Introduction
- Computing is essential in science and (almost) all data are digital
- A set of good enough practices can make you more efficient
- Future you will thank past you for adopting good practices
- Shared Principles: planning, modular organisation, names, documentation
Data Management
- Raw data is the data as originally generated – it should be kept read-only
- Raw data has to be backed up in more than one location
- Create the data you wished you have received
- Keeping track of your actions is a key part of data management
- The Digital object identifiers (DOIs) is a unique identifier that permanently identifies data and makes it findable
- Finding a repository tailored to your data is key to making it findable and accessible by the broader community
Code and Software
- Any code that runs on your research data is research software
- Write your code to be read by other people, including future you
- Decompose your code into modules: scripts and functions, with meaningful names
- Be explicit about requirements and dependencies such as input files, arguments and expected behaviour
Collaboration
- Create an overview of your project
- Create a shared “to-do” list
- Decide on communication strategies
- Make the license explicit
- Make the project citable
Project Organization
- A good file name suggests the file content
- Good project organization saves you time
Keeping Track of Changes
- Small, frequent changes are easier to track
- Tracking change systematically with checklists is helpful
- Version control systems help adhere to good practices
Manuscripts
- Have all authors agree on a workflow before the writing starts
- Email-based workflows work better with informative filenames and clear co-ordination
- Text-based documents with version control scale better, if co-authors are familiar with the tools
- Single Master Online approaches can be an effective compromise
What To Do Next
- Learning good practices is a long-term process
- Different people make different contributions to good practice