Data for Policy 2016 – Frontiers of Data Science for Government: Ideas, Practices, and Projections
15-16 September 2016, Cambridge
Data Science is emerging as a key interdisciplinary research field to address major contemporary challenges across sectors. Particular focus on the government sector offers huge potentials to advance citizen services and collective decision-making processes. To reflect the diversity of skills and knowledge required to tackle challenges in this domain, the conference offers an open discussion forum for all stakeholders. 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 modeling, 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.
- 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
Publications from the Data for Policy 2016 Conference:
Anderson, C. Leigh, Biscaye, Pierre, Harris, Katie Panhorst, Merfeld, Josh, & Reynolds, Travis. (2017). Proxy errors with policy consequences: How common crop yield measures can bias estimates of management-based agricultural productivity gains. Zenodo. http://doi.org/10.5281/zenodo.571408 Read more >>
Binder, Clemens. (2017). The emergence of Big Drone Data? Analyzing debates on drones as data gathering means in intelligence. Zenodo. http://doi.org/10.5281/zenodo.571552. Read more >>
Blancas Reyes, Eduardo, Helsby, Jennifer, Rasch, Katharina, van der Boor, Paul, Ghani, Rayid, Haynes, Lauren, & Cunningham, Edward P. (2017). Early detection of properties at risk of blight using spatiotemporal data. Zenodo. http://doi.org/10.5281/zenodo.556510 Read more >>
Cameron, Kath, Janusz, Stefan, Pilley, Vanessa, Amin, Farhana, Wilkinson, Steve, Hicks, Jonathan, … Boyd, Ian. (2017). Sharing more widely: data at the heart of evidence, policy and transformation at Defra. Zenodo. http://doi.org/10.5281/zenodo.571399. Read more >>
Chessell, Mandy. (2017). The Case for Open Metadata. Zenodo. http://doi.org/10.5281/zenodo.556504 Read more >>
De-Arteaga, Maria, & Dubrawski, Artur. (2017). Discovery of complex anomalous patterns of sexual violence in El Salvador. Zenodo. http://doi.org/10.5281/zenodo.571551. Read more >>
Drosou, A., Dimitriou, N., Sarris, N., Konstantinidinis, A., & Tzovaras, Dimitrios. (2017). Research directions for harvesting cross-sectorial correlations towards improved policy making. Zenodo. http://doi.org/10.5281/zenodo.571543. Read more >>
Faul, Anita C., & Pilikos, Georgios. (2017). The Model is Simple, Until Proven Otherwise: How to Cope in an ever-changing world. Zenodo. http://doi.org/10.5281/zenodo.556502. Read more >>
Fazekas, Mihaly, & Czibik, Agnes. (2017). Diverse uses of government contracting data to improve spending of public funds. Zenodo. http://doi.org/10.5281/zenodo.810049. Read more >>
Garg, Sachin. (2017). Policy for Big Data: An Investigation Using Land Records. Zenodo. http://doi.org/10.5281/zenodo.545800. Read more>>
Gonzales, Frederic, Miroudot, Sebastien, & Vebr, Thierry. (2017). Developing a semantic legal research interface for the OECD Services Trade Restrictiveness Index. Zenodo. http://doi.org/10.5281/zenodo.571398. Read more >>
Huynh, Kim P., Chu, Ba M., Jacho-Chavez, David T., Kryvtsov, Oleksiy, & Shane Wood. (2017). Inflation and Price Dispersion: Evidence from the UK CPI Micro Data. Zenodo. http://doi.org/10.5281/zenodo.809935. Read more >>
Koussouris, Sotirios, Kokkinakos, Panagiotis, Markaki, Ourania, Panopuoulos, Dimitrios, Glickman, Yuri, & Lee, Habin. (2017). Policy Compass – Indicator-based Accountable Policy Analysis and Evaluation via Open Data Exploitation. Zenodo. http://doi.org/10.5281/zenodo.571392. Read more>>
Kowalski, Radoslaw, Mikhaylov, Slava, & Esteve, Marc. (2017). Treating End-User Feedback Seriously. Zenodo. http://doi.org/10.5281/zenodo.556506. Read more >>
Palfrey, Quentin. (2017). Translating Rigorous Evidence into Policies That Benefit the Poor Zenodo. http://doi.org/10.5281/zenodo.841745 Read more >>
Perez, Iker, Brown, Michael, Pinchin, James, Martindale, Sarah, Sharples, Sarah, Shaw, Dominick, & Blakey, John. (2017). Informatics in Out of Hours Service Delivery: Methods and Applications to Inform Healthcare Policy and Management. Zenodo. http://doi.org/10.5281/zenodo.810011. Read more >>
Persson, Jen. (2017). When the chips come out: is our public research infrastructure fit for the future?. Zenodo. http://doi.org/10.5281/zenodo.570489. Read more >>
Usher, Derval, Hodge, George, Amin, Imaduddin, & Lee, Jong Gun. (2016). Haze Gazer: A Crisis Analysis and Visualisation Tool to Better Inform Peatland Fire and Haze Management. Zenodo. http://doi.org/10.5281/zenodo.824995. Read more >>
van Renswouw, Loes, Bogers, Sander, & Vos, Steven. (2017). Urban Planning for Active and Healthy Public Spaces with User-Generated Big Data. Zenodo. http://doi.org/10.5281/zenodo.570550. Read more >>
Veale, Michael. (2017). Connecting diverse public sector values with the procurement of machine learning systems. Zenodo. http://doi.org/10.5281/zenodo.571786. Read more>>
Waldo, Jim. (2017). Big Data and the Social Sciences: Can Accuracy and Privacy Co-Exist?. Zenodo. http://doi.org/10.5281/zenodo.832070. Read more >>
Multimedia from Data for Policy 2016 Conference: