We want to help make data more accessible and more useful; our purpose is to develop and support methods to locate, identify and cite data and other research objects.
The 'German Network for Bioinformatics Infrastructure – de.NBI' is a national, academic and non-profit infrastructure supported by the Federal Ministry of Education and Research providing bioinformatics services to users in life sciences research and biomedicine in Germany and Europe. The partners organize training events, courses and summer schools on tools, standards and compute services provided by de.NBI to assist researchers to more effectively exploit their data.
SSHOC will create the social sciences and humanities area of the European Open Science Cloud (EOSC) thereby facilitating access to flexible, scalable research data and related services streamlined to the precise needs of the SSH community.
Im Onlineportal finden Nutzerinnen und Nutzer zunächst die bereits bekannten Leitlinien und ihre Erläuterungen. Neu hinzu kommen nun allgemeine und fachspezifische Kommentierungen, Fallbeispiele, eine Übersicht über häufig gestellte Fragen, Verweise auf Gesetze und andere Normen, zugehörige DFG-Stellungnahmen sowie externe Quellen. Für die Nutzerinnen und Nutzer des Portals existieren verschiedene Such- und Zugangsmodi. Eine englische Fassung soll 2021 freigeschaltet werden.
A. Seeland. Software, (2020)Related to: Selent, B., Kraus, H., Hansen, N., Schembera, B., Seeland, A. & Iglezakis, D. (forthcoming). Management of Research Data in Computational Fluid Dynamics and Thermodynamics. In: Proceedings der E-Science-Tage 2019.
M. Gärtner, U. Hahn, und S. Hermann. Language Technologies for the Challenges of the Digital Age, Seite 284-291. Cham, Springer International Publishing, (2018)
O. Gundersen, und S. Kjensmo. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), Association for the Advancement of Artificial Intelligence, (2018)
N. Micic, D. Neagu, I. Campean, und E. Habib Zadeh. (2017)Every industry has significant data output as a product of their working process, and with the recent advent of big data mining and integrated data warehousing it is the case for a robust methodology for assessing the quality for sustainable and consistent processing. In this paper a review is conducted on Data Quality (DQ) in multiple domains in order to propose connections between their methodologies. This critical review suggests that within the process of DQ assessment of heterogeneous data sets, not often are they treated as separate types of data in need of an alternate data quality assessment framework. We discuss the need for such a directed DQ framework and the opportunities that are foreseen in this research area and propose to address it through degrees of heterogeneity..