In 1997, Congress, in the conference report, H.R. 105-271, to the FY1998 Energy and Water Development Appropriation Bill, directed the National Research Council (NRC) to carry out a series of assessments of project management at the Department of Energy (DOE). The final report in that series noted that DOE lacked an objective set of measures for assessing project management quality. The department set up a committee to develop performance measures and benchmarking procedures and asked the NRC for assistance in this effort. This report presents information and guidance for use as a first step toward development of a viable methodology to suit DOE's needs. It provides a number of possible performance measures, an analysis of the benchmarking process, and a description ways to implement the measures and benchmarking process.

Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.

"Quality of life"..."livability"..."sense of place." Communities across America are striving to define these terms and to bring them to life, as they make decisions about transportation systems and other aspects of planning and development. Community and Quality of Life discusses important concepts that undergird community life and offers recommendations for collaborative planning across space and time. The book explores: Livability as an ensemble concept, embracing notions such as quality of place and sustainability. It discusses how to measure the "three legs" of livability (social, economic, ecological) while accounting for politics and personal values. And the book examines how to translate broad ideas about livability into guidelines for policymaking Place as more than location, including the natural, human-built, and social environments. The book discusses the impact of population changes over time, the links between regional and local identity, and other issues Tools for decision making in transportation and community planning. It reviews a variety of decision models and tools such as geographic information systems (GIS)â€"as well as public and private sources of relevant data. Including several case examples, this book will be important to planners, planning decision makers, planning educators and students, social scientists, community activists, and interested individuals.

This book identifies challenges and opportunities in the development and implementation of software that contain significant statistical content. While emphasizing the relevance of using rigorous statistical and probabilistic techniques in software engineering contexts, it presents opportunities for further research in the statistical sciences and their applications to software engineering. It is intended to motivate and attract new researchers from statistics and the mathematical sciences to attack relevant and pressing problems in the software engineering setting. It describes the "big picture," as this approach provides the context in which statistical methods must be developed. The book's survey nature is directed at the mathematical sciences audience, but software engineers should also find the statistical emphasis refreshing and stimulating. It is hoped that the book will have the effect of seeding the field of statistical software engineering by its indication of opportunities where statistical thinking can help to increase understanding, productivity, and quality of software and software production.