Data for policy 2021
The sixth edition of the Data for Policy conference will take place virtually from Sept 14th – 16th. Due to the ongoing Covid-19 pandemic, and uncertainties about the safety of travel, the International Conference Organisation Committee has decided to hold the Data for Policy 2021 meeting virtually replacing the physical meeting scheduled for 14-16 September at UCL, London. This decision was not taken lightly but we believe this is the correct decision since we are, first and foremost, committed to protecting our delegates’ health and safety while fulfilling our central purpose as a top international forum bringing together key stakeholders in this space.
We are now planning the virtual meeting. As new information becomes available, we will share it on our website and social media accounts, and via email to our subscribers.
The conference relies on support from our sustainer partners, the Turing Institute, the Office for National Statistics and UCL.
Zeynep Engin - UCL / Data for Policy
C. Leigh Anderson - University of Washington, USA
Emanuele Baldacci - European Commission
Jon Crowcroft - University of Cambridge & Alan Turing Institute
Andrew Hyde - Data & Policy Commissioning Editor
Innar Liiv - Tallinn University of Technology, Estonia
Christoph Lütge - Technical University of Munich, Germany
H. Scott Matthews - Carnegie Mellon University, USA
Barbara Ubaldi - OECD
Stefaan Verhulst - GovLab, New York University, USA
Masaru Yarime Hong - Kong University of Science and Technology
Laura Acion - University of Buenos Aires, Argentina
Omar Asensio - Georgia Tech, US
Feras Batarseh - Virginia Tech, USA
Eleonore Fournier-Tombs - University of Ottawa and World Bank, Canada
Giz Gulnerman - Committee coordinator
Anushri Gupta - Coventry University, UK
Shan Jiang - Tufts University, US
Joanna Kulesza - University of Lodz, Poland
Tian Lan - UCL, UK
Xiao Liu - McGill University / World Economic Forum, Canada
Lauren Maffeo - Steampunk, US
Keegan McBride - Hertie School, Germany
Alexander Monea - Geroge Mason University, US
Catherine Moore - Committee coordinator
Francesco Mureddu - Lisbon Council, Belgium
Jaron Porciello - Cornell University, US
Robby Cobby Avaria - Data & Policy Communications Editor
Zeynep Engin - Director
Emily Gardner - Community Manager
Andrew Hyde Data & Policy Commissioning Editor
Itzelle Medina Perea - Data for Policy Communications Editor
Call for Papers General Information
The Data for Policy conference series is the premier global forum for multiple disciplinary and cross-sector discussions around the theories, applications and implications of data science innovation in governance and the public sector. Its associated journal, Data & Policy, published by Cambridge University Press has quickly established itself as a major venue for publishing research in the field of data-policy interactions. Both the conference and the journal receive valuable support from their sustainer partners: the Alan Turing Institute, the Office for National Statistics and UCL.
Convening for the sixth time in September 2021, the International Organisation Committee for the conference invites Paper and Panel Session proposals at the conference to be also considered for potential post-conference publications in Data & Policy (subject to peer-review).
There are six broad, interdisciplinary, cross-sectoral areas of interest, which form the standard tracks of the conference. The six areas are overseen by newly appointed editorial committees, who are working to develop each area.
Following the momentous events of 2020, the International Committee and organisers wish to recognise the additional focus on data throughout the whole of society as a consequence of the Covid-19 pandemic, and the opportunities this creates for learning about and developing data and policy interactions. This focus should not be seen as limiting, and we welcome submissions across domains, sectors and applications. In addition to the standard tracks, nine Special Tracks have also been shortlisted this year.
Submissions will be accepted in the following categories:
1. Individual Research/Policy/Practitioner Proposals to Standard or Special Tracks in the form of i) full research papers or ii) extended abstracts
2. Session Proposals
The review process is likely to include multiple iterations that extend beyond the timeline of the conference, and publication is subject to reviewer comments being reflected in the final paper.
Note that Conference acceptance does not guarantee publication in Data & Policy.
