Dark Data
Title | Dark Data PDF eBook |
Author | David J. Hand |
Publisher | Princeton University Press |
Pages | 344 |
Release | 2022-02-15 |
Genre | Computers |
ISBN | 0691234469 |
"Data describe and represent the world. However, no matter how big they may be, data sets don't - indeed cannot - capture everything. Data are measurements - and, as such, they represent only what has been measured. They don't necessarily capture all the information that is relevant to the questions we may want to ask. If we do not take into account what may be missing/unknown in the data we have, we may find ourselves unwittingly asking questions that our data cannot actually address, come to mistaken conclusions, and make disastrous decisions. In this book, David Hand looks at the ubiquitous phenomenon of "missing data." He calls this "dark data" (making a comparison to "dark matter" - i.e., matter in the universe that we know is there, but which is invisible to direct measurement). He reveals how we can detect when data is missing, the types of settings in which missing data are likely to be found, and what to do about it. It can arise for many reasons, which themselves may not be obvious - for example, asymmetric information in wars; time delays in financial trading; dropouts in clinical trials; deliberate selection to enhance apparent performance in hospitals, policing, and schools; etc. What becomes clear is that measuring and collecting more and more data (big data) will not necessarily lead us to better understanding or to better decisions. We need to be vigilant to what is missing or unknown in our data, so that we can try to control for it. How do we do that? We can be alert to the causes of dark data, design better data-collection strategies that sidestep some of these causes - and, we can ask better questions of our data, which will lead us to deeper insights and better decisions"--
Data Matters
Title | Data Matters PDF eBook |
Author | National Academies of Sciences, Engineering, and Medicine |
Publisher | National Academies Press |
Pages | 103 |
Release | 2019-01-28 |
Genre | Science |
ISBN | 030948247X |
In an increasingly interconnected world, perhaps it should come as no surprise that international collaboration in science and technology research is growing at a remarkable rate. As science and technology capabilities grow around the world, U.S.-based organizations are finding that international collaborations and partnerships provide unique opportunities to enhance research and training. International research agreements can serve many purposes, but data are always involved in these collaborations. The kinds of data in play within international research agreements varies widely and may range from financial and consumer data, to Earth and space data, to population behavior and health data, to specific project-generated dataâ€"this is just a narrow set of examples of research data but illustrates the breadth of possibilities. The uses of these data are various and require accounting for the effects of data access, use, and sharing on many different parties. Cultural, legal, policy, and technical concerns are also important determinants of what can be done in the realms of maintaining privacy, confidentiality, and security, and ethics is a lens through which the issues of data, data sharing, and research agreements can be viewed as well. A workshop held on March 14-16, 2018, in Washington, DC explored the changing opportunities and risks of data management and use across disciplinary domains. The third workshop in a series, participants gathered to examine advisory principles for consideration when developing international research agreements, in the pursuit of highlighting promising practices for sustaining and enabling international research collaborations at the highest ethical level possible. The intent of the workshop was to explore, through an ethical lens, the changing opportunities and risks associated with data management and use across disciplinary domainsâ€"all within the context of international research agreements. This publication summarizes the presentations and discussions from the workshop.
Matters of Life and Data
Title | Matters of Life and Data PDF eBook |
Author | Charles D. Morgan |
Publisher | Morgan James Publishing |
Pages | 346 |
Release | 2015-01-20 |
Genre | Business & Economics |
ISBN | 1630474665 |
Thanks to Edward Snowden and the N.S.A., “Big Data” is a hot---and controversial---topic these days. In Charles D. Morgan’s lively memoir, "Matters of Life and Data", he shows that data gathering itself is neither good nor bad---it’s how it’s used that matters. But Big Data isn’t the whole story here---Morgan is also a champion race car driver, a jet pilot, and an all-around gadget-geek-turned-business-visionary. Life is about solving the problems we’re faced with, and Charles Morgan’s life has been one of trial, error, and great achievement. His story will inspire all who read it.
Open Scientific Data
Title | Open Scientific Data PDF eBook |
Author | Vera Lipton |
Publisher | BoD – Books on Demand |
Pages | 232 |
Release | 2020-01-22 |
Genre | Computers |
ISBN | 1838809848 |
This book shows how the vision for open access to scientific data can be more readily achieved through a staged model that research funders, policy makers, scientists, and research organizations can adopt in their practice. Drawing on her own experiences with data processing, on early findings with open scientific data at CERN (the European Organization for Nuclear Research), and from case studies of shared clinical trial data, the author updates our understanding of research data - what it is; how it dynamically evolves across different scientific disciplines and across various stages of research practice; and how it can, and indeed should, be shared at any of those stages. The result is a flexible and pragmatic path for implementing open scientific data.
Data Feminism
Title | Data Feminism PDF eBook |
Author | Catherine D'Ignazio |
Publisher | MIT Press |
Pages | 328 |
Release | 2020-03-31 |
Genre | Social Science |
ISBN | 0262358530 |
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.
Data Matters
Title | Data Matters PDF eBook |
Author | Nicholas Maxwell |
Publisher | Springer |
Pages | 644 |
Release | 2004 |
Genre | History |
ISBN |
Today’s reader is increasingly inundated with data and statistics, yet has little mathematical or statistical training to help him understand the storm of data in which he lives. Data Matters talks directly to those readers, without formulas, without heavy mathematics. It will appeal to any motivated reader who wants help understanding the data he reads in the newspaper, sees on TV, or encounters on the job. Author Nicholas Maxwell uses easy-to-understand explanations, real-world contexts and a focus on statistical concepts to help bring a deeper understanding of the world. Each chapter is packed with real data and quotes from today’s news media. Interesting and relevant topics are used to illustrate the core mathematical ideas. The text is written with non-math, non-science readers in mind. Yet it still provides the resources needed to fully understand and apply statistics.
Measuring Race
Title | Measuring Race PDF eBook |
Author | Robert T. Teranishi |
Publisher | Multicultural Education |
Pages | 0 |
Release | 2020 |
Genre | Education |
ISBN | 9780807763612 |
"Understanding the complexity of racial categories is essential for achieving equity and reducing inequality in the United States. The authors show how that by disaggregating data on race, researchers and policymakers can more fully understand how race is factored in educational settings"--