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Verlagsausgabe
DOI: 10.5445/IR/1000076854
Originalveröffentlichung
DOI: 10.1088/1742-6596/898/8/082026

Advancing data management and analysis in different scientific disciplines

Fischer, Max; Gasthuber, Martin; Giesler, Andre; Hardt, Marcus; Meyer, Jörg; Prabhune, Ajinkya; Rigoll, Fabian; Schwarz, Kilian; Streit, Achim

Abstract:
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.


Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 1742-6588, 1742-6596
URN: urn:nbn:de:swb:90-768546
KITopen ID: 1000076854
HGF-Programm 46.12.01; LK 01
Erschienen in Journal of physics / Conference Series
Band 898
Seiten Art.Nr. 082026
Bemerkung zur Veröffentlichung 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, San Francisco, California, USA, 10th - 14th October 2016
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