Improve CITATION.cff (#77)

* Improve CITATION.cff

See https://carpentries.github.io/sandpaper-docs/editing.html#creating-a-cff-for-a-lesson

* Resolve uninformative link text

* Performance of Python should be at the same level as premature optimisation.

Author: Robadob

CITATION.cff

@@ -3,8 +3,17 @@
 
 cff-version: 1.2.0
 title: Performance Profiling & Optimisation (Python)
-message: Please cite this course using these metadata.
-type: software
+message: >-
+  Please cite this lesson using the information in this file
+  when you refer to it in publications, and/or if you
+  re-use, adapt, or expand on the content in your own
+  training material. To cite the Workbench software itself,
+  please refer to the websites for the individual
+  components:
+  https://carpentries.github.io/sandpaper/authors.html#citation,
+  https://carpentries.github.io/pegboard/authors.html#citation,
+  https://carpentries.github.io/varnish/authors.html#citation
+type: dataset
 authors:
   - given-names: Robert
     family-names: Chisholm

episodes/optimisation-introduction.md

@@ -47,7 +47,7 @@ Profiling is a valuable tool for prioritising optimisations. Should effort be ex
 
 This doesn't mean you should ignore performance when initially writing code. Choosing the right algorithms and data structures, as we will discuss in this course, is good practice. However, there's no need to obsess over micro-optimising every tiny component of your code—focus on the bigger picture.
 
-### Performance of Python
+## Performance of Python
 
 If you've read about different programming languages, you may have heard that there’s a difference between "interpreted" languages (like Python) and "compiled" languages (like C). You may have heard that Python is slow *because* it is an interpreted language.
 To understand where this comes from (and how to get around it), let's talk a little bit about how Python works.

learners/ppp.md

@@ -14,4 +14,4 @@ The 25 minute talk, which introduces many of the Python patterns from the Optimi
 
 The talk has an accompanying Jupyter notebook, allowing people to benchmark the patterns on their own hardware.
 
-The notebook can be downloaded [here](https://drive.google.com/file/d/1YTEXJlDaaZBAGo-Aodukuw0ArVJakdBB/view?usp=sharing). 
+[Download the notebook here!](https://drive.google.com/file/d/1YTEXJlDaaZBAGo-Aodukuw0ArVJakdBB/view?usp=sharing).