The Experts below are selected from a list of 2946 Experts worldwide ranked by ideXlab platform

Vivian Hutchison - One of the best experts on this subject based on the ideXlab platform.

  • The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model
    Journal of eScience Librarianship, 2017
    Co-Authors: John Faundeen, Vivian Hutchison
    Abstract:

    This paper details how the U.S. Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies and data management applications. Starting with an extensive literature review of existing data Lifecycle Models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined. Refinements and reviews spanned two years, leading to finalization of the Model and documentation in a formal agency publication

John Faundeen - One of the best experts on this subject based on the ideXlab platform.

  • The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model
    Journal of eScience Librarianship, 2017
    Co-Authors: John Faundeen, Vivian Hutchison
    Abstract:

    This paper details how the U.S. Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies and data management applications. Starting with an extensive literature review of existing data Lifecycle Models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined. Refinements and reviews spanned two years, leading to finalization of the Model and documentation in a formal agency publication

John L. Faundeen - One of the best experts on this subject based on the ideXlab platform.

  • The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model
    Journal of eScience Librarianship, 2017
    Co-Authors: John L. Faundeen, Vivian B. Hutchison
    Abstract:

    This paper details how the U.S. Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies and data management applications. Starting with an extensive literature review of existing data Lifecycle Models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined. Refinements and reviews spanned two years, leading to finalization of the Model and documentation in a formal agency publication. The Model serves as a critical framework for data management policy, instructional resources, and tools. The Model helps the USGS address both the Office of Science and Technology Policy (OSTP)2 for increased public access to federally funded research, and the Office of Management and Budget (OMB)3 2013 Open Data directives, as the foundation for a series of agency policies related to data management planning, metadata development, data release procedures, and the long-term preservation of data. Additionally, the agency website devoted to data management instruction and best practices (www2.usgs.gov/datamanagement) is designed around the Model’s structure and concepts. This paper also illustrates how the Model is being used to develop tools for supporting USGS research and data management processes

  • the united states geological survey science data Lifecycle Model
    Open-File Report, 2014
    Co-Authors: John L. Faundeen, Thomas E. Burley, Jennifer A. Carlino, David L. Govoni, Heather S. Henkel, Sally L. Holl, Vivian B. Hutchison, Ellyn T. Montgomery, Elizabeth Martin, Cassandra C. Ladino
    Abstract:

    1 Background 1 The Data Lifecycle Model 2 Primary Model Elements 2 Cross-Cutting Model Elements 3 Data Management Roles and Responsibilities in Research Projects 3 Summary 4 Acknowledgments 4 References Cited 4

  • The United States Geological Survey Science Data Lifecycle Model
    U.S. Geological Survey Open-File Report 2013–1265, 2013
    Co-Authors: John L. Faundeen, Thomas E. Burley, Jennifer A. Carlino, David L. Govoni, Heather S. Henkel, Sally L. Holl, Vivian B. Hutchison, Ellyn T. Montgomery, Elizabeth Martin, Cassandra C. Ladino
    Abstract:

    U.S. Geological Survey (USGS) data represent corporate assets with potential value beyond any immediate research use, and therefore need to be accounted for and properly managed throughout their Lifecycle. Recognizing these motives, a USGS team developed a Science Data Lifecycle Model (SDLM) as a high-level view of data—from conception through preservation and sharing—to illustrate how data management activities relate to project workflows, and to assist with understanding the expectations of proper data management. In applying the Model to research activities, USGS scientists can ensure that data products will be well-described, preserved, accessible, and fit for re-use. The Model also serves as a structure to help the USGS evaluate and improve policies and practices for managing scientific data, and to identify areas in which new tools and standards are needed.

Morten Lovbjerg - One of the best experts on this subject based on the ideXlab platform.

  • PPSN - The Lifecycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
    Parallel Problem Solving from Nature — PPSN VII, 2002
    Co-Authors: Thiemo Krink, Morten Lovbjerg
    Abstract:

    Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the Lifecycle Model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs), and stochastic hill climbing to create a generally well-performing search heuristics. In the Lifecycle Model, we consider candidate solutions and their fitness as individuals, which, based on their recent search progress, can decide to become either a GA individual, a particle of a PSO, or a single stochastic hill climber. First results from a comparison of our new approach with the single search algorithms indicate a generally good performance in numerical optimization.

  • the Lifecycle Model combining particle swarm optimisation genetic algorithms and hillclimbers
    Parallel Problem Solving from Nature, 2002
    Co-Authors: Thiemo Krink, Morten Lovbjerg
    Abstract:

    Adaptive search heuristics are known to be valuable in approximating solutions to hard search problems. However, these techniques are problem dependent. Inspired by the idea of life cycle stages found in nature, we introduce a hybrid approach called the Lifecycle Model that simultaneously applies genetic algorithms (GAs), particle swarm optimisation (PSOs), and stochastic hill climbing to create a generally well-performing search heuristics. In the Lifecycle Model, we consider candidate solutions and their fitness as individuals, which, based on their recent search progress, can decide to become either a GA individual, a particle of a PSO, or a single stochastic hill climber. First results from a comparison of our new approach with the single search algorithms indicate a generally good performance in numerical optimization.

Vivian B. Hutchison - One of the best experts on this subject based on the ideXlab platform.

  • The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model
    Journal of eScience Librarianship, 2017
    Co-Authors: John L. Faundeen, Vivian B. Hutchison
    Abstract:

    This paper details how the U.S. Geological Survey (USGS) Community for Data Integration (CDI) Data Management Working Group developed a Science Data Lifecycle Model, and the role the Model plays in shaping agency-wide policies and data management applications. Starting with an extensive literature review of existing data Lifecycle Models, representatives from various backgrounds in USGS attended a two-day meeting where the basic elements for the Science Data Lifecycle Model were determined. Refinements and reviews spanned two years, leading to finalization of the Model and documentation in a formal agency publication. The Model serves as a critical framework for data management policy, instructional resources, and tools. The Model helps the USGS address both the Office of Science and Technology Policy (OSTP)2 for increased public access to federally funded research, and the Office of Management and Budget (OMB)3 2013 Open Data directives, as the foundation for a series of agency policies related to data management planning, metadata development, data release procedures, and the long-term preservation of data. Additionally, the agency website devoted to data management instruction and best practices (www2.usgs.gov/datamanagement) is designed around the Model’s structure and concepts. This paper also illustrates how the Model is being used to develop tools for supporting USGS research and data management processes

  • the united states geological survey science data Lifecycle Model
    Open-File Report, 2014
    Co-Authors: John L. Faundeen, Thomas E. Burley, Jennifer A. Carlino, David L. Govoni, Heather S. Henkel, Sally L. Holl, Vivian B. Hutchison, Ellyn T. Montgomery, Elizabeth Martin, Cassandra C. Ladino
    Abstract:

    1 Background 1 The Data Lifecycle Model 2 Primary Model Elements 2 Cross-Cutting Model Elements 3 Data Management Roles and Responsibilities in Research Projects 3 Summary 4 Acknowledgments 4 References Cited 4

  • The United States Geological Survey Science Data Lifecycle Model
    U.S. Geological Survey Open-File Report 2013–1265, 2013
    Co-Authors: John L. Faundeen, Thomas E. Burley, Jennifer A. Carlino, David L. Govoni, Heather S. Henkel, Sally L. Holl, Vivian B. Hutchison, Ellyn T. Montgomery, Elizabeth Martin, Cassandra C. Ladino
    Abstract:

    U.S. Geological Survey (USGS) data represent corporate assets with potential value beyond any immediate research use, and therefore need to be accounted for and properly managed throughout their Lifecycle. Recognizing these motives, a USGS team developed a Science Data Lifecycle Model (SDLM) as a high-level view of data—from conception through preservation and sharing—to illustrate how data management activities relate to project workflows, and to assist with understanding the expectations of proper data management. In applying the Model to research activities, USGS scientists can ensure that data products will be well-described, preserved, accessible, and fit for re-use. The Model also serves as a structure to help the USGS evaluate and improve policies and practices for managing scientific data, and to identify areas in which new tools and standards are needed.