File naming conventions

A file naming convention is a framework for naming your files that describes what they contain and how they relate to other files. You should establish a convention and naming practice that aligns with your discipline's standard before you begin collecting data files.
File names should:

• be machine-readable, human-readable, and work well with the default ordering
• consistently named
• have short but descriptive filenames (<25 characters)
• use underscores in place of spaces, dots or slashes
• Use date format ISO 8601: YYYYMMDD
• include a version number if applicable.

Folder structure and naming

Whether you are storing your data in cloud storage, local drives, or network locations, a well-planned, logical, and consistent folder and file structure will help to make the data findable and reusable in the future. Before you start, check for established procedures within your team or department. One best practice is to structure folders hierarchically and be consistent with names. Use names that describe the file's contents, selecting 3-4 key pieces of information such as project number, team name, facility ID, date or version number.

Tidy data principles

Tidy data is a standard way to organise data, making it easier to analyse and visualise. It follows three core principles:

• Each variable forms a column - ensuring that all values for a specific attribute are grouped together.
• Each observation forms a row - making it easier to track and analyse individual data points.
• Each value has its own cell - avoiding the issues associated with merged cells or multiple values in a single cell.

File versioning

To ensure clarity and consistency when managing files, follow these best practices for file versioning:

• Keep a copy of the raw data in its original format.
• Make a working copy and save new versions.
• Include a version number at the end of the file name such as v01. Change this version number each time the file is saved.
• For the final version, substitute the word FINAL for the version number. This is especially important if files are being shared.
• Turn on file versioning in OneDrive.

Using external digital resources for data analysis

As per the Digital Services Implementation Policy, all acquisitions of digital services (free, purchased or downloaded) for research projects are required to complete a Risk and Compliance Determination (RCD) (login to IT portal required) form prior to its installation and / access. This allows the IT team to evaluate IT risks and compliance issues associated with the software and develop recommendations on how any identified risks can be addressed.
This includes the use of Artificial Intelligence tools for data collection and analysis.

Excel for data analysis

These resources are available to learn data analysis with Excel.

• Microsoft Analyse data in Excel - This resource introduces the Analyze Data functionality that is available to Microsoft Office 365 users.
• Excel for researchers training - This one-day course is conducted regularly by WSU partner Intersect. The course will teach you to use Microsoft Excel to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. Check the Research Events calendar for upcoming sessions.
• LinkedIn Learning Excel data analysis - This self-guided learning course provides instruction on using the data analysis and visualisation tools built into Excel.

Training sessions and workshops

Western Sydney University conducts regular research training sessions through training partner Intersect including:

• Data manipulation and visualisation using R
• Python
• Excel for data analysis
• Qualtrics survey tool
• REDCap
• Text analysis with NVivo

Intersect Online Training: Introduction to Research Data Management at Western Sydney - This two-hour workshop is ideal for researchers who want to know how to create a research data management plan. Check the Research Events calendar for upcoming sessions.
Register for an upcoming event via the Research calendar.

Self-guided learning