Edward Elgar

Editors: Sarah Giest, Bram Klievink, Alex Ingrams, and Matthew Young

Institute of Public Administration, Leiden University

I. Summary

The book aims to focus on the intersection of Governance and Data Science by covering a variety of theoretical and empirical issues. In current research, we see a lot of work being done parallel to each other – focusing on either more technical and data-related aspects or more governance and policy aspects. In addition, current scholarship has limited linkages across geographic locations. The book addresses these gaps by focusing on five themes in particular.

First, an (1) introduction, which includes theoretical chapters on e.g., the intersection of governance and data science, machine learning theory and the types of (public) data. (2) Regulatory Governance and Data Science, covering topics such as data management, data-driven risk management, compiling and storing data as well as regulation through data/algorithms. (3) Good Governance and Data Science, which addresses topics around accountability, transparency, ethics as well as data gaps and bias. (4) Collaborative Governance and Data Science, addressing public private collaborations, NGOs, citizen science and other forms of participatory governance. (5) Global Governance and Data Science, including data ecosystems, intergovernmental collaborations, and data science for SDGs.

By compiling original theoretical and empirical chapters, we mainly target an academic audience including students and colleagues around the world. We aim to bring together research streams and findings across disciplines and identify future research questions. With this, we seek contributions from diverse locations and angles with the goal of shedding light on the intersection of governance and data science.

II. Chapter outline - sub-headings are examples and can be modified to the authors’ preferences

 

1. Introduction

2.Regulatory Governance & Data Science

• Data Mangement
• Data-driven risk management
• Compiling, storing & analyzing data
• Regulation through data/Algorithmic regulation

3. Good Governance & Data Science

• Accountability in governance of data
• Transparency of data use in government
• Ethics of data use in government
• Data gaps and data bias
• Open Data

4. Collaborative Governance & Data Science

• Public-Private Collaborations
• NGOs
• Participatory governance
• Citizen Science
• Hackathons

5. Global Governance & Data Science

• Data Ecosystems
• Intergovernmental Collaborations
• Data Science for Sustainable Development Goals

6. Conclusion: Governance and Data Science

III. Formatting, and Stylistic expectations

Further information for authors about formatting and stylistic expectations:

• Writing style and approach should consider the following points:

1. Your chapter should be approximately 8,000 words (including references).
2. The book is meant for a scholarly (faculty and university students) audience.

• Contributions should address theory or policy in a novel way through new argumentation, data collection or conceptual approaches.
• Key terms should be defined to help clarify discussion around these topics.

For formatting guidelines, please follow author guidelines from Edward Elgar

For any additional questions, you can email the editorial team: gdshandbook@fgga.leidenuniv.nl.