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Observations:

The information on this page was collected and verified on October 9, 2020.  The information here was collected for research purposes and is related to the following blog post co-authored by Amanda Hunter and Tracey P. Lauriault.

  1. All provincial and territorial, as well as the federal governments are publicly publishing up to date COVID-19 data.
  2. None of official public provincial and territorial, as well as the federal governments health sites publish COVID-19 data under an open data licence. Each claims copyright with the exception of Nunavut that has no statements.
  3. ONLY Saskatchewan, Manitoba*, Nunavut and the North West Territories DO NOT HAVE open government and open data initiatives.  Manitoba has an open government initiative but not with an open data licence.
  4. ONLY British Columbia and Ontario, as well as the Federal Government include COVID-19 data in their open Data Portals / Catalogues. Quebec republishes 4 COVID-19 related datasets submitted by the cities of Montreal and Sherbrooke.

The following includes a list of official COVID-19 provincial and territorial, and federal websites including links to their data and information copyright, terms of use and disclaimers. Also, included are links to open government and open data initiatives, including policies, directives, and open data licences. Finally, each of the existing open data sites were searched on Oct. 9 to assess if they disseminate COVID-19 data.

British Columbia

BC Centre for Disease Control BC COVID-19 Data http://www.bccdc.ca/health-info/diseases-conditions/covid-19/data

© Province of British Columbia

Terms of Use http://www.bccdc.ca/Health-Info-Site/Documents/BC_COVID-19_Disclaimer_Data_Notes.pdf

Disclaimer / Information http://www.bccdc.ca/Health-Info-Site/Documents/BC_COVID-19_Disclaimer_Data_Notes.pdf

Open Information and Open Data Policy https://www2.gov.bc.ca/assets/gov/british-columbians-our-governments/services-policies-for-government/information-management-technology/information-privacy/resources/policies-guidelines/open-information-open-data-policy.pdf

“While the Open Information and Data Policy applies to all government information and Data, legal, policy, and contractual obligations, limit the application of this Policy in some cases. In addition, this Policy sets out specific criteria that must be met before government information is designated for Proactive Disclosure or Routine Release, or before Data can be considered Open Data.”

Open Data https://www2.gov.bc.ca/gov/content/data/open-data

Open Data Licence https://www2.gov.bc.ca/gov/content/data/open-data/open-government-licence-bc

YES – COVID-19 Data in the Open Data Portal

Yukon

COVID-19 information https://yukon.ca/en/covid-19-information

© Copyright 2020 Government of Yukon

Copyright https://yukon.ca/en/copyright

Disclaimer https://yukon.ca/en/disclaimer

Mandate Letter Commitment to Open Data https://yukon.ca/sites/yukon.ca/files/eco/eco-mandate-richard-mostyn_en.pdf

Open Data https://open.yukon.ca/data/

Open Government Licence https://open.yukon.ca/data/open-government-licence-yukon

NO – COVID-19 Data in the Open Data Portal

Alberta

COVID-19 info for Albertans https://www.alberta.ca/covid-19-alberta-data.aspx

© 2020 Government of Alberta

Terms of Use https://www.alberta.ca/disclaimer.aspx#toc-0

Disclaimer and Copyright https://www.alberta.ca/disclaimer.aspx#toc-0

Government of Alberta Open Information and Open Data Policy https://open.alberta.ca/policy

“provides a framework to establish the operational responsibilities, organization, processes, tools and other resources required for a single approach to the open data and open information programs. The policy also provides foundational assurance and guidance to staff from across the Government of Alberta with respect to identifying, preparing, and publishing data and information through the open data and open information portals on a routine basis going forward.”

Open Government Alberta https://www.alberta.ca/open-government-program.aspx

Open Data https://open.alberta.ca/opendata

Open Government Licence https://open.alberta.ca/licence

NO – COVID-19 Data in the Open Data Portal

Northwest Territories

GNWT’s Response to COVID-19 https://www.gov.nt.ca/covid-19/

Copyright https://www.gov.nt.ca/en/terms#2-copyright-and-trademarks

Disclaimer https://www.gov.nt.ca/en/terms

Open Government Policy https://www.eia.gov.nt.ca/sites/eia/files/2018-01-08_open_government_policy_-_signed.pdf

There is a Discovery Portal with environmental geospatial data, but not released under an open data licence; the Copyright Act applies.

Saskatchewan

Saskatchewan Health and Wellness Dashboard https://dashboard.saskatchewan.ca/health-wellness

© 2019, Government of Saskatchewan.

Terms of Use (disclaimer) https://dashboard.saskatchewan.ca/terms

Copyright https://www.saskatchewan.ca/copyright

N/A – No open government or open data initiative.

There is an open geospatial data portal: https://geohub.saskatchewan.ca/ with a Standard Unrestricted Use Data Licence: https://gisappl.saskatchewan.ca/Html5Ext/Resources/GOS_Standard_Unrestricted_Use_Data_Licence_v2.0.pdf

Manitoba

Manitoba COVID-19 Updates https://www.gov.mb.ca/covid19/updates/index.html

Copyright © 2017, Province of Manitoba

Disclaimer https://www.gov.mb.ca/legal/disclaimer.html

Copyright https://www.gov.mb.ca/legal/copyright.html    

Open Government Portal https://www.gov.mb.ca/openmb/index.html

“provides Manitobans with a place to engage with government to share your ideas, stories and knowledge. It’s also an easy way to find government reports and data.”

