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Unit 5.1 Activity: Create study metadata activity

Unit overview

Unit study time

  • 45 minutes

Intended Learning Outcomes

By the end of the unit, you will ...

  • Identify the key elements of study-level metadata
  • Create clear, structured study-level metadata
  • Apply good metadata practices to support discovery and reuse

How to approach metadata creation

How do you decide what metadata to create?

What metadata you choose to create depends on the aims of your research and what you want to do with it. If your data is only for personal use, your metadata might only need to capture key details that will be useful for your current and future work. However, if you’re planning to share your research more widely and/or deposit it in a data repository or catalogue, you’ll likely need to create more metadata to help others understand and use your data. A data repository or catalogue, may also require you to follow a metadata schema to ensure your metadata is interoperable with the other studies on the site.

Creating metadata for a study

In this task you will create clear, useful study-level metadata that supports discovery and reuse.

You are preparing metadata for a study that will be added to your university repository. Others will rely on your metadata to understand what the study is about and whether it is relevant to their work.

Use the information below to create study metadata:

Connection to greenspaces in capital cities across the UK' conducted by Joe Smith and Mary Jones collects data on residents of capital cities across the UK and their feelings toward greenspaces. Face-to-face interviews were conducted in September 2022 - June 2023 (London, Cardiff, Edinburgh, Belfast). The project consists of 4 datasets, one for each city. The interviews used Greenspace Connection Survey v2.0. Green Brick Org provided logistical support for the interviews. The dataset is published by Green City Data in 08/09/25. The dataset is stored in CSV and conducted in English. The data is open access with CC BY-NC rights.

Note this is a fictional case study example for practice purposes only.

Your metadata should allow a researcher to:

  • discover the study
  • understand its purpose and design
  • decide whether it is relevant to their research

A strong metadata record will:

  • Be clear and unambiguous
  • Include enough detail to support understanding and reuse
  • Use consistent terminology
  • Support decisions about whether the data are suitable
  • Avoid assumptions about prior knowledge

Use clear, concise language and avoid ambiguity. Keep in mind this is not like writing an abstract for a journal article. For example avoid vague terms (e.g. “examines”, “looks at”) without specifying what is actually measured.

Task 1: Create study metadata

Using the information above, complete the following:

Title

Write a clear and specific title

Description

Write 2–3 sentences that explain:

  • What the study is about
  • What it measures
  • Why it was conducted

Population

Describe the population included in the study

Time period

State when the data were collected

Data collection method

Describe how the data were collected

Geographic coverage

State where the data were collected

Access and licence

State how the data can be accessed and under what conditions

Task 2 Review your metadata

  • Is anything unclear, vague, or open to interpretation?
  • Is the title specific and informative?
  • Does the description clearly explain what is measured and why?
  • Are the population, time period, and coverage clearly defined?

Make one or two improvements.

Example answers

Title

Resident attitudes on greenspaces in UK Capital Cities (2022–2023)

Description

This study consists of four datasets collected as part of 'Connection to greenspaces in capital cities across the UK' project. The study examined how residents of London, Cardiff, Edinburgh and Belfast perceive and interact with urban greenspaces. It measured attitudes, reported behaviours, and feelings towards greenspace use using structured face-to-face interviews. The study was conducted to better understand public engagement with urban environments and to inform urban planning and policy development.

Population

Residents of capital cities in the UK (London, Cardiff, Edinburgh, and Belfast) 2022-2023.

Time period

September 2022 – June 2023

Data collection method

Face-to-face interviews using the Greenspace Connection Survey v2.0.

Geographic coverage

London (GB-LND), Cardiff (GB-CRF), Edinburgh (GB-EDH), Belfast (GB-BFS)

Access and licence

Open access under a Creative Commons Attribution-NonCommercial (CC BY-NC) licence.

Task 3 Reflection

  • What information did you find difficult to describe, and why?
  • What important information is still missing that would improve this metadata further?
Example answers

There is no correct answer. The goal is to recognise which aspects of metadata require more interpretation or judgement. You may have thought the following was difficult...

  • deciding what information was most important to include e.g. study description
  • avoiding vague wording e.g. clearly explaining what the study measures
  • being specific when details were missing e.g. age range or inclusion criteria
  • writing in a way that was clear but concise
  • deciding whether to repeat information or not e.g. geographic coverage

You may have identified some of the following information as missing:

  • Sample size (e.g. how many participants were interviewed)
  • Language
  • Sampling method (e.g. random, convenience, recruited how)
  • More detail on variables
  • Access details beyond licence (e.g. where the data are stored, URL or DOI)
  • Creator affiliations (e.g. institution of the researchers)
  • File format

Summary

Creating effective metadata requires more than simply describing a study. It involves selecting the right information, expressing it clearly, and anticipating what others will need to find, understand, and reuse the data. Clear, well-structured and detailed metadata reduces uncertainty, supports informed decision making, and makes research processes easier and more efficient. By focusing on clarity, completeness, and the user’s perspective, you can create metadata that enables others to confidently discover and reuse your data.