To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

Data interoperability is extremely important for Geoscience Australia. GA's ability to produce data in a useable format for stakeholders and the public is key to maximising the uptake and appropriate use of our data. One of the easiest and quickest ways for GA to make data available in a way that can be accessed regardless of the client's software is through the use of web services, in particular OGC web services. GA is involved in two major projects that are working on getting data out to stakeholders and the public.

Spatial data interoperability is particularly important to community safety; when responding to emergencies it is vital for information to reach those who need it quickly, and in a format that can be used. Geoscience Australia is involved in developing the National Situational Awareness Tool project that aims to integrate State, Territory and Commonwealth emergency management information into one National Common Operating Procedure. The primary focus of the project is to make web services available from jurisdictions and Commonwealth agencies that can help decision makers during times of crisis.

GA in collaboration with the Department of Communications and NICTA is working on The National Map, an open source web application that will allow public and government users to have intuitive access through a map interface to all of Australia's open geospatial data. The National Map architecture is based on open protocols and formats to allow straight-forward incorporation of data services from many existing systems and initiatives, particularly those existing GIS systems managed by government agencies that publish their web services through data.gov.au.

BackgroundAccording to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).ObjectiveOur objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.MethodsWe conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1–22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data.Results5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809).ConclusionThis paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.

As part of the EOSC project family the FAIRsFAIR - Fostering Fair Data Practices in Europe - project aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. The work package "WP2 FAIR Practices: Semantics, Interoperability, and Services" will produce three reports on FAIR requirements for persistence and interoperability to identify domain-specific standards and practices in use. These will review and document commonalities and possible gaps regarding semantic interoperability, and the use of metadata and persistent identifiers across infrastructures. They will also look into differences in terms of standards, vocabularies and ontologies. The collected information will be updated during the course of the project in cooperation with other tasks and EOSC projects.

This survey was done to complement and validate the information from desk research for the first of these reports. It was aimed at data managers and data support experts. We hoped to get information about tools and services we might have missed, but also some reflections on the thinking around identifiers and ontologies and other semantic artefacts. The information was also collected to support preparing workshops on semantics and interoperability that are forthcoming in the project, as well as the work on software and services. The survey covers questions about metadata, use of persistent identifiers, use of semantic artefacts and handling research software.

The survey was conducted as a joint effort with WP3, FAIR Policy and Practice and its open consultation, and was disseminated on the fairsfair.eu web pages, social media channels and via email lists. We received 66 answers during the period the survey was open, that is between 15 July to 2 October 2019.

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Healthcare Interoperability Solution Market Size 2024-2028

The healthcare interoperability solution market size is forecast to increase by USD 4 billion at a CAGR of 14.27% between 2023 and 2028. The market is experiencing robust growth, fueled by several key factors. The increasing adoption of Electronic Health Records (EHRs) is a significant driver, as healthcare providers seek to streamline processes and improve patient care. Moreover, the growing number of acquisitions and partnerships among industry players reflects the market's competitiveness and the need for innovation. Additionally, increased spending on healthcare infrastructure indicates a commitment to modernizing healthcare delivery systems. These trends are influenced by consumer preferences for more efficient, accessible, and standardized care. As a result, companies in this market are prioritizing sustainability and operational efficiency to maintain a competitive edge. The transition towards advanced interoperability solutions is expanding the market's scope, ensuring its continued evolution. This growth is underpinned by the demand for seamless data exchange between various healthcare providers and systems, enabling better patient outcomes and population health management.

What will be the size of the market during the forecast period?

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Healthcare Interoperability Solution Market Segmentation

The healthcare interoperability solution market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Deployment

  On-premises
  Cloud based


Type

  Structural
  Semantic
  Foundational


Geography

  North America

    US


  Europe

    Germany
    UK


  Asia

    China
    India


  Rest of World (ROW)

Which is the largest segment driving market growth?

