Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.

NOTE: An updated Introduction to ArcGIS GeoEvent Server Tutorial is available here. It is recommended you use the new tutorial for getting started with GeoEvent Server. The old Introduction Tutorial available on this page is relevant for 10.8.x and earlier and will not be updated.The Introduction to GeoEvent Server Tutorial (10.8.x and earlier) introduces you to the Real-Time Visualization and Analytic capabilities of ArcGIS GeoEvent Server. GeoEvent Server allows you to:

Incorporate real-time data feeds in your existing GIS data and IT infrastructure. Perform continuous processing and analysis on streaming data, as it is received. Produce new streams of data that can be leveraged across the ArcGIS system.

Once you have completed the exercises in this tutorial you should be able to:

Use ArcGIS GeoEvent Manager to monitor and perform administrative tasks. Create and maintain GeoEvent Service elements such as inputs, outputs, and processors. Use GeoEvent Simulator to simulate event data into GeoEvent Server. Configure GeoEvent Services to append and update features in a published feature service. Work with processors and filters to enhance and direct GeoEvents from event data.

The knowledge gained from this tutorial will prepare you for other GeoEvent Server tutorials available in the ArcGIS GeoEvent Server Gallery.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 7 - March 30, 2018 - Compatible with ArcGIS GeoEvent Server 10.6 and later.

Previous

Release 6 - January 12, 2018 - Compatible with ArcGIS GeoEvent Server 10.5 thru 10.8.

Release 5 - July 30, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 4 - July 30, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x.

Release 3 - April 24, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 2 - January 22, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 1 - April 11, 2014 - Compatible with ArcGIS GeoEvent Server 10.2.x.

Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.

The SPARC Knowledge base of the Autonomic Nervous System (SCKAN) is an integrated graph database composed of three parts: the SPARC dataset metadata graph, ApiNATOMY and NPO models of connectivity, and the larger ontology used by SPARC which is a combination of the NIF-Ontology and community ontologies.

The fastest way to get querying is to follow the instructions in the SCKAN readme file.

For background information please see https://scicrunch.org/sawg/about/SCKAN and the SPARC portal resource page about SCKAN.

This release contains the raw and compiled data for SCKAN. The release-*.zip contains raw data inputs along with the Blazegraph journal file, the sparc-sckan-graph-*.zip contains the SciGraph database, and sckan-data-*.tar.gz is a Docker image that contains the Blazegraph journal file and the SciGraph database along with the configuration files for running each of the servers. The image is intended to be used as a data volume with another Docker container that runs the SciGraph and Blazegraph server software.

The Docker image containing this data is available live and is likely easier to use than the archived image included in this release. See the SCKAN readme file for the most up-to-date instructions.

We would like to thank the members of the SAWG (SPARC Anatomy Working Group, RRID:SCR_018709) for their work on the various connectivity models included in this release.

This work was funded by the NIH Common Fund under 3OT2OD030541-01S1.

Server for the vocabulary of the Natural Environment Research Council. NVS2.0 makes use of the World Wide Web Consortium's Simple Knowledge Organization System (SKOS) to represent knowledge in a format understandable by computers.

