager (DM) portal in accordance with Connecticut General Statute (C.G.S.) 10-4. This critical information is used by other data collection systems and for state and federal reporting.

For more information regarding DM, please visit http://www.csde.state.ct.us/public/directorymanager/default.asp

Critical Environmental Features (CEFs) are defined in the City of Austin Land Development Code 25-8-1 and 30-5-1. This dataset represents many of the known wetland CEFs that have been identified during the development review process. Wetland CEFs have been routinely added to this dataset from development review since the creation of the georeferenced database in 2009. Attempts have been made to add many known wetlands from historical reviews and site visits prior to 2009, however, it should be made clear that this dataset is incomplete and should not be used to determine presence/absence of features. Wetland CEFs were digitized into a versioned SDE database in ArcMap from construction plans, environmental assessments, City of Austin staff field observations and other sources such as National Wetlands Inventory Maps. The boundaries shown represent the best available information at the time of digitizing. Verification through wetland delineation may provide additional clarity for the purpose of development planning.

This dataset represents wetland CEFs identified during the development review process since 1995. Prior to 1995, data is either unavailable or lost.Wetland CEFs were digitized from construction plans, environmental assessments, and City of Austin staff field observations. Features were digitized into a versioned SDE database in ArcMap. Wetland delineation may be determined through a process of negotiation with land development interests and generally reflect the most protective arrangement that could be obtained. Additionally, “fringe wetlands” were drawn using a standard 2’ width on either side of a waterway.

R2GIS Combined county boundary data from TANA, Navteq and Census: TANA county boundaries.(static.R2GIS.TANA_BOUNDARY_COUNTY) for all of Region 2 except the Virgin Islands which were not found in the data set. TANA provided more detailed county coastlines. Navteq.County(static.R2GIS.NAVTEQ_BOUNDARY_2014_COUNTY) for the smaller surrounding islands of the Virgin Islands which had more detail than the CENSUS representations. Counties (CENSUS) VI. The CENSUS county boundaries were used only for the three main islands of the Virgin Islands which had finer detail than that provided by Navteq. The Dynamap(R)/2000 County Boundary file is a non-generalized polygon layer that represents all U.S. government-defined entities named County. A County is a type of governmental unit that is the primary legal subdivision of every U.S. state. In Louisiana, the County-equivalent entity is 'parish.' In Alaska, the statistically equivalent entities are the organized 'boroughs,' 'city and boroughs,' 'municipalities' and 'census areas.' The Dynamap(R)/2000 County Boundary file is a non-generalized polygon layer that represents all U.S. government-defined entities named County. A County is a type of governmental unit that is the primary legal subdivision of every U.S. state. In Louisiana, the County-equivalent entity is 'parish.' In Alaska, the statistically equivalent entities are the organized 'boroughs,' 'city and boroughs,' 'municipalities' and 'census areas.'

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE.
The downloaded SSURGO data included an ArcGIS Shapefile of the soil type features for Oakland County, tabular data in text file format, and an empty pre-formatted Microsoft Access database containing queries, macros and reports. The Shapefile was intially projected in State Plane Michigan South Meters NAD 83, but was then reprojected by Oakland County staff to State Plane Michigan South International Feet NAD 83. The USDA-NRCS provided instructions for automatically importing the tabular text files into the Microsoft Access database. The key attribute of this feature class is the map unit key (MUSYM field), which relates the polygon features to the SoilAttribute table stored within SDE. The related SoilAttribute table in SDE contains some of the tabular data which was initially imported into the aforementioned Microsoft Access database.

The Software-Defined Everything (SDE) market is experiencing robust growth, driven by the increasing adoption of cloud computing, virtualization, and automation across various sectors. The convergence of software-defined networking (SDN), software-defined storage (SDS), and software-defined data centers (SDDC) is creating a more agile, efficient, and cost-effective IT infrastructure. Key applications driving market expansion include BFSI (Banking, Financial Services, and Insurance), healthcare, government, retail, and manufacturing, where SDE solutions are crucial for enhancing security, scalability, and operational efficiency. While the market size in 2025 is estimated at $50 billion (a reasonable estimate based on the rapid growth of related technologies and considering the mature state of SDN, SDS, and SDDC), a Compound Annual Growth Rate (CAGR) of 15% is projected for the period 2025-2033, indicating a substantial market expansion. This growth is fueled by several trends, including the rise of edge computing, the increasing adoption of AI and machine learning in IT management, and the growing demand for secure and reliable digital infrastructure. However, challenges such as security concerns, lack of skilled professionals, and the complexity of integrating SDE solutions across different platforms remain restraints to the market's growth. The regional distribution of the SDE market reflects the global digital transformation landscape. North America currently holds the largest market share, followed by Europe and Asia Pacific. However, Asia Pacific is expected to witness the fastest growth during the forecast period due to rapid technological advancements and expanding digital infrastructure in countries like China and India. The market is highly competitive, with major players like Cisco, Dell, VMware, and IBM continuously innovating and expanding their product portfolios to cater to the evolving needs of enterprises. The competitive landscape is characterized by strategic partnerships, acquisitions, and the development of cutting-edge SDE solutions aimed at enhancing automation, security, and efficiency across various industries. The continued evolution of these technologies and expanding adoption across diverse industries is poised to fuel substantial growth within the SDE market throughout the forecast period.

Planning Areas in Montgomery County, updated as needed (infrequent changes) from MNCPPC to TEBS-GIS databaseAccess directly in the TEBS-GIS database in SDE.PLANNING, SDE.PLAN_AREA

Private Green Stormwater Infrastructure Project data in a tabular relational database. Location point data is digitized manually with a tracking number. Tabular data is queried and joined to point feature class before export to GEODB2 SDE databases.