Data Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales. - Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery - Includes comprehensible, theoretical chapters written for large and diverse audiences - Provides a wealth of selected application to the topics included

The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses.* Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes.* Provides examples of routines that are easily adapted to the processes investigated by the reader* 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered

The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition - high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems.The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an 'omics' platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry. - Discusses a broad range of applications from a variety of sectors - Provides a unique perspective on work normally performed behind closed doors - Highlights the criteria used to compare and assess different approaches to solving problems

Innovative technologies are changing the way research is performed, preserved, and communicated. Managing Scientific Information and Research Data explores how these technologies are used and provides detailed analysis of the approaches and tools developed to manage scientific information and data. Following an introduction, the book is then divided into 15 chapters discussing the changes in scientific communication; new models of publishing and peer review; ethics in scientific communication; preservation of data; discovery tools; discipline-specific practices of researchers for gathering and using scientific information; academic social networks; bibliographic management tools; information literacy and the information needs of students and researchers; the involvement of academic libraries in eScience and the new opportunities it presents to librarians; and interviews with experts in scientific information and publishing. - Promotes innovative technologies for creating, sharing and managing scientific content - Presents new models of scientific publishing, peer review, and dissemination of information - Serves as a practical guide for researchers, students, and librarians on how to discover, filter, and manage scientific information - Advocates for the adoption of unique author identifiers such as ORCID and ResearcherID - Looks into new tools that make scientific information easy to discover and manage - Shows what eScience is and why it is becoming a priority for academic libraries - Demonstrates how Electronic Laboratory Notebooks can be used to record, store, share, and manage research data - Shows how social media and the new area of Altmetrics increase researchers' visibility and measure attention to their research - Directs to sources for datasets - Provides directions on choosing and using bibliographic management tools - Critically examines the metrics used to evaluate research impact - Aids strategic thinking and informs decision making

Packed with worked examples and problems, this book will help the reader improve their confidence and skill in data-handling. The mathematical methods needed for problem-solving are described in the first part of the book, with chapters covering topics such as indices, graphs and logarithms. The following eight chapters explore data-handling in different areas of microbiology and biochemistry including microbial growth, enzymes and radioactivity. Each chapter is fully illustrated with worked examples that provide a step-by-step guide to the solution of the most common problems. Over 30 exercises, ranging in difficulty and length, allow you to practise your skills and are accompanied by a full set of hints and solutions.

The workshop was organized by the San Diego Supercomputer Center (SDSC) and took place July 20 –22, 2005 at the University of California, San Diego.

Early in 1984, NASA asked the Space Science Board to undertake a study to determine the principal scientific issues that the disciplines of space science would face during the period from about 1995 to 2015. The findings of this study are published in this volume.