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What Is Background in a Research Paper?

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So you have carefully written your research paper  and probably ran it through your colleagues ten to fifteen times. While there are many elements to a good research article, one of the most important elements for your readers is the background of your study.

What is Background of the Study in Research

The background of your study will provide context to the information discussed throughout the research paper . Background information may include both important and relevant studies. This is particularly important if a study either supports or refutes your thesis.

Why is Background of the Study Necessary in Research?

The background of the study discusses your problem statement, rationale, and research questions. It links  introduction to your research topic  and ensures a logical flow of ideas.  Thus, it helps readers understand your reasons for conducting the study.

Providing Background Information

The reader should be able to understand your topic and its importance. The length and detail of your background also depend on the degree to which you need to demonstrate your understanding of the topic. Paying close attention to the following questions will help you in writing background information:

  • Are there any theories, concepts, terms, and ideas that may be unfamiliar to the target audience and will require you to provide any additional explanation?
  • Any historical data that need to be shared in order to provide context on why the current issue emerged?
  • Are there any concepts that may have been borrowed from other disciplines that may be unfamiliar to the reader and need an explanation?
Related: Ready with the background and searching for more information on journal ranking? Check this infographic on the SCImago Journal Rank today!

Is the research study unique for which additional explanation is needed? For instance, you may have used a completely new method

How to Write a Background of the Study

The structure of a background study in a research paper generally follows a logical sequence to provide context, justification, and an understanding of the research problem. It includes an introduction, general background, literature review , rationale , objectives, scope and limitations , significance of the study and the research hypothesis . Following the structure can provide a comprehensive and well-organized background for your research.

Here are the steps to effectively write a background of the study.

1. Identify Your Audience:

Determine the level of expertise of your target audience. Tailor the depth and complexity of your background information accordingly.

2. Understand the Research Problem:

Define the research problem or question your study aims to address. Identify the significance of the problem within the broader context of the field.

3. Review Existing Literature:

Conduct a thorough literature review to understand what is already known in the area. Summarize key findings, theories, and concepts relevant to your research.

4. Include Historical Data:

Integrate historical data if relevant to the research, as current issues often trace back to historical events.

5. Identify Controversies and Gaps:

Note any controversies or debates within the existing literature. Identify gaps , limitations, or unanswered questions that your research can address.

6. Select Key Components:

Choose the most critical elements to include in the background based on their relevance to your research problem. Prioritize information that helps build a strong foundation for your study.

7. Craft a Logical Flow:

Organize the background information in a logical sequence. Start with general context, move to specific theories and concepts, and then focus on the specific problem.

8. Highlight the Novelty of Your Research:

Clearly explain the unique aspects or contributions of your study. Emphasize why your research is different from or builds upon existing work.

Here are some extra tips to increase the quality of your research background:

Example of a Research Background

Here is an example of a research background to help you understand better.

The above hypothetical example provides a research background, addresses the gap and highlights the potential outcome of the study; thereby aiding a better understanding of the proposed research.

What Makes the Introduction Different from the Background?

Your introduction is different from your background in a number of ways.

  • The introduction contains preliminary data about your topic that  the reader will most likely read , whereas the background clarifies the importance of the paper.
  • The background of your study discusses in depth about the topic, whereas the introduction only gives an overview.
  • The introduction should end with your research questions, aims, and objectives, whereas your background should not (except in some cases where your background is integrated into your introduction). For instance, the C.A.R.S. ( Creating a Research Space ) model, created by John Swales is based on his analysis of journal articles. This model attempts to explain and describe the organizational pattern of writing the introduction in social sciences.

Points to Note

Your background should begin with defining a topic and audience. It is important that you identify which topic you need to review and what your audience already knows about the topic. You should proceed by searching and researching the relevant literature. In this case, it is advisable to keep track of the search terms you used and the articles that you downloaded. It is helpful to use one of the research paper management systems such as Papers, Mendeley, Evernote, or Sente. Next, it is helpful to take notes while reading. Be careful when copying quotes verbatim and make sure to put them in quotation marks and cite the sources. In addition, you should keep your background focused but balanced enough so that it is relevant to a broader audience. Aside from these, your background should be critical, consistent, and logically structured.

Writing the background of your study should not be an overly daunting task. Many guides that can help you organize your thoughts as you write the background. The background of the study is the key to introduce your audience to your research topic and should be done with strong knowledge and thoughtful writing.

The background of a research paper typically ranges from one to two paragraphs, summarizing the relevant literature and context of the study. It should be concise, providing enough information to contextualize the research problem and justify the need for the study. Journal instructions about any word count limits should be kept in mind while deciding on the length of the final content.

The background of a research paper provides the context and relevant literature to understand the research problem, while the introduction also introduces the specific research topic, states the research objectives, and outlines the scope of the study. The background focuses on the broader context, whereas the introduction focuses on the specific research project and its objectives.

When writing the background for a study, start by providing a brief overview of the research topic and its significance in the field. Then, highlight the gaps in existing knowledge or unresolved issues that the study aims to address. Finally, summarize the key findings from relevant literature to establish the context and rationale for conducting the research, emphasizing the need and importance of the study within the broader academic landscape.

