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Due to their strong regenerative capabilities, freshwater planarians are a wellsuited model system for studying the effects of chemicals on stem cell biology and regeneration. After amputation, a planarian will regenerate the missing body parts within 1 to 2 weeks. Because planarians have a distinct head morphology that can be easily identified, head and eye regeneration has been a popular qualitative measure of toxicity. However, qualitative measures can only detect strong defects. Here, we present protocols for quantifying the rate of blastema growth to measure regeneration defects for assessment of chemical toxicity. Following amputation, a regenerative blastema forms at the wound site. Over the course of several days, the blastema grows and subsequently reforms the missing anatomical structures. This growth can be measured by imaging the regenerating planarian. As the blastema tissue is unpigmented, it can be easily distinguished from the remaining pigmented body using standard image analysis techniques. Basic Protocol 1 provides a step-by-step guide for imaging regenerating planarians over several days of regeneration. Basic Protocol 2 describes the necessary steps for the quantification of blastema size using freeware. It is accompanied by video tutorials to facilitate adaptation. Basic Protocol 3 shows how to calculate the growth rate using linear curve fitting in a spreadsheet. The ease of implementation and low cost make this procedure suitable for an undergraduate laboratory teaching setting, in addition to typical research settings. Although we focus on head regeneration in Dugesia japonica, these protocols are adaptable to other wound sites and planarian species.

One year after identifying the first case of the 2019 coronavirus disease (COVID-19) in Canada, federal and provincial governments are still struggling to manage the pandemic. Provincial governments across Canada have experimented with widely varying policies in order to limit the burden of COVID-19. However, to date, the effectiveness of these policies has been difficult to ascertain. This is partly due to the lack of a publicly available, high-quality dataset on COVID-19 interventions and outcomes for Canada. The present paper provides a dataset containing important, Canadian-specific data that is known to affect COVID-19 outcomes, including sociodemographic, climatic, mobility and health system related information for all 10 Canadian provinces and their health regions. This dataset also includes longitudinal data on the daily number of COVID-19 cases, deaths, and the constantly changing intervention policies that have been implemented by each province in an attempt to control the pandemic.

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