This title appears in the Scientific Report :
2017
Please use the identifier:
http://hdl.handle.net/2128/16292 in citations.
Please use the identifier: http://dx.doi.org/10.1088/1742-6596/898/8/082026 in citations.
Advancing data management and analysis in different scientific disciplines
Advancing data management and analysis in different scientific disciplines
Over the past several years, rapid growth of data has affected many fields of science. This has often resulted in the need for overhauling or exchanging the tools and approaches in the disciplines' data life cycles. However, this allows the application of new data analysis methods and facilitat...
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Personal Name(s): | Fischer, M. (Corresponding author) |
---|---|
Gasthuber, M. / Giesler, A. / Hardt, M. / Meyer, J. / Prabhune, A. / Rigoll, F. / Schwarz, K. / Streit, A. | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: | Journal of physics / Conference Series, 898 (2017) S. 082026 |
Imprint: |
Bristol
IOP Publ.
2017
|
DOI: |
10.1088/1742-6596/898/8/082026 |
Conference: | 22nd International Conference on Computing in High Energy and Nuclear Physics, San Francisco (USA), |
Document Type: |
Journal Article |
Research Program: |
Large Scale Data Management and Analysis Data-Intensive Science and Federated Computing |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1088/1742-6596/898/8/082026 in citations.
Over the past several years, rapid growth of data has affected many fields of science. This has often resulted in the need for overhauling or exchanging the tools and approaches in the disciplines' data life cycles. However, this allows the application of new data analysis methods and facilitates improved data sharing.The project Large-Scale Data Management and Analysis (LSDMA) of the German Helmholtz Association has been addressing both specific and generic requirements in its data life cycle successfully since 2012. Its data scientists work together with researchers from the fields such as climatology, energy and neuroscience to improve the community-specific data life cycles, in several cases even all stages of the data life cycle, i.e. from data acquisition to data archival. LSDMA scientists also study methods and tools that are of importance to many communities, e.g. data repositories and authentication and authorization infrastructure. |