Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Archaeology --- Archaeology. --- archaeology data --- datasets --- archaeology

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Like many other research fields, over the last two decades neuroscience has turned towards data-driven discovery, a change which has dramatically reshaped the field. Through large collaborative projects and concerted data collection and data sharing efforts, the field is gaining access to large and heterogeneous data sets, at scales that have never been possible before. While these data present tremendous opportunities, their effective management, storage, and analysis presents serious challenges for many researchers. The tools and techniques of data science - a field which draws on software engineering, statistics, and machine learning to increase the efficiency and reproducibility of data extraction and analysis - have much to offer neuroscientists, but unfortunately these concepts are not taught within the standard neuroscience curriculum. This book offers an introduction to contemporary data science and its application in neuroimaging research. Taking a "hands-on" approach, the book explains common methods and approaches in a reader-friendly style, and includes numerous applications to openly available neuroscience datasets, including extensive code examples in Python. In contrast to most other neuroimaging-focused books, which place heavy emphasis on the process of acquiring and statistically analyzing neuroimaging data, the focus of this book is on developing and managing scalable and reproducible data analysis pipelines, broadly relevant skills that will readily translate to students' own research questions. Throughout, there is an emphasis on best-practices in data sharing and reporting, including how to apply principles of fairness, accountability, and transparency in neuroscience applications."

COMPUTERS / Data Science / Data Analytics. --- Data Science --- Datasets as Topic. --- Neuroimaging. --- Programming Languages. --- SCIENCE / Life Sciences / Neuroscience. --- Methods. --- Neuroimaging --- Programming Languages --- Datasets as Topic

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Crystallography --- Crystallography. --- Leptology --- crystal structure --- crystallography --- crystallographic data --- datasets --- Physical sciences --- Mineralogy --- Crystallographies

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it. In sum, this volume presents cognitive scientists and those in related fields with a detailed, stimulating, and realistic introduction to big data - and to show how it may greatly advance our understanding of the principles of human memory, perception, categorization, decision-making, language, problem-solving, and representation."--Provided by publisher.

Big data. --- Cognitive Science --- Cognitive science --- Data Mining. --- Data mining. --- Datasets as Topic. --- Datenanalyse. --- Kognitionswissenschaft. --- Massendaten. --- Methods. --- Research --- Data processing.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Research --- Science --- Research --- Science --- Datasets as Topic. --- Sciences - General --- Data processing --- Data processing --- Data processing. --- Data processing. --- Sciences - General