No Open Data Licence

NO – COVID-19 Data in OpenMB

Nunavut

Department of Health COVID-19 (Novel Coronavirus) https://www.gov.nu.ca/health/information/covid-19-novel-coronavirus

No copyright or disclaimer notifications

N/A – No Open Data or Open Government Initiative.

Ontario

COVID-19 case data: All Ontario https://covid-19.ontario.ca/data

© Queen’s Printer for Ontario, 2012-2020

Terms of Use https://www.ontario.ca/page/terms-use

Copyright https://www.ontario.ca/page/copyright-information-c-queens-printer-ontario

Open Government https://www.ontario.ca/page/open-government

“We’re creating a more open and transparent government by sharing our data and information, and consulting with the people of Ontario. Learn more about open government and the digital transformation taking place within the Ontario Digital Service.”

Open Data Directive https://www.ontario.ca/page/ontarios-open-data-directive

Adopting the International Open Data Charter https://www.ontario.ca/page/adopting-international-open-data-charter

Open Data Catalogue https://data.ontario.ca/

Open Government Licence https://www.ontario.ca/page/open-government-licence-ontario

YES – COVID data in the Open Data Catalogue

Québec

Institut national de santé publique du Québec Données COVID-19 https://www.inspq.qc.ca/covid-19/donnees

© Gouvernement du Québec, 2020

Dispositions de protections des droits de propriété intellectuelle (Copyright/Droits d’auteur) et Intégrité de l’information (Disclaimer)

http://www.droitauteur.gouv.qc.ca/copyright.php

Gouvernement ouvert https://www.quebec.ca/gouv/politiques-orientations/vitrine-numeriqc/gouvernement-ouvert/

Plan d’action pour l’accessibilité et le partage des données ouvertes des ministères et des organismes publics https://cdn-contenu.quebec.ca/cdn-contenu/gouvernement/SCT/vitrine_numeriQc/gouvernement_ouvert/plan_action_gouvernement_ouvert.pdf?1595962618

« constitue la démarche structurée que nous entreprendrons, parce que nous avons la ferme conviction que les affaires de l’État sont également celles de la population. Ce plan d’action représente également l’occasion d’engager une collaboration avec toute la société afin de valoriser la transparence et l’ouverture ».

Données ouvertes https://www.donneesquebec.ca/fr/

License Creative Commons https://www.donneesquebec.ca/fr/licence/#cc-by

NO – Provincial COVID-19 Data in the Open Data Portal only 4 related data republished from the Cities of Montreal and Sherbrooke

Newfoundland and Labrador

Newfoundland and Labrador COVID-19 Pandemic Update Data Hub https://covid-19-newfoundland-and-labrador-gnl.hub.arcgis.com/

Copyright, Government of Newfoundland and Labrador, all rights reserved

Disclaimer/Copyright/Privacy Statement https://www.gov.nl.ca/disclaimer/

Open Government Framework https://open.gov.nl.ca/pdf/OpenGovernmentInitiativeFramework.pdf

Open Government https://open.gov.nl.ca/

“is guided by the principles of transparency, accountability, participation and collaboration. Open governments recognize that true democracy involves working with citizens and stakeholders, not just for them. Open governments acknowledge and benefit from the input, knowledge and expertise that citizens can contribute to the operations and decision-making of government.”

Open Data https://opendata.gov.nl.ca/

Open Data Licence https://opendata.gov.nl.ca/public/opendata/page/?page-id=licence

NO – COVID-19 Data in the Open Data Portal

Newfoundland and Labrador do publish some COVID-19 open datasets on their open geospatial portal with an open data licence.

New Brunswick

New Brunswick COVID-19 Dashboard https://experience.arcgis.com/experience/8eeb9a2052d641c996dba5de8f25a8aa

All content © Government of New Brunswick. All rights reserved.

Copyright and Disclaimer https://www2.gnb.ca/content/gnb/en/departments/jag/attorney-general/content/acts_regulations/content/disclaimer_and_copyright.html

Disclaimer https://www2.gnb.ca/content/gnb/en/corporate/promo/covid-19/about_dashboard.html#disclaimer

Open Data Policy???

Digital Strategy https://www2.gnb.ca/content/dam/gnb/Departments/eco-bce/Promo/digitalnb/digital_new_brunswick.pdf

Open Data New Brunswick https://gnb.socrata.com/

Open Government Licence http://www.snb.ca/e/2000/data-E.html

NO – COVID-19 Data in the Open Data Portal

Prince Edward Island

PEI COVID-19 Case Data https://www.princeedwardisland.ca/en/information/health-and-wellness/pei-covid-19-case-data

© 2020 Government of Prince Edward Island

Website Disclaimer and Copyright Policy https://www.princeedwardisland.ca/en/information/executive-council-office/website-disclaimer-and-copyright-policy

Open Data Principles https://www.princeedwardisland.ca/en/information/finance/open-data-principles

Open Data https://www.princeedwardisland.ca/en/service/open-data

Open Government Licence https://www.princeedwardisland.ca/en/information/finance/open-government-licence-prince-edward-island

NO – COVID-19 Data in the Open Data Portal

Nova Scotia

Coronavirus (COVID-19): case data https://novascotia.ca/coronavirus/data/

Crown copyright © Government of Nova Scotia

Copyright https://beta.novascotia.ca/copyright

Terms https://beta.novascotia.ca/terms

Open Data https://data.novascotia.ca/

Open Government Licence https://novascotia.ca/opendata/licence.asp

NO – COVID-19 Data in the Open Data Portal

Health Canada

Coronavirus disease (COVID-19): Outbreak update https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

Information posted by the Government of Canada is subject to the Copyright Act https://laws-lois.justice.gc.ca/eng/acts/C-42/index.html

Also a link to https://open.canada.ca/en (open.canada.ca/coronavirus)

Government of Canada Open Government https://open.canada.ca/en

“Open Government is about making government more accessible to everyone. Participate in conversations, find data and digital records, and learn about open government.”

