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Sekundärnutzung von Sozial- und Gesundheitsdaten – Rechtliche Rahmenbedingungen

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ISBN: 9783954664849 9783954665181 Year: Language: German
Publisher: MWV Medizinisch Wissenschaftliche Verlagsgesellschaft Grant: Knowledge Unlatched - 104966
Subject: Internal medicine
Added to DOAB on : 2020-02-15 11:21:04
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Viele der im System der Gesetzlichen Krankenversicherung erhobenen Daten sind für die Weiterentwicklung und Verbesserung der Gesundheitsversorgung von hohem Wert. Sozial- und Gesundheitsdaten sind als personenbezogene Daten aber auch datenschutzrechtlich einem engmaschigen Rechtsrahmen unterworfen. Ihre Nutzung in anderen Kontexten bedarf eines tiefgründigen Verständnisses der Regelungen auf europäischer und nationaler Ebene. Dieser Band bietet einen Überblick über den aktuellen Rechtsrahmen und eine wichtige Orientierung zur rechtssicheren Nutzung von Sozial- und Gesundheitsdaten. Das Werk ermöglicht es, Lösungen und Produkte zu entwickeln, die die Qualität der Versorgung verbessern. Im ersten Teil wird der sozialrechtliche Rahmen zur Nutzung von Sozialdaten für die Forschung dargelegt. Im zweiten Teil wird ein Überblick zum Umgang mit Forschungsdaten nach Anwendung der DSGVO und entsprechender nationaler Anpassungen des Rechtsrahmens gegeben.

Data Sciences: From First-Order Logic to the Web

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ISBN: 9782722601796 Year: Language: English
Publisher: Collège de France
Added to DOAB on : 2014-02-27 14:16:29
License: OpenEdition licence for Books

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Relational database management systems, using as foundations a formal language, first-order logic, serve as mediators between individuals and machines. With the increase in the volume of data disseminated on the Web, a “collective intelligence” is currently emerging, shaped by large search engines whose monopoly raises ethical and political questions. One of the main challenges for the coming years is the development of technologies that will make it possible to find, evaluate, validate, veri...

Archives in Liquid Times

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Book Series: Jaarboek ISBN: 9789071251450 Year: Pages: 326 Language: English
Publisher: Stichting Archiefpublicaties Grant: Amsterdam University of Applied Sciences / Archiefschool DEVENTit BV Doxis Informatiemanagers Karmac Informatie & Innovatie BV Picturae DE REE archiefsystemen
Subject: Information theory
Added to DOAB on : 2018-01-11 11:01:53
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Archives in Liquid Times aims to broaden and deepen the thinking about archives in today’s digital environment. It is a book that tries to fuel the debate about archives in different fields of research. It shows that in these liquid times, archives need and deserve to be considered from different angles. Archives in Liquid Times is a publication in which archival science is linked to philosophy (of information) and data science. Not only do the contributors try to open windows to new concepts and perspectives, but also to new uses of existing concepts concerning archives. The articles in this book contain philosophical reflections, speculative essays and presentations of new models and concepts alongside well-known topics in archival theory. Among the contributors are scholars from different fields of research, like Anne Gilliland, Wolfgang Ernst, Geoffrey Yeo, Martijn van Otterlo, Charles Jeurgens and Geert-Jan van Bussel. This book includes interviews with Luciano Floridi and Eric Ketelaar, in which they reflect on key issues arising from the contributions. The editors are Frans Smit, Arnoud Glaudemans and Rienk Jonker.

Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)

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ISBN: 9783731506959 Year: Pages: V, 259 p. DOI: 10.5445/KSP/1000071931 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Computer Science
Added to DOAB on : 2019-07-30 20:02:00
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The Helmholtz Association funded the ""Large-Scale Data Management and Analysis"" portfolio theme from 2012-2016. Four Helmholtz centres, six universities and another research institution in Germany joined to enable data-intensive science by optimising data life cycles in selected scientific communities. In our Data Life cycle Labs, data experts performed joint R&D together with scientific communities. The Data Services Integration Team focused on generic solutions applied by several communities.

At the Crossroads: Lessons and Challenges in Computational Social Science

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889450213 Year: Pages: 98 DOI: 10.3389/978-2-88945-021-3 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Physics (General)
Added to DOAB on : 2018-02-27 16:16:44
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The interest of physicists in economic and social questions is not new: for over four decades, we have witnessed the emergence of what is called nowadays “sociophysics” and “econophysics”, vigorous and challenging areas within the wider “Interdisciplinary Physics”. With tools borrowed from Statistical Physics and Complexity, this new area of study have already made important contributions, which in turn have fostered the development of novel theoretical foundations in Social Science and Economics, via mathematical approaches, agent-based modelling and numerical simulations. From these foundations, Computational Social Science has grown to incorporate as well the empirical component —aided by the recent data deluge from the Web 2.0 and 3.0—, closing in this way the experiment-theory cycle in the best tradition of Physics.

Earth Observation Open Science and Innovation

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Book Series: ISSI Scientific Report Series ISBN: 9783319656328 9783319656335 Year: Pages: 332 DOI: https://doi.org/10.1007/978-3-319-65633-5 Language: English
Publisher: Springer Nature Grant: International Space Science Institute (ISSI)
Subject: Technology (General) --- Geography
Added to DOAB on : 2018-06-26 12:48:07
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Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites.This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

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ISBN: 9783658205393 9783658205409 Year: Pages: 201 DOI: https://doi.org/10.1007/978-3-658-20540-9 Language: English
Publisher: Springer Nature Grant: Philipps-Universität Marburg
Subject: Mathematics
Added to DOAB on : 2018-06-29 15:12:23
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This book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining.

Introduction to Scientific Programming with Python

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Book Series: Simula SpringerBriefs on Computing ISBN: 9783030503567 Year: Pages: 148 DOI: 10.1007/978-3-030-50356-7 Language: English
Publisher: Springer Nature
Subject: Science (General) --- Computer Science
Added to DOAB on : 2020-07-15 23:58:26
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This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

Data Journeys in the Sciences

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ISBN: 9783030371777 Year: Pages: 412 DOI: 10.1007/978-3-030-37177-7 Language: English
Publisher: Springer Nature
Subject: Science (General)
Added to DOAB on : 2020-07-15 23:58:32
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This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.

Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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ISBN: 9783039214099 9783039214105 Year: Pages: 254 DOI: 10.3390/books978-3-03921-410-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

Keywords

parameter-dependent model --- surrogate modeling --- tensor-train decomposition --- gappy POD --- heterogeneous data --- elasto-viscoplasticity --- archive --- model reduction --- 3D reconstruction --- inverse problem plasticity --- data science --- model order reduction --- POD --- DEIM --- gappy POD --- GNAT --- ECSW --- empirical cubature --- hyper-reduction --- reduced integration domain --- computational homogenisation --- model order reduction (MOR) --- low-rank approximation --- proper generalised decomposition (PGD) --- PGD compression --- randomised SVD --- nonlinear material behaviour --- machine learning --- artificial neural networks --- computational homogenization --- nonlinear reduced order model --- elastoviscoplastic behavior --- nonlinear structural mechanics --- proper orthogonal decomposition --- empirical cubature method --- error indicator --- symplectic model order reduction --- proper symplectic decomposition (PSD) --- structure preservation of symplecticity --- Hamiltonian system --- reduced order modeling (ROM) --- proper orthogonal decomposition (POD) --- enhanced POD --- a priori enrichment --- modal analysis --- stabilization --- dynamic extrapolation --- computational homogenization --- large strain --- finite deformation --- geometric nonlinearity --- reduced basis --- reduced-order model --- sampling --- Hencky strain --- microstructure property linkage --- unsupervised machine learning --- supervised machine learning --- neural network --- snapshot proper orthogonal decomposition

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