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Building on the foundation of sequenced genomes and metagenomes, the program focuses on a tightly coupled approach that combines experimental physiology, omics- driven analytical techniques, and computational modeling of functional biological networks. Program Goal. Achieve a predictive, systems- level understanding of plants, microbes, and biological communities to enable biobased solutions to DOE mission challenges in energy and environment. Objectives. The program's ultimate objectives are to: Determine the molecular mechanisms, regulatory elements, and integrated networks needed to understand genome- scale functional properties of biological systems. Develop omics experimental capabilities and enabling technologies needed to achieve dynamic, systems- level understanding of organism and community function. Flexibly scale understanding of biological processes from defined subsystems to individual organisms, consortial assemblies of multiple organisms, or complex communities operating at ecosystem scales. Understand the foundational rules and “design principles” governing living systems and develop tools for more sophisticated biosystems design, enabling the targeted modification of functional properties at the genome scale. Develop the knowledgebase, computational infrastructure, and modeling capabilities to advance predictive understanding and manipulation of biological systems. Advancing fundamental knowledge of these systems bridges critical knowledge gaps that must be addressed to enable biological solutions to crucial DOE missions. Genomic Science program research also employs the omics- driven tools of modern systems biology to analyze interactions between organisms that form biological communities and their surrounding environments. Understanding the relationships between molecular- scale functional biology and ecosystem- scale environmental processes illuminates the basic mechanisms that drive biogeochemical cycling of metals and nutrients, carbon cycling, and greenhouse gas emissions in both terrestrial ecosystems impacted by climate change and in agricultural systems producing bioenergy feedstocks. As new understanding emerges, strategic emphasis will be placed on overcoming knowledge gaps and fostering transformational breakthroughs that most effectively address DOE mission- critical research objectives. Multiscale Explorations. For biological systems central to DOE missions in energy and the environment, Genomic Science program research analyzes properties and processes on three fundamental levels. Molecular: Focusing on genes, proteins, macromolecular complexes, and other biomolecules that provide structure and perform a cellular function. Such an approach aids in understanding how the genome determines dynamic biological structure and function at all scales, from genes to ecosystems, and how proteins and protein complexes function individually or in interactions with other cellular components. Cellular: Investigating dynamic molecular processes, networks, and subsystems controlled and coordinated to enable complex cellular processes such as growth and metabolism. Multicellular and Multiorganismal Systems: Exploring diverse cellular systems that interact to carry out coordinated complex processes that both respond to and alter their environments to determine how cells work in the context of multicellular tissues of plants and multiorganism communities of microbes and plants. The myriad biological structures and processes that exist within these three system levels are interconnected and coordinated by an intricate set of regulatory controls and continuous interactions with the surrounding environment. To investigate biology at multiple scales, the Genomic Science program is building new multidisciplinary research communities and advancing development of next- generation, automated technologies that increase sample throughput and analytical reliability while reducing analysis time. Key research technology and methodology development areas for the Genomic Science program include genomics, analytical omics, molecular imaging and structural analysis, modeling and simulation, and genome- scale engineering tools that span all three levels of organization. From Genome Sequences to Understanding As a leader in systems biology research, the Genomic Science program builds on a foundation of genome sequences, genetic regulatory networks, densely arrayed metabolic pathways, and higher- scale organismal interactions to identify the common fundamental principles that drive living systems. Knowledge of these common principles revealed by studying organisms relevant to any one DOE mission facilitates breakthroughs in basic biology important to other DOE and national needs. The human genome is the genome of Homo sapiens. It is made up of 23 chromosome pairs with a total of about 3 billion DNA base pairs. There are 24 distinct human chromosomes: 22 autosomal chromosomes, plus the sex-determining X.By leveraging the increasing availability of sequences from whole organism genomes and environmental samples (metagenomes), Genomic Science program researchers are developing advanced methods to facilitate the translation of genome sequence into predictive understanding of function. These methods cut across DOE missions in energy and the environment. Genomic Science program systems biology research extends traditional scientific methodology by addressing complex problems through coordinated research among interdisciplinary teams with complementary expertise in the biological, physical, and computational sciences. A nested array of experimental techniques and analytical technologies must be created, refined, and deployed to investigate and understand biological systems, linking different levels of biological discovery to gain a predictive understanding of whole systems—from cells to ecosystems. To accomplish the desired in- depth understanding of biological systems, an unprecedented integration of experimental biology, analytical technologies, data and computation, theory, modeling, simulation, and experimentation must occur. 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Proposals must fall within the scope of any of the clusters in the Division of Environmental Biology (DEB) or the Behavioral Systems Cluster in the Division of Integrative Organismal Systems (IOS). These grants provide partial support of doctoral dissertation research for improvement beyond the already existing project. Allowed are costs for doctoral candidates to participate in scientific meetings, to conduct research in specialized facilities or field settings, and to expand an existing body of dissertation research. EDUCATIONAL OPPORTUNITYThis program provides educational opportunities for. Graduate Students. SBE Doctoral Dissertation Research Improvement Grants . CONTACTSFor a list of cognizant program officers, please visit the SBE Doctoral Dissertation Contact List. PROGRAM GUIDELINESSolicitation. Important Information for Proposers. A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 1. January 2. 