1 i) Individual Research/Policy/Practitioner Proposals to Standard or Special Tracks: Extended abstract
The process is essentially unchanged from the 2020 conference. This should be 1,000 words maximum, including a title, research/policy question, research methodology and data used, and key findings. Authors who decide to just submit an extended abstract – in order to present at the Conference – will still have the option of submitting to the Journal at a later date if they wish to do so, but will not benefit from the integrated review procedure.
Note also that some Special Track Chairs are intending to guest-edit thematic collections of articles in Data & Policy, so you may be separately contacted by the Chair about the potential of submitting to the Journal.
2) Session Proposals
Session proposals should comprise a combination of 3-4 presentations from researchers and/or practitioners, each of whom must provide an abstract (1,000 words maximum). A description of the panel should also be submitted (500 words maximum).
This standard track focuses on the high-level vision for philosophy, ideation, formulation and implementation of new approaches leading to paradigm shifts, innovation and efficiency gains in collective decision making processes. Topics may include:
- Data-driven innovation in public, private and voluntary sector governance and policy-making at all levels (international; national and local): applications for real-time management, future planning, and rethinking/reframing governance and policy-making in the digital era;
- Data and evidence-based policy-making;
- Government-private sector-citizen interactions: data and digital power dynamics, asymmetry of information; democracy, public opinion and deliberation; citizen services;
- Interactions between human, institutional and algorithmic decision-making processes, psychology and behaviour of decision-making;
- Global policy-making: global existential debates on utilizing data-driven innovation with impact beyond individual institutions and states;
- Socio-technical and cyber-physical systems, and their policy and governance implications.
The remaining categories represent more specifically the current applications, methodologies, strategies which underpin the broad aims of Data for Policy’s vision:
This track is concerned with data in its variety of forms and sources, and infrastructure and methods for its utilisation in policy and governance:
- Data sources: Personal and proprietary data, administrative data and official statistics, open and public data, organic vs designed data, sensory and mobile data, digital footprints, crowdsourced data, and other relevant data;
- Technologies: Artificial Intelligence, Blockchain, Internet of Things, Platform Technologies, Digital Twins, Visualisation and User Interaction Technologies, data and analytics infrastructures, cloud and mobile technologies;
- Methodologies and Analytics: Mathematical and Statistical models, Computational Statistics, Machine Learning, Edge Analytics, Federated Learning, theory and data-driven knowledge generation, multiple disciplinary methodologies, real-time and historical data processing, geospatial analysis, simulation, gaps in theory and practice.
This track focusses on governance practices and management issues involved in implementation of data-driven solutions:
- Data and algorithm design principles and accountability
- Local, national and international governance models and frameworks for data and associated technologies;
- Data and algorithms in the law;
- General Data Protection Regulation (GDPR) and other regulatory frameworks;
- Data intermediaries, trusts and collaboratives;
- Meta-data, interoperability and standards;
- Data ownership, provenance, sharing, supply chains, linkage, curation and expiration;
- Data sovereignty and data spaces.
This track examines the issues which must be considered in technology design and assessment:
- Digital Ethics: Data, algorithms, models and dynamic interactions between them
- Digital trust, and human-data-machine interactions in policy context
- Responsible technology design and assessment
- Privacy and data sharing
- Digital identification, personhood, and services
- Uncertainties, bias, and imperfections in data and data-driven systems
- Algorithmic behaviour: equity and fairness, transparency and explainability, accountability, and interpretability
- Human-machine collaboration in strategic decision making and algorithm agency
- Human control, rights, democratic values, and self-determination.
The following are areas which fall within the above categories, but are highlighted as being of special interest:
- Data-driven insights in governance decision making, black-box processing;
- Algorithm agency along with human and institutional decision-making processes;
- Government automation: citizen service delivery, supporting civil servants, managing national public records and physical infrastructure, statutes and compliance, and public policy development;
- Algorithmic ‘good’ governance: participation, consensus orientation, accountability, transparency, responsiveness, effectiveness and efficiency, equity and inclusiveness, and the rule of law.