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premise healthcare software solutions are preferred by large healthcare organizations due to their enhanced data security features. Unlike cloud-based solutions, on-premise software is installed and runs on the organization's dedicated servers, providing better control and physical access to critical patient information. This deployment model is particularly important In the healthcare sector, where data security and patient safety are paramount. Operational efficiency, patient safety, and healthcare supply chain are key areas where on-premise solutions offer significant benefits. Electronic Health Records (EHRs), software technology, and digital health tools are essential components of the healthcare IT landscape that require interoperability to ensure seamless data exchange.

On-premise Enterprise Interoperability Solutions, such as EHR interoperability solutions, enable data sharing and automation technology between different healthcare providers and facilities. Patient outcomes can be improved through the elimination of duplicate clinical interventions and the integration of medical equipment and smart gadgets. Healthcare enterprises, authorities, and organizations are investing in digitalization and cloud technology to enhance patient-centric care and preventive care services. The healthcare system capacity, public health, and pharmaceutical effectiveness can also benefit from digital interoperability solutions, including video conferencing and PHR applications. The services segment, including on-premise and cloud-based solutions, is expected to grow significantly In the coming years due to government funding and the increasing adoption of digitalization In the healthcare industry.

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The On-premises segment was valued at USD 1.72 billion in 2018 and showed a gradual increase during the forecast period.

Which region is leading the market?

North America is estimated to contribute 42% to the growth of the global market during the forecast period.

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Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.The North American healthcare market is set to dominate the interoperability solution sector due to escalating investments in digital healthcare infrastructure, increasing healthcare expenditure, and the introduction of new products. The demand for operational efficiency, patient safety, and cost reduction In the industry is driving the adoption of interoperability solutions. The US is a significant market for healthcare interoperability solutions in North America, given its extensive use of electronic health records (EHR) and digital health tools. Interoperability networks, health infor

IntroductionObtaining real-world data from routine clinical care is of growing interest for scientific research and personalized medicine. Despite the abundance of medical data across various facilities — including hospitals, outpatient clinics, and physician practices — the intersectoral exchange of information remains largely hindered due to differences in data structure, content, and adherence to data protection regulations. In response to this challenge, the Medical Informatics Initiative (MII) was launched in Germany, focusing initially on university hospitals to foster the exchange and utilization of real-world data through the development of standardized methods and tools, including the creation of a common core dataset. Our aim, as part of the Medical Informatics Research Hub in Saxony (MiHUBx), is to extend the MII concepts to non-university healthcare providers in a more seamless manner to enable the exchange of real-world data among intersectoral medical sites.MethodsWe investigated what services are needed to facilitate the provision of harmonized real-world data for cross-site research. On this basis, we designed a Service Platform Prototype that hosts services for data harmonization, adhering to the globally recognized Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) international standard communication format and the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Leveraging these standards, we implemented additional services facilitating data utilization, exchange and analysis. Throughout the development phase, we collaborated with an interdisciplinary team of experts from the fields of system administration, software engineering and technology acceptance to ensure that the solution is sustainable and reusable in the long term.ResultsWe have developed the pre-built packages “ResearchData-to-FHIR,” “FHIR-to-OMOP,” and “Addons,” which provide the services for data harmonization and provision of project-related real-world data in both the FHIR MII Core dataset format (CDS) and the OMOP CDM format as well as utilization and a Service Platform Prototype to streamline data management and use.ConclusionOur development shows a possible approach to extend the MII concepts to non-university healthcare providers to enable cross-site research on real-world data. Our Service Platform Prototype can thus pave the way for intersectoral data sharing, federated analysis, and provision of SMART-on-FHIR applications to support clinical decision making.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.

Introduction

Geoscience data are being generated at exponentially increasing volumes, and it is no longer feasible to develop centralized warehouses from which data are accessed. Efficient access to such data online in real time from distributed sources is rapidly becoming one of the major challenges in building cyberinfrastructures for the Earth Sciences. EXtensible Markup Language (XML) and web-based data delivery is a proven technology which allows access to standardized data on the fly via the internet. GeoSciML (GeoScience Markup Language) is a geoscience specific, XML-based, GML (Geography Markup Language) application that supports interchange of geoscience information. It has been built from various existing geoscience data model sources, particularly the North American Data Model (NADM) and XMML (eXtensible Mining Markup Language). It is being developed through the Interoperability Working Group of the Commission for the Management and Application of Geoscience Information (CGI), which is a commission of the International Union of Geological Sciences (IUGS). The Working Group is (currently) comprised of geology and information technology specialists from agencies in North America, Europe, Australia and Asia.

