docs: add blurb about converters to the readme

README.md

@@ -24,6 +24,32 @@ and someone will be able to help out.
 
 The environment image allows you to work in Python or R in JupyterLab or RStudio/Shiny.
 
+### Working with the data
+
+A summary of the datasets available in this project is in the table below. In
+order to work more efficiently with the data, we have implemented a set of
+"converters" to standardize the various datasets to a subset of useful fields.
+Each converter is aware of the details of each dataset and produces a view of
+the dataset that is homogenized with the others. In this way, we are able to
+visualize with simple commands data of very different origins using very simple
+procedures.
+
+For example, to work with the JHU-CSSE country-level data as well as the more
+detailed dataset on Spain:
+
+```python
+from covid_19_utils.converters import CaseConverter
+
+converter = CaseConverter('./data/atlas')
+jhu_df = converter.read_convert('./data/covid-19_jhu-csse')
+spain_df = converter.read_convert('./data/covid-19-spain')
+```
+
+The resulting DataFrames have exactly the same structure so they can be used
+interchangably in any analysis or plotting code. See the [standardization
+notebook]('notebooks/process/standardize_datasets.ipynb') for a more complete
+example.
+
 ### Updating your branch or fork
 
 The data in the main master branch of this project is updated daily - how can