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Research Summary – Structure, Examples and Writing Guide

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Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

• Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
• Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
• Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
• Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
• Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
• References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

• Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
• Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
• Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
• Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
• Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
• Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
• Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
• Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
• Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

• Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
• Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
• Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

• Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
• Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
• Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
• Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

• Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
• Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
• Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
• Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
• Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
• Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
• Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
• Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

• Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
• Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
• Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
• Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
• Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

• Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
• Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
• Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
• Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
• Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
• Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

• Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
• Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
• Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
• Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
• Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
• Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

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Researcher, Academic Writer, Web developer

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Writing a Summary – Explanation & Examples

Published by Alvin Nicolas at October 17th, 2023 , Revised On October 17, 2023

In a world bombarded with vast amounts of information, condensing and presenting data in a digestible format becomes invaluable. Enter summaries. 

A summary is a brief and concise account of the main points of a larger body of work. It distils complex ideas, narratives, or data into a version that is quicker to read and easier to understand yet still retains the essence of the original content.

Importance of Summaries

The importance of summarising extends far beyond just making reading more manageable. In academic settings, summaries aid students in understanding and retaining complex materials, from textbook chapters to research articles. They also serve as tools to showcase one’s grasp of the subject in essays and reports. 

In professional arenas, summaries are pivotal in business reports, executive briefings, and even emails where key points need to be conveyed quickly to decision-makers. Meanwhile, summarising skills come into play in our personal lives when we relay news stories to friends, recap a movie plot, or even scroll through condensed news or app notifications on our smartphones.

Why Do We Write Summaries?

In our modern information age, the sheer volume of content available can be overwhelming. From detailed research papers to comprehensive news articles, the quest for knowledge is often met with lengthy and complex resources. This is where the power of a well-crafted summary comes into play. But what drives us to create or seek out summaries? Let’s discuss.

Makes Important Things Easy to Remember

At the heart of summarisation is the goal to understand. A well-written summary aids in digesting complex material. By distilling larger works into their core points, we reinforce the primary messages, making them easier to remember. This is especially crucial for students who need to retain knowledge for exams or professionals prepping for a meeting based on a lengthy report.

Simplification of Complex Topics

Not everyone is an expert in every field. Often, topics come laden with jargon, intricate details, and nuanced arguments. Summaries act as a bridge, translating this complexity into accessible and straightforward content. This is especially beneficial for individuals new to a topic or those who need just the highlights without the intricacies.

Aid in Researching and Understanding Diverse Sources

Researchers, writers, and academics often wade through many sources when working on a project. This involves finding sources of different types, such as primary or secondary sources , and then understanding their content. Sifting through each source in its entirety can be time-consuming. Summaries offer a streamlined way to understand each source’s main arguments or findings, making synthesising information from diverse materials more efficient.

Condensing Information for Presentation or Sharing

In professional settings, there is often a need to present findings, updates, or recommendations to stakeholders. An executive might not have the time to go through a 50-page report, but they would certainly appreciate a concise summary highlighting the key points. Similarly, in our personal lives, we often summarise movie plots, book stories, or news events when sharing with friends or family.

Characteristics of a Good Summary

Crafting an effective summary is an art. It’s more than just shortening a piece of content; it is about capturing the essence of the original work in a manner that is both accessible and true to its intent. Let’s explore the primary characteristics that distinguish a good summary from a mediocre one:

Conciseness

At the core of a summary is the concept of brevity. But being concise doesn’t mean leaving out vital information. A good summary will:

• Eliminate superfluous details or repetitive points.
• Focus on the primary arguments, events, or findings.
• Use succinct language without compromising the message.

Objectivity

Summarising is not about infusing personal opinions or interpretations. A quality summary will:

• Stick to the facts as presented in the original content.
• Avoid introducing personal biases or perspectives.
• Represent the original author’s intent faithfully.

A summary is meant to simplify and make content accessible. This is only possible if the summary itself is easy to understand. Ensuring clarity involves:

• Avoiding jargon or technical terms unless they are essential to the content. If they are used, they should be clearly defined.
• Structuring sentences in a straightforward manner.
• Making sure ideas are presented in a way that even someone unfamiliar with the topic can grasp the primary points.

A jumble of ideas, no matter how concise, will not make for a good summary. Coherence ensures that there’s a logical flow to the summarised content. A coherent summary will:

• Maintain a logical sequence, often following the structure of the original content.
• Use transition words or phrases to connect ideas and ensure smooth progression.
• Group related ideas together to provide structure and avoid confusion.

Steps of Writing a Summary

The process of creating a compelling summary is not merely about cutting down content. It involves understanding, discerning, and crafting. Here is a step-by-step guide to writing a summary that encapsulates the essence of the original work:

Reading Actively

Engage deeply with the content to ensure a thorough understanding.

• Read the entire document or work first to grasp its overall intent and structure.
• On the second read, underline or highlight the standout points or pivotal moments.
• Make brief notes in the margins or on a separate sheet, capturing the core ideas in your own words.

Identifying the Main Idea

Determine the backbone of the content, around which all other details revolve.

• Ask yourself: “What is the primary message or theme the author wants to convey?”
• This can often be found in the title, introduction, or conclusion of a piece.
• Frame the main idea in a clear and concise statement to guide your summary.

List Key Supporting Points

Understand the pillars that uphold the main idea, providing evidence or depth to the primary message.

• Refer back to the points you underlined or highlighted during your active reading.
• Note major arguments, evidence, or examples that the author uses to back up the main idea.
• Prioritise these points based on their significance to the main idea.

Draft the Summary

Convert your understanding into a condensed, coherent version of the original.

• Start with a statement of the main idea.
• Follow with the key supporting points, maintaining logical order.
• Avoid including trivial details or examples unless they’re crucial to the primary message.
• Use your own words, ensuring you are not plagiarising the original content.

Fine-tune your draft to ensure clarity, accuracy, and brevity.

• Read your draft aloud to check for flow and coherence.
• Ensure that your summary remains objective, avoiding any personal interpretations or biases.
• Check the length. See if any non-essential details can be removed without sacrificing understanding if it is too lengthy.
• Ensure clarity by ensuring the language is straightforward, and the main ideas are easily grasped.

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Dos and Don’ts of Summarising Key Points

Summarising, while seemingly straightforward, comes with its nuances. Properly condensing content demands a balance between brevity and fidelity to the original work. To aid in crafting exemplary summaries, here is a guide on the essential dos and don’ts:

Use your Own Words

This ensures that you have truly understood the content and are not merely parroting it. It also prevents issues of plagiarism.

Tip: After reading the original content, take a moment to reflect on it. Then, without looking at the source, write down the main points in your own words.

Attribute Sources Properly

Giving credit is both ethical and provides context to readers, helping them trace back to the original work if needed. How to cite sources correctly is a skill every writer should master.

Tip: Use signal phrases like “According to [Author/Source]…” or “As [Author/Source] points out…” to seamlessly incorporate attributions.

Ensure Accuracy of the Summarised Content

A summary should be a reliable reflection of the original content. Distorting or misrepresenting the original ideas compromises the integrity of the summary.

Tip: After drafting your summary, cross-check with the original content to ensure all key points are represented accurately and ensure you are referencing credible sources .

Avoid Copy-Pasting Chunks of Original Content

This not only raises plagiarism concerns but also shows a lack of genuine engagement with the material.

Tip: If a particular phrase or sentence from the original is pivotal and cannot be reworded without losing its essence, use block quotes , quotation marks, and attribute the source.

Do not Inject your Personal Opinion

A summary should be an objective reflection of the source material. Introducing personal biases or interpretations can mislead readers.

Tip: Stick to the facts and arguments presented in the original content. If you find yourself writing “I think” or “In my opinion,” reevaluate the sentence.

Do not Omit Crucial Information

While a summary is meant to be concise, it shouldn’t be at the expense of vital details that are essential to understanding the original content’s core message.

Tip: Prioritise information. Always include the main idea and its primary supports. If you are unsure whether a detail is crucial, consider its impact on the overall message.

Examples of Summaries

Here are a few examples that will help you get a clearer view of how to write a summary. 

Example 1: Summary of a News Article

Original Article: The article reports on the recent discovery of a rare species of frog in the Amazon rainforest. The frog, named the “Emerald Whisperer” due to its unique green hue and the soft chirping sounds it makes, was found by a team of researchers from the University of Texas. The discovery is significant as it offers insights into the biodiversity of the region, and the Emerald Whisperer might also play a pivotal role in understanding the ecosystem balance.

Summary: Researchers from the University of Texas have discovered a unique frog, termed the “Emerald Whisperer,” in the Amazon rainforest. This finding sheds light on the region’s biodiversity and underscores the importance of the frog in ecological studies.

Example 2: Summary of a Research Paper

Original Paper: In a study titled “The Impact of Urbanisation on Bee Populations,” researchers conducted a year-long observation on bee colonies in three urban areas and three rural areas. Using specific metrics like colony health, bee productivity, and population size, the study found that urban environments saw a 30% decline in bee populations compared to rural settings. The research attributes this decline to factors like pollution, reduced green spaces, and increased temperatures in urban areas.

Summary: A study analysing the effects of urbanisation on bee colonies found a significant 30% decrease in bee populations in urban settings compared to rural areas. The decline is linked to urban factors such as pollution, diminished greenery, and elevated temperatures.

Example 3: Summary of a Novel

Original Story: In the novel “Winds of Fate,” protagonist Clara is trapped in a timeless city where memories dictate reality. Throughout her journey, she encounters characters from her past, present, and imagined future. Battling her own perceptions and a menacing shadow figure, Clara seeks an elusive gateway to return to her real world. In the climax, she confronts the shadow, which turns out to be her own fear, and upon overcoming it, she finds her way back, realising that reality is subjective.

Summary: “Winds of Fate” follows Clara’s adventures in a surreal city shaped by memories. Confronting figures from various phases of her life and battling a symbolic shadow of her own fear, Clara eventually discovers that reality’s perception is malleable and subjective.

Frequently Asked Questions

How long is a summary.

A summary condenses a larger piece of content, capturing its main points and essence.  It is usually one-fourth of the original content.

What is a summary?

A summary is a concise representation of a larger text or content, highlighting its main ideas and points. It distils complex information into a shorter form, allowing readers to quickly grasp the essence of the original material without delving into extensive details. Summaries prioritise clarity, brevity, and accuracy.

When should I write a summary?

Write a summary when you need to condense lengthy content for easier comprehension and recall. It’s useful in academic settings, professional reports, presentations, and research to highlight key points. Summaries aid in comparing multiple sources, preparing for discussions, and sharing essential details of extensive materials efficiently with others.

How can I summarise a source without plagiarising?

To summarise without plagiarising: Read the source thoroughly, understand its main ideas, and then write the summary in your own words. Avoid copying phrases verbatim. Attribute the source properly. Use paraphrasing techniques and cross-check your summary against the original to ensure distinctiveness while retaining accuracy. Always prioritise understanding over direct replication.

What is the difference between a summary and an abstract?

A summary condenses a text, capturing its main points from various content types like books, articles, or movies. An abstract, typically found in research papers and scientific articles, provides a brief overview of the study’s purpose, methodology, results, and conclusions. Both offer concise versions, but abstracts are more structured and specific.

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Academic sources, also known as scholarly sources or academic references, are materials used by researchers, scholars, and students to support their academic work. These sources are specifically created for use in academic contexts and contribute to the body of knowledge in a particular field of study.

From academic research to personal blogs, the bedrock of trust and credibility is often established by one simple act: source citing. Whether we are constructing a thesis for a graduate program or debunking a myth on a personal blog, providing the origins of our information bolsters our arguments and pays homage to the original creators of that knowledge.

In academia, research, journalism, and writing, the skill of quoting sources is fundamental. Accurate and proper quoting adds credibility to your work and demonstrates respect for the original authors and their ideas.

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• How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on 25 September 2022 by Shona McCombes . Revised on 12 May 2023.

Summarising , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

• Read the text
• Break it down into sections
• Identify the key points in each section
• Write the summary
• Check the summary against the article

Writing a summary does not involve critiquing or analysing the source. You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

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Table of contents

When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, frequently asked questions.

There are many situations in which you might have to summarise an article or other source:

• As a stand-alone assignment to show you’ve understood the material
• To keep notes that will help you remember what you’ve read
• To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyse or critique it.

In any case, the goal of summarising is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

• Scan the article quickly to get a sense of its topic and overall shape.
• Read the article carefully, highlighting important points and taking notes as you read.
• Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

• Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
• Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
• Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organised into clearly marked sections, usually including an introduction, methods, results, and discussion.

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarise this article , which scientifically investigates the old saying ‘an apple a day keeps the doctor away’.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or research paper, you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

• You’ve accurately represented the author’s work
• You haven’t missed any essential information
• The phrasing is not too similar to any sentences in the original.

If you’re summarising many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

A summary is a short overview of the main points of an article or other source, written entirely in your own words.

Save yourself some time with the free summariser.

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarising, and on the purpose of the summary.

With the summariser tool you can easily adjust the length of your summary.

You might have to write a summary of a source:

• As a stand-alone assignment to prove you understand the material
• For your own use, to keep notes on your reading
• To provide an overview of other researchers’ work in a literature review
• In a paper , to summarise or introduce a relevant study

To avoid plagiarism when summarising an article or other source, follow these two rules:

• Write the summary entirely in your own words by   paraphrasing the author’s ideas.
• Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarise the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarise a source in a paper , in a literature review , or as a standalone assignment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, May 12). How to Write a Summary | Guide & Examples. Scribbr. Retrieved 6 May 2024, from https://www.scribbr.co.uk/working-sources/how-to-write-a-summary/

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How to write a summary of a research paper (with template)

by Diana Ribeiro Last updated Jul 20, 2020 | Published on Jun 27, 2020 Writing Skills 0 comments

In our daily work as medical writers, we have to read many scholarly articles and extract the main information from them. Having a process to retrieve that information and create a short summary that you can easily access will save you precious time. That’s why I decided to guide you through my process of summarising a research article and created a handy template.

Having short summaries of academic papers is useful to create news articles, press releases, social media posts, blog articles, or curated news reports, like the one I write weekly for my newsletter subscribers .

What’s the importance of summarising research articles?

If you don’t have a system to extract the main information from a scholarly paper, you may have to re-read it repeatedly, looking for that piece of information you know it’s there. Sure, you can use a highlighter pen to mark the main points, but sometimes what happens is that you end up with yellow walls of text. Or green. Or even a rainbow. Which may be pretty, but it’s quite useless as a retrieval system.

What also happens when you highlight text is that you end up with a diverse array of writing styles, none of them being your own. This way, when you try to write a text with information from multiple sources, you have to search for the information and write it in a consistent style.

In this article, I’ll show you how to retrieve the most relevant information from a scientific paper, how to write it in a compelling way, and how to present it in a news-worthy style that’s easily adaptable to your audience. Ready?

Three steps to summarise a research paper

1. scan and extract the main points.

First things first, so you have to read the paper. But that doesn’t mean you have to read it from start to finish. Start by scanning the article for its main points.

Here’s the essential information to extract from the research paper you have in front of you:

• Authors, year, doi
• Study question: look in the introduction for a phrase like “the aim of this study was”
• Hypothesis tested
• Study methods: design, participants, materials, procedure, what was manipulated (independent variables), what was measured (dependent variables), how data were analysed.
• Findings: from the results section; fill this before you look at the discussion section, if possible. Write bullet points.
• Interpretation: how did the authors interpreted their findings? Use short sentences, in your own words.

After extracting the key information , revisit the article and read it more attentively, to see if you missed something. Add some notes to your summary, but take care to avoid plagiarism. Write notes in your own words. If you can’t do that at this moment, use quotation marks to indicate that your note came straight from the study. You can rewrite it later, when you have a better grasp of the study.

2. Use a journalistic approach for the first draft

Some sources advise you to keep the same structure as the scientific article, but I like to use the journalistic approach of news articles and flush out the more relevant information first, followed by the details. This is more enticing for readers, making them want to continue reading. Yes, I know that your reader may be just you, but I know I have lost myself in some of the things I’ve written, so…keep it interesting, even for a future self 😊.

This is the main information you have to put together:

Title of the article: I like to keep the original article title for the summary, because it’s easier to refer back to the original article if I need to. Sometimes I add a second title, just for me, if the article title is too obscure or long.

• 1 st paragraph: Answer the 5 W’s in 3-4 sentences.

Who? (the authors)

What? (main finding)

When and where? (journal, date of publication)

Why? (relevance)

This should be a standalone paragraph, meaning that the reader should be able to take out the main information even if they just read this paragraph.

• Subsequent paragraphs: In 2-3 paragraphs or less, provide context and more information about the research done. If you’re not sure if a detail is important or not, you can include it here and edit it out in the next step.

3. Polish the rough edges

In this stage, you’re going to make a quick edit, checking for completeness and accuracy. Make sure you’ve included all the main points without repeating yourself. Double-check all the numbers. Stay focused on the research questions to avoid tangents. Avoid using jargon and the passive voice whenever possible.

Final summary

Using this approach, you’ll end up with a short summary of your article that you can use to craft other types of writing, such as press releases, news articles, social media blurbs, and many others.

The advantages of summarising research articles are that you can better understand what the article is about, and you’ll have a text written by you, so it’s easier to adapt and you avoid unintentional plagiarism.

That’s it! My guide to write a research paper summary 😊

I’ve created a handout with all the information in this blog post plus a fill-in-the-blanks template that you can use to summarise research articles, you can download it using the form below. You’ll be signed up to my mailing list, and receive a weekly roundup of news in the biomedical industry as a bonus!

If you have any comments or questions, please let me know in the comment box below.

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About Diana Ribeiro

Diana Ribeiro  is a pharmacist and  freelance medical writer based in Cascais, Portugal.  Before starting her career in medical writing, Diana worked 10+ years in hospital and community pharmacies, where she helped patients and healthcare professionals with drug management and information. Nowadays, she helps pharma, biotech, and meddev companies communicate with their audiences in a clear, accurate, and compelling way. Diana is an active member of the European Medical Writers Association, where she volunteers for the webinar team. You can find more about her on  LinkedIn .

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How to Write a Summary of a Research Paper

Last Updated: July 10, 2020 References

This article was co-authored by wikiHow staff writer, Hannah Madden . Hannah Madden is a writer, editor, and artist currently living in Portland, Oregon. In 2018, she graduated from Portland State University with a B.S. in Environmental Studies. Hannah enjoys writing articles about conservation, sustainability, and eco-friendly products. When she isn’t writing, you can find Hannah working on hand embroidery projects and listening to music. This article has been viewed 28,323 times.

Writing a summary of an academic research paper is an important skill, and it shows that you understand all of the relevant information presented to you. However, writing a summary can be tough, since it requires you to be completely objective and keep any analysis or criticisms to yourself. By keeping your goal in mind as you read the paper and focusing on the key points, you can write a succinct, accurate summary of a research paper to prove that you understood the overall conclusion.

Reading the Research Paper

• For instance, if you’re supporting an argument in your own research paper, focus on the elements that are similar to yours.
• Or, if you’re comparing and contrasting methodology, focus on the methods and the significance of the results.

• You can also read the abstract of the paper as a good example of what the authors find to be important in their article.

• Depending on how long and dense the paper is, your initial reading could take you up to an hour or more.

• The important information will usually be toward the end of the paper as the authors explain their findings and conclusions.

• Writing a summary without plagiarizing, or copying the paper, is really important. Writing notes in your own words will help you get into the mindset of relaying information in your own way.

Including Relevant Information

• For example, “The methods used in this paper are not up to standards and require more testing to be conclusive.” is an analysis.
• ”The methods used in this paper include an in-depth survey and interview session with each candidate.” is a summary.

• If you’re writing a summary for class, your professor may specify how long your summary should be.
• Some summaries can even be as short as one sentence.

• ”Environmental conditions in North Carolina pose a threat to frogs and toads.”

• For example: “According to the climate model, frog and toad populations have been decreasing at a rapid rate over the past 10 years, and are on track to decrease even further in the coming years.”

• For example: “Smith and Herman (2008) argue that by decreasing greenhouse gases, frog and toad populations could reach historical levels within 20 years, and the climate model projections support that statement.”
• You can add in the authors and year of publication at any time during your summary.

• If you have time, try reading your summary to someone who hasn’t read the original paper and see if they understand the key points of the article.

Expert Q&A

• Make sure you fully understand the paper before you start writing the summary. Thanks Helpful 2 Not Helpful 0
• Plagiarism can have serious consequences in the academic world, so make sure you’re writing your summary in your own words. [12] X Research source Thanks Helpful 0 Not Helpful 0

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• ↑ https://writingcenter.uconn.edu/wp-content/uploads/sites/593/2014/06/How_to_Summarize_a_Research_Article1.pdf
• ↑ https://www.ufv.ca/media/assets/academic-success-centre/handouts/Summarizing-a-Scholarly-Journal-Article-rev2018.pdf
• ↑ https://integrity.mit.edu/handbook/academic-writing/summarizing
• ↑ https://writingcenter.unc.edu/tips-and-tools/summary-using-it-wisely/
• ↑ https://davidson.libguides.com/c.php?g=349327&p=2361763

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• Research Summary: What Is It & How To Write One

Introduction

A research summary is a requirement during academic research and sometimes you might need to prepare a research summary during a research project for an organization.

Most people find a research summary a daunting task as you are required to condense complex research material into an informative, easy-to-understand article most times with a minimum of 300-500 words.

In this post, we will guide you through all the steps required to make writing your research summary an easier task. 

What is a Research Summary?

A research summary is a piece of writing that summarizes the research of a specific topic into bite-size easy-to-read and comprehend articles. The primary goal is to give the reader a detailed outline of the key findings of a research.

It is an unavoidable requirement in colleges and universities. To write a good research summary, you must understand the goal of your research, as this would help make the process easier. 

A research summary preserves the structure and sections of the article it is derived from.

Research Summary or Abstract: What’s The Difference?

The Research Summary and Abstract are similar, especially as they are both brief, straight to the point, and provide an overview of the entire research paper. However, there are very clear differences.

To begin with, a Research summary is written at the end of a research activity, while the Abstract is written at the beginning of a research paper. 

A Research Summary captures the main points of a study, with an emphasis on the topic, method , and discoveries, an Abstract is a description of what your research paper would talk about and the reason for your research or the hypothesis you are trying to validate.

Let us take a deeper look at the difference between both terms.

What is an Abstract?

An abstract is a short version of a research paper. It is written to convey the findings of the research to the reader. It provides the reader with information that would help them understand the research, by giving them a clear idea about the subject matter of a research paper. It is usually submitted before the presentation of a research paper.

What is a Summary?

A summary is a short form of an essay, a research paper, or a chapter in a book. A research summary is a narration of a research study, condensing the focal points of research to a shorter form, usually aligned with the same structure of the research study, from which the summary is derived.

What Is The Difference Between an Abstract and a Summary?

An abstract communicates the main points of a research paper, it includes the questions, major findings, the importance of the findings, etc.

An abstract reflects the perceptions of the author about a topic, while a research summary reflects the ideology of the research study that is being summarized.

Getting Started with a Research Summary

Before commencing a research summary, there is a need to understand the style and organization of the content you plan to summarize. There are three fundamental areas of the research that should be the focal point:

• When deciding on the content include a section that speaks to the importance of the research, and the techniques and tools used to arrive at your conclusion.
• Keep the summary well organized, and use paragraphs to discuss the various sections of the research.
• Restrict your research to 300-400 words which is the standard practice for research summaries globally. However, if the research paper you want to summarize is a lengthy one, do not exceed 10% of the entire research material.

