[custom] fix lesson contents

episodes/02-common-mistakes.md

@@ -28,10 +28,10 @@ There are a few potential errors to be on the lookout for in your own data as we
 - [Not filling in zeros](#zeros)
 - [Using problematic null values](#null)
 - [Using formatting to convey information](#formatting)
-- [Using formatting to make the data sheet look pretty](#formatting_pretty)
+- [Using formatting to make the data sheet look pretty](#formatting-pretty)
 - [Placing comments or units in cells](#units)
 - [Entering more than one piece of information in a cell](#info)
-- [Using problematic field names](#field_name)
+- [Using problematic field names](#field-name)
 - [Using special characters in data](#special)
 - [Inclusion of metadata in data table](#metadata)
 - [Date formatting](03-dates-as-data.md)
@@ -190,7 +190,7 @@ Blanks (most applications) and NA (for R) are good choices. White et al, 2013, e
 
 ![](fig/good_formatting.png){alt='good formatting'}
 
-## Using formatting to make the data sheet look pretty {#formatting\_pretty}
+## Using formatting to make the data sheet look pretty {#formatting-pretty}
 
 **Example**: merging cells.
 
@@ -216,7 +216,7 @@ another field and specify the units the cell is in.
 If you need both these measurements, design your data sheet to include this information. For example, include one column for number of
 individuals and a separate column for sex.
 
-## Using problematic field names {#field\_name}
+## Using problematic field names {#field-name}
 
 Choose descriptive field names, but be careful not to include spaces, numbers, or special characters of any kind. Spaces can be
 misinterpreted by parsers that use whitespace as delimiters and some programs don't like field names that are text strings that start

learners/reference.md

@@ -4,50 +4,49 @@ title: 'Glossary'
 
 ## Glossary
 
-{:auto\_ids}  
-cleaned data  
+[cleaned data  ]{#cleaned-data}
 :   data that has been manipulated post-collection to remove errors or inaccuracies, introduce desired formatting changes, or otherwise prepare the data for analysis
 
-conditional formatting  
+[conditional formatting  ]{#conditional-formatting}
 :   formatting that is applied to a specific cell or range of cells depending on a set of criteria
 
-CSV (comma separated values) format  
+[CSV (comma separated values) format  ]{#csv-comma-separated-values-format}
 :   a plain text file format in which values are separated by commas
 
-factor  
+[factor  ]{#factor}
 :   a variable that takes on a limited number of possible values (i.e. categorical data)
 
-metadata  
+[metadata  ]{#metadata}
 :   data which describes other data
 
-null value  
+[null value  ]{#null-value}
 :   a value used to record observations missing from a dataset
 
-observation  
+[observation  ]{#observation}
 :   a single measurement or record of the object being recorded (e.g. the weight of a particular mouse)
 
-plain text
+[plain text]{#plain-text}
 :   unformatted text
 
-quality assurance  
+[quality assurance  ]{#quality-assurance}
 :   any process which checks data for validity during entry
 
-quality control  
+[quality control  ]{#quality-control}
 :   any process which removes problematic data from a dataset
 
-raw data  
+[raw data  ]{#raw-data}
 :   data that has not been manipulated and represents actual recorded values
 
-rich text  
+[rich text  ]{#rich-text}
 :  formatted text (e.g. text that appears bolded, colored or italicized)
 
-string  
+[string  ]{#string}
 :   a collection of characters (e.g. "thisisastring")
 
-TSV (tab separated values) format  
+[TSV (tab separated values) format  ]{#tsv-tab-separated-values-format}
 :   a plain text file format in which values are separated by tabs
 
-variable  
+[variable  ]{#variable}
 :   a category of data being collected on the object being recorded (e.g. a mouse's weight)