Policy on Service and Digital: https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32603 (4.3.2.8 and 4.3.2.8 on Open Information and Open Data)

Directive on Open Government https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=28108

Open Data https://open.canada.ca/en/open-data

Open Government Licence https://open.canada.ca/en/open-government-licence-canada

YES – COVID-19 Data in the Open Data Portal

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Article written by: Kit Chokly & Tracey P. Lauriault

The phrase “flatten the curve” has recently come into the vernacular to encourage slowing the spread of COVID-19. The concept comes from the Centre for Disease Control (CDC) report (p.18), where the curve renders the daily number of COVID-19 infections on the Y-axis and the day these are counted on X-axis of a line chart. A flatter curve visually depicts when the illness is under control, while a higher peak curve emerges when the health case system becomes overwhelmed and instances of the virus are high. Together we work to “flatten the curve” by following the recommendations of our Chief Medical Officers of Health.

This is not the only visual metaphor used to refer to COVID-19, however; the “second wave” has also been used to describe the spread of COVID-19. This term refers to another peak in the line chart that may appear as restrictions are lifted and cases spike as a result. By visualizing health data, we can see the spread of COVID-19 and set public health goals through the peaks and valleys of a line chart.

In addition to demonstrating a growing public awareness of the pandemic, the popularity of these phrases shows the importance of data visualization in understanding—and thus managing and communicating—the virus. Following this prevalence, many official sources now share live data to track and communicate COVID-19 data. John Hopkins University, for example, rendered COVID-19 datasets into the first global and US dashboards. Health Canada provides an interactive map of Canada featuring case counts and rates. Some city public health departments such as Ottawa Public Health have also launched dashboards to communicate the current status of local COVID-19 cases.

These dashboards get complex information across quickly and include many types of indicators. For instance, as seen below, the City of Ottawa dashboard includes key indicators such as the number of COVID-19 cases, hospitalization levels, and other case data. These are depicted in a cumulative curve of confirmed cases, their rate across time, bar charts of cases reported by age, and a pie chart that shows cases by gender. Visualizing these data in a dashboard makes it easy to see patterns and trends.

A screenshot of the city of Ottawa’s COVID-19 dashboard, which effectively condenses complex data on COVID-19 cases and outcomes into bite-sized visualizations (Ottawa Public Health, 2020).

A screenshot of the city of Ottawa’s COVID-19 dashboard, which effectively condenses complex data on COVID-19 cases and outcomes into bite-sized visualizations (Ottawa Public Health, 2020).

While the city and all the other public health agencies across Canada should be lauded for their dashboards, these, like all other demographic reporting systems, suffer from a lack of a nuanced and intersectional approach to collecting and reporting data. For example, this dashboard alongside those just mentioned do not report how COVID-19 affects people of low socio-economic status. They do not include how living in a densely populated area may also influence health outcomes. There is also little to no data on how COVID-19 may disproportionately affect racialized groups such as Indigenous, Black, and other people of colour. And most importantly, they do not demonstrate how all of these variables intersect. As Jamie Bartlett and Nathaniel Tkacz (2017) explain,

“Like all visualisations of data, dashboards necessarily distort the information that they are attempting to present neutrally by defining how a variable is to be understood and by excluding any data which isn’t compatible with this definition” (p. 15).

As Maggie Walter and Chris Andersen (2013) discuss in their book Indigenous Statistics, data dashboards—like all data and technological systems—are more than neutral numerics as they play,

“a powerful role in constituting reality through their underpinning methodologies by virtue of the social, cultural, and racial terrain in which they are conceived, collected, analyzed, and interpreted” (p. 9).

They also emphasize that there is a distinction to be made between groups of people being “statistical creations based on aggregated individual-level data, rather than ‘real world’ concrete groups” (Walter & Andersen, 2013, p.9).

It is not uncommon that dashboards miss these subtleties. We are only just coming to terms with the idea that data are not neutral objects, and that data dashboards are cultural artifacts. As such, they reflect the limitations of the normative values of the institutions that create them, of our health data collection systems, and of this very form of communication.

Overcoming these limitations means that we can improve the information environment of decision makers to target limited resources when and where they are needed most, while not reinforcing nor perpetuating inequality. Making things visible makes them actionable. Doing so also prevents the deep systemic societal problems that have led to such poor health outcomes for some during the pandemic. Below, we consider some ideas on how to re-think data and data dashboards by framing their creation with an intersectional design approach.

What is intersectionality?

Intersectionality is a term conceptualized by Black feminist legal scholar Kimberlé Crenshaw in 1989. The concept has earlier roots in Black feminism and builds on the work and struggles of Sojourner Truth, Ida B. Wells, Louise Thompson Patterson, and Audre Lorde (to name only a few). Intersectionality refers to the notion that different forms of oppression interact with and multiply each other, demonstrating their inseparability. Health scientists Chandra Ford and Collins Airhihenbuwa (2010) write:

“Intersectionality posits that social categories (e.g., race, gender) and the forms of social stratification that maintain them (e.g., racism, sexism) are interlocking, not discrete” (p. 1396).