Once you have satisfied the requirements of the fundamentals for starting your research summary, you can now begin to write using the following format:

• Why was this research done?   – A clear description of the reason the research was embarked on and the hypothesis being tested.
• Who was surveyed? – Your research study should have details of the source of your information. If it was via a survey, you should document who the participants of the survey were and the reason that they were selected.
• What was the methodology? – Discuss the methodology, in terms of what kind of survey method did you adopt. Was it a face-to-face interview, a phone interview, or a focus group setting?
• What were the key findings? – This is perhaps the most vital part of the process. What discoveries did you make after the testing? This part should be based on raw facts free from any personal bias.
• Conclusion – What conclusions did you draw from the findings?
• Takeaways and action points – This is where your views and perception can be reflected. Here, you can now share your recommendations or action points.
• Identify the focal point of the article –  In other to get a grasp of the content covered in the research paper, you can skim the article first, in a bid to understand the most essential part of the research paper. 
• Analyze and understand the topic and article – Writing a summary of a research paper involves being familiar with the topic –  the current state of knowledge, key definitions, concepts, and models. This is often gleaned while reading the literature review. Please note that only a deep understanding ensures efficient and accurate summarization of the content.
• Make notes as you read – Highlight and summarize each paragraph as you read. Your notes are what you would further condense to create a draft that would form your research summary.

How to Structure Your Research Summary

• Title – This highlights the area of analysis, and can be formulated to briefly highlight key findings.
• Abstract – this is a very brief and comprehensive description of the study, required in every academic article, with a length of 100-500 words at most. 
• Introduction – this is a vital part of any research summary, it provides the context and the literature review that gently introduces readers to the subject matter. The introduction usually covers definitions, questions, and hypotheses of the research study. 
• Methodology –This section emphasizes the process and or data analysis methods used, in terms of experiments, surveys, sampling, or statistical analysis. 
• Results section – this section lists in detail the results derived from the research with evidence obtained from all the experiments conducted.
• Discussion – these parts discuss the results within the context of current knowledge among subject matter experts. Interpretation of results and theoretical models explaining the observed results, the strengths of the study, and the limitations experienced are going to be a part of the discussion. 
• Conclusion – In a conclusion, hypotheses are discussed and revalidated or denied, based on how convincing the evidence is.
• References – this section is for giving credit to those who work you studied to create your summary. You do this by providing appropriate citations as you write.

Research Summary Example 1

Below are some defining elements of a sample research summary.

Title – “The probability of an unexpected volcanic eruption in Greenwich”

Introduction – this section would list the catastrophic consequences that occurred in the country and the importance of analyzing this event. 

Hypothesis –  An eruption of the Greenwich supervolcano would be preceded by intense preliminary activity manifesting in advance, before the eruption.

Results – these could contain a report of statistical data from various volcanic eruptions happening globally while looking critically at the activity that occurred before these events. 

Discussion and conclusion – Given that Greenwich is now consistently monitored by scientists and that signs of an eruption are usually detected before the volcanic eruption, this confirms the hypothesis. Hence creating an emergency plan outlining other intervention measures and ultimately evacuation is essential. 

Research Summary Example 2

Below is another sample sketch.

Title – “The frequency of extreme weather events in the UK in 2000-2008 as compared to the ‘60s”

Introduction – Weather events bring intense material damage and cause pain to the victims affected.

Hypothesis – Extreme weather events are more frequent in recent times compared to the ‘50s

Results – The frequency of several categories of extreme events now and then are listed here, such as droughts, fires, massive rainfall/snowfalls, floods, hurricanes, tornadoes, etc.

Discussion and conclusion – Several types of extreme events have become more commonplace in recent times, confirming the hypothesis. This rise in extreme weather events can be traced to rising CO2 levels and increasing temperatures and global warming explain the rising frequency of these disasters. Addressing the rising CO2 levels and paying attention to climate change is the only to combat this phenomenon.

A research summary is the short form of a research paper, analyzing the important aspect of the study. Everyone who reads a research summary has a full grasp of the main idea being discussed in the original research paper. Conducting any research means you will write a summary, which is an important part of your project and would be the most read part of your project.

Having a guideline before you start helps, this would form your checklist which would guide your actions as you write your research summary. It is important to note that a Research Summary is different from an Abstract paper written at the beginning of a research paper, describing the idea behind a research paper.

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When writing a summary, the goal is to compose a concise and objective overview of the original article. The summary should focus only on the article's main ideas and important details that support those ideas.

Guidelines for summarizing an article:

• State the main ideas.
• Identify the most important details that support the main ideas.
• Summarize in your own words.
• Do not copy phrases or sentences unless they are being used as direct quotations.
• Express the underlying meaning of the article, but do not critique or analyze.
• The summary should be about one third the length of the original article. 

Your summary should include:

• Give an overview of the article, including the title and the name of the author.
• Provide a thesis statement that states the main idea of the article.
• Use the body paragraphs to explain the supporting ideas of your thesis statement.
• One-paragraph summary - one sentence per supporting detail, providing 1-2 examples for each.
• Multi-paragraph summary - one paragraph per supporting detail, providing 2-3 examples for each.
• Start each paragraph with a topic sentence.
• Use transitional words and phrases to connect ideas.
• Summarize your thesis statement and the underlying meaning of the article.

 Adapted from "Guidelines for Using In-Text Citations in a Summary (or Research Paper)" by Christine Bauer-Ramazani, 2020

Additional Resources

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How to Write a Summary - Guide & Examples  (from Scribbr.com)

Writing a Summary  (from The University of Arizona Global Campus Writing Center)

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How to Write a Research Paper Summary

One of the most important skills you can imbibe as an academician is to know how to summarize a research paper. During your academic journey, you may need to write a summary of findings in research quite often and for varied reasons – be it to write an introduction for a peer-reviewed publication , to submit a critical review, or to simply create a useful database for future referencing.

It can be quite challenging to effectively write a research paper summary for often complex work, which is where a pre-determined workflow can help you optimize the process. Investing time in developing this skill can also help you improve your scientific acumen, increasing your efficiency and productivity at work. This article illustrates some useful advice on how to write a research summary effectively. But, what is research summary in the first place?  

A research paper summary is a crisp, comprehensive overview of a research paper, which encapsulates the purpose, findings, methods, conclusions, and relevance of a study. A well-written research paper summary is an indicator of how well you have understood the author’s work. 

Table of Contents

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• 2. Invest enough time to understand the topic deeply 

Use Paperpal to summarize your research paper. Click here to get started!

• Mistakes to avoid while writing your research paper summary 

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Frequently asked questions (faq), how to write a research paper summary.

Writing a good research paper summary comes with practice and skill. Here is some useful advice on how to write a research paper summary effectively.  

1. Determine the focus of your summary

Before you begin to write a summary of research papers, determine the aim of your research paper summary. This will give you more clarity on how to summarize a research paper, including what to highlight and where to find the information you need, which accelerates the entire process. If you are aiming for the summary to be a supporting document or a proof of principle for your current research findings, then you can look for elements that are relevant to your work.

On the other hand, if your research summary is intended to be a critical review of the research article, you may need to use a completely different lens while reading the paper and conduct your own research regarding the accuracy of the data presented. Then again, if the research summary is intended to be a source of information for future referencing, you will likely have a different approach. This makes determining the focus of your summary a key step in the process of writing an effective research paper summary. 

2. Invest enough time to understand the topic deeply

In order to author an effective research paper summary, you need to dive into the topic of the research article. Begin by doing a quick scan for relevant information under each section of the paper. The abstract is a great starting point as it helps you to quickly identify the top highlights of the research article, speeding up the process of understanding the key findings in the paper. Be sure to do a careful read of the research paper, preparing notes that describe each section in your own words to put together a summary of research example or a first draft. This will save your time and energy in revisiting the paper to confirm relevant details and ease the entire process of writing a research paper summary.

When reading papers, be sure to acknowledge and ignore any pre-conceived notions that you might have regarding the research topic. This will not only help you understand the topic better but will also help you develop a more balanced perspective, ensuring that your research paper summary is devoid of any personal opinions or biases. 

3. Keep the summary crisp, brief and engaging

A research paper summary is usually intended to highlight and explain the key points of any study, saving the time required to read through the entire article. Thus, your primary goal while compiling the summary should be to keep it as brief, crisp and readable as possible. Usually, a short introduction followed by 1-2 paragraphs is adequate for an effective research article summary. Avoid going into too much technical detail while describing the main results and conclusions of the study. Rather focus on connecting the main findings of the study to the hypothesis , which can make the summary more engaging. For example, instead of simply reporting an original finding – “the graph showed a decrease in the mortality rates…”, you can say, “there was a decline in the number of deaths, as predicted by the authors while beginning the study…” or “there was a decline in the number of deaths, which came as a surprise to the authors as this was completely unexpected…”.

Unless you are writing a critical review of the research article, the language used in your research paper summaries should revolve around reporting the findings, not assessing them. On the other hand, if you intend to submit your summary as a critical review, make sure to provide sufficient external evidence to support your final analysis. Invest sufficient time in editing and proofreading your research paper summary thoroughly to ensure you’ve captured the findings accurately. You can also get an external opinion on the preliminary draft of the research paper summary from colleagues or peers who have not worked on the research topic. 

Mistakes to avoid while writing your research paper summary

Now that you’ve understood how to summarize a research paper, watch out for these red flags while writing your summary. 

• Not paying attention to the word limit and recommended format, especially while submitting a critical review 
• Evaluating the findings instead of maintaining an objective , unbiased view while reading the research paper 
• Skipping the essential editing step , which can help eliminate avoidable errors and ensure that the language does not misrepresent the findings 
• Plagiarism, it is critical to write in your own words or paraphrase appropriately when reporting the findings in your scientific article summary 

We hope the recommendations listed above will help answer the question of how to summarize a research paper and enable you to tackle the process effectively. 

Summarize your research paper with Paperpal

Paperpal, an AI academic writing assistant, is designed to support academics at every step of the academic writing process. Built on over two decades of experience helping researchers get published and trained on millions of published research articles, Paperpal offers human precision at machine speed. Paperpal Copilot, with advanced generative AI features, can help academics achieve 2x the writing in half the time, while transforming how they research and write.

How to summarize a research paper with Paperpal?

To generate your research paper summary, simply login to the platform and use the Paperpal Copilot Summary feature to create a flawless summary of your work. Here’s a step-by-step process to help you craft a summary in minutes:

• Paste relevant research articles to be summarized into Paperpal; the AI will scan each section and extract key information.
• In minutes, Paperpal will generate a comprehensive summary that showcases the main paper highlights while adhering to academic writing conventions.
• Check the content to polish and refine the language, ensure your own voice, and add citations or references as needed.

The abstract and research paper summary serve similar purposes but differ in scope, length, and placement. The abstract is a concise yet detailed overview of the research, placed at the beginning of a paper, with the aim of providing readers with a quick understanding of the paper’s content and to help them decide whether to read the full article. Usually limited to a few hundred words, it highlights the main objectives, methods, results, and conclusions of the study. On the other hand, a research paper summary provides a crisp account of the entire research paper. Its purpose is to provide a brief recap for readers who may want to quickly grasp the main points of the research without reading the entire paper in detail.

The structure of a research summary can vary depending on the specific requirements or guidelines provided by the target publication or institution. A typical research summary includes the following key sections: introduction (including the research question or objective), methodology (briefly describing the research design and methods), results (summarizing the key findings), discussion (highlighting the implications and significance of the findings), and conclusion (providing a summary of the main points and potential future directions).

The summary of a research paper is important because it provides a condensed overview of the study’s purpose, methods, results, and conclusions. It allows you to quickly grasp the main points and relevance of the research without having to read the entire paper. Research summaries can also be an invaluable way to communicate research findings to a broader audience, such as policymakers or the general public.

