Why has the biotechnology industry failed to perform up to expectations? This book attempts to answer this question by providing a critique of the industry. It reveals the causes of biotech's problems and offers an analysis on how the industry works. It also provides prescriptions for companies, seeking ways to improve the industry's performance.

Cooperation between science and business (S2B cooperation) is a very important issue from the points of view of the domestic economy, companies, and universities. This is mainly because such cooperation is a catalyst for faster and reliable development - not only of enterprises, but also of the entire economy. Thanks to S2B cooperation, enterprises can gain and commercialize new and ground-breaking solutions. Universities in turn can give their research a more practical dimension to increase its economic applicability, which helps universities prepare future staff to work in modern enterprises. The aim of this publication is to indicate how cooperation between universities and business can be implemented in an international dimension. It shows the role of modern universities in supporting the development of enterprise and entire economies as well as the role of modern enterprises that use resources located in universities (including knowledge resources). This unique combination has a positive impact on the results and developmental opportunities for each of them. Any potential obtained in this manner is the basis for creating a competitive advantage on the market. This book is devoted to a specific area of cooperation between enterprises and universities and will be of interest to researchers, academics, practitioners, and students in the fields of entrepreneurship, knowledge management, international relations, and higher education.

In today's knowledge-based society universities and firms must learn to engage more effectively. Universities focus on generating new knowledge, while firms are increasingly drawing on external collaborations to add value to their offerings. This book shows managers how to work with universities to boost their competitive position and revenue.

Cooperation between science and business (S2B cooperation) is a very important issue from the points of view of the domestic economy, companies, and universities. This is mainly because such cooperation is a catalyst for faster and reliable development – not only of enterprises, but also of the entire economy. Thanks to S2B cooperation, enterprises can gain and commercialize new and ground-breaking solutions. Universities in turn can give their research a more practical dimension to increase its economic applicability, which helps universities prepare future staff to work in modern enterprises. The aim of this publication is to indicate how cooperation between universities and business can be implemented in an international dimension. It shows the role of modern universities in supporting the development of enterprise and entire economies as well as the role of modern enterprises that use resources located in universities (including knowledge resources). This unique combination has a positive impact on the results and developmental opportunities for each of them. Any potential obtained in this manner is the basis for creating a competitive advantage on the market. This book is devoted to a specific area of cooperation between enterprises and universities and will be of interest to researchers, academics, practitioners, and students in the fields of entrepreneurship, knowledge management, international relations, and higher education.

University start-ups are unique in the world of business and entrepreneurship, translating research conducted at and owned by universities into market-ready products--a complex process that requires a combination of scientific, technical, legal, business, and financial skills to be successful. Start-ups have the potential to generate revenue for universities, enhance faculty recruitment and retention, create jobs, and create investment opportunities for venture capitalists and entrepreneurs. Research to Revenue presents the first-ever comprehensive guide to understanding, starting, and managing university startups. By systematically describing the process of translating academic research into commercial enterprises, Don Rose and Cam Patterson give a thorough, process-oriented, and practical set of guidelines that cover not only best practices but also common--and avoidable--mistakes. They detail the key factors and components that contribute to a successful start-up, explain what makes university start-ups unique, delineate the steps of building and managing them, and describe how to foster and maintain start-ups at a university. Written for faculty and staff working on campus, tech-transfer officers, university administrators, and venture capitalists unfamiliar with university structures, Research to Revenue ensures that any reader unfamiliar with technology commercialization and entrepreneurship will understand the fundamentals of the process, including intellectual property rights, fund-raising, and business models. This work is an invaluable resource for the successful formation and well-managed operation of university start-ups.

Too many universities remain wedded to outmoded ways of teaching science in spite of extensive research showing that there are much more effective methods. Too few departments ask whether what happens in their lecture halls is effective at helping students to learn and how they can encourage their faculty to teach better. But real change is possible, and Carl Wieman shows us how it can be brought about. Improving How Universities Teach Science draws on Wieman’s unparalleled experience to provide a blueprint for educators seeking sustainable improvements in science teaching. Wieman created the Science Education Initiative (SEI), a program implemented across thirteen science departments at the universities of Colorado and British Columbia, to support the widespread adoption of the best research-based approaches to science teaching. The program’s data show that in the most successful departments 90 percent of faculty adopted better methods. Wieman identifies what factors helped and hindered the adoption of good teaching methods. He also gives detailed, effective, and tested strategies for departments and institutions to measure and improve the quality of their teaching while limiting the demands on faculty time. Among all of the commentary addressing shortcomings in higher education, Wieman’s lessons on improving teaching and learning stand out. His analysis and solutions are not limited to just one lecture hall or course but deal with changing entire departments and universities. For those who want to improve how universities teach science to the next generation, Wieman’s work is a critical first step.

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates