- Describe FAIR principles
- Apply FAIR principles throughout research project
- List good resources for FAIR data management information These will be achieved with written information as lesson notes + examples + exercies, practical parts
In 2014, members of the scientific community got together in a workshop called “Jointly Designing a Data Fairport” in Leiden, the Netherlands, to define the principles on how you could make your data more findable, accessible, interoperable, and reusable. These 15 principles are now called The FAIR Principles. The GO-FAIR organization has annoted the principles.
- Promote good data management practice
- More citations of your published research articles
- Greater discoverability and enhanced visibility
- Credit for your work, helping you to gain recognition.
- The absence of FAIR research data costs the European economy at least €10.2 billion annually. Biggest parameters that influence this are time spent on non-FAIR research (finding the right data) and cost of storage (redundancy) Source
- Students in PhD programmes spend up to 80% of their time on ‘data munging’, fixing formatting and minor mistakes to make data suitable for analysis — wasting time and talent. Source
- Not domain specific; you can apply them in any field
- FAIR principles are not rules or standards. These are guidelines, not mandatory principles.
- Data can be different levels of FAIR
- The principles emphasize machine-actionability because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
- FAIR ≠ Open.
- Open data is available without restriction. The ‘A’ in FAIR stands for ‘Accessible under well defined conditions’. There may be legitimate reasons to shield data and services generated with public funding from public access. These include personal privacy, national security, and competitiveness. The FAIR principles, although inspired by Open Science, explicitly and deliberately do not address moral and ethical issues pertaining to the openness of data. They do, however, require clarity and transparency around the conditions governing access and reuse. Source DOI: 10.3233/ISU-170824
The FAIR Principles are divided into four categories:
- Findable
- Accessible,
- Interoperable
- Reusable;
and each of these are divided up to specific principles about data and metadata.
The data is all the resources that have been used or produced during and at the end of the research project.
Examples: text, spreadsheets, images, 3D models, software, audio files, video files, reports, surveys, patient records, abstract ideas, measurements, statistics, raw biological sequence (DNA, RNA, amino acid), slides, workflows, algorithms, codes, databases.
The metadata is all of the descriptive information that helps to find/reuse/interpret the digital resource. “Data about data”
Examples: image files contain metadata about the date picture was taken, resolution, size, what equipment was used etc.
RELEVANT TOOLS AND RESOURCES: RDMkit - Best practices and guidelines to help you make your data FAIR (Findable, Accessible, Interoperable and Reusable)
There are 4 principles regarding findability: F1. (Meta)data are assigned a globally unique and persistent identifier
F2. Data are described with rich metadata
F3. Metadata clearly and explicitly include the identifier of the data they describe
F4. (Meta)data are registered or indexed in a searchable resource
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers.
PICTURE OF F COLORFUL HERE
Example
Example 1
Example 2
Example 2.2
Task 1
testing question is very long
Solution example
blahdiblah
Task 2
testing question is short
Solution example
blahdiblah
- FAIR data by SangyaPundir. CC BY-SA https://commons.wikimedia.org/wiki/File:FAIR_data_principles.jpg