The global rack server market is experiencing robust growth, driven by the increasing demand for data storage and processing capabilities across various sectors. The market, estimated at $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This expansion is fueled by several key factors, including the proliferation of cloud computing, big data analytics, and the growing adoption of artificial intelligence (AI) and machine learning (ML) applications. Businesses across IT & Telecommunications, BFSI, Manufacturing, Retail, Healthcare, and Media & Entertainment are investing heavily in upgrading their IT infrastructure to support these technologies, leading to heightened demand for rack servers. The diverse range of operating systems (Linux, Windows, UNIX) and the availability of diverse server configurations cater to varied organizational needs and budgets. Significant regional growth is anticipated in Asia Pacific, driven by rapid technological advancements and increasing digitalization initiatives in countries like China and India. While potential restraints include supply chain disruptions and fluctuations in component costs, the long-term outlook remains positive, given the sustained demand for efficient and scalable computing solutions. The market segmentation reveals a strong preference for Linux-based rack servers, reflecting its cost-effectiveness and open-source nature. Major players like Hewlett-Packard, Dell Inc., IBM, and Cisco Systems dominate the market landscape, leveraging their established brand reputation and extensive product portfolios. However, the increasing presence of ODMs (Original Design Manufacturers) is intensifying competition and driving down prices, making rack servers more accessible to a wider range of businesses. Future market trends point towards increased demand for high-performance computing (HPC) servers, edge computing solutions, and environmentally friendly rack servers with enhanced energy efficiency. Continued innovation in server technologies, coupled with the expansion of 5G networks and the Internet of Things (IoT), will further propel market expansion in the coming years. This comprehensive report delves into the dynamic world of rack servers, a multi-billion dollar market projected to surpass $100 billion in revenue by 2028. We provide in-depth analysis of market size, key players, emerging trends, and future growth projections, empowering businesses to make informed decisions in this rapidly evolving landscape. This report uses data informed by industry knowledge and analysis to provide estimates when precise figures are unavailable.

Chile Data Center Server Market size was valued at USD 1.53 Billion in 2024 and is projected to reach USD 3.82 Billion by 2032, growing at a CAGR of 12.1% during the forecast period 2026-2032.

Chile Data Center Server Market Drivers

1. Growing Need for Digital Services and Cloud Computing:

Data centre infrastructures in Chile have had to grow as a result of the broad use of cloud computing, big data analytics, and Internet of Things (IoT) technologies. Strong data processing and storage skills are becoming more and more necessary as businesses from a variety of industries move to digital platforms. Investments in data centres outfitted with cutting-edge server technologies to effectively manage complicated workloads are being driven by this change. ​

1. Government Programs and Assistance with Policies:

The development of digital infrastructure is being aggressively supported by the Chilean government. The Chilean National Data Centres Plan 2024-2030 was introduced in December 2024 by the Ministry of Science, Technology, Knowledge, and Innovation with the goal of making Chile a preeminent Latin American digital hub. These programs foster an atmosphere that is favourable to investments in data centres and technical developments.

Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.

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.

SSD KVM VPS Market Outlook

The global SSD KVM VPS market size is projected to grow from USD 1.2 billion in 2023 to USD 3.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.5% during the forecast period. This remarkable growth is driven by the increasing demand for high-performance virtual private servers (VPS) and the growing adoption of advanced technologies across various industries. Factors such as the rapid digital transformation, the proliferation of cloud services, and the need for efficient and scalable IT infrastructure are significantly contributing to the market's expansion.

One of the primary growth factors for the SSD KVM VPS market is the escalating demand for high-speed and reliable virtual private servers. As businesses increasingly rely on digital operations, the need for robust and efficient server solutions has never been greater. SSD KVM VPS offers enhanced performance, faster data access speeds, and improved reliability compared to traditional HDD-based VPS. This makes it an attractive option for enterprises that require high-speed data processing and minimal downtime. Additionally, the rise of e-commerce, online gaming, and content streaming services has further fueled the demand for high-performance VPS solutions.

Another significant growth driver is the widespread adoption of cloud computing services. With the increasing shift towards cloud-based solutions, enterprises are seeking scalable and flexible virtual server options. SSD KVM VPS provides the necessary infrastructure to support cloud-based applications, offering seamless scalability and improved resource management. The growing trend of remote work and the need for efficient collaboration tools have also contributed to the demand for cloud-based VPS solutions, driving the market's growth.

