As the global ‘data revolution’ accelerates, how can the data rights and interests of indigenous peoples be secured? Premised on the United Nations Declaration on the Rights of Indigenous Peoples, this book argues that indigenous peoples have inherent and inalienable rights relating to the collection, ownership and application of data about them, and about their lifeways and territories. As…
In her first inquiry toward a decelerationist aesthetics, Katherine Behar explores in this essay chapbook the rise of two “big deal” contemporary phenomena, big data and obesity. In both, scale rearticulates the human as a diffuse informational pattern, causing important shifts in political form as well as aesthetic form. Bigness redraws relationships between the singular and the collective…
Current big data practices are largely guided by deliberations concerning their efficiency, and optimisation. Yet there is another perspective. This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raise significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that…
The United Nations Sustainable Development Goals initiative has the potential to set the direction for a future world that works for everyone. Approved by 193 United Nations member countries in September 2016 to help guide global and national development policies in the period to 2030, the 17 goals build on the successes of the Millennium Development Goals, but also include new priority areas,…
With changes in technology and a renewed effort to catalog the world's biodiversity, huge amounts of data are being generated on biodiversity issues. As response to the call for better information systems to manage the biodiversity crisis, a wide range of solutions are being developed for inventorying, managing, and disseminating taxonomic data. This book brings together a diverse array of auth…
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining th…
Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries and graphs. Focusing on a mathematically rigorous approach that is fast, practical and efficient, Morin clearly and briskly presents instruction along…
Computational Intelligence in Software Cost Estimation: Evolving Conditional Sets of Effort Value Ranges
An introductory text for college or graduate course in computer networks, with a balance between practical matters and underlying principles. It covers the LAN, internetworking and transport layers, focusing primarily on TCP/IP.
It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase…