Reusing Terms — Search and Integrate Existing Vocabularies

Last updated on 2025-11-11 | Edit this page

Overview

Questions

  • Are the terms I need already defined somewhere else?
  • How can I responsibly reuse existing terms and URIs?
  • What are the benefits of aligning early rather than reinventing?

Objectives

  • Learn how to discover and evaluate existing vocabularies relevant to your domain (e.g., Darwin Core, WoRMS, OBO ontologies).
  • Understand how to reuse URIs and integrate external definitions into your own data dictionary.
  • Practice linking your data elements to authoritative terms where appropriate.

Introduction

Every dataset — whether from your lab, your agency, or another research group — uses terms to describe its contents. Column headers, variable names, and codes all hold meaning, but often those meanings are assumed rather than shared.

When everyone invents their own terms for the same concept (e.g., SmoltCond, ConditionFactor, CF), it becomes difficult to integrate or compare data across projects.

Reusing existing terms — with clear definitions and persistent identifiers (URIs) — makes your data:

  • Easier to share and integrate

  • More interoperable and transparent

  • Aligned with others in your community

  • Future-proof for modeling and ontology building

This session helps you learn where to find existing vocabularies, how to decide what to reuse, and how to incorporate those terms into your own data dictionary.

Callout

🧩 Core Ideas

Term reuse means adopting existing, well-defined concepts instead of inventing new ones.

Each reused term has a URI (Uniform Resource Identifier) that makes it globally recognizable.

Reusing does not mean losing your local context — you can still describe how your project uses a term, while referencing a shared definition.

This is a key first step in making your data “semantic” — meaning it can be understood by both humans and machines.

Discussion

Challenge 1: Find and reuse (30 min)

Goal: Identify existing vocabulary terms that match your own dataset.

Steps:

  1. Select 3–5 column names from your dataset.

  2. Search for equivalent terms in one or more repositories.

  3. Record matches in the Data Dictionary Template:

  • Your local term
  • External URI
  • Source vocabulary name
  • Notes on whether it’s an exact or close match

Updated data dictionary with at least three reused terms and their URIs.

Learners understand how to find, evaluate, and record external vocabularies.

Key Points
  • Controlled vocabularies capture shared meaning of terms.
  • Reusing existing URIs improves interoperability and credibility.
  • Reuse saves time, avoids duplication, and makes future integration easier.