Welcome
- Do not be shy
- Be nice
- Remember, you can do better research if you plan to share your outputs!
Introduction to Open Science
- Open Science increases transparency in research
- Publicly funded science should be publicly available
- While both You and the research community benefit from open practices, they are costs involved in making outputs open
Being FAIR
- FAIR stands for Findable Accessible Interoperable Reusable
- FAIR assures easy reuse of data underlying scientific findings
Intellectual Property, Licensing and Openness
- A license is a promise not to sue - therefore attach license files
- For data use Creative Commons Attribution (CC BY) license (CC0 is even more permissive)
- For code use open source licenses such as MIT, BSD, or Apache license
Introduction to metadata
- Metadata provides contextual information so that other people can understand the data.
- Metadata is key for data reuse and complying with FAIR guidelines.
- Metadata should be added incrementally through out the project
Being precise
- Public identifiers and ontologies are key to knowledge discovery
- Automatic data aggregations needs standardised metadata formats and values
(Meta)data in Excel
- Never use formatting to encode information
- Include only one piece of information in a cell
- It is easier to store data in the correct form than to clean data for reuse
Laboratory records
- Good record keeping ensures transparency and reproducibility.
- Record keeping is an integral part of data FAIRification.
- Record keeping is key to good data management practices.
- Having experimental details already in electronic form makes it easier to include them in READMEs or repository records
Working with files
- A good file name suggests the file content
- Good project organization saves you time
- Describe your files organization in PROJECT_STRUCTURE or README including naming convention
Reusable analysis
- Jupyter Notebooks are useful tools to share analysis with non-programmers
- One single document can visualise background, results, formulae/code and metadata
- One single document helps to make your work more understandable, repeatable and shareable
Version control
- Version control system helps maintaining good practices
- Version control system keeps your work safe
Templates for consistency
- Templates save time
- Templates enforce best practices and ensure consistency
- Templates allow for automatisation of processes
Public repositories
- Repositories are the main means for sharing research data.
- You should use data-type specific repository whenever possible.
- Repositories are the key players in data reuse.
It's all about planning
- Data within a project undergo a set of steps known as the research data life cycle.
- Planning can help make your data FAIR.
- Data management is a continuous process during a project.
- A DMP is the best way to prepare for a new project.
Putting it all together
- There are simple steps to help make your data more FAIR throughout the research data lifecycle
- Implement these steps throughout to keep track of your data and changes