Synthesizing Quality Open Data Assets from Private Health Research Studies

Generating synthetic data represents an attractive solution for creating open data, enabling health research and education while preserving patient privacy. This work reproduces the research outcomes obtained on two previously published studies, which used private health data, using synthetic data generated with a method that used in this publication, called HealthGAN.

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Enhancing the Interactive Visualisation of a Data Preparation Tool from in-Memory Fitting to Big Data Sets

In order to derive reliable insights or make evidence-based decisions, the starting point is to assess and meet a minimum quality of data, either by those that publish the data (preferably) or alternatively by those that prepare data for analysis and develop specific analytics.

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Semantic Data Integration and Quality Assurance of Thematic Maps in the German Federal Agency for Cartography and Geodesy

This paper presents a new concept of geospatial quality assurance that is currently planned to be implemented in the German Federal Agency of Cartography and Geodesy. Linked open data is being enriched with Semantic Web data in order to create thematic maps relevant to the population.

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Evaluating the Quantity of Incident-Related Information in an Open Cyber Security Dataset

However, some questions remain over the quality and quantity of such open data. This paper presents the results of a recent case study that considers how feasible it is to answer a common question in Cyber security incident investigations, namely that “in an incident, who did what to which asset or victim, and with what result and impact”, for one such open Cyber security database.

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