Thread by @Cghlewis

(1/4) When publicly sharing your data it's important to share documentation along with your data. Readmes, data dictionaries, and project documentation can ensure that future users understand both the contents and context of your data. Templates in comments below.
#edresearch

(2/4) It can be helpful to add Readmes to the top of your directories to provide information on what datasets are available in the directory and pertinent information about those datasets.
docs.google.com/document/d/1rb…

(3/4) A data dictionary is a rectangular formatted collection of names, definitions, and attributes about variables in a dataset. It helps users clearly see what variables exist in a dataset and how they should be interpreted.
docs.google.com/spreadsheets/d…

(4/4) Project documentation is a comprehensive document that describes the what, who, when, where, and how of your study. Some information in your project documentation may also overlap with metadata collected from your repository.
docs.google.com/document/d/1wO…

One last thought - When sharing multiple datasets in a data folder, it can also be helpful to include a Readme that provides high-level information about datasets and how they are related.
docs.google.com/document/d/1JW…