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Article written by: Amanda Hunter & Tracey P. Lauriault

The Organisation for Economic Co-operation and Development (OECD) recently published a policy response to COVID-19 in which they suggest that open science, and the policies & standards that support it, can accelerate the health, social, and economic responses to the virus as barriers to information access are eliminated.

As the first in a series of blog posts about Open Science (OS) and FAIR principles in Canada, here we highlight the key role open science plays in communicating and disseminating official COVID-19 research and public health data before assessing if official COVID-19 reporting in Canada adheres to OS principles.

In a next post, we will analyze official COVID-19 reporting in Canada to assess whether or not these follow Open Science, FAIR principles, and the Open Data Charter in the sharing of COVID-19 data.

What is Open Science?

The OECD Open Science program states that the benefits of open science is that it promotes a more accurate verification of scientific results, reduces duplication, increases productivity, and promotes trust in science.

https://www.oecd.org/science/inno/open-science.htm

Open science (OS) is a movement, a practice and a policy toward transparent, accessible, reliable, trusted and reproducible science. This is achieved by sharing how research and data collection are done so as to make research results accessible and standardized, created once and reused by many. This includes techniques, tools, technologies, and platforms should also be open source wherever possible.

In OS the outputs of the scientific process are considered to be a public good, thus wherever possible articles are published in open access (OA) journals, and research data are shared with the public and other scientists who may want to re-purpose those data in new work, or by people who want to verify the veracity of research results. Reporting COVID-19 Cases by normalizing an open by default approaches means that health scientists, population health experts and government officials make this part of their workflow (maintaining individual privacy of course), and by doing so decision makers beyond government, can scrutinize the results, leading to trust the results while also increasing data sharing.

What role does open science play in combating COVID-19?

In the early stages of the pandemic, knowing the genome provided crucial information to help scientists and researchers identify the origin of the outbreak, treat the infection, develop a diagnostic test and work on the vaccine. In other words, the easier—and quicker—researchers can produce, share and access scientific data, the quicker and the more informed is the collaborative response to the virus.

During the 2002-03 SARS outbreak it took five months to publish a full genome of the virus largely due to information blackouts and lack of data sharing. In contrast, the full genome of COVID-19 was published to an open-access platform nearly a month after the first patient was admitted to the hospital in Wuhan. This provided researchers around the world with a head start. Since OS policies have been operationalized during the pandemic, the resulting free flow of ideas in terms of biomedical research has accelerated (OECD).

The implementation of OS standards during COVID-19 has indeed been largely successful. OECD described how collaborative research and  thee global sharing of information reached unprecedented levels, for example:

  • In March 2020, 12 countries (including Canada) launched the Public Health Emergency COVID-19 Initiative at the level of Chief Science advisors, calling for open access to publications and machine-readable access to data related to COVID-19.
  • Open online platform Vivli offers an easy way to request anonymized data from clinical trials.
  • A COVID-19 Open Research Dataset [CORD-19] was developed that hosts 157,000 + scholarly articles about COVID-19 and related coronaviruses; 75,000 of which are full-text machine-readable data that can be used for AI and natural language processing.

These online, open-source platforms have supported rapid scientific COVID-19 research. OS, facilitated by standards, shared infrastructure and techniques, policies and licences, has been instrumental in the global fight against the pandemic.

Yet, despite the numerous successes, many challenges remain. For example, not all COVID-19 related health research and data adhere to the FAIR principles. FAIR principles are a standards approach which support the application of open science by making data Findable, Accessible, Interoperable, and Reusable. Failure to adhere to FAIR principles has led to an overall lack of communication and coordination during the pandemic. In Canada, data should also adhere to CARE principles, which address issues of Indigenous data governance with respect to Indigenous knowledge along with the OCAP Principles of the First Nation Information Governance Centre (FNIGC). More on this in the following section.