These scholars and social justice leaders emphasize that rather than adding these co-occurring categories together in public health research, it is essential that the interactions between categories are also considered (Ford & Airhihenbuwa, 2010). As an example of intersectional public health, Ford and Airhihenbuwa (2010) describe the importance of considering race alongside gender and sexuality when approaching HIV risk behaviours because of the way “racism operates via gendered and sexualized proscriptions” (p. 1394).

How does intersectionality relate to the Tracing COVID-19 Data project?

Epidemiologist Greta Bauer (2014) describes how datasets about public health and marginalized populations tend to only examine a singular axis of oppression, even though intersectional data reporting can help reduce health inequalities.

One of the aims of the Tracing COVID-19 Data project is to identify these asymmetries in COVID-19 data reports, as well as rapidly and effectively mobilizing this knowledge and communicating our findings with decision makers. As demonstrated by the growing use of dashboards in reporting COVID-19 data, data visualization can be a useful communication tool. We thus ask:

How can we communicate COVID-19 data in a way that improves health outcomes for all? How can we use data visualization techniques to communicate intersectional issues effectively?

We aim to provide answers to questions such as these for those responsible for official COVID-19 reporting. Our hope is that our recommendations will lead to an intersectional approach to communicating COVID-19 impacts and health outcomes.

How have people tried to visualize intersectionality already?

Visual metaphors are useful to describe intersectionality. Crenshaw (1989) herself uses the metaphor of the traffic intersection, writing:

“Discrimination, like traffic through an intersection, may flow in one direction, and it may flow in another. If an accident happens in an intersection, it can be caused by cars traveling from any number of directions and, sometimes, from all of them. Similarly, if a Black woman is harmed because she is in the intersection, her injury could result from sex discrimination or race discrimination (p. 149).”

In another metaphor, provided by Black feminist scholar Patricia Hill Collins (1990) is the “matrix of domination”. Here, she uses a multi-level matrix to describe how oppression—and privilege—must be understood through an interlocking structural model (Hill Collins, 1990).

More recently, communications scholars Jenna Abetz and Julia Moore (2019) describe how visual metaphors for intersectionality often focus on centralizing difference and are often conceptualized as linear. They suggest the use of fractals—repeating and irregular geometric patterns—as a metaphor for the scalability and recursion of oppression (Abetz & Moore, 2019).

An example of a von Koch curve fractal from Abetz and Moore’s (2019) article, which they use as a metaphor to explore the scalability and recursion of oppression.

An example of a von Koch curve fractal from Abetz and Moore’s (2019) article, which they use as a metaphor to explore the scalability and recursion of oppression.

The issue with metaphors

While these visual metaphors are extremely useful to conceptualize intersectionality, Bauer (2014) points to the issue with their use in quantitative research. She writes:

“Interestingly, quantitative applications of intersectionality can be obfuscated by the predominance of mathematical-like language in intersectionality theory, though its use there is conceptual rather than strictly mathematical” (Bauer, 2014, p. 12).Georgia apostille. Florida apostille. Florida apostille.

While illustrations of traffic intersections, graphic matrices, and fractal patterns can be used to explain intersectionality, they do not easily map onto the visualization of quantitative data in a data dashboard and can actually obscure the meanings of these data. They may, however, be useful instruments to help model which data should be collected and rendered visually.

Feminist Data Visualization

To address this issue, data scientists Catherine D’Ignazio and Lauren Klein (2016) suggest applying feminist theory to data visualization. Rather than resign ourselves to the limitations of current dashboards, feminist data visualzation offers the possibility of “challeng[ing] the validity of a variety of binaristic and hierarchical configurations” (D’Ignazio & Klein, 2016, p. 1). This includes non-intersectional data analysis.

D’Ignazio and Klein (2016) thus suggest that data visualization begins with the way data are collected and organized—even before they are visualized.

This means starting with the evidence collected and finding the best way to get the stories they tell across. This could mean enlisting the help of metaphors; however, it is important to find the stories from the data first and then use metaphor to communicate them as effectively as possible.

To find and interpret these data stories, D’Ignazio and Klein (2016) offer six starting principles:

  1. Rethink binaries
  2. Embrace pluralism
  3. Examine Power and Aspire to Empowerment
  4. Consider Context
  5. Legitimize Embodiment and Affect
  6. Make Labor Visible

Finding and using intersectional data

Despite the lack of intersectional data in current COVID-19 dashboards, a number of organizations are already making efforts to find and use intersectional data. The Data Standards for the Identification and Monitoring of Systemic Racism, produced by the Ontario Anti-Racism Directorate, not only takes an intersectional approach to the collection of data and data characteristics, but uses it to identify and monitor systemic racism. These ideals align with the Research, Evaluation, Data Collection, and Ethics (REDE) Protocol for Black Populations in Canada Protocol (or the REDE4BlackLives Protocol for short), which also suggests that these data should be part of ongoing conversations in pre-existing communities.

This is not unlike the work of:

The cover of the First Nations Data Governance Strategy, produced by the FNIGC as a response to direction received from First Nations leadership.

The cover of the First Nations Data Governance Strategy, produced by the FNIGC as a response to direction received from First Nations leadership.

A graphic from the Global Indigenous Data Alliance (2019) encouraging the use of CARE principles to encourage Indigenous data sovereignty alongside Wilkinson et al’s (2016) FAIR open data principles.

A graphic from the Global Indigenous Data Alliance (2019) encouraging the use of CARE principles to encourage Indigenous data sovereignty alongside Wilkinson et al’s (2016) FAIR open data principles.

The Tracing COVID-19 Data project is working to bring together some of these datasets as they pertain to COVID-19. For example, with the help of Aidan Battley, we are looking into social models of disability and data such as the International Classification of Functioning, Disability and Health (ICF) by the World Health Organization (WHO). These models and data fall well into the intentions of this project, which is to encourage technological citizenship and a rights based approach to data during a crisis. Alongside collecting these data, visualization is key to reach our goals.

But how do we align these principles, protocols, standards, and practices? How might one model these in a population health data system? And how should these data be rendered visually? These are challenges worth pursuing, as lives are quite literally on the line.

What efforts might be useful to adapt for the Tracing COVID-19 Data project?

Following these principles, protocols, standards and practices, the Tracing COVID-19 Data project is critically thinking about data and data systems. We are currently collecting data from all over the country, including data on the way location, age, ability, race, Indigeneity, gender, and income may intersect with COVID-19 outcomes. From there, we aim to develop a series of rapid and archivable visualizations and blog posts to communicate our findings in ways best suited to both researchers and the communities they describe, as well as provide recommendations to decision makers on how to improve their data dashboards and other data visualizations techniques.

We are also mindful of becoming flexible and adaptable to new solutions (and issues) as they arise. The work of visualizing the intersectional impacts of COVID-19 is important, but loses its value if it becomes too brittle to be used effectively. We have already heard words of caution from racialized communities who rightfully fear becoming further stigmatized by being described through COVID-19 data. Listening to and working with the communities who are being multiply impacted by COVID-19 is critical to the success of this project. For this work to truly be intersectional, it is essential that we are all on board to listen and work together.

Slides from a presentation given on this topic (Sept 22, 2020)

References, Links, & Resources

Abetz, J., & Moore, J. (2018). Visualizing intersectionality through a fractal metaphor. In J. Dunn & J. Manning (Eds.), Transgressing Feminist Theory And Discourse (1st ed., pp. 31–43). Routledge. https://doi.org/10.4324/9781351209793-3

Bartlett, J., & Tkacz, N. (2017). Governance by Dashboard. https://core.ac.uk/download/pdf/80851285.pdf

Bauer, G. R. (2014). Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Social Science & Medicine, 110, 10–17. https://doi.org/10.1016/j.socscimed.2014.03.022

Centre for Disease Control. (2007). Interim Pre-pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States. 97. https://www.cdc.gov/flu/pandemic-resources/pdf/community_mitigation-sm.pdf

Columbia Law School. (2017, June 8). Kimberlé Crenshaw on Intersectionality, More than Two Decades Later. https://www.law.columbia.edu/news/archive/kimberle-crenshaw-intersectionality-more-two-decades-later

Crenshaw, K. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics. University of Chicago Legal Forum, 1989(1), 31. https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1052&context=uclf

D’Ignazio, C., & Klein, L. F. (2016). Feminist Data Visualization. 5. http://www.kanarinka.com/wp-content/uploads/2015/07/IEEE_Feminist_Data_Visualization.pdf

First Nations Information Governance Centre. (2020). The First Nations Information Governance Centre. https://fnigc.ca/index.php

Ford, C. L., & Airhihenbuwa, C. O. (2010). The public health critical race methodology: Praxis for antiracism research. Social Science & Medicine, 71(8), 1390–1398. https://doi.org/10.1016/j.socscimed.2010.07.030

Fundamentals of OCAP®. (2020). The First Nations Information Governance Centre. https://fnigc.ca/training/fundamentals-ocap.html

Gilyard, K. (2017). Louise Thompson Patterson: A Life of Struggle for Justice. Duke University Press. https://www.dukeupress.edu/louise-thompson-patterson

Government of Canada. (2020, July 8). Coronavirus disease (COVID-19): Outbreak update. https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

Government of Ontario. (2016, June 28). Anti-Racism Directorate. https://www.ontario.ca/page/anti-racism-directorate

Government of Ontario. (2019, August 27). Data Standards for the Identification and Monitoring of Systemic Racism. https://www.ontario.ca/document/data-standards-identification-and-monitoring-systemic-racism

Government of Ontario. (2020, July 8). COVID-19 (coronavirus) in Ontario. https://covid-19.ontario.ca/

Greenwood, F., Howarth, C., Poole, D. E., Raymond, N. A., & Scarnecchia, D. P. (2017). The Signal Code: A human rights approach to information during crisis. Harvard Humanitarian Initiative. https://hhi.harvard.edu/publications/signal-code-human-rights-approach-information-during-crisis#:~:text=The%20Signal%20Code%20is%20the,have%20to%20information%20during%20disasters.