Moreover, the advancements in virtualization technologies are playing a crucial role in the market's expansion. Kernel-based Virtual Machine (KVM) technology, in particular, has gained popularity due to its ability to provide better isolation and security for virtual machines. The integration of KVM with SSD storage enhances the overall performance and efficiency of VPS, making it an ideal choice for businesses with high-performance computing needs. The continuous development of virtualization technologies and their integration with advanced storage solutions are expected to further propel the market's growth in the coming years.

Regionally, North America holds the largest share of the SSD KVM VPS market, driven by the presence of major technology companies and a highly developed IT infrastructure. The region's strong emphasis on digital innovation and early adoption of advanced technologies have positioned it as a leader in the market. Additionally, the growing demand for cloud services and the increasing number of data centers in the region are contributing to the market's growth. Europe and the Asia Pacific are also witnessing significant growth, with increasing investments in IT infrastructure and the rising adoption of cloud computing services.

Type Analysis

The SSD KVM VPS market is segmented by type into managed and unmanaged services. Managed SSD KVM VPS services are gaining popularity due to the increasing demand for comprehensive solutions that include server maintenance, security, and technical support. Businesses with limited IT resources often opt for managed services to ensure their virtual servers are properly maintained and secured. This segment is expected to witness significant growth as more enterprises look for hassle-free and reliable VPS solutions that allow them to focus on their core business activities.

On the other hand, unmanaged SSD KVM VPS services cater to businesses that have the technical expertise and resources to manage their virtual servers independently. This type of service offers greater control and flexibility, allowing enterprises to customize their server environments according to their specific needs. While unmanaged services may require more technical knowledge, they provide cost savings and the ability to optimize server performance based on individual requirements. The demand for unmanaged services is particularly high among tech-savvy businesses and startups that prefer to have full control over their server infrastructure.

The choice between managed and unmanaged SSD KVM VPS services often depends on the size and nature of the business. Small and medium enterprises (SMEs) typically lean towards managed services due to the convenience and reduced burden of serve

This dataset is based on the model developed with the Ph.D. students of the Communication and Information Sciences Ph.D. program at the University of Hawaii at Manoa, intended to help new students get relevant information. The model was first presented at the iConference 2023, in a paper "Community Design of a Knowledge Graph to Support Interdisciplinary Ph.D. Students " by Stanislava Gardasevic and Rich Gazan (available at: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/9eebcea7-06fd-4db3-b420-347883e6379e/content)The database is created in Neo4J, and the .dump file can be imported to the cloud instance of this software. The dataset (.dump) contains publically available data collected from multiple web locations and indexes of the sample of publications from the people in this domain. Except for that, it contains my (first author's) personal graph demonstrating progress through a student's program in this degree, and activities they have done while in the program. This dataset was made possible with the huge help of my collaborator, Petar Popovic, who ingested the data in the database.The model and dataset were developed while involving the end users in the design and are based on the actual information needs of a population. It is intended to allow researchers to investigate multigraph visualization of the data modeled by the said model.The knowledge graph was evaluated with CIS student population, and the study results show that it is very helpful for decision-making, information discovery, and identification of people in one's surroundings who might be good collaborators or information points. We provide the .json file containing the Neo4J Bloom perspective with styling and queries used in these evaluation sessions.

FedE4RAG_Dataset

This is the dataset of the paper Privacy-Preserving Federal Embedding Learning for Localized Retrieval-Augmented Generation. FedE4RAG addresses data scarcity and privacy challenges in private RAG systems. It uses federated learning (FL) to collaboratively train client-side RAG retrieval models, keeping raw data localized. The framework employs knowledge distillation for effective server-client communication and homomorphic encryption to enhance parameter privacy.… See the full description on the dataset page: https://huggingface.co/datasets/DocAILab/FedE4RAG_Dataset.

Dedicated Hosting Service Market Outlook

The global market size for Dedicated Hosting Services was valued at approximately USD 10 billion in 2023 and is projected to reach a staggering USD 20.5 billion by 2032, exhibiting a robust CAGR of 8.5% during the forecast period. This significant growth is driven by the increasing need for secure, reliable, and scalable hosting solutions amid the rising adoption of digital transformation across various industries.