The reporting COVID-19 demographic data and reports in Canada to date falls short on standardized classifications in terms of demographics, as we discussed in an earlier blog post, which makes doing a comparative analysis difficult or impossible: for example, many countries define “recoveries” differently, and in Canada, since health is the jurisdictional responsibility of the provinces and territories, each report in their own way. Even though numerous official organizations publish COVID-19 and health related data, as open data databases or in open data portals, there remains an overall lack of interoperability, comparability and standards.

Where does Canada stand on Open Science?

Canada was implementing an open science framework before the pandemic as follows.

National Action Plan on Open Government

The Government of Canada recently published Canada’s 2018-2020 National Action Plan on Open Government, listing ten commitments to furthering the open government initiative. The plan asserts five commitments to implementing OS in Canada by the end of 2020, as seen below:

A screenshot showing a portion of Canada's 2018-2020 National Action Plan on  Open Government. The main issue addressed here is the difficulty for Canadians to access scientific research outputs: thus the commitments focus on making federal science, scientific data, and scientists themselves more accessible.

The OS portion of Canada’s 2018-2020 National Action Plan on Open Government. It aims to address the difficulty for Canadians to access scientific research: thus the commitments on making federal science, scientific data, and scientists themselves more accessible (Government of Canada, 2018).

The Action Plan addresses issues of accessibility and transparency of scientific research and outlines 5 commitments to amending these issues. These commitments include:

  1. Development of an OS roadmap,
  2. Providing an open access platform for publications,
  3. Raising awareness of federal scientists’ work,
  4. Promoting OS and soliciting feedback on stakeholder needs, and
  5. Measuring progress & benefits of the OS implementation.

Despite the comprehensiveness of the Roadmap (see below), Canada has not yet moved past the Action Plan’s second commitment—to provide a platform for Canadians to find and access open access (OA) publications from federal scientists—despite the projected March 2020 deadline. Also, at the time of writing, there is no federal open science platform or portal for users to access open science data in Canada even though there is an open data portal. The New Digital Research Infrastructure Organization (NDRIO) does show promise.

There are however some open data initiatives, such as the Federal Open Government and COVID-19 section on the Open Government Canada Portal.  Here Epidemiological and economic research data, with mathematical modeling reports, a map of cases and deaths by province, daily and weekly detailed epidemiological reports, and an ongoing dataset of COVID-19 cases, deaths, recoveries, and testing rates in Canada’s provinces and territories are made available. This is a significant improvement from the early days of reporting, as data journalist Kenyon Wallace discovered that on a daily basis, the Province of Ontario published new data but each time they did they overrode the previous day’s reports. His article and some work by Lauriault with the Ontario Open Government team resulted in changing that practice and raw data are now updated daily and reported. Open data is but one part of the OS process as we will see when we look at the FAIR principles.

Open Science Roadmap

The plan’s first commitment, to “develop a Canada Open Science Roadmap…” was completed and published in February 2020. The document provides ten recommendations made by Chief Science Advisor, Dr. Mona Nemer, to advance Canada’s OS initiatives. Like the policy brief by OECD, the roadmap is driven by the importance of trust among collaborators, inclusiveness of varying perspectives, and transparent processes throughout.

A screenshot of the cover of Canada’s Roadmap for Open Science (Government of Canada, 2020)

Canada’s Roadmap for Open Science (Government of Canada, 2020)

Most importantly, the Roadmap describes a commitment to developing an OS framework, including adopting the FAIR principles and “open by design and by default” specifications. The roadmap asserts Canada’s commitment to upholding these standards and policies via 10 recommendations:

10 recommendations made in the Roadmap for Open Science. Key points include the adoption of an OS framework in Canada, making federal scientific research outputs ‘open by default’, and implementing FAIR principles. (Government of Canada, 2020).

10 recommendations in the Roadmap for Open Science. Key points include the adoption of an OS framework, making federal scientific research outputs ‘open by default’, and implementing FAIR principles (Government of Canada, 2020).