Hill Collins, P. (1990). Black feminist thought: Knowledge, consciousness, and the politics of empowerment. Unwin Hyman. http://www.hartford-hwp.com/archives/45a/252.html

John Hopkins University. (2020, July 8). COVID-19 Map—Johns Hopkins Coronavirus Resource Center. https://coronavirus.jhu.edu/map.html

Lauriault, T. (2020, June 1). Tracing COVID-19 Data: Data and Technological Citizenship during the COVID-19 Pandemic. Datalibre. Retrieved 8 July 2020, from https://www.datalibre.ca/2020/06/01/tracing-covid-19-data-data-and-technological-citizenship-during-the-covid-19-pandemic/

Lorde, A. (1981). The Uses of Anger: Women Responding to Racism. BlackPast. https://www.blackpast.org/african-american-history/speeches-african-american-history/1981-audre-lorde-uses-anger-women-responding-racism/

National Underground Railroad Freedom Center. (n.d.). Ida B. Wells. Retrieved 8 July 2020, from https://freedomcenter.org/content/ida-b-wells

Ottawa Public Health. (2020, July 8). Daily COVID-19 Dashboard. https://www.ottawapublichealth.ca/en/reports-research-and-statistics/daily-covid19-dashboard.aspx

Podell, L. (n.d.). The Sojourner Truth Project. The Sojourner Truth Project. Retrieved 8 July 2020, from https://www.thesojournertruthproject.com

Research Data Alliance International Indigenous Data Sovereignty Interest Group. (2019). CARE Principles for Indigenous Data Governance. The Global Indigenous Data Alliance. https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab221ce848fb2/1571419335217/CARE+Principles_One+Pagers+FINAL_Oct_17_2019.pdf

The First Nations Information Governance Centre. (2020). A First Nations Data Governance Strategy. https://fnigc.inlibro.net/cgi-bin/koha/opac-retrieve-file.pl?id=9c677f3dcf8adbf18fcda96c6244c459

The Protocol: REDE4BlackLives. (n.d.). REDE4BlackLives. Retrieved 8 July 2020, from https://rede4blacklives.com/the-protocol/

Walter, M. (2013). Indigenous statistics: A quantitative research methodology. Left Coast Press. https://www.routledge.com/Indigenous-Statistics-A-Quantitative-Research-Methodology/Walter-Andersen/p/book/9781611322934

Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18

World Health Organization. (2018, March 2). International Classification of Functioning, Disability and Health (ICF). https://www.who.int/classifications/icf/en/

World Health Organization. (2020). https://www.who.int

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I was just awarded a small but not insignificant award as part of the Carleton University COVID-19 Rapid Response Research Grants. Below is a description of what I will be up to, along with some great students and expert advisors.  I will share everyone’s names later.  Results of the work will be published here as it becomes available!  Stay tuned. Also, let me know if you want to contribute in any way! Tracey dot Lauriault at Carleton dot CA

Research Summary

There is much official COVID-19 data reporting by federal, provincial, territorial and Indigenous Communities. As the pandemic evolves, and more information comes to light, there is a call to add data attributes about Indigenous, Black and Racialized groups and of the affected labour force, and to report where cases predominate. The pandemic also revealed that foundational datasets are missing, such as a national list of elder care homes, maps of local health regions and data about the digital divide. This project will embrace technological citizenship, adopt a critical data studies theoretical framework and a data humanitarian approach to rapidly assess data shortfalls, identify standards, and support the building of infrastructure. This involves training students, conducting rapid response research, developing a network of experts, learning by doing and a transdisciplinary team of peer reviewers to assess results. The knowledge will be mobilized in open access blog posts, infographics, policy briefs and scholarly publications.

Research challenge:

Official COVID-19 public heath reports by Federal, Provincial, and Territorial (F/P/T) and First Nation Communities are uneven and there are calls to improve them ( 1 CBC News, Toronto Star). Asymmetries can be attributed to dynamically evolving challenges associated with the pandemic, such as working while practicing social distancing; jurisdictional divisions of power in terms of health delivery; and responding to a humanitarian crisis, where resources are stretched and infrastructures are splintered (i.e. digital divide, nursing home conditions).

The Harvard Humanitarian Initiative (HHI) developed a rights-based approach to the management of data and technologies during crisis situations which includes the right to: be informed, protection, privacy and security, data agency and rectification and redress (2). These apply to contact tracing (3 ITWorld, Scassa) and to equity groups calling for demographic data (1). Other have conducted rapid response data reporting, for example after the Haiti Earthquake volunteers developed real-time crowdsourcing data collection systems to support humanitarian responders (4 Meier) and WeRobotics mobilizes local drone expertise to objectively assess proposed pandemic response technologies (5 WeRobotics).

This research will apply a critical data studies (CDS) theoretical framework (6 Kitchin & Lauriault), the principles of the HHI and, practice technological citizenship (7 Feenbert) to the study of the Canadian COVID-19 data response. Lauriault will leverage her expertise and Canadian and international network of open data, open government, civic technology experts in government, civil society, and Indigenous Communities (see CV) as seen in the policy briefs published on DataLibre.ca (8) to rapidly assess and support COVID-19 data management and reporting.

The objective is to carry out the following activities:

  1. Compare official COVID-19 public health data reports to identify gaps and best practices (9 Lauriault & Shields).
  2. Identify and support the building of framework datasets to standardize reporting (10 Lauriault).
  3. Analyze data standards and protocols to support data management, interoperability and cross-jurisdictional reporting (11 GeoConnections).
  4. Publish case-studies, resources, an archives of official reporting, and a glossary and
  5. Rapidly conduct expert analysis, peer review, knowledge mobilization and provide evidence-based recommendations to improve data reporting.