- Human existence and the planet;
- International collaboration for global risk management and disaster recovery;
- Global health, emergency response, Covid-19 and pandemics;
- Sustainable development, climate change and the environment;
- Humanitarian data science, international migration, gender-based issues and racial justice;
- International competition and cultures of digital transformation.
Jaron Porciello, Cornell University (corresponding author)
Ulrike Hahn, Birkbeck College
Stephan Lewandowsky, University of Bristol
The COVID crisis has turned the world upside down. It has revealed societies’ fissures and pressure points as it has mercilessly revealed any lurking weaknesses in our existing systems and structures. The public and scientists have witnessed an explosion of scientific research across all disciplines –much it of understanding the nature of the virus itself—in addition to a well-spring of data science, meta-science and science communication, some of it drawing on state-of-the-art AI and machine learning tools designed to help scientists and non-scientists keep current on the explosion of knowledge.
The pandemic has brought into sharp focus questions surrounding the development, discussion, and diffusion of research. The wider issues they raise as they pertain to the ways science is and could be conducted in online information environments, whether this is among scientists themselves, in the interaction between scientists and policy-makers, or in interaction with the general public.
This special track will consider what we have learned as we emerge from the COVID-19 pandemic. What are the tools, systems, data governance models and types of experts that we need to foster science and help maximize its societal benefits well beyond the pandemic context? We will pay special attention to the role of media in the dissemination of new scientific findings alongside misinformation: expediency, if nothing else, during the pandemic has necessitated the use of extant social media platforms for science-to-science, science-to-policy, and science-to-public discourse.
We will bring together a mix of contributed papers and panel discussions to explore the relationship between discourse quality and algorithmic mediation. More specifically, we invite contributions on the following topics:
- COVID-19 required urgency to produce, react and make decisions based on scientific data. How have platforms tuned to the maximization of advertising revenue failed and succeeded to serve the epistemic goals of scientists? How did the fine-tuning of algorithmic behaviours on platforms like Twitter and YouTube end up impacting decision-making processes, psychology and behaviour of the individuals using these platforms? Are there new opportunities to re-purpose and extend existing tools to promote high-quality science discourse?
- Preliminary scientific findings find themselves in a complex set of dynamics between science, policy makers, public opinion, non-traditional `outlets’ for scientific research and the media that seem deeply problematic for the integrity of the scientific process. Many of the traditional systems of peer-review and science policy were arguably already challenged by the pre-crisis state of affairs, and the pandemic has placed huge additional demands on these system. Platforms that could reward expediency and transparency, in part by increasing usage of existing machine-learning algorithms, saw dramatic increases in usage and content. They have replaced some quality-control and review functions that were previously designed for humans. If the principles of expediency, transparency and informal review are to become part of the new normal, then what models of data governance and science policy do we need to encourage? What types of sociotechnical models do we need to encourage unbiased decision-making?
- The pandemic clearly challenged the traditional science-to-policy interface. Scientists in all disciplines were called on to act as intermediaries and build trust between the general public and policy-makers. Scientists were vetting “emergent science” as it was published on social media platforms, resulting in at least one high-profile retraction. This has pushed the job of technical discussions and scientific peer-review into a new social space and one where publishers do not wield as much power over the dissemination of scientific ideas. However, oversight is important, and in this panel we invite novel ideas for governance systems, online tools and financing mechanisms that we need in order to make successful and long-lasting changes towards creating new systems for science.
- There is evidence that shows that the same misformation tactics and campaigns that have been used for climate change (among other issues) are the same ones casting aspersions about the COVID-19 vaccines. How can we prevent bad actors from overwhelming public systems and reduce opportunities for misinformation campaigns based on expedient science now, and in the future?
Masaru Yarime, Hong Kong University of Science and Technology
Data-driven innovation, including the Internet of Things (IoT), blockchain, and artificial intelligence (AI), has significant potential to address various challenges identified in the Sustainable Development Goals (SDGs). Smart energy grids based on blockchain make it easier to integrate renewable energy sources and balance energy supply and demand smoothly, improving energy efficiency and reducing carbon dioxide emissions and air pollution. Smart infrastructure monitoring systems equipped with IoT sensors enable us to measure weather conditions precisely and strengthen urban resilience to natural disasters such as floods and typhoons. AI-based medical devices support conducting diagnosis and treatment efficiently, providing accessible and inclusive health services to all.
