"Reinventing Discovery argues that we are in the early days of the most dramatic change in how science is done in more than 300 years. This change is being driven by new online tools, which are transforming and radically accelerating scientific discovery"--

The bestselling author of Dogs That Know When Their Owners Are Coming Home offers an intriguing new assessment of modern day science that will radically change the way we view what is possible. In Science Set Free (originally published to acclaim in the UK as The Science Delusion), Dr. Rupert Sheldrake, one of the world's most innovative scientists, shows the ways in which science is being constricted by assumptions that have, over the years, hardened into dogmas. Such dogmas are not only limiting, but dangerous for the future of humanity. According to these principles, all of reality is material or physical; the world is a machine, made up of inanimate matter; nature is purposeless; consciousness is nothing but the physical activity of the brain; free will is an illusion; God exists only as an idea in human minds, imprisoned within our skulls. But should science be a belief-system, or a method of enquiry? Sheldrake shows that the materialist ideology is moribund; under its sway, increasingly expensive research is reaping diminishing returns while societies around the world are paying the price. In the skeptical spirit of true science, Sheldrake turns the ten fundamental dogmas of materialism into exciting questions, and shows how all of them open up startling new possibilities for discovery. Science Set Free will radically change your view of what is real and what is possible.

An intriguing and illuminating read for science buffs, those fascinated by the lives and minds of great men and women, and anyone curious about how we came to understand the physical world The ideas, experiments, and inventions of great scientists have revolutionized our understanding of the world around us. Theories, discoveries, and technologies—from relativity, the genetic code, and the periodic table to synthetic drugs, nuclear weapons, and brain scans—have transformed the physical world and our lives. Copernicus, Crick, Watson, Galileo, Marie Curie: these are some of the forty pioneers behind modern science whose stories are explored here. The scientists come from around the globe and represent multiple nationalities—American, English, German, French, Dutch, Czech, Indian, Japanese, and more. Often unorthodox thinkers, they frequently had to struggle against hostile contemporaries to gain recognition for their ideas and discoveries. All the major scientific disciplines are covered, including astronomy, biology, biochemistry, chemistry, computing, ecology, geology, medicine, neurology, physics, and psychology, as well as mathematics.

True stories of everyday volunteers participating in scientific research that “may well prompt readers to join the growing community” (Booklist). Think you need a degree in science to contribute to important scientific discoveries? Think again. All around the world, in fields ranging from meteorology to ornithology to public health, millions of everyday people are choosing to participate in the scientific process. Working in cooperation with scientists in pursuit of information, innovation, and discovery, these volunteers are following protocols, collecting and reviewing data, and sharing their observations. They’re our neighbors, in-laws, and coworkers. Their story, along with the story of the social good that can result from citizen science, has largely been untold, until now. Citizen scientists are challenging old notions about who can conduct research, where knowledge can be acquired, and even how solutions to some of our biggest societal problems might emerge. In telling their story, Caren Cooper just might inspire you to rethink your own assumptions about the role that individuals can play in gaining scientific understanding—and putting that understanding to use as a steward of our world. “Engaging.” —Library Journal (starred review)

A general elementary encyclopedia with brief illustrated articles covering an alphabetical array of topics.

This book constitutes the proceedings of the 21st International Conference on Discovery Science, DS 2018, held in Limassol, Cyprus, in October 2018, co-located with the International Symposium on Methodologies for Intelligent Systems, ISMIS 2018. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 71 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Classification; meta-learning; reinforcement learning; streams and time series; subgroup and subgraph discovery; text mining; and applications.

These are the conference proceedings of the 4th International Conference on Discovery Science (DS 2001). Although discovery is naturally ubiquitous in s- ence, and scientific discovery itself has been subject to scientific investigation for centuries, the term Discovery Science is comparably new. It came up in conn- tion with the Japanese Discovery Science project (cf. Arikawa's invited lecture on The Discovery Science Project in Japan in the present volume) some time during the last few years. Setsuo Arikawa is the father in spirit of the Discovery Science conference series. He led the above mentioned project, and he is currently serving as the chairman of the international steering committee for the Discovery Science c- ference series. The other members of this board are currently (in alphabetical order) Klaus P. Jantke, Masahiko Sato, Ayumi Shinohara, Carl H. Smith, and Thomas Zeugmann. Colleagues and friends from all over the world took the opportunity of me- ing for this conference to celebrate Arikawa's 60th birthday and to pay tribute to his manifold contributions to science, in general, and to Learning Theory and Discovery Science, in particular. Algorithmic Learning Theory (ALT, for short) is another conference series initiated by Setsuo Arikawa in Japan in 1990. In 1994, it amalgamated with the conference series on Analogical and Inductive Inference (AII), when ALT was held outside of Japan for the first time.