  When writing a research paper summary, it is crucial to avoid plagiarism by properly attributing the original authors’ work. To learn how to summarize a research paper while avoiding plagiarism, follow these critical guidelines: (1) Read the paper thoroughly to understand the main points and key findings. (2) Use your own words and sentence structures to restate the information, ensuring that the research paper summary reflects your understanding of the paper. (3) Clearly indicate when you are paraphrasing or quoting directly from the original paper by using appropriate citation styles. (4) Cite the original source for any specific ideas, concepts, or data that you include in your summary. (5) Review your summary to ensure it accurately represents the research paper while giving credit to the original authors.

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An executive summary is a thorough overview of a research report or other type of document that synthesizes key points for its readers, saving them time and preparing them to understand the study's overall content. It is a separate, stand-alone document of sufficient detail and clarity to ensure that the reader can completely understand the contents of the main research study. An executive summary can be anywhere from 1-10 pages long depending on the length of the report, or it can be the summary of more than one document [e.g., papers submitted for a group project].

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80 Todorovic, Zelimir William and Marietta Wolczacka Frye. “Writing Effective Executive Summaries: An Interdisciplinary Examination.” In United States Association for Small Business and Entrepreneurship. Conference Proceedings . (Decatur, IL: United States Association for Small Business and Entrepreneurship, 2009): pp. 662-691.

Importance of a Good Executive Summary

Although an executive summary is similar to an abstract in that they both summarize the contents of a research study, there are several key differences. With research abstracts, the author's recommendations are rarely included, or if they are, they are implicit rather than explicit. Recommendations are generally not stated in academic abstracts because scholars operate in a discursive environment, where debates, discussions, and dialogs are meant to precede the implementation of any new research findings. The conceptual nature of much academic writing also means that recommendations arising from the findings are distributed widely and not easily or usefully encapsulated. Executive summaries are used mainly when a research study has been developed for an organizational partner, funding entity, or other external group that participated in the research . In such cases, the research report and executive summary are often written for policy makers outside of academe, while abstracts are written for the academic community. Professors, therefore, assign the writing of executive summaries so students can practice synthesizing and writing about the contents of comprehensive research studies for external stakeholder groups.

When preparing to write, keep in mind that:

• An executive summary is not an abstract.
• An executive summary is not an introduction.
• An executive summary is not a preface.
• An executive summary is not a random collection of highlights.

Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Clayton, John. "Writing an Executive Summary that Means Business." Harvard Management Communication Letter (July 2003): 2-4; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; Murphy, Herta A., Herbert W. Hildebrandt, and Jane P. Thomas. Effective Business Communications . New York: McGraw-Hill, 1997; Vassallo, Philip. "Executive Summaries: Where Less Really is More." ETC.: A Review of General Semantics 60 (Spring 2003): 83-90 .

Structure and Writing Style

Writing an Executive Summary

Read the Entire Document This may go without saying, but it is critically important that you read the entire research study thoroughly from start to finish before you begin to write the executive summary. Take notes as you go along, highlighting important statements of fact, key findings, and recommended courses of action. This will better prepare you for how to organize and summarize the study. Remember this is not a brief abstract of 300 words or less but, essentially, a mini-paper of your paper, with a focus on recommendations.

Isolate the Major Points Within the Original Document Choose which parts of the document are the most important to those who will read it. These points must be included within the executive summary in order to provide a thorough and complete explanation of what the document is trying to convey.

Separate the Main Sections Closely examine each section of the original document and discern the main differences in each. After you have a firm understanding about what each section offers in respect to the other sections, write a few sentences for each section describing the main ideas. Although the format may vary, the main sections of an executive summary likely will include the following:

• An opening statement, with brief background information,
• The purpose of research study,
• Method of data gathering and analysis,
• Overview of findings, and,
• A description of each recommendation, accompanied by a justification. Note that the recommendations are sometimes quoted verbatim from the research study.

Combine the Information Use the information gathered to combine them into an executive summary that is no longer than 10% of the original document. Be concise! The purpose is to provide a brief explanation of the entire document with a focus on the recommendations that have emerged from your research. How you word this will likely differ depending on your audience and what they care about most. If necessary, selectively incorporate bullet points for emphasis and brevity. Re-read your Executive Summary After you've completed your executive summary, let it sit for a while before coming back to re-read it. Check to make sure that the summary will make sense as a separate document from the full research study. By taking some time before re-reading it, you allow yourself to see the summary with fresh, unbiased eyes.

Common Mistakes to Avoid

Length of the Executive Summary As a general rule, the correct length of an executive summary is that it meets the criteria of no more pages than 10% of the number of pages in the original document, with an upper limit of no more than ten pages [i.e., ten pages for a 100 page document]. This requirement keeps the document short enough to be read by your audience, but long enough to allow it to be a complete, stand-alone synopsis. Cutting and Pasting With the exception of specific recommendations made in the study, do not simply cut and paste whole sections of the original document into the executive summary. You should paraphrase information from the longer document. Avoid taking up space with excessive subtitles and lists, unless they are absolutely necessary for the reader to have a complete understanding of the original document. Consider the Audience Although unlikely to be required by your professor, there is the possibility that more than one executive summary will have to be written for a given document [e.g., one for policy-makers, one for private industry, one for philanthropists]. This may only necessitate the rewriting of the introduction and conclusion, but it could require rewriting the entire summary in order to fit the needs of the reader. If necessary, be sure to consider the types of audiences who may benefit from your study and make adjustments accordingly. Clarity in Writing One of the biggest mistakes you can make is related to the clarity of your executive summary. Always note that your audience [or audiences] are likely seeing your research study for the first time. The best way to avoid a disorganized or cluttered executive summary is to write it after the study is completed. Always follow the same strategies for proofreading that you would for any research paper. Use Strong and Positive Language Don’t weaken your executive summary with passive, imprecise language. The executive summary is a stand-alone document intended to convince the reader to make a decision concerning whether to implement the recommendations you make. Once convinced, it is assumed that the full document will provide the details needed to implement the recommendations. Although you should resist the temptation to pad your summary with pleas or biased statements, do pay particular attention to ensuring that a sense of urgency is created in the implications, recommendations, and conclusions presented in the executive summary. Be sure to target readers who are likely to implement the recommendations.

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80; Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Executive Summaries. Writing@CSU. Colorado State University; Clayton, John. "Writing an Executive Summary That Means Business." Harvard Management Communication Letter , 2003; Executive Summary. University Writing Center. Texas A&M University;  Green, Duncan. Writing an Executive Summary.   Oxfam’s Research Guidelines series ; Guidelines for Writing an Executive Summary. Astia.org; Markowitz, Eric. How to Write an Executive Summary. Inc. Magazine, September, 15, 2010; Kawaski, Guy. The Art of the Executive Summary. "How to Change the World" blog; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; The Report Abstract and Executive Summary. The Writing Lab and The OWL. Purdue University; Writing Executive Summaries. Effective Writing Center. University of Maryland; Kolin, Philip. Successful Writing at Work . 10th edition. (Boston, MA: Cengage Learning, 2013), p. 435-437; Moral, Mary. "Writing Recommendations and Executive Summaries." Keeping Good Companies 64 (June 2012): 274-278; Todorovic, Zelimir William and Marietta Wolczacka Frye. “Writing Effective Executive Summaries: An Interdisciplinary Examination.” In United States Association for Small Business and Entrepreneurship. Conference Proceedings . (Decatur, IL: United States Association for Small Business and Entrepreneurship, 2009): pp. 662-691.

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Research Paper Summary

Ai generator.

Whether you are a student, an academic scholar, or even working in business, there is no denying that a research paper summary is the one tool that you are going to expect when it comes to writing your research paper or research studies. There is also no denying how useful the summary is going to be when you have to report it to your superiors or your professors without having to go through the entire research paper. Students know for themselves that writing a summary of their research paper is useful. With that, here are examples of research paper summaries to download.

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What Is a Research Paper Summary?

Research paper summaries are short but descriptive writings that are expected in a research paper . What goes in a research paper summary is the main topic or the main plot of your research paper. However, what is and should never be included are any new discoveries, arguments and new leads that help your research. The purpose of the summary is to simply give out the general point of view or the outline of your research paper and nothing else. This is often the mistake made by students when they think of a research paper summary. The need to add all new leads to help their research in the summary. The only main thing to focus on your summary is the overview and the general outline . 

How to Write a Research Paper Summary

Being able to write a research paper summary is important and quite a useful skill. As this does not only work for students on their research paper, but it also works for employees who are given the task to write a project summary. It basically works just the same. To get a glimpse of what you can do to make your research paper summary, here are simple steps you can follow.

Step 1: Take the Main Part of Your Research

When you make your summary, the first paragraph will mainly be about your research paper. The first part is to take the main part of your research. The main part or the main topic should be what it is about. Make sure what you are writing is what your research paper is about, as there are times when your topic may not be the main goal of your paper.

Step 2: Break It Down to Smaller Topics

Since the first paragraph is focused on the introduction and the main topic, the second paragraph will focus mainly on breaking down your main or general topic into smaller subtopics. By doing this, it is easier for you to divide and explain every single important detail of your research paper. Students are often tasked to do this in order for them to get a better outlook of their research paper and how they are able to piece together the smaller topics to the main topic.

Step 3: Get the Gist

The third and final paragraph will be the gist of your research paper. This includes the heart or the main part, the findings and the conclusion. The gist has to be a general summary of your research paper. It should have the facts that support it, the findings of your research and the hypothesis. Add in your conclusion at the end.

Step 4: Proofread Your Work

Lastly, make sure to proofread your entire research paper summary. This is just to make sure you did not misspell any words, your punctuations are in the correct place and the tone of your writing fits the paper you are making.

What is a research paper summary?

Research paper summaries are short but descriptive writings  that are expected in a research paper. What goes in a research paper summary is the main topic or the main plot of your research paper.

What are the characteristics of a research paper summary?

The characteristics of a research paper summary are the following:

• The introduction and the main topic
• The breaking of the main topic to sub topics
• The gist of the research paper summary
• The conclusion

How lengthy can a research paper summary be?

The normal length of a research paper summary should not exceed more than a page. However, when it comes to the number of words for a summary, your wording should not exceed the maximum number of four hundred words.

When it comes to writing a research paper, there is no denying that you must also write a summary for it. Since a research paper can sometimes be overwhelming to those who will be listening to you talk about it, you can relieve it by making a summary of your paper. This will also help them follow what you are discussing and what it is about.

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Research Papers

Harmonizing gcw cryosphere vocabularies with envo and sweet. towards a general model for semantic harmonization.

• Pier Luigi Buttigieg
• Gary Berg Cross
• Kai Lewis Blumberg
• Brandon Whitehead
• Nancy Wiegand

This paper presents the specific process used by members of the Earth Science Information Partners (ESIP) Semantic Harmonization Cluster, to harmonize cryospheric terms gathered by the Global Cryosphere Watch (GCW) with two leading semantic resources used in the Earth and Environmental science communities—the Semantic Web for Earth and Environmental Terminology (SWEET) and the Environment Ontology (ENVO). This process led to updates to both ENVO and SWEET as well as the development of an alignment file relating cryospheric terms in ENVO to those in SWEET. In addition, we summarize several leading practices which may be applied to other projects/realms within Earth and Environmental science and perhaps beyond, as well as suggest a generalized process for doing so. This paper describes the history of the effort, the technical and decision-making processes used to resolve differences between semantic resources, and describes several issues encountered, with a focus on those that were addressed during the effort. Lessons learned, examples of the problems encountered and a summary of resulting leading practices growing out of this work is provided.

• semantic resource
• semantic harmonization
• lessons learned
• leading practices

Introduction

Over the last decades it has become apparent that to solve any of humanity’s pressing issues, inter- and trans-disciplinary research is needed. This requires that data that are collected, developed, and described for one community become readily accessible and understandable by other communities, that the data become globally FAIR (Findable, Accessible, Interoperable, Reusable) ( Wilkinson et al. 2016 ).