One of the primary growth factors for the Dedicated Hosting Service market is the increasing demand for enhanced security and performance. Unlike shared hosting, dedicated hosting provides an entire server to a single client, which significantly reduces the risk of security breaches and ensures high performance and uptime. This is particularly crucial for industries handling sensitive data, such as BFSI and healthcare, where data breaches can lead to severe penalties and loss of reputation.

Furthermore, the proliferation of online businesses and e-commerce platforms is another significant driver of market growth. As companies expand their online presence, the need for robust and reliable hosting solutions has skyrocketed. Dedicated hosting services offer unparalleled reliability and customization options, which are essential for businesses aiming to provide a seamless user experience. The ability to tailor server configurations to meet specific business needs is a significant advantage that dedicated hosting offers over other types of hosting services.

Additionally, advancements in technology and the increasing integration of AI and machine learning in hosting services are propelling the market forward. These technologies enable predictive maintenance, enhanced security features, and efficient resource management, thereby enhancing the overall performance and reliability of hosting services. The integration of advanced technologies provides a competitive edge and is a significant factor encouraging businesses to opt for dedicated hosting solutions.

Regionally, North America holds the largest market share, driven by the presence of numerous key players and the high adoption rate of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate, driven by the rapid digitization of economies, increasing internet penetration, and the growing number of small and medium enterprises (SMEs) seeking reliable hosting solutions. The favorable government policies promoting digital transformation in countries like India and China further bolster the market outlook in this region.

Type Analysis

The Dedicated Hosting Service market is segmented into two primary types: Managed Hosting and Unmanaged Hosting. Managed hosting services are those where the service provider takes full responsibility for managing the server, including maintenance, updates, and security. This type of hosting is particularly popular among organizations that lack in-house IT expertise. The demand for managed hosting is on the rise due to the increasing complexity of IT infrastructure and the need for specialized skills to manage it. Businesses prefer managed hosting to ensure that their servers are always up-to-date and secure, without having to invest in a dedicated IT team.

On the other hand, unmanaged hosting provides the client with full control over the server, but the responsibility of managing it lies with the client. This type of hosting is preferred by businesses with robust IT teams that have the expertise to handle server management. Unmanaged hosting offers greater flexibility and customization options, making it suitable for businesses with specific hosting requirements. However, the need for skilled personnel to manage the server can be a limiting factor for many organizations.

The choice between managed and unmanaged hosting often depends on the size and IT capabilities of the organization. Small and medium enterprises (SMEs) typically opt for managed hosting due to the lack of in-house IT expertise. In contrast, larger enterprises with dedicated IT teams may prefer unmanaged hosting to have complete control over their servers.

Additionally, the cost of these services also plays a significant role in decision-making. Managed hosting services usually come at a higher price due to the additional management services included. However, the peace of mind and reliability that comes with managed hosting often outweigh the higher costs. Unmanaged hosting, while cheaper, requires a significant investment in IT resources, which

Help Desk Ticketing System Servers Market Outlook

The global help desk ticketing system servers market size was valued at USD 3.67 billion in 2023 and is projected to reach USD 8.45 billion by 2032, growing at a CAGR of 9.8% during the forecast period. This significant growth can be attributed to the increasing need for efficient customer service systems and the rapid adoption of digital transformation strategies across various industries. The demand for help desk ticketing systems is rising as more companies recognize the importance of streamlined customer support for enhancing customer satisfaction and operational efficiency.

One of the major growth factors driving the help desk ticketing system servers market is the increasing complexity of business operations, which necessitates sophisticated customer service solutions. Organizations today handle a myriad of customer queries and issues across multiple channels, requiring an integrated system for effective management and resolution. This growing complexity is pushing businesses to invest in advanced ticketing systems that can handle high volumes of customer interactions efficiently. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in ticketing systems is enhancing their capability to automate tasks, predict issues, and provide faster resolutions, thus driving market growth.

