In late 2017, Databrew partnered with the International Finance Coorperation - a member of the World Bank Group - to build dashboards for the Financial Institution Group's work in Sub-Saharan Africa.
Since the fall of 2017 we’ve been processing, analyzing, and visualizing Canadian Census and other survey data for Youth Research and Evaluation Exchange (YouthREX), an organization based out of York University’s School of Social Work. We have also built an interactive data dashboard that allows researchers and the public to explore, visualize, and download large amounts of data about youth wellbeing in a user friendly way.
In 2016-17, we helped researchers in rural Mozambique roll out and evaluate a trial testing the efficacy of a house fumigation program for the prevention of malaria in children. This collaboration, still in progress, has lead to advances in algorithmic data cleaning, and insights into malaria risk and surveillance.
In 2017, we carried out a 12-week training course for medical research professionals at the Centro de Investigação em Saude de Manhiça. Using the R statistical language, we taught data management, visualization, and analysis, so that our students - largely doctors - were equipped to glean insight from their data without having to waste funds on software and licensing fees.
Ben is involved in numerous projects through his work as a bioinformatician in genetics and genome biology at the Hospital for Sick Children (SickKids) in Toronto. Currently Ben is using germline methylation data to predict age of onset in patients with Li Fraumeni Syndrome, a rare hereditary cancer predisposition disorder. He is also currently using machine learning to predict tumor growth in patient derived xenograft models, testing the efficacy of lung cancer chemotherapy drugs.
In 2015, as part of the Data Science for Social Good Fellowship, Ben’s team helped the US Environmental Protection Agency (EPA) identify hazardous material facilities at risk of major violations. They implemented several machine learning methods and used historical data to predict the risk of serious violations, helping the EPA prioritize their inspections of hazardous material facilities around the United States.
In 2014, we helped the Florida Department of Health in Alachua County quantify the reduction in absenteeism associated with a mass influenza immunization program. The results of our analysis were used to help school leaders and decision-makers better understand the costs and benefits of in-school immunizations.
In 2014, as part of his Data Science for Social Good Fellowship, Joe worked with the Chicago Department of Public Health and University of Chicago to build a statistical model for predicting which Chicago residents were most at risk of lead poisoning.
We are active researchers in the academic world as well, with focuses on economics, epidemiology, biostatistics, and data science. Our peer-reviewed research ranges from the effectiveness of flu vaccination to tuberculosis trends, cancer diagnostics, and childhood obesity. For a full list of our publications, click here .