What is often not understood by researchers is that for data to be FAIR, both the data and its metadata must be amenable to reasoning by both humans and computers ( ‘FAIR Principles’ 2015 ). This implies that formally defined language be used to describe the structure and content of both the data and its metadata ( ‘FAIR Principle I1’ 2015 ). Consequently, understanding and harmonizing disciplinary semantic resources with those in other fields is necessary ( Gil et al. 2018 ).

Historically, the data systems used by the research community were independently developed and customized to suit their requirements. Underpinning these systems are a variety of semantically heterogeneous resources, including controlled vocabularies, glossaries, thesauri, and ontologies (see Figure 1 and section Types of Semantic Resources). Moreover, these underlying resources come in a wide variety of formats, including spreadsheets, documents, programming languages, and schemas, which are typically embedded with a non-trivial amount of tacit domain knowledge. Consequently, these data systems, which may support large, well-established user communities such as those of the Global Cryosphere Watch, are unlikely to naturally merge with those of other disciplines without a great deal of effort. In light of this problem, it is increasingly clear there is a pressing need for a sound and sustainable way to align and harmonize these underlying semantic resources in order to allow for inter-, cross- and trans-disciplinary data discovery and use .

A depiction of the semantic ladder illustrating the extent of machine-aided interoperability of semantic resources, loosely based on Dan McCreary’s 2006 presentation ( McCreary 2006 ).

The World Meteorological Organization’s (WMO) Global Cryosphere Watch (GCW) supports many historical, or legacy, discipline-specific research data. The term ‘cryosphere’ refers collectively to the portions of the earth where water is in solid form, including snow and ice cover, sea ice, river ice, lake ice, glaciers, ice caps, ice sheets, and seasonally and perennially frozen ground (permafrost). Given the geographic scope of the cryosphere, its data comprise several scientific and sociological disciplines and is thus extremely heterogeneous. A few examples include remotely sensed data acquired by satellites, airplanes, and drones; long-term time-series data gathered at stations such as permafrost borehole temperature profiles and ship-born sea ice and ocean temperature profiles; ‘in-situ’ sample data such as snow depth, density and water equivalent, ice cores, sea ice, or permafrost soil samples; laboratory measurements and experimentally derived data; and computational environmental models.

The cryosphere is an integral part of the global climate system. The presence or absence of snow and ice affects heating and cooling over the Earth’s surface, influencing the entire planet’s energy balance. Indeed, as the 2023 Global Tipping Points Report ( Lenton et al. 2023 ) notes, of the five major systems currently at risk of crossing tipping points, four of them—the Greenland and West Antarctic ice sheets, the North Atlantic Subpolar Gyre circulation and permafrost regions—all have cryospheric components. Thus, harmonizing the semantic resources underlying data systems holding cryospheric data is critical to enabling the inter-, cross-, and trans-disciplinary research needed to understand the impacts of and to mitigate climate change .

The Earth Science Information Partners (ESIP) is a non-profit organization with a mission to ‘empower innovative use and stewardship of Earth Science data to solve our planet’s greatest challenges’ ( ESIP 2023 ). Supported by the US National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and the US Geological Survey (USGS), and with more than 130 member organizations, ESIP provides a neutral, open, and welcoming space for collaboration between researchers, educators, industry, and government agencies to accomplish these goals.

In 2009, ESIP convened a Semantic Web Cluster to help its community adopt a wide range of technologies to digitally represent knowledge from diverse scientific domains and bridge between them. As the popularity and importance of semantic technologies grew, this cluster was promoted to become the Semantic Technologies Committee in 2016 to address needs in this operational space. In ESIP, Committees can convene their own clusters, and as recognition of the substantial expertise and domain knowledge present within the ESIP community, several subsidiary clusters were formed to address specific aspects of semantics.

One of these clusters was the ESIP Semantic Harmonization Cluster which was formed in 2018 to propose a route towards sustainably bridging terminologies across the Earth Sciences to other domains, as well as to disseminate best practices for harmonizing semantic resources . Successful bridges need to be usable across implementation scenarios and user communities, as well as applicable across the spectrum of semantic resource types—that is, from resources with weak expressivity such as controlled vocabularies and glossaries (see Figure 1 ), through those that support best practices for publishing structured scientific data on the Web ( Shepherd et al. 2022 ), and to those that enable computational reasoning—that is, ontologies.

In this paper, we describe the methods used to harmonize cryosphere terms from the 27 semantic resources in the Global Cryosphere Watch (GCW) glossary compilation with two major Earth science ontologies, ENVO and SWEET, and propose a general process for harmonizing semantic resources across the semantic ladder. This work was done as a project through ESIP to fulfill the mandate of the ESIP Semantic Harmonization Cluster .

Background: Types of Semantic Resources

In the Earth Sciences there is no single semantic resource or semantic resource type to rule them all. The phrase semantic resource typically refers to a spectrum of artifacts ranging from simple controlled vocabularies (e.g., term lists) to complex, logically consistent, and formally rigorous structures (e.g., ontologies), each providing a level of interoperability to innumerable applications (see Figure 1 ). The terminology describing semantic resources varies significantly depending on the community with which it is employed. As such, the following are the types of semantic resources considered during this work along with our definitions for each.

• Example: AGU Index of terms ( AGU 2021 )
• Example: Glossary of Geology ( Neuendorf, Mehl, Jr., and Jackson 2011 )
• Example: The USGS Thesaurus ( “USGS Thesaurus” 2023 )
• Example: The classification of living organisms by their Kingdom, Phylum, Class, Order, Family, Genus, and Species
• Example: ENVO ( Buttigieg et al. 2023 )

Each type of semantic resource defined above has been placed on the semantic ladder depicted in Figure 1 along with the three resources used in this work (GCW glossaries, SWEET, and ENVO).

As previously described, the ESIP Semantic Harmonization Cluster was formed to develop processes for sustainably bridging terminologies across the Earth Sciences and to other related domains, as well as to disseminate best practices for harmonizing semantic resources. Figure 2 depicts the general process used here, which is reproducible across other projects and disciplines.

Overview of the harmonization process used in the project and described below.

Step 1: Find and compile existing resources

The first task was to select the set of semantic resources to harmonize and the discipline to cover. Given the expertise within the group and the critical importance of the cryosphere to climate change impacts, we agreed that cryospheric terminology would be our focus.

This task was greatly aided by previous work commissioned by the GCW to analyze the 27 cryospheric semantic resources they had gathered ( Duerr 2018b ; 2018a ). One result of that work are tables containing terms:

• In this case usually because only one of the glossaries defined the term or where multiple definitions are exact copies of each other and therefore do not conflict.
• For example, the term adfreezing is defined as ‘the process by which two objects are bonded together by ice formed between them’ in the International Permafrost Association’s glossary ( van Everdingen 2005 ) and as ‘the process by which one object becomes adhered to another by the binding action of ice’ in the AMS Glossary of Meteorology ( American Meteorological Society 2024 ). These two definitions were combined into the ENVO definition ‘a freezing process during which two objects adhere to each other via ice.’
• For example, the term ‘blizzard’ is defined as ‘violent and very cold wind which is loaded with snow, some of which has been raised from snow covered ground’ by the Australian Bureau of Meteorology ( Australian Government, n.d. ); as ‘a severe weather condition characterized by reduced visibility from falling and/or blowing snow and strong winds that may be accompanied by low temperatures’ by Canada ( Government of Canada n.d. ); and having ‘sustained wind or frequent gusts of 16 m per second (30 kt or 35 mi per hour) or greater, accompanied by falling and/or blowing snow, frequently reducing visibility to less than 400 m (0.25 mi) for 3 hours or longer’ by the US National Weather Service ( NOAA/NWS 2009 ). There are additional definitions for other regions such as France, England, and Russia as well, each with some distinguishing set of criteria that usually differs in some way from the examples given here. This example is discussed further below.
• See the detailed discussion of the term calving and the calving process in the Results section and in Figure 5 .

Terms from the categories above formed the initial scope of this project.

A recent survey identified both the Semantic Web for Earth and Environmental Terminology (SWEET) and the Environment Ontology (ENVO) as amongst the five most important semantic resources within the community ( Whitehead 2022 ). Of the other three resources in the group, neither QUDT ( FAIRsharing Team 2015 ) nor the Sensor, Observation, Sampler, and Actuator/Semantic Sensor Network (SOSA/SSN) ( Haller et al. 2019 ; Janowicz et al. 2019 ) contains cryospheric terminology. The last member, the UK Natural Environment Research Council (NERC Vocabularies) ( British Oceanographic Data Centre 2023 ), is focused on marine science but not the cryosphere. Moreover, Wolodkin, Welland, and Grieb explicitly mention the need to bridge between SWEET and ENVO in order to facilitate reuse of biodiversity data ( Wolodkin, Weiland & Grieb 2023 ). The previously mentioned survey also noted that SWEET should be harmonized with other semantic resources. Consequently, the cluster agreed to harmonize GCW terminology within and between both SWEET and ENVO.

SWEET ( McGibbney et al. 2022 ) organizes over 11,000 Earth and Environmental concepts into roughly 200 separate ontology modules based on nine top-level categories (below), some of which contain subcategories with cryosphere-related terms ( Table 1 ):

• Representation – Math, Space, Science, Time, Data,
• Realm – Ocean, Land Surface, Terrestrial Hydrosphere, Atmosphere, Heliosphere, Cryosphere, Geosphere,
• Phenomena (macro-scale) – Ecological, Physical,
• Process (micro-scale) – Physical, Biological, Chemical, and Mathematical,
• Matter – Living Thing, Material Thing, Chemical,
• Human Activities – Decision, Commerce, Jurisdiction, Environmental, Research,
• Property (observation) – Binary Property, Quantity, Categorical Property, Ordinal Property
• State (adjective, adverb) – Role, Biological, Physical, Space, Chemical, and
• Relation (verb) – Human, Chemical, Physical, Space, Time

SWEET files addressed during this work.

Initially developed at NASA’s Jet Propulsion Lab ( Raskin & Pan 2005 ) and originally based on the Global Change Master Directory (GCMD) keywords ( Nagendra et al. 2001 ), SWEET is now officially under the governance of the ESIP federation. Despite the broad coverage, historically, SWEET did not include terminology definitions or their equivalent machine readable axioms, so despite routinely being referred to as a set of ontologies in relation to the semantic spectrum, in many areas SWEET is more along the lines of a taxonomy or lightweight ontology ( Giunchiglia & Zaihrayeu 2017 ).

ENVO was initially created to represent environmental characteristics in which biological entities are found. ENVO includes, for example, descriptions of physical environments such as geological, ecological, or astronomical ( Buttigieg et al. 2013 ; 2016 ). As such, expanding ENVO to include cryospheric terms enhances ENVO’s coverage of physical environments.

In relation to the semantic ladder (see Figure 1 ), ENVO is an ontology with both human and machine-readable axiomatic definitions. It is being developed following the recommendations and principles of the Open Biological and Biomedical Ontologies (OBO) Foundry and Library ( OBO Technical Working Group 2022 ) and can be formally represented in the Ontology Web Language (OWL) or OBO formats. ENVO is aligned with the Basic Formal Ontology ( Arp, Smith & Spear 2015 ; Brochhausen et al. 2019 ) at an upper level, so that ENVO is interoperable with other OBO ontologies. Compared to SWEET, ENVO has numerous defining axioms and overall is a more formally rigorous ontology.

Step 2: Semantic harmonization

Work proceeded by identifying SWEET terms that were cryospheric from within that subset of SWEET files whose name indicated that they were likely to contain relevant terms (see the list of files addressed in Table 1 ). A Google sheet containing the relevant SWEET terms was created for each SWEET file addressed ( Semantic Harmonization Cluster 2023 ).

