Edward Curry – författare

In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the ability to plan suitable analyses of biological datasets, and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained, including linear and rank-based correlation, distance metrics and hierarchical clustering, hypothesis testing using linear regression, proportional hazards regression for survival data, and principal component analysis. These methods are then applied as appropriate throughout the case studies, illustrating how they can be used to answer research questions.Key Features:· Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming.· Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles· Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves.· Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq, genome sequencing, ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens.· Gives worked-out examples geared towards problems encountered in cancer research, which can also be applied across many areas of molecular biology and medical research.This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills.

In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the ability to plan suitable analyses of biological datasets, and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained, including linear and rank-based correlation, distance metrics and hierarchical clustering, hypothesis testing using linear regression, proportional hazards regression for survival data, and principal component analysis. These methods are then applied as appropriate throughout the case studies, illustrating how they can be used to answer research questions.Key Features:· Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming.· Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles· Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves.· Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq, genome sequencing, ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens.· Gives worked-out examples geared towards problems encountered in cancer research, which can also be applied across many areas of molecular biology and medical research.This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills.

The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018. The contributions were organized in topical sections named as follows: Part I: adversarial learning;

This open access book explores the dataspace paradigm as a best-effort approach to data management within data ecosystems.

This open access book explores the dataspace paradigm as a best-effort approach to data management within data ecosystems.

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society.

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society.

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas.The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry.The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas.The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry.The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces.The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces.The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy.The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing.The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical.

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces.The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces.The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy.The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing.The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical.

This two-volume set LNCS 15718-15719 constitutes the refereed proceedings of the 22nd European Semantic Web Conference, ESWC 2025, held in Portoroz, Slovenia, during June 1–5, 2025.The 45 full papers presented were carefully reviewed and selected from 155 submissions.The ESWC’s Research track addresses the theoretical, analytical, and empirical aspects of the semantic web, semantic technologies, knowledge graphs and semantics on the Web in general. The In-use track focuses on contributions that reuse and apply state-of-the-art semantic technologies or resources to real-world settings. The resource track deals with resource contributions that are, on the one hand innovative or novel, and on the other hand sharable and reusable (e.g. datasets, knowledge graphs, ontologies, workflows, benchmarks, frameworks), and provide the necessary scaffolding to support the generation of scientific work and advance the state of the art.

This two-volume set LNCS 15718-15719 constitutes the refereed proceedings of the 22nd European Semantic Web Conference, ESWC 2025, held in Portoroz, Slovenia, during June 1–5, 2025.The 45 full papers presented were carefully reviewed and selected from 155 submissions.The ESWC’s Research track addresses the theoretical, analytical, and empirical aspects of the semantic web, semantic technologies, knowledge graphs and semantics on the Web in general. The In-use track focuses on contributions that reuse and apply state-of-the-art semantic technologies or resources to real-world settings. The resource track deals with resource contributions that are, on the one hand innovative or novel, and on the other hand sharable and reusable (e.g. datasets, knowledge graphs, ontologies, workflows, benchmarks, frameworks), and provide the necessary scaffolding to support the generation of scientific work and advance the state of the art.

This book constitutes the proceedings of the satellite events held at the XXX Semantic Web Conference, ESWC 2025, held in Portoroz, Slovenia, during June 1–2, 2025.The 31 Posters & Demos papers are presented together with 12 PhD Symposium papers and 7 Industry papers in this book were carefully reviewed and selected from 43 Posters& Demo papers, 15 PHD Symsposium papers and 14 Industry papers. The theme of  the conference was “Empowering Knowledge through Semantics: From Knowledge Graphs to Neurosemantics”. The satellite events are an important aspect of facilitating the main conference discussion as well as fundamental questions about how we acquire, represent, use, and interact with knowledge and semantics.The satellite events of ESWC 2025 include the poster and demo session, the PhD symposium, the industry track, project networking, and workshops and tutorials.

This open access book explores the rapidly evolving landscape of artificial intelligence, data and robotics (ADR), highlighting their foundational principles, transformative applications and future directions. Emphasising ethical considerations, human-centric design and sustainability, it sets the stage for understanding how multidisciplinary collaboration and strategic foresight are critical to unlocking the full potential of ADR in creating a competitive, inclusive and resilient digital society.The chapters are organized in three parts and provide a comprehensive overview of the key research agendas, innovative frameworks and real-world deployments that define the current and emerging state of ADR. First, Foundations introduces the strategic, technical and conceptual foundations that underpin Europe’s ADR ecosystem. Collectively, the contributions establish a robust foundation strategically guided by the vision put forward through the European AI, Data and Robotics Association and practically informed by emerging technologies and methodologies. Next, the chapters in Transformations examine the transformative impact of ADR technologies across diverse industrial and societal domains. They provide a comprehensive and nuanced view of how ADR are reshaping industries, infrastructure and human-centred applications and emphasise the importance of interdisciplinary approaches, ethical considerations and technological innovation. Eventually, Future Directions explores the critical pathways for advancing ADR technologies through strategic planning, applied industrial innovation and human-centred approaches. The selected chapters present a coherent and forward-looking narrative: setting strategic priorities, translating them into transformative industrial applications and addressing the nuanced human factors essential for the responsible and effective deployment of ADR technologies.By exploring cutting-edge theory, technologies, methodologies, and best practices within industrial and public sector scenarios, the book provides researchers, professionals and policy makers with a basis for understanding the scientific foundations, the transformation potential, and the future research challenges of ADR technologies.

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe.This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.