As environmental, economic, and social aspects are increasingly interwound, various kinds of data that are getting available from a variety of sources through sophisticated equipment and devices need to be deployed effectively to tackle multifaceted sustainability goals and targets. It is hence critical to collect, share, and use relevant data through cooperation and collaboration among stakeholders. The open data policy will facilitate data disclosure and exchange, contributing to creating innovation.
There are many policy challenges, however, that we need to consider in properly utilizing data for innovation for sustainability. Technical issues related to data, such as metadata tagging, quality control, cleaning and error elimination, and interoperability between various standards, must be addressed to support data sharing. Stakeholders have different interests and motivations and would not necessarily be willing to disclose or exchange data with each other, which would require us to consider a proper balance between open and proprietary data. Serious concerns are also raised about dealing with sensitive data in terms of security and privacy. Micro-targeted nudging for sustainable behaviour based on detailed personal data could involve manipulation or paternalism.
Various policy approaches can be considered for data governance. The government can be in charge of governing public data, whereas platform enterprises in the private sector also play a critical role in assembling and managing an increasing amount of data. A data trust can be established as an independent institution to make decisions about who has access to data under what conditions, how that data is used and shared for what purposes, and who can benefit from it. Transparency and citizens’ participation and engagement in the processes of data governance are particularly emphasized in encouraging social acceptance and inclusiveness in pursuing sustainable objectives.
Data-driven innovation poses a difficult challenge to policymaking. The speed of technological change is rapid, and the path of its evolution is not entirely predictable or explainable. That would produce a widening gap between technological change and institutional readiness. Also, various sectors, including energy, transportation, and health, which were not connected previously, are increasingly integrated through data in cyber-physical systems. Novel policy approaches, such as regulatory sandboxes, would be required to incorporate the ability to learn from real-world use and experience and improve performance through adaptation.
In-depth research needs to investigate what kinds of policy frameworks and measures would be effective in collecting, sharing, and using data among stakeholders and what impacts would be made on facilitating data-driven innovation while addressing societal concerns, including data security and privacy. This special track invites theoretical as well as empirical studies that examine various policy measures and approaches to facilitating data-driven innovation and addressing key issues involved, such as the ownership of and accessibility to data, interoperability and integration of data, incentives to the collection, disclosure, and sharing of data, the protection of data security and privacy, and trust and engagement in data governance.
Possible questions we would discuss in this special track include, but not limited to, the following:
- How are various kinds of data collected, shared, and used for innovation among stakeholders?
- What incentives are provided to stakeholders with different interests and motivations to facilitate data sharing?
- What kinds of governance systems are established to manage data availability, accessibility, and ownership?
- What policy measures and institutional arrangements are introduced to deal with sensitive data in terms of data security and privacy?
- What are the impacts and consequences of policy measures on facilitating innovation while addressing societal concerns?
Case studies in different countries and regions are particularly welcome to examine the mechanisms and processes involved in data collection, sharing, and use for innovation, as local specificities of the relevant actors and institutions would be significant. Policy implications and recommendations are explored for maximizing the potential of data-driven innovation while minimizing risks to individuals and communities in addressing SDGs.
Dr. Ronit Purian and Avi Cohen, SYN-RG-Ai, Tel Aviv University, and CODATA
Participants in this track will present approaches and methods to better understand urban dynamics, identities and spatial behaviors that incorporate collective actions in cities today. Specifically, willingness to share data in decentralized systems through careful design is at focus, assembling data trusts for communities – in different ecosystems and social groups – while reconstructing trust in government and in the nation state and institutional practices.
