CFP Topics
Data for Policy 2016 invited individual and/or group submissions from all relevant disciplines and application domains. Topics covered included but were not limited to the following:
Government & Policy: Digital era governance and citizen services, public demand vs. government response, using data in the policy process, open source and open data movements, policy laboratories, citizen expertise for government, public opinion and participation in democratic processes, distributed data bases and data streams, information and evidence in policy context, case studies and best practices.
Policy for Data & Management: Data collection, storage, and access; psychology/behaviour of decision; privacy, trust, public rights, free speech, ethics and law; data security/ownership/linkage; provenance, curation, expiration; private/public sector/non-profit collaboration and partnership, etc.
Data Analysis: Computational procedures for data collection, storage, and access; large-scale data processing, dealing with biased/imperfect/uncertain data, human interaction with data, statistical/computational models, technical challenges, communicating results, visualisation, etc.
Methodologies: Qualitative/quantitative/mixed methods, gaps in theory and practice, secondary data analysis, web scraping, randomised controlled trials, sentiment analysis, Bayesian approaches and graphical models, biologically inspired models, real-time and historical data processing, simulation and modelling, small area estimation, correlation & causality-based models, and other relevant methods.
Data Sources: Government administrative data, official statistics, commercial and non-profit data, user-generated web content (blogs, wikis, discussion forums, posts, chats, tweets, podcasting, pins, digital images, video, audio files, advertisements, etc.), search engine data, data gathered by connected people and devices (e.g. wearable technology, mobile devices, Internet of Things), tracking data (including GPS/geolocation data, traffic and other transport sensor data, CCTV images etc.,), satellite and aerial imagery, and other relevant data sources.
Policy/Application Domains: Security, health, cities, public administration, economy, science and innovation, finance, energy, environment, social policy areas (education, migration, etc.) and other relevant domains.
Summaries from Report
15-16 September 2016, Cambridge
The 2016 conference theme “Frontiers of Data Science for Government: Ideas, Practices, and Projections” was intended to capture two main developments at the time. The first was the cooling-off period from the hype of theory-free big data speaking for itself (Anderson, 2008), and its replacement by the more general term ‘data science’ to encompass both established theory-driven and new data-driven methods of creating value from all available data at hand. The second emphasis was on the move from abstract thinking and formulations of the Data for Policy space to real-world practices and projections, and capturing cutting edge research in this new area. Returning to the University of Cambridge, the conference hosted 191 delegates and extended its supporting partner network to include the newly founded Alan Turing Institute (the UK’s National Institute for Data Science), the University of Oxford (Oxford Internet Institute), the European Commission, UCL, Leiden University (Centre for Innovation), Technopolis Group, and New York University (GovLab). Data for Policy’s Zenodo community profile [hyperlink https://zenodo.org/communities/dfp17/?page=1&size=20] was also set up as an open-access repository to share conference discussion papers and other pre-conference material.
Keynote Speakers
Enrico Giovannini, UN Data Revolution Group; University of Rome
Jim Waldo, Harvard University
Plenary Speakers
Quentin Palfrey, J-PAL North America, Massachusetts Institute of Technology
Barbara Ubaldi, The Organisation for Economic Co-operation and Development (OECD)
Rayid Ghani, University of Chicago
Maive Rute, European Commission
Helen Margetts, Oxford Internet Institute, University of Oxford
Philip Treleaven, UCL
Partners
University of Cambridge – Computer Laboratory, Centre for Science and Policy, Cambridge Big Data Strategic Research Initiative, Digital Humanities Network, Cambridge Public Policy Initiative
European Commission • Alan Turing Institute • Imperial College London – Data Science Institute
London School of Economics & Political Sciences – Department of Methodology
University College London – Department of Computer Science, UCL Public Policy, The Bartlett – UCL Faculty of the Built Environment
University of Oxford – Oxford Internet Institute
Office for National Statistics
Royal Statistical Society • New York University – The GovLab, Open Governance Research Exchange
Leiden University – Centre for Innovation
Technopolis Group