5, 2. 01. Doctoral candidates currently enrolled in accredited programs are eligible for the Doctoral Dissertation Research Grant program. They may be in any academic. The NSF Graduate Research Fellowship Program recognizes and supports outstanding graduate students in NSF-supported science, technology. Division of Environmental Biology Doctoral Dissertation Improvement Grants in the Directorate for Biological Sciences (DDIG) DDIG Update. Summer session: 8 Tips for funding your dissertation. Getting money for your research may not be as hard as you think. By Christopher Munsey. Please be advised that, depending. NSF 1. 6- 1 may apply to proposals submitted in response to this. DUE DATES. Archived. SYNOPSISThe National Science Foundation's Division of Behavioral and Cognitive Sciences (BCS), Division of Social and Economic Sciences (SES), National Center for Science and Engineering Statistics (NCSES), and the SBE Office of Multidisciplinary Activities (SMA) award grants to doctoral students to improve the quality of dissertation research. These grants provide funds for items not normally available through the student's university. Additionally, these grants allow doctoral students to undertake significant data- gathering projects and to conduct field research in settings away from their campus that would not otherwise be possible. Proposals are judged on the basis of their scientific merit, including the theoretical importance of the research question and the appropriateness of the proposed data and methodology to be used in addressing the question. In an effort to improve the quality of dissertation research, many programs in both BCS and SES, the Research on Science and Technology Surveys and Statistics program within NCSES, and the Science of Science and Innovation Policy program in SMA accept doctoral dissertation improvement grant proposals. Requirements vary across programs, so proposers are advised to consult the relevant program's webpage for specific information and contact the program director if necessary. The following Programs support dissertation research: Division of Behavioral and Cognitive Sciences (BCS)Archaeology. Cultural Anthropology. Documenting Endangered Languages. Geography and Spatial Sciences. Linguistics. Biological Anthropology. Division of Social and Economic Sciences (SES)Decision, Risk and Management Sciences. Economics. Law and Social Science. Methodology, Measurement, and Statistics. Political Science. Science, Technology, and Society. Sociology. National Center for Science and Engineering Statistics (NCSES)Research on Science and Technology Surveys and Statistics Program. SBE Office of Multidisciplinary Activities. Science of Science and Innovation Policy. For a list of cognizant program officers for the programs listed above, please visit the SBE Doctoral Dissertation Contact List. EDUCATIONAL OPPORTUNITYThis program provides educational opportunities for. Graduate Students. S& T Competitiveness, STEM Education, S& T Workforce. Science of Science and Innovation Policy. Science, Technology, and Society. Sociology. REVISIONS AND UPDATESWhat Has Been Funded (Recent Awards Made Through This Program, with Abstracts)Map of Recent Awards Made Through This Program. Columbia Digital Library Collections. ABOUT IFPThe IFP program began in November 2. In Asia, the program was initiated in Vietnam. Tanzania-International Fellowships Program. The International Fellowships Program (IFP) is a program based in New York, and supported by the Ford Foundation. It is administered in Tanzania as IFP-Tanzania in. The Ford International Fellowships Program has ended and we are no longer offering fellowships. The Ford Foundation met with several IFP alumni as part of a learning trip to. Ford Foundation International Fellowships Program; Multinational: Ford Foundation International Fellowships. Mozambique, Nigeria, Palestinian Territories, Peru, Philippines, Russia, Senegal, South Africa, Tanzania. 4.3 Ford Foundation International Fellowships Program. IFP Ford Foundation - International Fellowships. Ford Foundation International Fellowships Program applicants must be. Philippines, Russia, Senegal, South Africa, Tanzania, Thailand. Foundation's areas of endeavor. INTERNATIONAL FELLOWSHIPS PROGRAM. Fellowships Program (IFP) of the Ford Foundation for resident citizens and other residents of Kenya. 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This is a collection of programs that enables you to enter. There are many different types of DBMSs. Benefits of Grid Computing Compared to other models of computing, IT systems designed and implemented in the grid style deliver higher quality of service, lower cost, and greater flexibility. Higher quality of service results from having no single point of failure, a robust security infrastructure, and centralized, policy- driven management. Lower costs derive from increasing the utilization of resources and dramatically reducing management and maintenance costs. Rather than dedicating a stack of software and hardware to a specific task, all resources are pooled and allocated on demand, thus eliminating under utilized capacity and redundant capabilities. Grid computing also enables the use of smaller individual hardware components, thus reducing the cost of each individual component and providing more flexibility to devote resources in accordance with changing needs. Grid Computing Defined. The grid style of computing treats collections of similar IT resources holistically as a single pool, while exploiting the distinct nature of individual resources within the pool. To address simultaneously the problems of monolithic systems and fragmented resources, grid computing achieves a balance between the benefits of holistic resource management and flexible independent resource control. IT resources managed in a grid include: Infrastructure: the hardware and software that create a data storage and program execution environment. Applications: the program logic and flow that define specific business processes. Information: the meanings inherent in all different types of data used to conduct business. Core Tenets of Grid Computing Two core tenets uniquely distinguish grid computing from other styles of computing, such as mainframe, client- server, or multi- tier: virtualization and provisioning. With virtualization, individual resources (e. Virtualization means breaking hard- coded connections between providers and consumers of resources, and preparing a resource to serve a particular need without the consumer caring how that is accomplished. With provisioning, when consumers request resources through a virtualization layer, behind the scenes a specific resource is identified to fulfill the request and then it is allocated to the consumer. Provisioning as part of grid computing means that the system