The rationale for this research is as follows:

  1. Official COVID-19 public health data are inconsistently reported, impeding comparability, and the ability to assess impact and target actions. Also, predictions missed seniors’ homes, precarious labour, and Indigenous communities and social determinants (12 Global News, NCCDH), resulting in an increase in cases and deaths. Currently job classifications and Indigenous, Black, and Racialized people classifications (13 CTV News) remain absent. This research will create a corpus of F/P/T and Indigenous Communities’ official reports, compare results, identify gaps.
  2. Framework data are standard information infrastructures upon which other analysis can consistently be done (14 Toronto Star). When this is lacking analysis is impeded, for example there is no national reporting by health region since no national framework dataset exists (15 Lauriault), and mitigating the digital divide is thwarted with a lack of broadband maps (16 Potter & Lauriault et al.). Other missing national datasets include senior care facilities, homeless shelters, precarious labour, and Indigenous Communities (17 Gaetz et al.). Needed framework datasets will be identified and if necessary coordinate their building (18 SPCOStatCan LODE), advocacy for the opening of public datasets such as corporate registries may be carried out (19 Fed. Registry,  Open Corporate, Open Contracting), and experts from public health , social planning, and Indigenous Communities will help identify localized frameworks.
  3. Consistent COVID-19 reporting requires an interoperable infrastructure which builds upon standards developed through consensus processes (20 CIHI, PHAC). Current uneven reporting may be attributed to a lack of standards adoption and formalization in terms of data flows. This research will develop a repository of standards and protocols and share these with decision-makers to improve interoperability (i.e. Data Standards for the Identification and Monitoring of Systemic Racism (21 ON Govt) and FNIGC OCAP Principles (22 FNIGC)).
  4. Rapidly mobilizing knowledge is important to improve reporting and manage data, and to build a crisis data reporting infrastructure for the future. This project will compile, and archive information, rapidly assess and peer review results with experts and report results on DataLibre.ca and other websites, will produce infographics and policy briefs, deliver online webinars, and help administrators and Indigenous Communities improve their data and technology policies.

A CDS framework recognizes that data have social and material shaping qualities and that they are never politically neutral while also being inseparable from the people and institutions who create them including practices, techniques, and infrastructures. This involves a team of data, technology, legal, social and health, and Indigenous experts to rapidly assess official COVID-19 data assemblages and to act as technological citizens by applying knowledge in real time and mobilize results to mitigate the data shortfalls witnessed during this crisis and support decision makers to respond with a data humanitarian and rights-based approach for now and to better respond in the future.

Expected Impact:

The target audience for this rapid response data and technology reporting is F/P/T public officials and Indigenous Community Leaders who manage public health, socio-economic, statistical and official record data flows; and civil society actors and the public involved in open data, open government and open contracting, transparency and accountability. This includes C-class executives, chief technology, information data, and digital officers.

The outcome of this research is to standardize and improve humanitarian crisis data management and data reporting in the short term to ensure consistent reporting, and in the long term establish standardized data workflows and operationalize data infrastructures for this pandemic in preparation for the next.

The timing to compile, inventory and build an open access archives of official data reporting is now as the fractures in the system have become apparent in real-time and have had negative consequences. It is important to monitor the response as it evolves so as to be able to improve it while our collective institutional memory is fresh and to have the evidence available as a reminder for if and when we forget, but also to build more robust systems.

The results of this research will be continuously reported and made openly accessible as it becomes available and will lead to the formation of a new research team.

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Study on Open Government: A view from local community and university based research

Watching this is a great New Years morning activity, and for Sep Kamvar I fell that data and statistics are the new black!  This is worth the 1 hour of your time!  dam, most online TV shows are 42 minutes and you learn way less…I should know 🙁

Merci Karl!

In between two big deliverables and a trip tomorrow with the gang of Montreal Ouvert to le  Salon du Logiciel Libre du Québec I thought I would go back a little to my other love beyond open data  – thinking about infrastructures.  The best place to tap into the pulse on the international scene of blogging, libraries, cyberdissidents, the Internet and human rights, free speech and infrastructure, is Ethan Zucherman‘s blog My Heart is in Accra.  This is where I read the best line I have I have come across in quite sometime:

Hosting your political movement on YouTube is a little like trying to hold a rally in a shopping mall. It looks like a public space, but it’s not – it’s a private space, and your use of it is governed by an agreement that works harder to protect YouTube’s fiscal viability than to protect your rights of free speech. Even if YouTube’s rulers take their function as a free speech platform seriously and work to ensure you’ve got rights to post content, they’re a benevolent despot, not a representative government. (Here I’m borrowing a formulation from Rebecca MacKinnon, who’s working on a book on this topic.) (Via blog post: Public Spaces, Private Infrastructure – Open Video Conference)

It reminded me once again why we need to think critically not just about the content on the Internet – Open Data –  in the case of this blog, but also reflecting on the infrastructure that deliver those data.  Open data is a practice and a philosophy, for some an ideology.  I, like many others get wrapped up in the practice and forget to look up, pause and think about the political economy, principles, and grounding practice in theory.

For example, during open access week where I was giving a talk on Open Data and Research, at Ottawa U I was surprised to hear that some were dissing the City on how it delivers content while proclaiming that Google is open data.  Well, Google ain’t open data!  They let you play with their content, and use it as a platform to showcase yours, but make no mistake, Google can decide to close the shop tomorrow and go fishing. Ergo your content goes with them.  For instance, the hydrographic community lobbied Google to remove the ice layer in Canada’s north and instead to show water features and coast lines.  Good for them, except that people live on the ice more than 60% of the year and it meant that their content – home, sled & skiddo routes – were now in the middle of the ocean.  Google listened to the formal scientific community while the Inuit were left stranded in the water so to speak.  Climate change does not help with that either.