For each SWEET term on the spreadsheets, the team determined whether there were equivalent terms in the GCW compilation. If not, the term was not addressed further. If the SWEET term was found in the GCW compilation, then we searched for the term in the ENVO ontology. If found, we paid attention to which hierarchy, that is, superclass, it was under compared to SWEET’s hierarchies to be sure we had a match. Then additions or updates to ENVO were made using guidelines developed by Seppälä et al. ( Seppälä, Ruttenberg & Smith 2017 ) and extended for ENVO ( Buttigieg 2021 ). This included creating minimal but robust definitions following the genus-differentia model which produces definitions of the form ‘X is a Y that Zs’ and numbering each discrete differentia in the definition (see Figure 4 for an example) as well as ensuring that the axioms for the term reflect the differentia in the definition (see Figure 3 for an example).

Term ice fog added to the ENVO ontology using a GCW derived definition showing parallel definition and axioms.

Term ice shelf added to ENVO with numbered differentia and added GCW comments.

Many of the terms in the GCW compilation included additional information that went well beyond a definition. These extra materials were not included as part of the ENVO definition, but instead kept as separate annotating comments on the ENVO term (see Figure 4 for an example). When revising definitions or adding terms to ENVO, we paid special attention to the taxonomically inherited axioms of each class, correcting issues higher in the ontology hierarchy or adding additional levels to the hierarchy as needed.

We initially intended to update SWEET directly as well—adding definitions and relationships to the equivalent terms from ENVO directly into SWEET. However, during the project a SWEET roadmap was debated within the larger ESIP Semantic Technologies Committee which might have invalidated our work. Instead, we opted to create GitHub Issues for anything related to SWEET, to defer the addition of definitions to after completion of the roadmap, and to record SWEET and ENVO term relationships using the recently developed Simple Standard for Sharing Ontological Mappings (SSSOM) ( Matentzoglu et al. 2022 ) (see Step 4 below).

Step 3: Encode in ontologies (OWL)

Initially, the examination of terms in ENVO occurred using the Protégé ontology editor ( Musen 2015 ) and the development branch of ENVO available from the ENVO GitHub repository. We were editing/updating ENVO one term at a time. However, later in the project, after having worked through many terms using this process, we switched to using a ROBOT spreadsheet ( Jackson et al. 2019 ; Overton et al. 2015 ) to automate the process of updating ENVO in bulk.

ROBOT is a general-purpose command-line tool for working with ontologies and is used by many projects contributing to the OBO Foundry. It provides commands for merging ontologies, extracting subsets, filtering for selected axioms, running reasoners, and converting between file formats. ROBOT commands can be chained together to form powerful, repeatable workflows.

In this work, we created the ENVO ROBOT template and merge workflow, which allowed us to update existing as well as to add new terms to ENVO. The workflow enables the use of collaborative spreadsheets to add information into ENVO. A generalized version of the workflow is available from the ENVO wiki ( Blumberg & Duerr 2022 ) involving the following steps:

• Creating a GitHub issue detailing the material to be added.
• Making a copy of the template spreadsheet formatted with headers necessary to compile a ROBOT template.
• Preparing new terminology by filling out the spreadsheet following the best documented practices ( Blumberg, Chong & Buttigieg 2021 ).
• Compiling the ROBOT template spreadsheet into OWL code.
• Using a GitHub pull request to merge the OWL code into the main ENVO codebase.

The spreadsheets we created while using the new workflow for this material added to ENVO discussed in this paper are available from our GitHub site ( Duerr 2023 ). Once finalized, the new information added to ENVO though the ROBOT template and merge workflow was made publicly available within a new release of ENVO using the standard ENVO release process.

Using ROBOT improved overall efficiency as well as decreased the conceptual workload for those team members without a great deal of ontology engineering experience, though it did not decrease the time required to assess the GCW definitions or any existing ENVO definitions and axioms.

Step 4: Technical harmonization

Finally, to formally record the relation between ENVO and SWEET terms, we used the recently developed Simple Standard for Sharing Ontological Mappings (SSSOM) ( Matentzoglu et al. 2022 ) to document the relationships between the identified SWEET terms and their related ENVO terms.

To use SSSOM, we first populated a spreadsheet with our newly entered ENVO terms alongside potential matching terms in SWEET. For each term, we determined a potential relationship that we expressed using Simple Knowledge Organization System (SKOS) predicates ( Miles & Bechhofer 2009 ), by analyzing the placement of the SWEET and ENVO terms in their class hierarchies, and comparing any available definitions and axioms (see the last column of Figure 2 for examples). While time consuming, this human curated approach proved to be much more accurate than other approaches which generally ignore both differences in the organization of the hierarchies of different resources as well as the richness of the subclasses and axioms underlying the mapped terms (see Results section below).

In addition to the SKOS relationship between terms, such as skos:broadMatch or skos:relatedMatch, we recorded a comment explaining the reasoning behind the type of match assigned. In many cases, these comments also include suggestions for future work and/or conditions for changing the type of match if either ontology is updated. For example, for the term Arete we recorded a comment to the effect that in SWEET an arete is a type of plain, but in ENVO an arete is a kind of ridge; so the SWEET hierarchy needed to be changed. The SSSOM file generated was added to the ENVO repository on GitHub and the ESIP Community Ontology Repository ( ESIPFed 2023 ).

Of the 626 terms currently in the polar subset of ENVO, a total of 302 terms were added or updated as a part of this work. This represents roughly 15% of the unique terms in the GCW compilation; though it should be noted that many of the other GCW terms had been addressed in ENVO prior to this project. Of these terms, 151 were mapped from ENVO to SWEET using the SSSOM mapping standard, mapping available in the ENVO GitHub repository ( Buttigieg et al. 2023 ).

Table 1 contains a list of the SWEET ontology files addressed during this work, the number of cryosphere terms identified in each file, the number of these that were also present in the GCW compilation and the number that were common between all three sources.

Of the almost 500 terms in the 12 SWEET files identified as containing some cryospheric terms, 124 or 26% of those were cryospheric terms. And, of those 124 cryosphere terms, 81 or 65% were also found in the Global Cryosphere Watch, and 70 or 56% were found among all three resources. Again, this overlap of similar terms found in multiple resources as well as the lack of comprehensiveness of terms relevant for a domain in any one resource shows the need and value of our work.

Figure 5 provides a graphic representation of the results of harmonizing ENVO terms related to the ‘ice calving process.’ This has the advantage of showing terms and relationships that are not immediately obvious when looking at one term at a time. In ENVO, ‘ice calving process’ is represented as a form of (subclass of) mass wasting. The subclasses of ice calving process captured differentia noted during our glossary review, in particular, ‘where’ the ice was calved, either into water or upon land, and ‘from’ which entity it was calved, that is, an iceberg or glacier. The definitions of these terms often reveal semantics which are implicitly obvious for domain scientists, but not apparent from their commonly used labels. Similarly, Land ice , is a term used to refer to ice formed over land masses, rather than present upon them, thus allowing marine icebergs to be a valid (sub)subclass. That is, by definition, icebergs come from land ice versus ice floes which are an expanse of sea ice. So, a marine iceberg is an iceberg which is a type of land ice mass, even though it’s no longer on land. Relationships between terms (i.e., axioms such as ‘has participant’) come from another OBO Foundry ontology, the Relations Ontology ( Huntley et al. 2014 ; Mungall et al. 2020 ), which supports reasoning and verifies logical coherence.

A partial ENVO representation of harmonized ‘ice calving process’ terms. Blue boxes represent terms within the ontology, the lines indicate subclass (i.e., is a) and other relationships between terms, while dotted gray boxes indicate that the enclosed terms inherit the relationships from other levels within the ontology.

As mentioned earlier, SSSOM was used to document the relationship between cryospheric terms in SWEET and ENVO. In total, 151 relationships between terms were developed. As you can see from Figure 6 , roughly 40% of the terms were categorized as being a skos:closeMatch which typically implies that positioning within each hierarchy is comparable but that SWEET’s lack of definitions inhibited assumptions of exact equivalence. An additional 40% of the terms were categorized as being related matches, which typically implies that while the terms are in some way related, that positioning within each hierarchy is sufficiently different to eliminate there being any possibility that the terms are equivalent. For example, if a term was considered to be a process in ENVO and a landform in SWEET, the match was deemed a related match. The remaining 20% of the terms were either categorized as being skos:broad or skos:narrow matches indicating that one of the terms is less specific than the other. skos:broad matches provided the bulk of these types of matches indicating that the ENVO term was more specific than the SWEET term.

Match types in the SSSOM created for ENVO and SWEET.

It is quite common in the field for folks to attempt lexical matching of concepts from multiple ontologies ( Euzenat & Shvaiko 2013 ; X. Liu et al. 2021 ), that is, matching based on similarity of the un-defined concept label only (or where the concept label is the most heavily weighted feature of the matching algorithm). To investigate the impact that this would have had on the ontology term relationships developed here, the match types assigned to the 61 lexically equivalent strings in the SSSOM file were examined. Figure 7 provides a summary of the match types found. Roughly half of the terms matched closely; while the other half did not; indicating that a purely lexical match would be wrong in our case roughly half the time. Moreover, we note that the majority of the terms for which we assigned a relationship could not be matched based on their labels, since they had little or no lexical similarity.

Match types for Lexically Equivalent Strings.

As summarized in Figure 8 , we also characterized the reasons for the match types chosen for those 61 lexically equivalent strings. While these characterizations are subjective and the number of terms addressed is small, the results are still instructive. As you might expect, most of the lexically equivalent terms rated as being close matches did not have definitions in SWEET (25 terms). However, there were six such terms where it also was not clear that the placement of the term in each hierarchy was equivalent. For example, SWEET considers fiords to be a type of estuary, while ENVO doesn’t. Similarly, ENVO considers rime to be a type of frost; while in SWEET frost and rime are parallel concepts placed in different parts of the overall hierarchy. In addition, there were 21 terms where the type of the term in each ontology was different. For example, in SWEET, terms such as permafrost are three-dimensional geometric objects, while in ENVO they are environmental materials. Moreover, in nine cases, the reasons for not equating the SWEET and ENVO terms were complex, typically involving both definitional and structural differences between the two resources. In one such case, the term had been deprecated in SWEET. In another such case, SWEET had two identical terms defined in different branches of the SWEET hierarchy. In five cases, the existing SWEET hierarchy was called into question. GitHub Issues have been created to address the concerns identified from these cases.

Reasons why lexically equivalent terms were not said to be semantically equivalent.

Lastly, over the last year interactions with other communities, both within ESIP and beyond, spurred us to generalize the harmonization process so that it could be tailored to the needs of other communities. Figure 2 depicts this general process using the GCW glossaries, ENVO and SWEET purely as examples of the types of resources that can be harmonized. A summary of the general process we developed follows:

• Existing thematic semantic resources in a variety of formats of term-definition pairs are identified by domain experts, who work together with semantic technology and ontology experts.
• Domain experts identify source/target terms for harmonization; usually those required to advance their work. If definitions, comments, or provenance do not accompany terms, more work will be needed to understand and describe each term. Semantic technology and ontology experts work with the domain experts to reduce ambiguity by comparing terms and definitions, splitting, or merging terms, and updating targets and formalizing definitions where necessary (see Discussion).
• The resulting terms and definitions can then be encoded in one or more semantic resources (including their provenance). To allow machine-actionable search and understanding of terms, formal axioms need to be written. This is best done by a collaboration of domain experts who know the field along with semantic technology and ontology experts who know the logic and technology. The result is a domain-correct and machine-readable final set of terms described and expressed with formal axioms. If OWL is used, reasoners can be used for quality assurance and control (QA/QC) and other logical analyses.
• Lastly, multiple semantic/ontology resources can be formally aligned, in our case documented with SSSOM.

Here, we discuss issues found regarding harmonizing terminology and definitions, harmonizing across different ontology hierarchies, and finally sociotechnical issues.

Harmonizing glossaries and ontologies

Harmonizing semantic resources developed by different groups over different periods of time is fraught with issues. However, using analysis methods such as those promulgated by the semantics community ( Seppälä, Ruttenberg & Smith 2017 ) can help clarify, simplify, and resolve many issues. Broadly over the course of this project two major kinds of glossary inconsistencies were encountered: terminology incoherence and imprecise definitions. How we dealt with each is described in the following sections.