determines how to meet the specific need of the consumer, while optimizing operation of the system as a whole. The specific ways in which information, application or infrastructure resources are virtualized and provisioned are specific to the type of resource, but the concepts apply universally. Similarly, the specific benefits derived from grid computing are particular to each type of resource, but all share the characteristics of better quality, lower costs and increased flexibility. Infrastructure Grid Infrastructure grid resources include hardware resources such as storage, processors, memory, and networks as well as software designed to manage this hardware, such as databases, storage management, system management, application servers, and operating systems. Virtualization and provisioning of infrastructure resources mean pooling resources together and allocating to the appropriate consumers based on policies. For example, one policy might be to dedicate enough processing power to a web server that it can always provide sub- second response time. That rule could be fulfilled in different ways by the provisioning software in order to balance the requests of all consumers. Treating infrastructure resources as a single pool and allocating those resources on demand saves money by eliminating under utilized capacity and redundant capabilities. Managing hardware and software resources holistically reduces the cost of labor and the opportunity for human error. Spreading computing capacity among many different computers and spreading storage capacity across multiple disks and disk groups removes single points of failure so that if any individual component fails, the system as a whole remains available. Furthermore, grid computing affords the option to use smaller individual hardware components, such as blade servers and low cost storage, which enables incremental scaling and reduces the cost of each individual component, thereby giving companies more flexibility and lower cost. Infrastructure is the dimension of grid computing that is most familiar and easy to understand, but the same concepts apply to applications and information. Applications Grid Application resources in the grid are the encodings of business logic and process flow within application software. These may be packaged applications or custom applications, written in any programming language, reflecting any level of complexity. For example, the software that takes an order from a customer and sends an acknowledgement, the process that prints payroll checks, and the logic that routes a particular customer call to a particular agent are all application resources. Historically, application logic has been intertwined with user interface code, data management code, and process or page flow and has lacked well- defined interfaces, which has resulted in monolithic applications that are difficult to change and difficult to integrate. Service oriented architecture has emerged as a superior model for building applications, and service oriented architecture concepts align exactly with the core tenets of grid computing. Virtualization and provisioning of application resources involves publishing application components as services for use by multiple consumers, which may be people or processes, then orchestrating those services into more powerful business flows. In the same way that grid computing enables better reuse and more flexibility of IT infrastructure resources, grid computing also treats bits of application logic as a resource, and enables greater reuse of application functionality and more flexibility in changing and building new composite applications. Furthermore, applications that are orchestrated from published services are able to view activities in a business as a single whole, so that processes are standardized across geography and business units and processes are automated end- to- end. This generates more reliable business processes and lowers cost through increased automation and reduced variability. Information Grid The third dimension to grid computing, after infrastructure and applications, is information. Today, information tends to be fragmented across a company, making it difficult to see the business as a whole or answer basic questions. Without information about who the customer is, and what they want to buy, information assets go underexploited. In contrast, grid computing treats information holistically as a resource, similar to infrastructure and applications resources, and thus extracts more of its latent value. Information grid resources include all data in the enterprise and all metadata required to make that data meaningful. This data may be structured, semi- structured, or unstructured, stored in any location, such as databases, local file systems, or e- mail servers, and created by any application. The core tenets of grid computing apply similarly to information as they do to infrastructure and applications. The infrastructure grid exploits the power of the network to allow multiple servers or storage devices to be combined toward a single task, then easily reconfigured as needs change. A service oriented architecture, or an applications grid, enables independently developed services, or application resources, to be combined into larger business processes, then adapted as needs change without breaking other parts of the composite application. Similarly, the information grid provides a way for information resources to be joined with related information resources to greater exploit the value of the inherent relationships among information, then for new connections to be made as situations change. The relational database, for example, was an early information virtualization technology. Unlike its predecessors, the network database and hierarchical database models, in which all relationships between data had to be predetermined, relational database enabled flexible access to a general- purpose information resource. Today, XML furthers information virtualization by providing a standard way to represent information along with metadata, which breaks the hard link between information and a specific application used to create and view that information. Information provisioning technologies include message queuing, data propagation, replication, extract- transform- load, as well as mapping and cleansing tools to ensure data quality. Data hubs, in which a central operational data store continually syncs with multiple live data sources, are emerging as a preferred model for establishing a single source of truth while maintaining the flexibility of distributed control. Grid Resources Work Well Independently and Best Together By managing any single IT resource – infrastructure, applications, or information - using grid computing, regardless of how the other resources are treated, enterprises can realize higher quality, more flexibility, and lower costs. |
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