Roberto Todeschini – författare

Analyzing observed or measured data is an important step in applied sciences. The recent increase in computer capacity has resulted in a revolution both in data collection and data analysis. An increasing number of scientists, researchers and students are venturing into statistical data analysis; hence the need for more guidance in this field, which was previously dominated mainly by statisticians.This handbook fills the gap in the range of textbooks on data analysis. Written in a dictionary format, it will serve as a comprehensive reference book in a rapidly growing field. However, this book is more structured than an ordinary dictionary, where each entry is a separate, self-contained entity. The authors provide not only definitions and short descriptions, but also offer an overview of the different topics. Therefore, the handbook can also be used as a companion to textbooks for undergraduate or graduate courses.1700 entries are given in alphabetical order grouped into 20 topics and each topic is organized in a hierarchical fashion. Additional specific entries on a topic can be easily found by following the cross-references in a top-down manner. Several figures and tables are provided to enhance the comprehension of the topics and a list of acronyms helps to locate the full terminologies. The bibliography offers suggestions for further reading.

This volume presents the basic mathematics of ranking methods through a didactic approach and the integration of relevant applications. Ranking methods can be applied in several different fields, including decision support, toxicology, environmental problems, proteomics and genomics, analytical chemistry, food chemistry, and QSAR.Covers a wide range of applications, from the environment and toxicology to DNA sequencingIncorporates contributions from renowned experts in the fieldMeets the increasing demand for literature concerned with ranking methods and their applications

Analyzing observed or measured data is an important step in applied sciences. The recent increase in computer capacity has resulted in a revolution both in data collection and data analysis. An increasing number of scientists, researchers and students are venturing into statistical data analysis; hence the need for more guidance in this field, which was previously dominated mainly by statisticians.

This handbook fills the gap in the range of textbooks on data analysis. Written in a dictionary format, it will serve as a comprehensive reference book in a rapidly growing field. However, this book is more structured than an ordinary dictionary, where each entry is a separate, self-contained entity. The authors provide not only definitions and short descriptions, but also offer an overview of the different topics. Therefore, the handbook can also be used as a companion to textbooks for undergraduate or graduate courses.

1700 entries are given in alphabetical order grouped into 20 topics and each topic is organized in a hierarchical fashion. Additional specific entries on a topic can be easily found by following the cross-references in a top-down manner. Several figures and tables are provided to enhance the comprehension of the topics and a list of acronyms helps to locate the full terminologies. The bibliography offers suggestions for further reading.

At last, the first systematic guide to the growing jungle of citation indices and other bibliometric indicators.Written with the aim of providing a complete and unbiased overview of all available statistical measures for scientific productivity, the core of this reference is an alphabetical dictionary of indices and other algorithms used to evaluate the importance and impact of researchers and their institutions. In 150 major articles, the authors describe all indices in strictly mathematical terms without passing judgement on their relative merit. From widely used measures, such as the journal impact factor or the h-index, to highly specialized indices, all indicators currently in use in the sciences and humanities are described, and their application explained. The introductory section and the appendix contain a wealth of valuable supporting information on data sources, tools and techniques for bibliometric and scientometric analysis - for individual researchers as well as their funders and publishers.

Quantitative studies on structure-activity and structure-property relationships are powerful tools in directed drug research. In recent years, various strategies have been developed to characterize and classify structural patterns by means of molecular descriptors. It has become possible not only to assess diversities or similarities of structure databases, but molecular descriptors also facilitate the identification of potential bioactive molecules from the rapidly increasing number of compound libraries. They even allow for a controlled de-novo design of new lead structures. This is the most comprehensive collection of molecular descriptors and presents a detailed review from the origins of this research field up to present day. This practically oriented reference book gives a thorough overview of the different molecular descriptors representations and their corresponding molecular descriptors. All descriptors are listed with their definition, symbols and labels, formulas, some numerical examples, data and molecular graphs, while numerous figures and tables aid comprehension of the definitions. Cross-references throughout, a list of acronyms and notations allow easy access to the information needed to solve a specific research problem. Examples of descriptor calculations along with tables of descriptor values for a set of selected reference compounds and an up-to-date reference list add to the practical value of the book, making it an invaluable guide for all those dealing with bioactive molecules as well as for researchers.

Molecular Descriptors for Chemoinformatics

As every chemist knows, there is a direct (if complex) relationship between the molecular structure of a compound and its chemical behavior. Predicting such behavior is possible by an abstract representation of its structure in terms of chemical similarity parameters, socalled ‘descriptors’. These are most useful in predicting the pharmacological properties of drug candidates, but are also used in predicting reactivity, toxicity and other important chemical characteristics.

The number-one reference on the topic now contains a wealth of new data: The entire relevant literature over the past eight years has been pain-stakingly surveyed, resulting in hundreds of new descriptors being added to the list, and some 3,000 new references in the bibliography section.

Volume 1 contains an alphabetical listing of around 3300 terms for the chemoinformatic analysis of chemical compound properties, while the second volume contains 6343 references selected from 450 journals with about 7000 authors quoted covering the period from the beginning of molecular descriptor research until the year 2008.

In this second edition, several sections have been completely rewritten and organized in a more effective way. The greatly expanded introductory section now contains several “walk-through” reading lists of selected keywords to make the data even more accessible for novice users.

At last, the first systematic guide to the growing jungle of citation indices and other bibliometric indicators. Written with the aim of providing a complete and unbiased overview of all available statistical measures for scientific productivity, the core of this reference is an alphabetical dictionary of indices and other algorithms used to evaluate the importance and impact of researchers and their institutions. In 150 major articles, the authors describe all indices in strictly mathematical terms without passing judgement on their relative merit. From widely used measures, such as the journal impact factor or the h-index, to highly specialized indices, all indicators currently in use in the sciences and humanities are described, and their application explained. The introductory section and the appendix contain a wealth of valuable supporting information on data sources, tools and techniques for bibliometric and scientometric analysis - for individual researchers as well as their funders and publishers.

At last, the first systematic guide to the growing jungle of citation indices and other bibliometric indicators. Written with the aim of providing a complete and unbiased overview of all available statistical measures for scientific productivity, the core of this reference is an alphabetical dictionary of indices and other algorithms used to evaluate the importance and impact of researchers and their institutions. In 150 major articles, the authors describe all indices in strictly mathematical terms without passing judgement on their relative merit. From widely used measures, such as the journal impact factor or the h-index, to highly specialized indices, all indicators currently in use in the sciences and humanities are described, and their application explained. The introductory section and the appendix contain a wealth of valuable supporting information on data sources, tools and techniques for bibliometric and scientometric analysis - for individual researchers as well as their funders and publishers.