This lesson aims to teach those just starting to undertake research how to manage their data and files.

• Masters/PhD/Postdoc researchers at the beginning of their projects.
• Basic digital skills required (e.g., file management, Excel, some version control exposure).
• No programming experience necessary.

• Basic Excel use (open/save tables)
• File/folder management on a computer
• A research project or dataset in progress

After completing this course, the learners should be able to:

• Define research data and distinguish between different data types.
• Structure research materials using clear file naming conventions and a logical folder hierarchy
• Describe methods of data collection that make data cleaner and easier to analyse
• Detect inconsistencies and errors in a tabular dataset ("dirty data")
• Use a set of basic techniques to remove/correct errors and inconsistencies in tabular data ("cleaning data")
• Use version control to track different versions of files, and switch between them.

• Victoria Yorke-Edwards (@vyorkeedwards)
• Kimberly Meechan (@K-Meech)
• Katie Buntic (@katiebuntic)

• Size:
• Types:
• Requires noise/messiness injection for teaching
• Licensing:

A fictional researcher, Alex, inherits disorganised MET data. Learners help clean and structure it.

• Unclear file naming (final_final_v3.csv)
• Scattered/misplaced files
• Dirty data: duplicates, missing values, format errors, inconsistent naming

1. What is Research Data?

• Data types
• Sources of data
• What is research data management (collection, storage, organisation, sharing, etc)?

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2. Structuring research materials

• Naming conventions
• Folder structures
• Version Control
• Introduction to version control software, Git/ Github

Objectives

After following this episode, learners will be able to:

• Organise their research data into a standard folder structure
• Name files with a consistent naming convention
• Understand why version control is important, and how to incorporate this into your naming conventions
• Explain why version control software such as Git/GitHub can be useful for certain types of data.

3. Tabular data collection

• Have a look at a 'dirty' data set
• Is there a standard set of responses?
• Is it free text?
• How do you control what data is being collected?
• Asking the right questions
• Data dictionaries

Objectives

After following this episode, learners will be able to:

• List variable types and formats
• Identify inconsistencies in data that can cause problems during analysis
• Describe methods that can be used during data collection and data entry that can prevent inconsistencies
• Write guidance for how to collect and enter data
• Create a data dictionary describing a dataset

4. How to clean a tabular dataset (using Excel)

• Finding inconsistencies
• Missing data
• Capitalisation
• Spelling mistakes
• Pros and cons of Excel

Objectives

After following this episode, learners will be able to:

• Describe what data cleaning is and why it is important
• Find and resolve inconsistencies within a tabular dataset programmatically (e.g datetime, numeric precision)
• Identify missing values within a tabular dataset using filters
• Correct spelling mistakes using spell check tools and find + replace
• Standardise text formats using spreadsheet functions
• Describe the pros and cons of using spreadsheets for data collection and cleaning
• [Note: update for using R?]

5. Introduction to R

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