Another significant growth factor is the rapid digital transformation across various industries. With the proliferation of digital channels and the growing dependence on IT infrastructure, organizations are increasingly implementing help desk ticketing systems to manage IT-related issues more effectively. These systems not only help in resolving IT issues promptly but also play a critical role in ensuring business continuity by minimizing downtime. Furthermore, the rising adoption of cloud-based solutions is making it easier and more cost-effective for organizations to deploy and scale help desk ticketing systems, contributing to market expansion.

The growing importance of customer experience as a key differentiator in competitive markets is also fueling the demand for help desk ticketing systems. Companies are investing heavily in customer service solutions to enhance their customer support capabilities and improve customer satisfaction levels. A robust help desk ticketing system allows companies to track, manage, and resolve customer issues efficiently, providing a seamless and positive customer experience. This focus on customer experience is particularly evident in sectors like BFSI, healthcare, and retail, where timely and efficient customer service is crucial for maintaining customer loyalty and trust.

Service Desk Software plays a pivotal role in enhancing the efficiency of help desk ticketing systems. By providing a centralized platform for managing customer interactions, Service Desk Software allows organizations to streamline their support processes, ensuring that customer queries are addressed promptly and effectively. This software often includes features such as automated ticket routing, knowledge base integration, and real-time reporting, which collectively enhance the ability of support teams to deliver high-quality service. As businesses continue to prioritize customer satisfaction, the adoption of advanced Service Desk Software is becoming increasingly essential, enabling companies to maintain a competitive edge in the market.

From a regional perspective, North America holds the largest share in the help desk ticketing system servers market, primarily due to the high adoption of advanced technology and the presence of major market players in the region. However, the Asia Pacific region is expected to witness the highest growth during the forecast period. The rapid growth of the IT and telecommunications sector, coupled with increasing investments in digital infrastructure and customer service solutions, is driving the market in this region. Additionally, the growing number of small and medium enterprises (SMEs) in countries like India and China is contributing to the rising demand for affordable and efficient help desk ticketing systems.

Component Analysis

The help desk ticketing system servers market can be segmented by component into three main categories: hardware, software, and services. The hardware segment includes servers and networking equipment necessary for the functioning of help desk systems. Although this segment represents a smaller portion of the market

Collections group multiple Human Reference Atlas (HRA) Digital Objects (DOs) for use in specific applications. The hra-ols collection is a subset that focuses solely on Anatomical Structures (AS), Cell Types (CTs), and Biomarkers (Bs)—i.e., the asct-b DOs. It is used by the Ontology Lookup Service (OLS, https://www.ebi.ac.uk/ols4) for validation purposes. The source files for this collection on the Linked Open Data (LOD) server are available on GitHub.

Bibliography:

• Bueckle, Andreas, Bruce W. Herr II, Josef Hardi, Ellen M. Quardokus, Mark A. Musen, and Katy Börner. 2025. “Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data.” bioRxiv. https://doi.org/10.1101/2024.12.22.630006.

This web map references the live tiled map service from the OpenStreetMap (OSM) project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in ESRI products under a Creative Commons Attribution-ShareAlike license. Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10. Tip: Here are some well known locations as they appear in this web map, accessed by launching the web map with a URL that contains location parameters:AthensCairoJakartaMoscowMumbaiNairobiParisRio De JaneiroShanghai

This web map references the live tiled map service from the OpenStreetMap (OSM) project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: https://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in ESRI products under a Creative Commons Attribution-ShareAlike license. Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

A scientific workflow describes a process for accomplishing a scientific objective, usually expressed in terms of tasks and their dependencies. We have collected publicly available workflows from Galaxy Main Server and tried to reuse them. This dataset contained our collected workflows.

Relation between the level of uncertainty and knowledge about possible string outcomes.