Management of scientific Big Data poses challenges at all stages of the data life cycle – acquisition, ingest, access, replication, preservation, etc. For scientific communities the data exploration – commonly considered as the 4th pillar besides experiment, theory and simulation – is of utmost importance to gain new scientific insights and knowledge.
The joint SCOSTEP-WDS workshop on ‘Global Data Activities for the Study of Solar-Terrestrial Variability’ will be held from 28 to 30 September 2015 at the National Institute of Information and Communications Technology, Tokyo, Japan.
The international GridKa School is one of the leading summer schools for advanced computing techniques. The school provides a forum for scientists and technology leaders, experts and novices to facilitate knowledge sharing and information exchange. The target audience are different groups like grid and cloud newbies, advanced users as well as administrators, graduate and PhD students. Organized by the Karlsruhe Institute of Technology (KIT), GridKa School is hosted by Steinbuch Centre for Computing (SCC).
The annual IEEE eScience conference will be held in Munich, Germany from 31st August to 4th September 2015. The objective of the eScience Conference is to promote and encourage all aspects of eScience and its associated technologies, applications, and tools.
eScience promotes innovation in collaborative, computationally- or data-intensive research across all disciplines, throughout the research lifecycle.
The 10th International Conference on Open Repositories (OR2015) will be held on June 8-11, 2015 in Indianapolis, Indiana, USA. The conference is being jointly hosted by Indiana University Bloomington Libraries, University of Illinois at Urbana-Champaign Library, and Virginia Tech University Libraries.
The key objective of the RDA Europe 2nd Science Workshop is to bring together leading scientists from different disciplines and European countries to discuss about the needs and directions with respect to make data science much more efficient and to guarantee that results emerging from data-driven science and related initiatives are reproducible.