Personal data sharing for the benefit of society at large is a goal inspired by the Covid-19 contact tracing applications. The voluntary use aimed at utilizing the value of crowdsourcing and self-reporting, to appropriate the very same principles of citizen science; however, acceptance rates were low (similarly, institutional distrust is so widespread that vaccination is at risk, even if provided voluntarily and free of charge). We wish to better understand the reason for this failure in terms of service design and institutional practices, i.e., what makes a trustworthy technological and organizational design.
On the continuum – between the elementary disclosure of information, to beliefs creation and trust building – lays a level in which a contract is established between the citizen and the dedicated authority. The contract level is core to technology use. Contract violation decreases users intention to reuse the system, and therefore, violating this psychological contract with the public is a moral hazard. Citizens were suspicious of the state-controlled applications, although government intrusion into their personal life with applications that applied a decentralized architecture was implausible. Is that possible that, rather than mechanisms to avoid privacy invasion, other design aspects should have been considered and emphasized? Dull interface and functionality could explain the evident failure of contact tracing applications. How can we identity design aspects and institutional practices that alienate participants?
In this track, we wish to extend our knowledge of information systems (IS) design. How can system use elicit empowerment, engagement and trust building? This question refers to data intermediaries, informed consent, data ownership, modularity (to be able to switch between decoupled apps and data servers, not losing data due to vendor lock-in) and the ability to easily reuse existing data from other apps – as proposed by SOLID, and more. In addition, the question refers to a series of new socioeconomic contingencies, mainly: the chronic-rural poverty – and the new-urban poor.
To illustrate this, the World Bank (2021) presents the “multidimensional character” of poverty in rural areas, where low levels of educational attainment are common to both poor and nonpoor; but at the same time, telework may challenge the education advantage of cities and the productivity effect of agglomeration in dense urban areas. Early results from rapid-response phone surveys show that a large share of the new poor will be urban. Moreover, “many of the new poor are likely to live in congested urban settings and to work in the sectors most affected by lockdowns and mobility restrictions; many are engaged in informal services and not reached by existing social safety nets” (World Bank, 2021; p. 143). Thus, the character of poverty is “multidimensional” also in urban areas, and Covid-19 is likely to have distinctive effects on poor people who are urban residents. An inclusive and sustainable mobility service should consider, accordingly, specific populations (e.g., undocumented work immigrants), point of departure (who live in congested informal settlements), and destination (who work in the informal sector). What should be the focus of data interoperability in such mobility services?
We believe that identifying the most vulnerable groups in society is important, not only to eliminate health disparities and adverse health effects related to climate variability and societal gaps, but to increase resilience in the sense of systemic engagement, caring about ecological and social systems, nurturing mutual responsibility and a sense of individual ownership.
The climate crisis, and health and economic problems are intertwined through our urban-digital life, and call for an urgent systemic transformation. Therefore, our overall aim is to further develop the systems and the societal practices that encourage healthier and more sustainable spatial behaviors.
We plan to carry out a collaborative interdisciplinary track that will define focus areas, activities, and priorities, e.g.: Needs assessment and knowledge gap analysis (the desirable data sources and tools); reflections on current studies (case studies – challenges, solutions; and how to generalize); operational research plans (national surveys, public concerns and perceptions in questionnaire items), and the like.
From the domain viewpoint: to define a cluster of mobility-related problems that are encountered by many cities.
From the data viewpoint: to define the data components we share in our research. The goal will be to facilitate open data, knowledge sharing, and data integration.
From the service viewpoint: to define the data sources and tools we use; what are the preferred integration and granularity (e.g., local-global); and how to achieve that.
To address such goals, we wish to invite researchers from different disciplines, who are interested in: Urban Resilience, spatial behavior, flexible mobility, data ownership, service design, and any other topic within the broad domains of smart cities, information systems, data science, and behavioral sciences.
