Minor notes on gress charge

doc/source/chapter02_beginner-tutorial/research-workflow.rst

@@ -14,7 +14,7 @@ Here's my typical research workflow for reference:
 3. Tweak model configurations as needed.
 4. Run simulations :ref:`with tmux <keep-running-label>`. Log out and go to sleep if the model runs for a long time.
 5. Use Python/Jupyter to analyze output data.
-6. After simulation and data analysis tasks are done, upload output data and customized model configuration (mostly run directories) to S3. Or download them to local machines if necessary (Recall that data egress charge is $90/TB).
+6. After simulation and data analysis tasks are done, upload output data and customized model configuration (mostly run directories) to S3. Or download them to local machines if necessary (Recall that :ref:`egress charge <data-egress-label>` is $90/TB; for several GBs the cost is negligible).
 7. Once important data safely live on S3 or on your local machine, shut down EC2 instances to stop paying for CPU charges.
 8. Go to write papers, attend meetings, do anything other than computing. During this time, no machines are running on the cloud, and the only cost is data storage on S3 ($23/TB/month). Consider `S3 - Infrequent Access <https://aws.amazon.com/blogs/aws/aws-storage-update-new-lower-cost-s3-storage-option-glacier-price-reduction/>`_ which costs half if the data will not be used for several months.
 9. Whenever need to continue computing, launch EC2 instances, pull stuff from S3, start coding again.