Model Science Integrity Policy

Canada also has a Model Science Integrity Policy (MSIP) for the public service. The MSIP represents an internal commitment to integrity and accountability in science. Various mandates in the MSIP state that their purpose is to increase public trust in the credibility and reliability of government research and scientific activities, and ensure that research and scientific information are made available in keeping with the Government of Canada’s Directive on Open Government. The MSIP echoes Canada’s commitment to OS.

Indigenous Data Governance 

Finally, Canada has some commitment to supporting Indigenous rights to self-determination and data governance, but does not incorporate standards such as CARE principles which support OS  nor the OCAP Principles when it comes to Indigenous data governance. These extend the FAIR principles.

The Global Indigenous Data Alliance (GIDA) introduced the CARE principles to complement the FAIR principles in 2019. The CARE principles for Indigenous data governance were developed to address a lack of engagement between the open science movement and Indigenous rights and interests (GIDA, 2019).

The FAIR principles focus on data accessibility of data and sharing but fail to address power differences and the impact of colonialism experienced by Indigenous peoples and their right to exercise control and ownership of data about them and local and traditional knowledge. The CARE principles are crucial for the recognition and advancement of these rights as they encourage open science (and other ‘open’ movements) to “consider both people and purpose in their advocacy and pursuits” (GIDA, 2019). The CARE principles are contrasted with the FAIR principles in the below image from the GIDA website:

The CARE principles, which are “collective benefit, authority to control, responsibility, and ethics”, contrasted with the FAIR principles, which are “findable, accessible, interoperable, and reusable” (GIDA, 2019)

The CARE principles are “collective benefit, authority to control, responsibility, and ethics”, contrasted with the FAIR principles, which are “findable, accessible, interoperable, and reusable” (GIDA, 2019)

The OCAP Principles of Ownership, Control, Access and Possession are another set of important principles, that are a better fit in the Canadian Context.  Members of our project currently taking the Fundamentals of OCAP course and we hope to better incorporate these approaches in our work and in how we assess official reporting. Though Indigenous data governance and handling of Indigenous knowledge are not addressed in the Open Science Roadmap, the Data Strategy Roadmap for the Federal Public Service does demonstrate a federal approach to supporting Indigenous data strategies (see below):

Recommendation #8 from the Data Strategy Roadmap for the Federal Public Service which states Canada’s recognition of the Indigenous right to self-determination and data governance (Government of Canada, 2019)

Recommendation #8 from the Data Strategy Roadmap for the Federal Public Service which states Canada’s recognition of the Indigenous right to self-determination and data governance (Government of Canada, 2019)

Next Steps

Much progress has been made in terms of publishing, reporting and communicating data in the short time since COVID-19 began (though not without pressure from the media!). Open access to scientific research and public health reports have been helpful to facilitate the rapid response to the virus and keeping the public informed on how science informs governments actions. There is, however, much left to be done.

  1. Open Science should consider bias in data as well as invisibilities for example interdisciplinary work that helps paint the fuller picture of the impact of the virus. For example, interdisciplinary and intersectional approaches to data categories, including research based in critical race theory (CRT), Indigenous perspectives, socio-demographics and gig labour groups for example.
  2. Second, as suggested by the OECD, making COVID-19 data Findable, Accessible, Interoperable and Reusable is critical for a more effective rapid response. Lack of adherence to FAIR principles currently presents challenges to open science research.
  3. Finally, a meaningful Canadian OS framework should also incorporate standards for Indigenous Data Governance such as CARE Principles and OCAP Principles ensure respectful data practices are followed.

The Tracing COVID-19 Data team is in the process of developing a framework to assess official COVID-19 reporting in Canada to see if they comply with OS, FAIR, CARE, OCAP, and open-by-default at all levels of government. We will draw on Canada’s commitments OS and FAIR in – Canada’s 2018-2020 National Action Plan on Open Government, Open Science Roadmap, the Model Science Integrity Policy and the Open Data Charter.

Is Canada FAIR?

Stay tuned!

