Diverse research backgrounds, skills and operational practices make our institute versatile and nimble to address research problems that crosses several domains. But they also enabled research analytical practices to remain fragmented and inefficient.
The Urban Institute data science team recognized the significance of reproducibility in analytical community research practice on two distinct contexts. 1) Operational efficiency via streamlined use and reuse of data, analytical tools and assets 2) developing a culture of transparency and trust that underpins reproducible research whose products become fully replicable and auditable.
Objective(s) and Scope
The project aims to improve reproducibility of our research practices, by adopting best practices in data management, coding, and reporting.
To this end, we will implement a reproducibility workflow that incorporates version control, clear documentation, and open data and code sharing. We will also adopt reproducibility-enhancing tools, such as R and Python packages for data management, visualization, and testing, and will participate in reproducibility initiatives and training opportunities.
This is an umbrella project that is further broken down to the following projects that seeks to address specific aspects of the research life cycle.
By improving the reproducibility of our research practices, we expect to:
Increase the transparency and accountability of our research, making it easier for others to verify and reproduce our findings.
Streamline our research workflow, making it easier to manage, analyze, and visualize our data.
Enhance our collaboration with other researchers, both within and outside of our organization.
Improve the quality of our research, by reducing the risk of errors and inconsistencies in our data and methods.
Establish a culture of reproducibility in our organization, promoting best practices in data management, coding, and reporting.
- Start Date: 08/02/2020
- Status: Active