Collaboration In Data Science
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Research Collaboration and Team Science
Today in most scientific and technical fields more than 90% of research studies and publications are collaborative, often resulting in high-impact research and development of commercial applications, as reflected in patents. Nowadays in many areas of science, collaboration is not a preference but, literally, a work prerequisite. The purpose of this book is to review and critique the burgeoning scholarship on research collaboration. The authors seek to identify gaps in theory and research and identify the ways in which existing research can be used to improve public policy for collaboration and to improve project-level management of collaborations using Scientific and Technical Human Capital (STHC) theory as a framework. Broadly speaking, STHC is the sum of scientific and technical and social knowledge, skills and resources embodied in a particular individual. It is both human capital endowments, such as formal education and training and social relations and network ties that bind scientists and the users of science together. STHC includes the human capital which is the unique set of resources the individual brings to his or her own work and to collaborative efforts. Generally, human capital models have developed separately from social capital models, but in the practice of science and the career growth of scientists, the two are not easily disentangled. Using a multi-factor model, the book explores various factors affecting collaboration outcomes, with particular attention on institutional factors such as industry-university relations and the rise of large-scale university research centers.
Human-Centered Data Science
Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.
Data Mining and Decision Support
Author: Dunja Mladenic
language: en
Publisher: Springer Science & Business Media
Release Date: 2003-09-30
Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation, visualization and modeling techniques, and software tools such as decision support systems, group decision support and mediation systems, expert systems, databases and data warehouses. Independently, data mining and decision support are well-developed research areas, but until now there has been no systematic attempt to integrate them. Data Mining and Decision Support: Integration and Collaboration, written by leading researchers in the field, presents a conceptual framework, plus the methods and tools for integrating the two disciplines and for applying this technology to business problems in a collaborative setting.