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FAIR in (biological) practice: Glossary

Key Points

  • 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