Engin, Zeynep;
Gardner, Emily;
Hyde, Andrew;
Verhulst, Stefaan;
Crowcroft, Jon;
(2024)
Unleashing collective intelligence for public decision-making: the Data for Policy community.
Data & Policy
, 6
, Article e23. 10.1017/dap.2024.2.
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Abstract
Since its establishment in 2014, Data for Policy (https://dataforpolicy.org) has emerged as a prominent global community promoting interdisciplinary research and cross-sector collaborations in the realm of data-driven innovation for governance and policymaking. This report presents an overview of the community’s evolution from 2014 to 2023 and introduces its six-area framework, which provides a comprehensive mapping of the data for policy research landscape. The framework is based on extensive consultations with key stakeholders involved in the international committees of the annual Data for Policy conference series and the open-access journal Data & Policy (https://www.cambridge.org/core/journals/data-and-policy), published by Cambridge University Press. By presenting this inclusive framework, along with the guiding principles and future outlook for the community, this report serves as a vital foundation for continued research and innovation in the field of data for policy.
| Type: | Article |
|---|---|
| Title: | Unleashing collective intelligence for public decision-making: the Data for Policy community |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1017/dap.2024.2 |
| Publisher version: | https://doi.org/10.1017/dap.2024.2 |
| Language: | English |
| Additional information: | Copyright © The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. |
| Keywords: | data for policy, collective intelligence, decision-making, digital transformation, governance, government |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Genetics and Genomic Medicine Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10191181 |
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