Suchergebnisse

Intro -- Preface -- Overview of the Book -- Intended Audience -- Prerequisites -- Contents -- Contributors -- Glossary -- Introduction to Emergency Management -- 1 What Is Emergency? -- 2 Emergency Management -- 3 Emergency Management in Social Media Age: Information Flows -- 4 Emergency Management using Big Data -- 5 Tasks in Data-Driven Emergency Management -- References -- Big Data -- 1 What Is Big Data? -- 2 Big Data Sources for Emergency Management -- 3 Big Data Benefits and Challenges -- 3.1 Benefits -- 3.2 Challenges -- 4 Big Data Techniques and Tools -- 5 General Engine for Big Data Processing: Spark -- 6 Ethical and Societal Issues -- Exercises -- References -- Learning Algorithms for Emergency Management -- 1 Machine Learning and Emergency Management -- 1.1 Preliminaries -- 1.2 Learning Algorithms and Its Usage -- 1.2.1 Decision Tree -- 1.2.2 Clustering -- 1.2.3 Support Vector Machine -- 1.2.4 Bayesian -- 1.2.5 Neural Networks -- 1.2.6 Deep Learning -- 2 Practices of Learning Techniques in Emergency Management -- 2.1 Data Sets -- 2.2 Decision Trees in R -- 2.3 Naïve Bayes in R -- 2.4 k-Means Clustering in R -- 2.5 Support Vector Machine in R -- 2.6 Artificial Neural Networks in R -- References -- Knowledge Graphs and Natural-Language Processing -- 1 What Are Knowledge Graphs? -- 2 Benefits and Challenges -- 2.1 Benefits -- 2.2 Challenges -- 3 Vocabularies for Emergency Response -- 4 Semantic Datasets for Emergency Management -- 5 Analysing Natural-Language Texts -- 5.1 Pre-processing -- 5.2 Word Embeddings -- 5.3 Analysis Problems -- 5.4 Discussion -- 6 Using a Sentiment Analyser -- Exercises -- References -- Social Media Mining for Disaster Management and Community Resilience -- 1 Social Media and Disasters -- 2 Scenarios of Using Social Media Mining -- 2.1 Filtering Social Data for Actionable Intelligence.

This Data Management Plan (DMP) characterises the existing and planned data management, data security, data access and policies within the EdiCitNet project. As a key element of professional data management, the EdiCitNet DMP documents the context in which research data will be created and how it is handled and serves in particular to interpret and reproduce research results in later years and to be able to use them afterwards. The DMP is a dynamic document and will be updated during the project if necessary, changes arise. Horizon 2020 recommends creating new versions whenever there are significant changes in the project, such as new research data, strategic changes, or changes in external factors. ; The EdiCitNet project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 776665

The Swedish government has decided that all research results in the form of research data and scientific publications financed with public funds should be openly accessible as far as possible. The question is whether the responsible actors and if the universities are ready to implement the change. The significance of open access has amplified in Sweden. Earlier research has brought to light that the collection and preservation of research data are often surrounded by ambiguous rules and lack a comprehensive structure. For example, archiving is not given enough consideration in connection to research projects and researchers often tend to save their material on platforms that are not persistent over time. This article is based upon a qualitative research approach where 15 semi-structured interviews have been used as primarily data sources to investigate the implementation of open access of research data and scientific publications. The article investigated how Swedish universities and public authorities were working with archiving and implementation of open research data and their opinions on open access. The results displayed a lack of coordination, resources and infrastructure but also that common agreed nomenclature were missing. The management of research data was not part of an overall recordkeeping strategy. One explanation could be differences in the information culture among researchers and archivists. Social sciences theory has been combined with archival theory in order to explain the reasons to this. These have been put in relation to the principles of the open data directive. ; This article is licensed under a Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/

Introduction Health research using routinely collected National Health Service (NHS) data derived from electronic health records (EHRs) and health service information systems has been growing in both importance and quantity. Wide population coverage and detailed patient-level information allow this data to be applied to a variety of research questions. However, the sensitivity, complexity and scale of such data also hamper researchers from fully exploiting this potential.Objective Here, we establish the current challenges preventing researchers from making optimal use of the data sets at their disposal, on both the legislative and practical levels, and give recommendations as to how these challenges can be overcome.Method A number of projects has recently been launched in the UK to address poor research data management practices. Rapid Organisation of Healthcare Research Data (ROHRD) at Imperial College, London produced a useful prototype that provides local researchers with a one-stop index of available data sets together with relevant metadata.Findings Increased transparency of data sets' availability and their provenance leads to better utilisation and facilitates compliance with regulatory requirements.Discussion Research data resulting from NHS data is often not utilised fully, or is handled in a haphazard manner that prevents full auditability of the research. Furthermore, lack of informatics and data management skills in research teams act as a barrier to implementing more advanced practices, such as provenance capture and detailed, regularly updated, data management strategies. Only by a concerted effort at the levels of research organisations, funding bodies and publishers, can we achieve full transparency and reproducibility of the research.

Part 1. The context of sport management research -- Part 2. Planning the sport management research process -- Part 3. Foundations of sport management research -- Part 4. Analysing the sport management data -- Part 5. Paradigms used in sport management research -- Part 6. Digital tools for qualitative research -- Part 7. Writing the sport management research report.