The GeoSciML Testbed

In 2006, representatives from geological surveys in USA, Canada, UK, France, Sweden and Australia came together to develop a testbed that would utilize GeoSciML to access globally distributed geoscience map data (Duffy et al, 2006). Data was served from seven sites in six countries with several different WMS/WFS (Web Feature Service/Web Map Service) software solutions employed. Geological surveys in Canada, USA and Sweden used an ESRI ArcIMS platform (and in one case a MapServer platform) with a Cocoon wrapper to handle queries and transformations of XML documents. The UK and Australian geological surveys employed the open source GeoServer software to serve data from ArcSDE and Oracle sources. The French geological survey implemented a system using an Ionic RedSpider server for WMS and client, and a custom development to implement a WFS. Web clients were constructed in Vancouver, Canada using Phoenix, and later in Canberra, Australia using Moximedia IMF software to test various use case for the WMS/WFS services. Generic web clients, such as Carbon Tools Gaia 2 were also used to test some use cases. In addition to geologic map data, the testbed also demonstrated the capacity to share borehole data as GeoSciML. Two WFS (French and British) provided borehole data to a client able to display the borehole logs.

ArcGIS is fundamentally an open platform. Esri president Jack Dangermond discusses how Esri ensures that ArcGIS is interoperable with other technology that users might need to integrate with ArcGIS. Esri’s approach is to help users achieve their interoperability goals. Esri supports open standards like OGC, WWW, and ISO standards, as well as industry data standards. The software has open APIs so developers can extend and build on top of the data and tools, and the ArcGIS platform is extendable and embeddable. Open source tools are also available in GitHub.

The size and share of the market is categorized based on Product (Health Information Exchange Platforms, Data Integration Solutions, Interoperability Tools) and Application (Data Sharing, System Integration, Patient Information Exchange) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The GIS shapefiles were exported using ArcGIS Quick Import Tool from the Data Interoperability Toolbox. The coverage files was imported into a file geodatabase then exported to a .shp file for long-term use without proprietary software. An example output of the final GIS file is include as a pdf, in addition, a scan of the original 1958 map (held in the UMN Borchert Map Library) is included as a pdf. Metadata was extracted as an xml file. Finally, all associated coverage files and original map scans were zipped into one file for download and reuse.Date completed4/28/2003Geographic coverageBounding box (W, S, E, N): -93.770810, 44.468717, -92.725647, 45.303848Persistent link to this itemhttps://dx.doi.org/10.13020/D6059Jhttps://hdl.handle.net/11299/160503ServicesFull Metadata (xml)View Usage StatisticsFunding Information:Sponsorship: MnDOT Report 2003-37Funding agency: Minnesota Department of TransportationFunding agency ID: Contract #: (c) 81655 (wo) 8Sponsorship grant: If They Come, Will You Build It? Urban Transportation Network Growth Models.Referenced byLevinson, David, and Wei Chen (2007) "Area Based Models of New Highway Route Growth." ASCE Journal of Urban Planning and Development 133(4) 250-254.https://doi.org/10.1061/(ASCE)0733-9488(2007)133:4(250)Levinson, David and Wei Chen (2005) "Paving New Ground" in Access to Destinations (ed. David Levinson and Kevin Krizek) Elsevier Publishers.

