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.
AWS Community Day CPH - Three problems of Terraform
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 Data studies
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 the complex 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
Dr Tracey 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 of Data
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
10. Types of Government Data
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
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
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
21. Conclusion
• There will be 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
Notes de l'éditeur
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.
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.
geomaticians, researchers, librarians, community developers and journalists