The track will integrate the following aspects:
- Accountability and privacy by design, security, open code
- Beliefs creation and trust building, attitudes, communicative action
- Civic mindedness in global cities
- Data integration and interoperability
- Global citizens and work immigrants
- Human-machine interface and rich system use
- Mechanism design, moral hazard, PPP, P2P
- Smart cities and urban networks
 World Bank (2021). Global Economic Prospects, January 2021. Washington, DC: World Bank. http://hdl.handle.net/10986/34710 and https://openknowledge.worldbank.org/handle/10986/34710
Review and Assessment process
for Conference submissions
All submissions to the Conference will be assessed by peer review for their suitability for the Conference, according to the following criteria:
- Potential contribution to the debates in the field
- Potential for stimulating debate in the Conference
- Freshness of the content, novelty and originality
- Formulation of the research/policy question
- Data and methodology
- Quality of writing and presentation
Conflicts of interest must be declared, and affected submissions will be moved from affected tracks for the purpose of review by the other members of the organising committee. Track Chairs will work with the organisers to achieve this.
Guidance for reviewers on using EasyChair is available as a pdf here
Further information for full papers
In addition to assessment of suitability for the conference, full papers will receive peer review which is aligned to that of the Data and Policy journal . Thus research papers will receive 3 reviews, and commentaries will receive 2. The reviewer is asked to comment on:
– the paper’s significance, noting what is original / interesting
– the overall quality
– the technical correctness and scientific soundness
– the clarity and length
– the suitability for the journal and conference
The reviewer will score to indicate whether the paper
– is accepted for the conference and suitable for invitation to submit to Data & Policy
– is suitable for the conference, but is not yet ready for publication
– is not suitable for the conference
Following peer review, the final decision on inclusion in the conference and invitation to submit a revised paper will rest with the International Organisation, who have an overview of the Conference programme as a whole. This is to ensure a varied and balance programme for the benefit of all conference attendees.
Further information for extended abstracts and session proposals
Extended abstracts and session proposals will be reviewed by the track chair and one other reviewer. The reviewer will score to indicate whether the submission is accepted for the conference, or rejected. Following peer review, the final decision on inclusion in the conference will rest with the International Organisation, who have an overview of the Conference programme as a whole. This is to ensure a varied and balance programme for the benefit of all conference attendees.
POST-ACCEPTANCE OF CONTRIBUTION
Important information for authors of accepted contributions to Data for Policy 2021
For all presenting authors: Video presentations for Data for Policy YouTube Channel
All presenters are required to submit a video of their presentation before the conference. This will be shared with all registered attendees via the Data for Policy YouTube channel. There is a playlist of 2020 videos available for examples.
Preparation and submission guidelines for videos:
The following are absolute requirements which we ask you to adhere to:
- Videos must be made in landscape orientation, on a neutral, natural background with adequate lighting.
- Presenter must start the video by introducing themselves, and giving the submission number and title.
- Videos must be 15-25 minutes in length.
- Videos must be at least 720p resolution. The optimum resolution for YouTube is 1080p. Videos do not need to be higher resolution than this.
- Videos must be in YouTube supported format (see YouTube page for details).
- Videos must be named in the format [submission number]_[family name of presenter], e.g., 1_gardner
To assist you in preparing your videos, we have compiled guidance for using Zoom and Microsoft Teams. The organisers regret that they are unable to offer support to individuals in recording their presentations.
Videos must be submitted by 20th August, by upload to the Data for Policy OneDrive. Instructions on how to do this will be sent to the submission’s first author by email. Videos may also be uploaded to the Data for Policy community on Zenodo, in which case the owner will be able to control sharing.
* Please notify us as soon as possible if you cannot meet any of these requirements *
For all authors: Discussion papers for Data for Policy Zenodo community
All authors, including those invited to submit papers to Data & Policy, are encouraged to upload a discussion paper to the Data for Policy community page on Zenodo. Other materials that they wish to share can be included – such as their presentation slides, or any related data sets. Any material submitted to Zenodo is citable, so it can be referenced in any future publications.
Zenodo templates and formatting instructions:
Click to download Word template: Data_for_Policy_Word_Template
Click to access LaTeX template via DropBox: Data for Policy LaTeX template
Click to view document formatting instructions in a new tab: Data for Policy Formatting Instructions
Instructions for uploading to Zenodo:
- After logging in to Zenodo click on the ‘Upload’ tab. Following this select the ‘New Upload’ option.