List of tested and analyzed data sharing tools (non-exhaustive)

Recent advances in ontology development support a rich description of entities that are modeled within a domain and how these entities relate to each other. However, even with ontology information, interoperability of a (sub-) system with other systems remains a serious issue. Interoperability issues may arise when two sub-systems that had not been designed as a unit must now work together. Interoperability issues also arise when extensions to a sub-system result in conflicts with the remainder of the system. In this work, we target (sub-) systems pertinent to advanced life support that are developed using software agent technology. Our innovation is to develop an ontology-aware meta-model to support designers and developers in exposing the information that must be captured in order to achieve the goal of 'designing for interoperability, extensibility, and re-use'. Additionally, the meta-model will be integrated into the agent-oriented software engineering (AOSE) design process. Even beyond development of the meta-model and methodology for coupling into the design process, we propose to develop tool support so that the meta-model can be easily utilized within the design process. The success of this innovative meta-model, process, and tool, will support agent-based software re-use and rapid, trouble-free integration of upgraded sub-system components.

List of tested and analyzed data sharing tools (non-exhaustive)

The Sassen BF1 soil moisture station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The soil moisture station station Sassen BF1 was installed in 2012. It is located next to a pylon on a crest of an undulating field. The station is equipped with sensor for measuring the following variables: ScemeSpadeSoilMoisture_Spade_2_Temperature, ScemeSpadeSoilMoisture_Spade_6_Temperature, ScemeSpadeSoilMoisture_Spade_1, ScemeSpadeSoilMoisture_Spade_2, ScemeSpadeSoilMoisture_Spade_3, ScemeSpadeSoilMoisture_Spade_4, ScemeSpadeSoilMoisture_Spade_5 and ScemeSpadeSoilMoisture_Spade_6. The current version of this dataset is 1.5. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. New authors were added for this new version: Alice Künzel (GFZ Potsdam), Christian Budach (GFZ Potsdam), Nils Brinckmann (GFZ Potsdam), Max Wegener (DLR Neustrelitz) and Klemens Schmidt (DLR Neustrelitz).A detailed overview on all changes is provided in the station description file. Older versions are available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/ containing the measured data only. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in data processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. During October 2020 a Bug in the published data was detected and a new version of the datasets was released from beginning until mid 2020. Data processing was done using DMRP version: 1.8.4. Metadataprocessing was done using DMETA version: 1.2.0.

High-quality GIS land use maps for the Twin Cities Metropolitan Area for 1968 that were developed from paper maps (no GIS version existed previously).The GIS shapefiles were exported using ArcGIS Quick Import Tool from the Data Interoperability Toolbox. The coverage files was imported into a file geodatabase then exported to a .shp file for long-term use without proprietary software. An example output of the final GIS file is include as a pdf, in addition, a scan of the original 1968 map (held in the UMN Borchert Map Library) is included as a pdf. Metadata was extracted as an xml file. Finally, all associated coverage files and original map scans were zipped into one file for download and reuse. Data was uploaded to ArcGIS Online 3/9/2020. Original dataset available from the Data Repository of the University of Minnesota: http://dx.doi.org/10.13020/D63W22

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The address service contains statewide address points and related landmark name alias table and street name alias table.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

Definitions and concepts in the context of the main paper.

You will be using tutorials (developed at the University of Guelph and Queen's University) and giving feedback on what you liked, what you found difficult, and further directions for the futher development of more-advanced tutorials.

The UGV Interoperability Lab provides the capability to verify vendor conformance against government-defined interoperability profiles (IOPs). This capability allows customers to verify a system or component against their program requirements, and helps facilitate increased commonality as well as reduce life-cycle costs.Capabilities: The UGV Interoperability Lab provides capabilities to verify conformance against the UGV IOPs, including overarching, payloads, controls and Joint Architecture for Unmanned Systems profiling rules. The lab's Conformance Verification Tool software and physical/electrical interface testing tools provide a flexible, automated way to test system/payload physical, electrical and logical interfaces, as part of the lab's payload/system conformance verification bench. The lab also provides reference architectures for use in testing, including both simulated and physical reference platforms and operator control units (OCUs).Benefits:•  Brings IOP conformance testing capability local to TARDEC customers.•  Provides ability to select best-value components to meet program needs.•  Helps ensure commonality of components for given platforms.•  Gives knowledgeable personnel the ability to help quickly identify and address IOP issues.•  Assists government vendors in developing IOP-compliant systems and subsystems.