Reproducible and FAIR data science

Week 6 - ANSCI 4940 - Spring 2026

Miel Hostens

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 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

Naming convention

Why do we need this

  • Dairy data science is multimodal, longitudinal, and collaborative
  • Naming is not cosmetic — it is infrastructure
  • Most downstream issues are caused by:
    • Ambiguity
    • Inconsistency
    • Silent semantic drift

40 Minutes, 4 Takeaways

  1. Naming conventions are scientific decisions
  2. Bad names silently break analyis and models
  3. Conventions must scale across projects & people
  4. Governance matters more than documentation

The Dairy Data Reality

  • Multiple farms
  • Multiple vendors
  • Multiple countries
  • Multiple disciplines:
    • Nutrition
    • Health
    • Behavior
    • Production

→ Names are the only shared language

What Is a Naming Convention?

A set of agreed‑upon rules for creating unique, informative, and stable names for data objects.

Applies to:

  • Variables

  • Tables

  • Files

  • Visuals

  • Ontology terms

Naming ≠ Style Preference

Bad naming causes:

  • Wrong joins

  • Broken pipelines

  • Invalid within/between farm comparisons

These errors do not throw exceptions.

Common Dairy Data Failures

  • DIM, days_in_milk, DaysInMilk
  • milk_yield, MilkYieldKg, MY
  • other, Other, OTHER
  • lactation_stage vs lactation_stages

Each creates a new semantic entity

Names Encode Meaning

A good name answers:

  • What?

  • For whom?

  • In what unit?

  • At what level?

  • Over what time window?

Core Design Principles

  1. Consistency over cleverness
  2. Explicit over implicit
  3. Stable over convenient
  4. Readable by humans and machines

Scope of a Naming Convention

You must define rules for:

  • ✅ Variables
  • ✅ Tables
  • ✅ Files
  • ✅ Features
  • ✅ Categories
  • ✅ Ontology terms

Partial conventions always fail.

Case Sensitivity Matters

Choose one and enforce it.

Recommended: - CamelCase for data schemas - lower_snake_case for code variables

Never mix within the same layer.

Variable Naming Pattern

It’s now the perfect time to decide on a convention used for the project