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Big Data in Context: Legal, Social and Technological Insights

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Book Series: SpringerBriefs in Law ISSN: 2192-8568 ISBN: 9783319624600 9783319624617 Year: Pages: 120 DOI: https://doi.org/10.1007/978-3-319-62461-7 Language: English
Publisher: Springer
Subject: Information theory
Added to DOAB on : 2017-11-24 10:19:35
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This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ’s Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.All contributions are based on papers submitted in connection with ABIDA (Assessing Big Data), an interdisciplinary research project exploring the societal aspects of big data and funded by the German Federal Ministry of Education and Research.This volume was produced as a part of the ABIDA project (Assessing Big Data, 01IS15016A-F). ABIDA is a four-year collaborative project funded by the Federal Ministry of Education and Research. However the views and opinions expressed in this book reflect only the authors’ point of view and not necessarily those of all members of the ABIDA project or the Federal Ministry of Education and Research.

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

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ISBN: 9783731505907 Year: Pages: XII, 243 p. DOI: 10.5445/KSP/1000060221 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:01:59
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Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.

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