Technical lead developing a global Platform (hosted on the UN Global Data Platform) for sharing standards for official statistics on the health impacts of climate change (£5m Wellcome Trust-funded project). Responsible for building the backend of the platform (Flask, CSS/HTML and Javascript), modularising scripts written in R and Python following RAP principles and converting them into open-source packages with an API endpoint accessible from the web platform.
Develop an open-source package in R containing globally generalisable statistical models on the impact of climate indicators on human health. Models developed by topic global experts.
Developed templates for code and documentation for topic leads (at ONS and international partner organisations) developing models of the impact of different climate indicators to feed into the open-source package. Lead and train topic leads on the adoption of RAP principles and use of collaboration/version controls tools such as Git/GitHub.
Created and manage the organisational GitHub workspace for the project. Onboarded all project members into respective teams and developed a kanban board using GitHub Projects to track issues, monitor progress and enhance collaboration. Trained and oversee the adoption of best practices such as protected (main) branch, use of pre-commit hooks via lint-staged, unit testing all functions, created easy-to-use pull request templates and ensured code is reviewed by two team members.
Contributed to an open-source package in Python that collects gridded climate data from open-sources such as Corpenicus (ERA5) and OpenAQ, and provides them in easily accessible dataframe-formats for augmenting health datasets in regions without adequate weather monitoring stations.
Regular stakeholder engagement: bi-weekly meeting with partners from Ghana and Rwanda; monthly expert advisory group (EAG) meeting to update on progress and learn from experts; monthly topic expert group (TEG) meetings with leading researchers from academic partner institutions to dissect proposed statistical models and review the underlying science; weekly catchups with UN technical team to review latest code/package updates prior to deployment on the UNGP.