Terminology differences

First, we need to simply acknowledge the fact that language is fluid, in some sense alive. Terminology meaning and usage varies and drifts over time, place, and community. Consequently, there may be multiple meanings for a term depending on the exact discipline or subdiscipline defining it. For example, in the permafrost community hummocks are ‘small lumps of soil pushed up by frost action, often found uniformly spaced in large groups’ ( NSIDC n.d. ), while in the sea ice community a hummock is ‘a hillock of broken ice that has been forced upwards by pressure’ ( WMO/OMM/BMO 1970 ). Both definitions are equally valid but specific to usage within a particular community. It would be pointless to argue about which of these is the right definition, since both clearly are ‘right’ and useful in their specific community. However, semantically speaking, these are two distinct terms that can each have their own unique identifier. For example, ENVO handles this by including the terms sea ice hummock (ENVO:01001537) and frost-formed hummock (ENVO:01001538) both under its elevated landforms branch.

Similarly, it is often the case that a term’s meaning depends either on the organization providing the definition or the region of the world from which the definition came. In either case, arguing over who is right is still pointless; simply acknowledging and understanding the differences and generating multiple terms in an ontology appropriately is sufficient. For example, there are differences in the definition of the term ‘blizzard’ depending on which country or continent the definition came from. Thus, in the US the Weather Service definition is not the same as that of the Australian Bureau of Meteorology. The real issue here becomes simply ensuring that there is a superclass concept able to account for all the variation and nuance of the more precise local variations as subclasses (in this case for any differences in the definition of the term blizzard from other meteorological services around the world).

Another case that often occurs is where the definitions of a term are not parallel concepts but are completely different but still related. For example, the term thermokarst can either be a type of landform or the process that results in those kinds of landforms. In these types of cases, resolution is simple – define multiple terms accordingly! In the case of thermokarst, the ENVO ontology includes the term thermokarst (ENVO:03000085) as ‘an irregular land surface which consists of marshy hollows, hummocks, thermokarst depressions and thermokarst lakes formed from the erosion of ice-rich thawing permafrost areas’ and the term thermokarst formation process (ENVO:01001498), which is ‘a process by which landforms are formed from the thawing of ice-rich permafrost or the melting of massive ground ice.’ The thing to remember here is that the labels thermokarst and thermokarst formation process are just that—labels—and as such are easily changed without impacting in any way the organization or structure of the ontology. The only reason why the label for the term ENVO:03000085 is not something like thermokarst landform is simply that it was inserted into the ontology first and the label wasn’t updated when the formation process was added to the ontology later on.

The situation when a term’s meaning changes over time is more complicated, for example, semantic drift. For example, when discussing snow and ice processes prior to 1980, the term ablation did not include mechanical removal of either snow or ice by processes such as wind erosion, avalanches, or calving. Now it does. While semantic technologies and languages such as OWL can deal with temporal and numeric constraints, their inclusion in ontologies such as those within the OBO Foundry has not yet been standardized. Even if such usage were standardized, it isn’t clear how such a temporal constraint could be operationalized without explicitly capturing the date the term was used wherever that term was used. For example, in natural language applications, associating the date when a particular text including that term was written, would be needed, and there would always be edge cases where it would be unclear which definition was used (e.g., papers written during or near 1980).

Worst yet are cases where there are disagreements over concepts. Unfortunately, ontology modeling cannot resolve disputes in the domain of discourse. In these situations, resolution will ultimately require discussion within the various communities involved. For example, within the cryospheric community as a whole there are disagreements about whether an ice sheet is a glacier, a glacier is an ice sheet or whether these are parallel concepts (A more complex case of calving is discussed in the next subsection). In these cases, there are two courses of action, with only one being considered practical. The practical alternative involves 1) acknowledging the problem, 2) include terms in ontologies wherever their inclusion is absolutely required and 3) include a note with the term itself, possibly as a skos:scopeNote, as well as to the editor of the ontology, about the problem and the likelihood that the term’s placement, axiomatization, and/or inclusion may need to change in the future. The other option would be to create a separate ontology capturing the alternate world view, but this option is often considered wildly impractical.

Precise definitions and their axiomatization

While scientists are often accused of using jargon and trying to be very precise, sometimes inhumanely so, it is surprising that many of the definitions in the various disciplinary glossaries and other vocabulary resources developed are often not semantically consistent or complete. This is one reason why formal semantics calls for 1) the careful creation of definitions using analysis methods such the genus-differentia definitional form (that is, dividing terms into classes and subclasses differentiated by properties) complemented by 2) machine-actionable axiomatization which uses a logical language to formally specify the vocabulary of concepts and the relationships among them and 3) by ensuring that the human-readable definition and the corresponding machine-actionable axioms are equivalent ( Seppälä, Ruttenberg & Smith 2017 ). Doing so can both call out and/or fix problems with existing glossaries. Inconsistencies between axioms represented in OWL, for example, can be shown by theorem provers available in tools like Protégé ( Musen 2015 ). However, it is up to the ontology developer(s) to ensure that the human readable definitions and their machine-actionable counterparts actually are equivalent, so that any machine made logical inferences are as expected by humans.

Let’s return to our example in Figure 5 . The term calving is an ablative process where chunks of ice fall off a parent body (e.g., a calving glacier). There is ambiguity in the existing dozen definitions in the GCW compilation for both the process and the resulting chunks of ice. Some definitions assume that the calving process can only happen going into water while others allow calving on land. Also some definitions allow calving to occur from any form of ice of land origin (e.g., ice sheets, ice caps, ice shelves), while others restrict it to glaciers or some other subset of all of the types of ice of land origin. What ice calved onto land would be called is not obvious, especially since the only definition of calved ice in the GCW compilation excludes ice falling onto land. To resolve the ambiguity with process terminology, we defined four subclasses in ENVO under the class ‘ice calving process’: calving of ice from an iceberg, calving of ice into water, calving of ice onto land (i.e., dry calving or terrestrial calving), and glacial ice calving process. While it is unlikely that there will ever be a need for other terms for what ice is falling onto (can ice fall onto or into anything other than water or land?), there may well be the need to add terms for other sources of the falling ice (e.g., ice sheet, ice cap, thick permafrost embedded in an eroding cliff, etc.) in the future, provided of course that there are use cases where such distinctions are important.

As an example of the genus-differentia definitional form, the definition of the term calving of ice into water is ‘An ice calving process during which a mass of ice falls from a larger mass into a body of water’ where ice calving process is the parent, more general class. The rest of the sentence describes how this term is specialized from its parent. In terms of the machine-actionable axiomatization of the term, the only difference in axiomatization of the term and its parent is the addition of a water body as a participant in the process (i.e., ‘has participant’ some ‘water body’).

Another example of axiomatization of an ENVO term is permafrost . We created formally defined axioms that specify that permafrost is a type of ‘environmental material’ which ‘has quality some decreased temperature’ and is ‘composed primarily of some (sediment or soil or rock).’ One of its sub-types is ‘ice-bearing permafrost’ which ‘has part some water ice.’ Permafrost also has a human-readable definition of ‘Soil or rock and included ice or organic material at or below the freezing point of water (0 degrees Celsius or 32 degrees Fahrenheit) for two or more years.’ This is a case where the human readable definition is more precise than the axiomatization. Clearly, when or if the larger semantic community promulgates a standard way of including numeric constraints into axioms, these axioms will need to be updated, perhaps as ‘has quality some freezing years’ >=2; where ‘freezing years’ axioms are something like ‘has quality maximum temperature < 0C’ and ‘has quality minimum duration.’

Mapping across inconsistent ontology hierarchies

Given the issues with harmonizing terms in glossaries as discussed above, and the vast number of glossaries, it would be surprising if two ontologies created by different groups, for different purposes at possibly different times had internal hierarchies that were the same. Yet, that doesn’t mean that it is impossible to harmonize across such resources; it is just not as straightforward as simply mapping lexically equivalent terms.

Consequently, when adding terms to an existing ontology the resulting contextual structure/hierarchy for the added terms may not necessarily be the same as would occur if adding to a different ontology or if creating a new and independent ontology, say a stand-alone cryosphere ontology. But, even when creating a new ontology, the order of adding classes can result in a functionally similar but different ontology structure. That is, which terms were added first can influence where later terms are placed. So, as we added cryosphere terms one by one to ENVO, the terms were subclassed into the most relevant existing classes. This scattered some terms that, on later inspection, could have been more closely related, and the initial result may eventually be slightly changed. The piecemeal process of adding terms and creating a new whole that makes sense is difficult regardless of creating a new ontology or adding to an existing ontology and is probably non-deterministic regarding the exact same hierarchical result. Accuracy can be retained, however. A few examples follow.

For example, the concept ‘greenhouse gas’ encompasses both a role and a material entity. In ENVO there is no material entity that is a ‘greenhouse’ gas, but certain gasses can bear this role. So in ENVO, greenhouse gas is a term from the Chemical Entities of Biological Interest (ChEBI) ontology (i.e., CHEBI_76413) and not a term under ‘gas molecular entity.’ However, in SWEET, greenhouse gas is a both a subclass of ‘chemical substance’ and a subclass of ‘chemical.’

As another example, in the ENVO ontology, ‘cryosol’ is a subclass of frozen soil, and ‘part_of’ is its relationship to ‘permafrost’; but in SWEET ‘cryosol’ is a ‘categorical property,’ specifically a subclass of ‘soil order.’ Also, in SWEET, ‘gelisol’ is listed as a sibling of ‘cryosol,’ whereas ‘gelisol’ is a synonym of ‘cryosol’ in ENVO.

‘Snowpack’ is a subclass under ‘thickness’ in SWEET, although immediately under ‘snow cover.’ In ENVO, ‘snowpack’ is under ‘snow mass,’ which is under ‘mass of compounded environmental materials.’ Given that SWEET considers the term to be a thickness and ENVO currently considers it a mass of snow, there is a mismatch. The definition in ENVO does refer to size, however, as in being large enough and persisting long enough to form layers under its own weight. Overall, the GCW analysis found eight definitions of snowpack over multiple glossaries, with many commonalities but also disagreements.

In ENVO, proglacial (ENVO:01001853) is a ‘positional quality which inheres in a bearer by virtue of the bearer in being in physical contact with, or close to, a glacial margin.’ But, in SWEET, ‘proglacial’ is not a concept that refers to being, say, in front of a glacier, but instead is a process, that is, found under ‘glacial process’ along with other processes such as ‘accumulation,’ ‘calving,’ and ‘glacial retreat’.

In each of the examples above, it was possible to generate a SSSOM relationship between the terms despite their differences.

In summary, the definitions and uses of terms can vary across ontologies such that hierarchies and conceptualizations differ. This makes alignment or harmonization imprecise. Delving into these differences, however, can expand one’s knowledge across disciplines and perspectives and may help the expert community reassess and standardize its definitions.

Sociotechnical issues

In addition to issues related to the often ambiguous or incomplete definitions, difficulties with inconsistent ontology structures and current limitations in axiomatization, we encountered several issues that were more on the social side of the sociotechnical spectrum that needed to be resolved.

First, many GCW terms are entirely missing from either ENVO or SWEET or both. Simply put, the GCW provides a much more comprehensive compilation of terms in use within this discipline. The question then becomes one of scoping—how much coverage of the terms in the GCW would be appropriate for this work? We decided to limit ourselves to terms that were present in SWEET or ENVO and to add related terms to ENVO as were judged relevant to the existing ENVO community. For example, several compaction and erosion related terms were added to ENVO because material transformation processes having inputs and outputs are an important branch of the ENVO ontology. This decision constrained the work to the limited bandwidth available within the ESIP harmonization cluster membership.