Open data is also not just about apps – which makes me sound scrooge like on International Hackathon day btw – it is about the data that go into these, how these are delivered, not just formats & standards, but licenses, fibre optic cables, telephone towers, radio wave signals, phone and data plans etc.  It is also about the policies around access and who is at the table doing the asking, their demographics and why they are asking.  Most of the apps we will see coming out of today,  will be delivered on iPhones, some for iPads.  Few will have regular websites, most will be around the faster and better delivery of services and few will be about critically reflecting on who gets and does not get access to those same services.

For instance, the bus apps are great for the business commuter, not so great for seniors, refugees, those who survive on fixed incomes such as disability pensions or social assistance etc.  That group cannot afford an IPhone, can barely afford the cost of a bus pass and do not really care about predicting the exact second the bus will come, but, do care about bus fees, off peak hour transit times and whether or not their social housing project or suburban home is on a bus route that will take them to the grocery store in the dead of winter, school, work or library.  Also, they just hope their #2, or #14 will actually show up.  There will be no apps to show us where the buses do not go, where they should go, nor apps that will inform transit committees on how to better serve non commuters.

I have really liked the transparency apps, spending visualizations and those focusing on electoral accountability

  1. http://howdtheyvote.ca/votes.php?s=13
  2. http://vote.ca/#192-booth-st-ottawa-on-k1r-7j4
  3. http://citizenfactory.com/debates
  4. http://openparliament.ca/
  5. http://representme.ca/K1R7J4
  6. http://www.punditsguide.ca/
  7. http://www.voteforenvironment.ca/node/563
  8. http://gcrc.carleton.ca/cne/proof_of_concepts/elect2004/JavaVersion/feo_applet.html

I look forward to accessing demographic data, health data, environmental data, spending data, administrative data, research data, and seeing those rendered in ways where we have to rethink policy and redirect efforts (Atlas of the Risk of Homelessness or ecological footprint calculators and ideas like Random Hacks for Kindness).  I was also really happy to see Apps4ClimateAction and workshops like Mapping Environmental Issues in the City.  You need subject matter expertise, grounded theory, scientific models, great data and more time to develop, which makes them harder to produce, but well worth the challenge if they improve our lot just a bit more.  I think I will also need to lay low for a while and think more about theory and infrastructures surrounding open data and less about the shiny tinsel and more about the intersection of society and technology.


Abstract: Canada’s Information Commissioners have adopted a resolution toward Open Government and part of the open government process is open access to public administrative, census, map and research data.  A number of Canadian Cities,  innovative government programs such as GeoConnections, forward thinking research funding such as International Polar Year have become OpenData cities, implemented data sharing infrastructures and fund data sharing science.  Access to data are one part of the open government conversation, and it is argued that opendata bring us closer to more informed democratic deliberations on public policy.

Event: Open Access Week 2010, Carleton University, October 21, Noon to 1PM.

1. Event: Open Access Week 2010, Carleton University, October 21, Noon to 1PM.

2. Event: Open Access Week, Université d’Ottawa, Apps4Ottawa Showcase, October 21, 5-7PM.

  • Title: OpenData & Public Research
  • Abstract: Researchers use OpenData to inform their work, and are also producers of data and software that can be re-shared to the public.  In Canada, much of university research is supported by public funds and an argument can be made that the results of that research should be accessible to the public.  The research at the Geomatics and Cartographic Research Centre will be featured as will community based social policy research in Ottawa.  In Canada some data are accessible, but mostly data are not, and if they are, cost recovery policies and regressive licensing impede their use.  The talk will feature examples where data are open and where opportunities for evidence based decision making are restricted.

3. Event: Statistical Society of Ottawa 8th annual seminar – Our Statistics Community on Monday the 25th of October.

  • Title: The Real Census informs Neighbourhood Research in Canada
  • Abstract: Ms. Tracey P. Lauriault will discuss neighbourhood scale research using Census data.  She will introduce the The Cybercartographic Pilot Atlas of the Risk of Homelessness created at the Geomatics and Cartographic Research and will feature community based research used to inform public policy as part of the Canadian Social Data Strategy (CSDS).  She will feature maps and data about social issues in Canadian cities & metropolitan areas (e.g. Calgary, Toronto, Halton, Sault Ste. Marie, Ottawa, Montreal, & others) and will focus on the importance of local analysis and what the loss of the Long-Form Census could mean to evidence based decision making to communities in Canada’s.

The Canadian Government cuts the Long-Form Census,creates a survey that costs  $ 35 million for less reliable data and then cuts the agency back again by $7 million!

Canadian Press: Troubled StatsCan facing $7M in cuts

Hamilton Spectator:  StatsCan to cut more 5 more surveys

The Article includes the following surveys – I think I have the correct links but I am unsure!:

  1. The Industrial Pollutant Release Survey (I cannot find a link)
  2. The article says The Quarterly Energy Use  (Households and the Environment: Energy Use or Quarterly Industrial Consumption of Energy Survey which one?) and the  Greenhouse Gas Emissions Survey (Greenhouse Gas Emissions from Private Vehicles in Canada, 1990 to 2007 or Greenhouse Gas Emissions Report which one?) both pilot projects;
  3. The National Population Health Survey;
  4. The Survey of the Suppliers of Business Financing; and
  5. The Survey on Financing of Small and Medium-Sized Enterprises.

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