Launched at the inaugural conference hosted by the University of Cambridge in 2015, Data for Policy is an independent initiative to promote interdisciplinary and cross-sectoral discussion between all stakeholders with interest in potentials of Data Science research to enhance government operations and policy-making. The inaugural conference “Policy-making in the Big Data Era: Opportunities and Challenges” hosted around 90 top-level presentations and over 170 delegates from UK and EU government bodies, prestigious national and international universities, non-profit institutions, and major industrial stakeholders.
The main topics to cover in this conference are:
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.
Public/ private sector, academia
University of Cambridge
University of Cambridge
William Gates Building
15 JJ Thomson Avenue
Cambridge CB3 0FD