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Herausgeber: 
  • Kerstin Kleese Van Dam
  • Critchlow Terence
  • Data-Intensive Science 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2013  
    Genre:  EDV / Informatik 
     
    American Regional Climate Change Assessment / Brazilian Amazon Rainforest / Bubble Count / collaborative research methods / Computational astrophysics / computational infrastructure / COMPUTERS / General / EAM
    ISBN:  9781439881392 
    EAN-Code: 
    9781439881392 
    Verlag:  Taylor and Francis 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Chapman & Hall/CRC Computational Science  
    Dimensionen:  H 234 mm / B 156 mm / D 32 mm 
    Gewicht:  748 gr 
    Seiten:  448 
    Illustration:  schwarz-weiss Illustrationen, Tabellen, schwarz-weiss 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Data-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative science is still lacking the effective access and exchange of knowledge among scientists, researchers, and policy makers across a range of disciplines. Bringing together leaders from multiple scientific disciplines, Data-Intensive Science shows how a comprehensive integration of various techniques and technological advances can effectively harness the vast amount of data being generated and significantly accelerate scientific progress to address some of the world's most challenging problems.

    In the book, a diverse cross-section of application, computer, and data scientists explores the impact of data-intensive science on current research and describes emerging technologies that will enable future scientific breakthroughs. The book identifies best practices used to tackle challenges facing data-intensive science as well as gaps in these approaches. It also focuses on the integration of data-intensive science into standard research practice, explaining how components in the data-intensive science environment need to work together to provide the necessary infrastructure for community-scale scientific collaborations.

    Organizing the material based on a high-level, data-intensive science workflow, this book provides an understanding of the scientific problems that would benefit from collaborative research, the current capabilities of data-intensive science, and the solutions to enable the next round of scientific advancements.

      



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