- After ‘Dragging/Choosing files’, search ‘Data for Policy’ under the ‘Communities’ section (users will need to scroll down the options and identify the Data for Policy logo).
- The next step is to select from the ‘Upload Type’. Here, those submitting Discussion/Forum Papers should select the ‘Publication’ option. Then, select ‘Conference Paper’ from the ‘Publication Type’ option. Those submitting Presentations should choose the ‘Presentation’ option.
- Zenodo will generate a DOI, as such under the ‘Basic Information’ section authors should fill only the ‘Tile’, ‘Authors’, ‘Description’ and ‘Key Words’.
- The default option under ‘Licencing’ is Open access. This option is encouraged, however, when appropriate authors can select the restricted options.
- At this point, authors will be able to ‘Publish’, all other information is optional.
Submissions should align with the instructions provided by the Data & Policy Journal.
Please name your file as [submission number]_[family name of corresponding author] for the paper, e.g., 1_gardner.
All submissions will be first used for conference discussions and then considered for conference proceedings and other post-conference publications (e.g. special journal issues and policy reports).
The deadline for all submissions to Zenodo is 20th August.
Please refer any questions to our team at email@example.com
For authors invited to submit a paper to Data & Policy
Invitations to submit a revised full paper to Data & Policy will be sent from the journal’s manuscript handling system. All details required for article preparation and submission are available on the journal website.
The deadline for submission of revised full papers is 20th August.
Data for Policy is a fee-paying conference. Register today to enjoy a 20% early bird discount
- Live keynotes, panels and individual presentations
- Live networking opportunities
- Videos of individual presentations (available before the conference)
The registration fee is £150 (£120 for registration up to 23rd July)
Presenting delegates must register by 23rd July to secure their time slot in the main conference programme. Each delegate can register to present only one paper at the conference and if multiple papers are accepted from the same author, they should either invite co-authors to present additional papers or indicate their preferred paper for presentation at the conference.
Attending delegates can register before 23rd July to take advantage of the 20% early bird discount. The final deadline for registration is 20th August.
We have limited funding to support student participation; those who wish to be considered for these grants should send a CV and cover letter explaining their case to firstname.lastname@example.org
Data for Policy is also committed to increasing diversity in its community. We understand that the cost of registration may be a barrier to participation, and are therefore delighted to offer Data for Policy diversity scholarships to suitable candidates from groups which are currently underrepresented in our community, in particular those from developing nations, who will extend our geographic diversity. Scholarship awards are based on the level of financial need, and the appropriateness of the opportunity afforded to the applicant. To be eligible, applicants must have no other source of funding to meet the cost of registration. Please send a CV and cover letter explaining their case to email@example.com
Registration via Bank Transfer:
We accept registrations via bank transfer* to:
Data for Policy CIC
National Westminster Bank (NatWest)
Account Number: 33915806
Sort Code: 56-00-31
* [IMPORTANT NOTE]: If you are registering via bank transfer, please send full delegate information – full name including title, email address, institution, and submission number (presenting delegates only) – and the payment reference to firstname.lastname@example.org after completing the bank transfer.
Terms & Conditions
We take receipt of a completed registration form as acceptance of the following terms and conditions:
Registration with full payment of the conference fees must be received before the registration deadlines:
In submitting your data, you agree to our privacy and data protection policies.
Public registration to the conference is limited and places will be offered on a first-come-first-served basis. If the spaces are filled earlier, the registration will be closed before the deadline. Please note that access to the conference will not be permitted without advance registration.
Registration fees are non-transferable and cancellations of registration with full refund are allowed until the registration deadline that applies to the delegate. Registration fees are non-refundable after deadlines.
Photography and video-recording will take place at conference venues to be used for post-conference publications and other related online/printed material to be produced by Data for Policy. Any reservations about this condition should be sent to email@example.com prior to the conference to avoid any disappointment in the future.
The organisers reserve the right to change conference programme and venue details, and to cancel the conference in case of any unpredictable event.