This engaging and highly regarded book takes readers through the key stages of their PhD research journey, from the initial ideas through to successful completion and publication. It gives helpful guidance on forming research questions, organising ideas, pulling together a final draft, handling the viva and getting published. Each chapter contains a wealth of practical suggestions and tips for readers to try out and adapt to their own research needs and disciplinary style. This text will be essential reading for PhD students and their supervisors in humanities, arts, social sciences, business, law, health and related disciplines.

A compilation of data on end stage renal disease (ESRD). With 1990, data includes incidence, mortality, and morbidity by primary diagnosis, treatment modality, and by other sociodemographic variables. Also includes data on kidney transplants and transplant outcomes.

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

1926/28- contains statistical tabulations relative to the public shcools of the state (Division of Research adn Statistics).