|Data management dimension||Lessons Learned.|
− 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 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 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 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.