Commit 1b2add92 authored by Jamie Forth's avatar Jamie Forth

t10 intro

parent d03b2c05
......@@ -39,7 +39,23 @@ nottype=incollection, nottype=inproceedings, nottype=manual]
** Introduction
Use topic description text.
The final topic we are going to cover in this data visualisation
module is /scientific data visualisation/. This represents a departure
from previous topics that have focused primarily on /information data
visualisation/. The basic premise of both information and scientific
data visualisation is to produce knowledge from data utilising human
visual capabilities, but the data itself – its form, what it
represents, and how it is collected – are fundamentally
different. Scientific data is primarily engaged with understanding
aspects of the physical world, such as the structure and behaviour of
physical objects or entities. Data gathered from observations of the
physical world tend to require many measurements made over time or
through space, or both; for example, taking high precision
measurements of the tissue density of a human brain in
three-dimensional space as is routine in medical imaging. Data of this
form can get very large very quickly, and specialised processing and
visualisation techniques are necessary to render it accessible to
human cognition.
** Lesson 1 – Scientific visualisation
*** Video 1 – Scientific visualisation
......@@ -1372,7 +1388,7 @@ How do signal to VisPy to display the canvas on the screen?
canvas.draw()
#+end_src
*** TODO Code examples from the lectures
*** Code examples from the lectures
** Lesson 3 – Multidimensional datasets
*** Video 4 – Multidimensional datasets
:PROPERTIES:
......@@ -2374,7 +2390,7 @@ Which of the following is true?
\fullcite{Liao2004}
*** TODO Code examples from the lectures
*** Code examples from the lectures
** Topic summary
This final topic in data visualisation has introduced scientific data
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