Second, this work reflects the understanding that practical and resource limitations mean that collaborative development of a single encompassing semantic resource for a domain is likely to be impossible. A better target is harmonizing semantic resources within a defined scope of work, the scope of work that participants in the harmonization process care about. This can start at the lower end of the semantic spectrum by harvesting well-established and well-defined terminologies as was done in this work. Agreement on the meaning of termed concepts is a first step toward alignment across the semantic spectrum and its impact on the overall ontological structure can be judged as work continues. A degree of interoperability, though minimal, is the reward.

In practice, what this also means is that it is likely that semantic modeling of any term in any ontology will only be as deep as is necessary to satisfy current use cases. For example, the term snow water equivalent describes the output of a method used to determine how much water is present in a given volume of snow. Snow covering a defined area is collected and then melted. The depth of the resulting snowmelt is measured after it has been transferred to a standardized container. A value for snow water equivalent (SWE) can also be inferred via remote sensing technologies. Complete semantic modeling of this term would require that the processes of identifying, collecting, and melting a volume of snow and subsequently measuring the volume of the resulting water be modeled for ground-based methods and the algorithms used to infer SWE from remote sensing observations also be modeled. Neither SWEET nor ENVO currently model this term or many comparable terms to that level of detail; though either could be updated to include deeper modeling if and when new use cases surfaced that require it.

In general, semantic resources of any type are living objects, subject to change over time, just as all languages in use (i.e., living languages) change over time. Both ENVO and SWEET have existed for more than a decade and some of the glossaries compiled by the GCW are well over 60 years old. What this meant in practical terms was that we needed to review the history of each term and its placement within the ENVO and SWEET hierarchies for every term addressed. In some cases this meant we needed to change an ontology to use better and more recently defined terms. For example, we switched to using the Chemical Entities of Biological Interest Ontology term for water, CHEBI:water, rather than the original ENVO term for water to handle issues of the hydrological precipitation process that arose when revising hailfall and snowfall in a systematic way.

As a corollary to these last several issues and given the hierarchy inconsistencies evident in comparing ontologies such as ENVO and SWEET, it should be noted that the need for semantic harmonization will only grow as long as people continue to reinvent the wheel each and every time they need to use semantic resources within their work. Currently the norm within the Earth and Environmental sciences is for folks who need to use semantics to invent their own semantic resources no matter how many resources either partially or totally covering that topic already exist. A better use of these people’s time would be for them to collaborate with the communities currently maintaining existing semantic resources and determining what extensions, refactoring, and so on of those resources are needed and contributing their efforts to the larger community. Having a well-maintained repository and ontology/term discovery resource for the Earth sciences, akin to the OBO Foundry and BioPortal resources in the Biomedical community, might go a long way to helping resolve this problem which is currently inhibiting uptake of semantics in our field.

Lessons Learned and Recommendations

Many lessons were learned along the way, with some noted as part of the previous discussion. The following are some of the main lessons along with recommendations for managing semantic harmonization.

Proper scope and interdisciplinary teams are needed

From a project perspective, starting with the right scope and an adequate, interdisciplinary team is important. Selecting a proper set of terms is important as is the value of building a coalition of interested parties around the selected set of concepts to harmonize. This starts with clearly identifying the conceptual space you are trying to describe and define. With the help of definitions one can analyze the conceptual space to understand the key concepts and relationships that are contained in a core subset of the terms looked at. Next is to evaluate the feasibility of a preliminary scope based on factors such as available resources and time constraints and prioritize a final set of semantic resources that need to be included in the scope based on the targeted conceptual space, stakeholders’ needs, domain analysis, and feasibility considerations. It is also important to identify the stakeholders who will likely use the harmonized vocabulary and ensure that the team has a good balance of domain and semantic technology experts with good communication skills for effective collaboration and resolving any conflicts that may arise.

Glossary harmonization is foundational

Merging and splitting of glossary terms at lower levels of the semantic ladder (as well as identification of sub meanings) is needed before the more difficult alignment at higher levels of the semantic ladder because many terms can have a variety of synonyms and closely related terms that make them similar. For example, the term tabular iceberg can be found in glossaries under the synonyms tabular berg and table iceberg , and it was formerly called a barrier iceberg . Similarly, ensuring that the same label is not re-used for another term within an ontology is important for minimizing confusion. This problem can be easily prevented simply by adding disambiguating phrases to the term, for example, thermokarst landscape and thermokarst process , as discussed earlier. Once mapped, the alignment of textual definitions with axiomized representations in ontologies can be performed. For all these reasons and to make the sequence of changes to the ontology clear (i.e., its provenance), there should be an item by item commit to updates and documentation of the changes made.

Use tools whenever possible

The well-documented ROBOT Templates ( Jackson et al. 2019 ) and their supporting scripts, described in Step 3 above, allow shared best practices with spreadsheet-like editing modality for more inclusivity. These tools help cross the domain expert to ontologist divide by allowing routine, asynchronous work within domain communities without relying on a trained ontology engineer.

Human expertise is important

A central lesson is that while automation, such as simple label matching and tools like ROBOT can help with routine tasks, a human-in-the-loop for things like ontology curation was needed. While time consuming, this human curated approach proved to be much more accurate than other approaches which generally ignore both differences in the organization of the hierarchies of different resources as well as the richness of the subclasses and axioms underlying the mapped terms.

As seen in the Discussion, there were many lessons learned in assigning the type of SKOS match between terms, especially when there is not an adequate definition in one of the ontologies. The most important lesson is that when alternate definitions exist from different points of view, arguing over who is right is less useful than simply acknowledging, understanding, and documenting the differences by appropriately generating multiple terms in an ontology.

Future Work

Based on the results of this work, the ESIP semantic community expects to continue working in three areas: 1) pushing the greater OBO Foundry and general semantics community to formalize the handling of numeric values and ranges in ontologies; 2) evolving the SWEET ontology in support of harmonization and 3) pursuing related semantic harmonization work in several other ESIP clusters. These topics are described in more detail in the following paragraphs.

Formalizing the handling of numeric values and ranges in ontologies

As has been mentioned previously it is often the case in science that the definition of a concept will include numeric values. For example, the composite definition for the term ice pellet from the 27 glossaries in the GCW compilation and included in the ENVO ontology is ‘An ice mass which is 1) transparent or translucent, 2) rounded, spherically, or cylindrically shaped, and 3) less than 5 millimeters in diameter.’ Similarly, nearly all of the terms in the WMO Sea Ice Nomenclature ( WMO/OMM/BMO 1970 ) include numeric criteria related to the age of the ice, the size of the floe, and so on. Currently, there is no agreement as to a uniform way of adding numeric values, with units, as an axiom. This is critical if ontologies are to be useful for characterizing and understanding scientific data. In particular, for this project it would have been very useful if the OBO Foundry consortium had agreed to a convention for this, since, as is, many terms within ENVO currently have incomplete axiomatization where the human readable definition is more accurate and complete than the computer processable axiomatization.

The SWEET ontology suite is a long-standing community resource and continues to evolve. Pursuant to the work described here, the harmonized GCW definitions now in ENVO are also being added to SWEET. As such, SWEET developers and the broader community of practice will soon be able to utilize SSSOM mappings to cross-reference back to ENVO and/or add further definition annotations which include the provenance available from that resource.

In addition to the SSSOM mappings, updates to the curation process, creation and enhancement of domain and observational concepts and properties, as well as the underlying technology stack supporting the resource, it was determined by the community that SWEET could fill a current gap by housing textual concept definitions from disparate Earth and Environmental science resources, thus making SWEET a hub for domain relevant concepts including, potentially, multiple independently sourced definitions which are not semantically equivalent. In this context, resources for definitions could be established vocabularies—for example, GCMD, USGS Thesaurus, and so on—as well as resources which currently exist in a static, unstructured format—for example, Dictionary of Geologic Terms ( Bates & Jackson 1984 ) or Glossary of Geology ( Neuendorf, Mehl, Jr., & Jackson 2011 ) currently available in hard copy format, or other resources perhaps only available as a PDF. Each candidate definition is to be added using annotation properties (i.e., it will not affect any axioms in the initial investigation) with proper citation and contributor information (i.e., creator and reviewer) attached to each recorded textual definition.

It is the hope that using SWEET as hub for concept definitions will highlight similarities and gaps in Earth science conceptual descriptions and knowledge as well as provide the groundwork for making concepts more precise and increasing their expressivity. This latter point will be crucial for the future development of the resource.

Future harmonization work

We believe that semantic harmonization is an important and often missing ingredient to help find, make sense of, and usefully employ digital data as well as being critical to making data FAIR. Our outcomes and progress with the cryosphere have motivated us to begin work with other ESIP clusters in harmonizing key terminological resources in the following domains (see Table 2 ).

Future harmonization work by Earth and Environmental science domain.

Alignment and semantic harmonization across the growing types of semantic resources is important for data interoperability and reuse, thus satisfying FAIR principles. In this work we have shown how a focused interdisciplinary team of domain experts and semantic technology developers can effectively harmonize semantic resources using a standard method. The process developed is to review and synthesize content in a stepwise fashion from a collection of thematic glossaries into a harmonized collection and then to align these and further document them along with richer, more machine-actionable resources higher on the semantic ladder (i.e., here, SWEET, and ENVO).

In piloting this process we encountered several issues and documented the lessons learned from these experiences. This includes many examples that we hope will help other communities attempting to perform similar activities.

Data Accessibility Statements

All of the data associated with this work is publicly available on Zenodo ( Semantic Harmonization Cluster 2023 ) as well as on the ESIP github ( Duerr 2023 ). The ENVO ontology can be found on the OBO Foundry ( OBO Technical Working Group 2024 ); while the SWEET ontology can be found on the ESIP Community Ontology Repository (COR) ( ESIPFed 2023 ).

Acknowledgements

This work is based on materials, programs, collaboration platform, and meeting spaces provided by the ESIP Community with support from the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). We’d especially like to thank Chantelle Verhey for her very helpful comments on drafts of this work and Mark Schildhauer and Anne Thessen for their insights and suggestions in defining the challenges and approaches early in the project. Some of the work included here was conducted using Protégé.

Funding Information

Pier Luigi-Buttigieg was supported by the Helmholtz Metadata Collaboration and the Frontiers in Arctic Marine Monitoring Programme of the Alfred Wegener Institute, Helmholtz Institute for Polar and Marine Research. Brandon Whitehead was supported by New Zealand’s Ministry of Business Innovation and Employment (MBIE) Infrastructure Platform. Kate Rose was supported by the Northern Gulf Institute under a grant from NOAA National Centers for Environmental Information.

Competing Interests

The authors have no competing interests to declare.

Author Contributions

Conceptualization: Ruth Duerr, Gary Berg-Cross, Brandon Whitehead, Mark Schildhauer, Anne Thessen, Pier Luigi Buttigieg

Data curation: Ruth Duerr

Formal analysis: Ruth Duerr, Gary Berg-Cross, Nancy Wiegand, Brandon Whitehead, Pier Luigi Buttigieg

Investigation: Ruth Duerr, Gary Berg-Cross, Nancy Wiegand, Brandon Whitehead, Anne Thessen, Pier Luigi Buttigieg

Methodology: Ruth Duerr, Gary Berg-Cross, Brandon Whitehead, Pier Luigi Buttigieg

Resources: Ruth Duerr, Pier Luigi-Buttigieg, ESIP

Software: Kai Blumberg, Pier Luigi-Buttigieg, Ruth Duerr, Brandon Whitehead

Validation: Ruth Duerr

Visualization: Ruth Duerr, Kate Rose, Pier Luigi Buttigieg

Writing, original draft: Gary Berg-Cross, Nancy Wiegand, Brandon Whitehead, Kate Rose, Ruth Duerr, Pier Luigi Buttigieg

Writing, review and editing: Gary Berg-Cross, Nancy Wiegand, Brandon Whitehead, Kate Rose, Ruth Duerr, Chantelle Verhey

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