A note on reproducible GIS by economists: it's mostly absent. Here are some tips.
Reproducibility means that the inputs and methods can be repeated by a (somewhat) knowledgeable person. For GIS, that *might* (should) mean code, but it *definitely* means at least SOME instructions. Even if they are manual....
Maps are data. While your typical Stata/Matlab/R/Julia graph is data projected into Cartesian coordinates, maps are data projected into geographic coordinates. So at a minimum, we need to know what the inputs to the map are, same as we need to know inputs to graphs.
So "data + code" + "graph twoway scatter x y" -> 📈📉, and "data + code" + "maptile x, geo(state)" -> 🗺️. Or "data + code" + "instructions(ArcGIS)" -> 🗺️. Note that for a map, "data" includes shapefiles (including provenance of the shapefile)
So: checklist for reproducible maps:
✅ data (+ provenance)
✅ code (manipulates data)
✅ shapefile (provides coordinates)
✅ code (preferred) or instructions (sufficient)
A few tentative resources (please improve!): https://social-science-data-editors.github.io/guidance/guidance-reproducible-gis-analysis.html with some other links below
@SciPyTip GeoPandas https://geopandas.org/  and others
@ArcGISPro can also be scripted (python) and integrate notebooks https://pro.arcgis.com/en/pro-app/arcpy/get-started/pro-notebooks.htm
Staying #opensource: @qgis has "Graphical Modeler" https://docs.qgis.org/3.10/en/docs/user_manual/processing/modeler.html and look for tutorials on "Automating GIS Workflows" https://courses.spatialthoughts.com/ 
Summary: Please try to create scripted maps, but always describe what data you are mapping, and where you got the shapefiles from (note: copyrights might apply, permissions might need to be obtained!)
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