Fix #160 -- changed to British English

episodes/00-intro.md

@@ -1,5 +1,5 @@
 ---
-title: Using spreadsheet programs for data organization
+title: Using spreadsheet programs for data organisation
 teaching: 10
 exercises: 5
 authors:
@@ -35,11 +35,11 @@ contributors:
 
 ### Narrative Guidance
 
-- Introduce that we're teaching data organization, and that we're using
+- Introduce that we're teaching data organisation, and that we're using
   spreadsheets, because most people do data entry in spreadsheets or
   have data in spreadsheets.
-- Emphasize that we are teaching good practice in data organization and that
-  this is the foundation of their research practice. Without organized and clean
+- Emphasize that we are teaching good practice in data organisation and that
+  this is the foundation of their research practice. Without organised and clean
   data, it will be difficult for them to apply the things we're teaching in the
   rest of the workshop to their data.
 - Much of their lives as a researcher will be spent on this 'data wrangling' stage, but
@@ -55,7 +55,7 @@ contributors:
 
 :::::::::::::::::::::::::::::::::::::::::::::::::::
 
-Good **data organization** is the foundation of much of our day-to-day
+Good **data organisation** is the foundation of much of our day-to-day
 work in libraries. Most **librarians** have data or do data entry in
 spreadsheets. Spreadsheet programs are very **useful graphical
 interfaces** for designing data tables and handling very basic data
@@ -93,7 +93,7 @@ In this lesson, we will look at:
 
 **Much of your time when you're producing a report will be spent in
 this 'data wrangling' stage.** It's not the most fun, but it's
-necessary. We'll teach you how to think about data organization and
+necessary. We'll teach you how to think about data organisation and
 some practices for more effective data wrangling.
 
 ***

episodes/01-format-data.md

@@ -66,10 +66,10 @@ we have to set up our data for the computer to be able to understand it
 (and computers are very literal).
 
 This is why it's extremely important to set up well-formatted tables from the outset **before** you even start collecting data to analyse.
-**Data organization is the foundation of your data-related work.**
-Unorganized data can make it harder to work with your data,
-so you should be mindful of your data organization when doing your data entry.
-You'll want to organize your data in a way that allows other programs and people to easily understand and use the data.
+**Data organisation is the foundation of your data-related work.**
+Unorganised data can make it harder to work with your data,
+so you should be mindful of your data organisation when doing your data entry.
+You'll want to organise your data in a way that allows other programs and people to easily understand and use the data.
 
 :::::::::::::::::::::::::::::::::::::::::  callout
 
@@ -93,7 +93,7 @@ you **should:**
 
 - create a new file with your cleaned or analyzed data. Don’t modify the original dataset, or you will never know where you started!
 - keep track of the steps you took in your clean up or analysis. You should track these steps as you would any step in an experiment. We recommend that you do this in a plain text file stored in the same folder as the data file.
-- record the steps you followed in your data cleanup or analysis. Just as you would document key actions in a research project, it's important to keep a clear record of your workflow. We suggest saving this documentation in a plain text file within the same folder as your data file for easy access and organization.
+- record the steps you followed in your data cleanup or analysis. Just as you would document key actions in a research project, it's important to keep a clear record of your workflow. We suggest saving this documentation in a plain text file within the same folder as your data file for easy access and organisation.
 
 This might be an example of the setup as you work:
 

episodes/02-common-mistakes.md

@@ -69,7 +69,7 @@ up into a usable form. The example below depicts the problem:
 
 ## Multiple tabs {#tabs}
 
-But what about worksheet tabs? That seems like an easy way to organize data, right? Well, yes and no. When you create extra tabs, you fail to allow the computer to see connections in the data that are there (you have to introduce spreadsheet application-specific functions or scripting to ensure this connection). Say, for instance, you make a separate tab for each year.
+But what about worksheet tabs? That seems like an easy way to organise data, right? Well, yes and no. When you create extra tabs, you fail to allow the computer to see connections in the data that are there (you have to introduce spreadsheet application-specific functions or scripting to ensure this connection). Say, for instance, you make a separate tab for each year.
 
 This is bad practice for two reasons:
 **1\)** you are more likely to accidentally add inconsistencies to your data if each time you take a measurement, you start recording data in a new tab, and
@@ -144,7 +144,7 @@ Choices for representing null values, as illustrated in the table above, are pro
 
 **Example**: merging cells.
 
-**Solution**: If you're not careful, formatting a worksheet to be more aesthetically pleasing can compromise your computer's ability to see associations in the data. Merged cells are an absolute formatting NO-NO if you want to make your data readable by statistics software. Consider restructuring your data in such a way that you will not need to merge cells to organize your data.
+**Solution**: If you're not careful, formatting a worksheet to be more aesthetically pleasing can compromise your computer's ability to see associations in the data. Merged cells are an absolute formatting NO-NO if you want to make your data readable by statistics software. Consider restructuring your data in such a way that you will not need to merge cells to organise your data.
 
 ## Placing comments or units in cells {#units}