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Data Diversity & Data Cultures = Flexible Open by Default Policy
1. The document summarizes a presentation on government data and open data policies. It discusses critical data studies and examines definitions of open data over time. 2. It categorizes different types of government data like survey data, geospatial data, scientific data, and administrative data. 3. The presentation also explores the concept of "data cultures" and how different communities interact with and use data in different ways like researchers, private sector, and citizens.
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Data Diversity & Data Cultures = Flexible Open by Default Policy
- 1. Government Information Day Oct.26, Library and Archives Canada 10:45 – 12:30 Government information & data ecosystem Data Diversity & Data Cultures = Flexible Open by Default Policy Dr. Tracey P. Lauriault Assistant Professor of Critical Media and Big Data School of Journalism and Communication Carleton University, Ottawa, ON, Canada Tracey.Lauriault@Carleton.ca ORCID: orcid.org/0000-0003-1847-2738
- 2. Table of Contents 1.Critical Data Studies 2. Open Definitions 3. Types of Data 4. Data Cultures 5. Conclusion Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 3. 1. Critical Datastudies Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 4. Premise Dr Tracey P.Lauriault, School of Journalism and Communication, Carleton University Data are more than the unique arrangement of objective and politically neutral facts & Data do not exist independently of the ideas, instruments, practices, contexts and knowledges used to generate, process and analyze them.
- 5. Material Platform (infrastructure –hardware) Code Platform (operating system) Code/algorithms (software) Data(base) Interface Reception/Operation (user/usage) Systems of thought Forms of knowledge Finance Political economies Governmentalities - legalities Organisations and institutions Subjectivities and communities Marketplace System/process performs a task Context frames the system/task Digital socio-technical assemblage HCI, Remediation studies Critical code studies Software studies New media studies Game studies Critical Social Science Science Technology Studies Platform studies Places Practices Flowline/Lifecycle Surveillance Studies Critical data studies Socio-Technological Data Assemblage (Rob Kitchin, 2014, Kitchin & Lauriault 2014) Algorithm studies
- 6. • Unpack thecomplex assemblages that produce, circulate, share/sell and utilise data in diverse ways; • Chart the diverse work they do and their consequences for how the world is known, governed and lived-in; • Survey the wider ecosystem of data assemblages and how they interact to form intersecting data products, services and markets and shape policy and regulation. Critical Data Studies Vision Rob Kitchin and Tracey P. Lauriault, 2018, Toward a Critical Data Studies: Charting and Unpacking Data Assemblages and their Work, in J. Eckert,, A. Shears & J. Thatcher, Geoweb and Big Data, University of Nebraska Press , Pre-Print http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112
- 7. 2. Open Definitions DrTracey P. Lauriault, School of Journalism and Communication, Carleton University
- 8. Open Data Definitions •1959 Antarctic Treaty • 1992 - UNCED – Agenda 21 Chapter 40, Information for Decision Making • 1996 Global Map • 2002 – UNCED – Agenda 21 + 10 Down To Earth • 2005 - Open Knowledge Foundation (OKNF) - 11 Principles (Licence specific) • 2007 GEOSS - Data Sharing Principles for the Global Earth Observing System of Systems • 2007 - US Open Government Working Group - 8 principles of Open Government Data • 2007 Science Commons Protocol for Implementing Open Access Data • 2007 Sunlight Foundation - 10 Principles for Opening Up Government Information • 2007 OECD, Principles and Guidelines for Access to Research Data from Public Funding • 2008 OECD, Recommendations on Public Sector Information • 2009 W3C - Publishing Open Government Data • 2010 Tim Berners-Lee 5 Star of Open Data • 2010 Panton Principles for Open Data in Science • 2010 Ontario Information Privacy Commissioner - 7 Principles • 2013 Open Economics Principles • US Association of Computing Machinery (USACM) – Recommendations on Open Government • American Library Association (ALA) – Access to Government Information Principles • 2013 G8 Open Data Charter • 2015 International Open Data Charter “In the context of Open Information, Open by Default is guided by the following set of principles: Complete and relevant: All government information that has value to the public is made available unless there are privacy, security or legal reasons for not doing so”. (Gov’t of Ontario Aug 11, 2017)
- 9. 3. Types ofData Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 10. Types of GovernmentData 1. Survey Data 2. Geospatial Data 3. Spatial & Social Media 4. Scientific Data 5. Research Data 6. Knowledge Institution 7. Administrative Data Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 11. 1. Survey Data •Public Opinion Data • Elections/Referenda • Census • Questionnaires
- 12. 2. Geospatial Data •Canadian Geospatial Data Infrastructure • Remote Sensing – • Satellite & Radar & • Drone & Air Photos • Sensor Derived Data • GeoDemographics • Location
- 13. 3. Spatial &Social Media • Crowdsourcing • Volunteered Geographic Information • Twitter • Facebook • Linkedin • GCTools
- 14. 4. Scientific Data •NRC • NRCAN • EnvCan • Health Canada • AAFC • CSA • Atomic Energy • …..
