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Table 2 Lessons learned by the GRAPH network on data management

From: Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response

Data management dimension Lessons Learned.
Data Collection − Quality control at the data cleaning step is primordial to preventing downstream problems.
− Quantitative evaluation and constructive feedback to data providers improves data quality.
Data Storage − Data files should systematically be archived, and file names standardized, searchable, and dated.
− Analysts need to be trained to foresee data infrastructure issues, such as how to avoid problems with large data file formats.
− Clear separation between original data, cleaned data and analytical results is key for reproducibility.
Data Use − Data utilisation approaches must involve and be agreed to by the data provider
− Studies must be designed with clear reasoning for how it serves the community from which the data originate.
Results Dissemination − Dissemination of data and/or results of analyses requires large levels of mutual trust and meaningful collaboration between the Network and the data providers.
Data Analysis − Data analysis and interpretation must be tailored to the context of each country or region’s specific situation.
− Source code versioning and review are key tools for the development of correct and well documented code.