Metadata and Documentation

Metadata describes the data, while documentation provides additional information about how the data were created, collected, processed, and organised.

Types of Metadata

Descriptive metadata — describes what the dataset is about, such as title, creators, keywords, abstract, and subject.

Example:

Title: Introduction to Digital Libraries
Author: John Smith
Description: An introduction to digital libraries, metadata, and digital preservation.
Subject: Digital Libraries; Metadata; Digital Preservation
Keywords: digital libraries, metadata, preservation
Publication Date: 2026
Language: English
ISBN: 978-1-23456-789-0

Structural metadata — describes how the data are organised and how different files or datasets are related.

Example:

<?xml version="1.0" encoding="UTF-8"?>

<book>
    <title>Introduction to Digital Libraries</title>

    <structure>
        <chapter order="1">
            <title>Chapter 1: Introduction</title>
            <file>chapter1.pdf</file>
        </chapter>

        <chapter order="2">
            <title>Chapter 2: Metadata</title>
            <file>chapter2.pdf</file>
        </chapter>

        <chapter order="3">
            <title>Chapter 3: Digital Preservation</title>
            <file>chapter3.pdf</file>
        </chapter>
    </structure>
</book>

The structural metadata tells us that the book has three chapters, their order, and which file belongs to each chapter.

Administrative metadata — includes information about access, rights, licences, ownership, and restrictions.

For a digital book, administrative metadata could look like this:

File Name: introduction_to_digital_libraries.pdf
File Format: PDF
File Size: 5 MB
Created Date: 2026-08-17
Creator: John Smith
Copyright: © 2026 John Smith
Access Rights: Open Access
License: Creative Commons Attribution 4.0

Metadata Standards

Using established metadata standards improves consistency, interoperability, and the discoverability of research data.

Dublin Core is a widely used metadata standard with 15 basic elements for describing digital resources. These include information such as the title, creator, date, subject, description, format, language, publisher, and rights. It is standardized as ISO 15836, ANSI/NISO Z39.85, and IETF RFC 5013.

The DataCite Metadata Schema  is a metadata standard designed specifically for datasets and other research outputs.

DDI (Data Documentation Initiative), commonly used for social science and survey data.

Darwin Core ,used for biodiversity and ecological data.

README File

A README file provides an overview of a dataset or research project and helps users understand its contents and how to use the accompanying files.

A README may include:

• title and description of the dataset;
• information about the creators and contributors;
• purpose and context of the research;
• description of the files and folders;
• data collection methods;
• software or tools required;
• important processing or analysis information;
• information about missing or restricted data;
• licence and conditions of reuse;
• citation information;
• contact details;
• version and date of the documentation.

Example of a README file:

File Name- 00_README_EYESAL_Exp1.txt 

It is part of the replication data for “The role of perceptual salience in L2 morphology acquisition: Attention, awareness, and intake.”

The dataset is available through DataverseNO, Version 1, and has a persistent DOI that makes it easy to identify and access:

https://doi.org/10.18710/GBMBYF/XBCPXR

Reference: Cipitria, Saioa; Knell, Georgia; Michel, Marije; Loerts, Hanneke; De Cuypere, Ludovic; Struys, Esli; Housen, Alex, 2026, "00_README_EYESAL_Exp1.txt", Replication data for: The role of perceptual salience in L2 morphology acquisition: Attention, awareness, and intake, https://doi.org/10.18710/GBMBYF/XBCPXR, DataverseNO, V1

Tools such as the USN README File Generator (URGE) can make creating a README file faster by automating some of the writing process.

Data Dictionary

A data dictionary provides detailed information about the variables or fields contained in a dataset. It is particularly useful for tabular, survey, statistical, and database data.

A data dictionary could look like this:

Electronic Laboratory Notebook (ELN)

An Electronic Laboratory Notebook (ELN) is a digital system used to document research activities, experiments, observations, protocols, results, and related information.

Example:

Experiment: Plant growth under different light conditions
Date: 2026-05-14
Researcher: A. Novák

Objective:
Compare plant growth under three light conditions.

Protocol:
Plants were grown for 14 days under conditions A, B and C.

Observations:
Condition A – average height: 12.4 cm
Condition B – average height: 15.1 cm
Condition C – average height: 10.8 cm

Data:
plant_growth_2026-05-14.csv

Notes:
Two plants in condition C showed signs of damage.

Electronic Laboratory Notebook (ELN) Tools:

eLabFTW

LabArchives

Benchling