- 15. 5. Research Data •Tri-Council Funded Data • CIHR • NSERC • SSHRC • Research Data Canada • Departments & Agencies • Scientific Data • Collaborations
- 16. 6. Knowledge Institutions •Museums • Archives • Libraries
- 17. 7. Administrative /Public Sector Data • Data produced as part of the outcome of delivering and administering programs, services, projects, administration • Performance & Accountability • Audits • Budgets • Expenditures • Contracts • Business registry • Grants & contributions
- 18.
- 19. Data Communities /Cultures Research/scientific Data GovData GeoData Physical Sciences AdminData Public Sector Data NGOs Access to Data Open Data Social Sciences 2005 Operations Data Infrastructural Data Sensor Data Social Media Data AI/Machine Learning Data Smart Open Data? 2015 Private Sector IOT - Smart Cities - Precision Agriculture - Autonomous Cars SM Platforms Algorithms AI P2P – Sharing Economy Predictive Policing Surveillance Digital Labour Drones 5G Public/Private Sector Data? Crowdsourcing Citizen Science Civic Teck OCAP Local and Traditional Knowledge 2017-Beyond
- 20.
- 21. Conclusion • There willbe many Open by Defaults • Data Types + Data Cultures • Need to think infrastructurally • Institutions • Standards - interoperability • Technology • Policy • Law • Regulation • Long-term thinking • Archives • Sustainable funding • Expertise - science • Not just data anymore • Algorithms • Artificial intelligence • Machine learning • Autonomous • Right to Repair • Right to Explanation - Algorithms • Data Subjects • Right to Access • Data Portability Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
Editor's Notes
- #6 Co-functioning heterogeneous elements of a large complex socio-technological system – these elements are loosely coupled. They contend that data do not exist independently from the context within which they were created, and the systems and processes that produce them. The Prime2 Data model and platform is no exception. In order to study data in their ‘habitat’ and ‘ecosystem’, Kitchin (2014) offers a socio-technological assemblage approach to guide the empirical analysis of data (See also Kitchin & Lauriault 2014). The assemblage can be conceptualized as a constellation of co-functioning, loosely-coupled heterogeneous elements, and it is these elements that guide data collection. Here, the assemblage is both a tool for research as well as a theoretical framing of data (Anderson et. al 2012). Furthermore, data modelling requires a particular form of logical abstract thinking, in the case of the OSi and 1Spatial those that were involved in the modelling exercise were very senior, experienced and renowned spatial data experts, all formally trained in spatial database design and maintenance as well as spatial analysis at the enterprise level. The design and testing of a model is very labour intensive, re-cursive, and incredibly expensive. At the OSi, this work was not done in house, thus requiring the enactment of a procurement process to cover this major expenditure, and because of this, and because the model is key, it is a high stakes tendering process. For example, infrastructure is not simply hardware and software it is the systems of thought that led to its creation including how object oriented modeling came to be and how that model materializes into code and algorithms which reformulated the entire data production flowline and its association with not only the equipment used by surveyors, but the entire database stack. It is only by looking at the model and how it came to be through database specifications and requirements, the observation of data production on site in real time and in communication with database designers and mangers, that attributes of an infrastructure’s assemblage can be observed in their state of play. The process of modelling is situated in the domain of object oriented programming, the semantic web, GIScience, modelling software, taxonomies, the burgeoning database and GIS industry, modelling schemas, mathematics, consulting firms, and offshore data re-engineering companies.
- #7 In addition to these, and as part of the work being done on the Programmable City Project, with the need for all of these provocations the following are added to the Dalton and Thatcher Provocations. Now lets look at research frameworks.
- #20 geomaticians, researchers, librarians, community developers and journalists