Lecture 2 - Reproducible & FAIR data science
ANSCI 4940 - Spring 2025
Reproducible & FAIR data science
What are the FAIR principles
FAIR data is data which meets the FAIR principles of findability, accessibility, interoperability, and reusability (FAIR).[1][2] The acronym and principles were defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations.[1]
Get to know the principles (1)
Get familiar with FAIR by looking at this video.
Read the intial publication on The FAIR Guiding Principles for scientific data management and stewardschip.
Explore the vision of Cornell University on FAIR principles and their cheatsheets.
Get to know the principles (2)
Check for reproducible advise in the tutorials at the bovi-analytics website.
Discuss with the entire team and Dr. Miel Hostens (contact him on his desk) on how this will reflect on your team project.
Findable
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
Findable
F1. (Meta)data are assigned a globally unique and persistent identifier
F2. Data are described with rich metadata (defined by R1 below)
F3. Metadata clearly and explicitly include the identifier of the data they describe
F4. (Meta)data are registered or indexed in a searchable resource
Accessible
Once the user finds the required data, they need to know how they can be accessed, possibly including authentication and authorisation.
Accessible
A1. (Meta)data are retrievable by their identifier using a standardised communications protocol
A1.1 The protocol is open, free, and universally implementable
A1.2 The protocol allows for an authentication and authorisation procedure, where necessary
A2. Metadata are accessible, even when the data are no longer available
Interoperable
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
Interoperable
I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2. (Meta)data use vocabularies that follow FAIR principles
I3. (Meta)data include qualified references to other (meta)data
Reusable
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
Reusable
R1. (Meta)data are richly described with a plurality of accurate and relevant attributes
R1.1. (Meta)data are released with a clear and accessible data usage license
R1.2. (Meta)data are associated with detailed provenance
R1.3. (Meta)data meet domain-relevant community standards