Recommendation

All official Federal, Provincial/Territorial and City public COVID-19 data reporting should be open data, open by design and by default, research should be published in open access (OA) Journals and should adhere to open science (OS) such as the FAIR principles , CARE Principles, OCAP Principles and the Open Data Charter.

References

Canadian Internet Policy and Public Interest Clinic. Open Data, Open Citizens? https://cippic.ca/en/open_governance/open_data_and_privacy

Centres for Disease Control and Prevention. (n.d.). SARS- Associated Coronavirus (SARS-CoV) Sequencing. https://www.cdc.gov/sars/lab/sequence.html

CTVNews. (2020). Project Pandemic: Reporting on COVID-19 in Canada. 
https://www.ctvnews.ca/health/coronavirus/project-pandemic

Federated Research Data Repository. (2018). FAIR Principles. 
https://www.frdr-dfdr.ca/docs/en/fair_principles/

Global Indigenous Data Alliance. (2019). CARE Principles for Indigenous Data Governance. 
https://www.gida-global.org/care

Government of Canada. (2014). Directive on open government. 
https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=28108

Government of Canada. (May, 2016). Open by default and modern, easy to use formats. 
https://open.canada.ca/en/content/open-default-and-modern-easy-use-formats

Government of Canada. (2017). Model policy on scientific integrity.
https://www.ic.gc.ca/eic/site/063.nsf/eng/h_97643.html

Government of Canada. (2018). Canada’s 2018-2020 National Action Plan on Open Government. https://open.canada.ca/en/content/canadas-2018-2020-national-action-plan-open-government#toc8

Government of Canada. (2018). Report to the Clerk of the Privy Council: A Data Strategy Roadmap for the Federal Public Service. https://www.canada.ca/content/dam/pco-bcp/documents/clk/Data_Strategy_Roadmap_ENG.pdf

Government of Canada. (2020). Coronavirus disease (COVID-19): Outbreak update.
https://www.canada.ca/en/public-health/services/diseases/coronavirus-disease-covid-19.html?utm_campaign=not-applicable&utm_medium=vanity-url&utm_source=canada-ca_coronavirus

Government of Canada. (2020). Office of the Chief Science Advisorhttps://www.ic.gc.ca/eic/site/063.nsf/eng/h_97646.html

Government of Canada. (2020). Open Government Portal.
https://open.canada.ca/data/en/dataset

Lauriault, T. (2020, April 17). Tracing COVID-19 Data: COVID-19 Demographic Reporting. Datalibre.
http://datalibre.ca/2020/04/17/covid-19-demographic-reporting/

National Centre for Biotechnology Information. (2020). Public Health Emergency COVID-19 Initiative.
https://www.ncbi.nlm.nih.gov/pmc/about/covid-19/?cmp=1

Open Data Charter. (n.d.). The International Open Data Charter.
https://opendatacharter.net

Organisation for Economic Co-operation and Development. (2020, May 12). OECD Policy Responses to Coronavirus (COVID-19): Why open science is critical to combatting COVID-19.
http://www.oecd.org/coronavirus/policy-responses/why-open-science-is-critical-to-combatting-covid-19-cd6ab2f9/

Ford & Airhihenbuwa. (2010). The public health critical race methodology: Praxis for antiracism research. Science Direct.
https://www.sciencedirect.com/science/article/abs/pii/S0277953610005800#!

Semantic Scholar. (2020). CORD-19: COVID-19 Open Research Dataset.
shorturl.at/wETZ5 

The Lancet. (January, 2020). Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding.
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30251-8/fulltext

The National Academies of Science, Engineering, Medicine. (2018). Open Science by Design: Realizing a Vision for 21st Century Research. Chapter 1, Front Matter. 
https://www.nap.edu/read/25116/chapter/1

The Star. (2020). Coronavirus & COVID-19 Data. https://www.thestar.com/coronavirus/data.html

Vivli. (2020).
https://vivli.org/about/overview-2/

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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).

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.

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

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