The background in a research paper is crucial as it sets the stage for the study by providing essential context and rationale. It helps readers understand the significance of the research problem and its relevance in the broader field. By presenting relevant literature and highlighting gaps, the background justifies the need for the study, building a strong foundation for the research and enhancing its credibility.

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Table of Contents

The background of a study is one of the most important components of a research paper. The quality of the background determines whether the reader will be interested in the rest of the study. Thus, to ensure that the audience is invested in reading the entire research paper, it is important to write an appealing and effective background. So, what constitutes the background of a study, and how must it be written?

What is the background of a study?

The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the research and its importance in a clear and logical manner. At times, the background may even explore whether the study builds on or refutes findings from previous studies. Any relevant information that the readers need to know before delving into the paper should be made available to them in the background.

How is a background different from the introduction?

The introduction of your research paper is presented before the background. Let’s find out what factors differentiate the background from the introduction.

  • The introduction only contains preliminary data about the research topic and does not state the purpose of the study. On the contrary, the background clarifies the importance of the study in detail.
  • The introduction provides an overview of the research topic from a broader perspective, while the background provides a detailed understanding of the topic.
  • The introduction should end with the mention of the research questions, aims, and objectives of the study. In contrast, the background follows no such format and only provides essential context to the study.

How should one write the background of a research paper?

The length and detail presented in the background varies for different research papers, depending on the complexity and novelty of the research topic. At times, a simple background suffices, even if the study is complex. Before writing and adding details in the background, take a note of these additional points:

  • Start with a strong beginning: Begin the background by defining the research topic and then identify the target audience.
  • Cover key components: Explain all theories, concepts, terms, and ideas that may feel unfamiliar to the target audience thoroughly.
  • Take note of important prerequisites: Go through the relevant literature in detail. Take notes while reading and cite the sources.
  • Maintain a balance: Make sure that the background is focused on important details, but also appeals to a broader audience.
  • Include historical data: Current issues largely originate from historical events or findings. If the research borrows information from a historical context, add relevant data in the background.
  • Explain novelty: If the research study or methodology is unique or novel, provide an explanation that helps to understand the research better.
  • Increase engagement: To make the background engaging, build a story around the central theme of the research

Avoid these mistakes while writing the background:

  • Ambiguity: Don’t be ambiguous. While writing, assume that the reader does not understand any intricate detail about your research.
  • Unrelated themes: Steer clear from topics that are not related to the key aspects of your research topic.
  • Poor organization: Do not place information without a structure. Make sure that the background reads in a chronological manner and organize the sub-sections so that it flows well.

Writing the background for a research paper should not be a daunting task. But directions to go about it can always help. At Elsevier Author Services we provide essential insights on how to write a high quality, appealing, and logically structured paper for publication, beginning with a robust background. For further queries, contact our experts now!

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Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

  • Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
  • Provide context: Provide a brief overview of the research area and highlight its significance in the field.
  • Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
  • Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
  • Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
  • Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
  • Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
  • Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

  • Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
  • Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
  • I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
  • Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
  • Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
  • State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
  • Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

  • Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
  • Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
  • Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
  • Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
  • State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
  • Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

  • Provide a clear understanding of the research problem and its context.
  • Identify the gap in knowledge that the study intends to fill.
  • Establish the significance of the research problem and its potential contribution to the field.
  • Highlight the key concepts, theories, and research findings related to the problem.
  • Provide a rationale for the research questions or hypotheses and the research design.
  • Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

  • Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
  • Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
  • Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
  • Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
  • Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

  • Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
  • Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
  • Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
  • Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
  • Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

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Background information identifies and describes the history and nature of a well-defined research problem with reference to contextualizing existing literature. The background information should indicate the root of the problem being studied, appropriate context of the problem in relation to theory, research, and/or practice , its scope, and the extent to which previous studies have successfully investigated the problem, noting, in particular, where gaps exist that your study attempts to address. Background information does not replace the literature review section of a research paper; it is intended to place the research problem within a specific context and an established plan for its solution.

Fitterling, Lori. Researching and Writing an Effective Background Section of a Research Paper. Kansas City University of Medicine & Biosciences; Creating a Research Paper: How to Write the Background to a Study. DurousseauElectricalInstitute.com; Background Information: Definition of Background Information. Literary Devices Definition and Examples of Literary Terms.

Importance of Having Enough Background Information

Background information expands upon the key points stated in the beginning of your introduction but is not intended to be the main focus of the paper. It generally supports the question, what is the most important information the reader needs to understand before continuing to read the paper? Sufficient background information helps the reader determine if you have a basic understanding of the research problem being investigated and promotes confidence in the overall quality of your analysis and findings. This information provides the reader with the essential context needed to conceptualize the research problem and its significance before moving on to a more thorough analysis of prior research.

Forms of contextualization included in background information can include describing one or more of the following:

  • Cultural -- placed within the learned behavior of a specific group or groups of people.
  • Economic -- of or relating to systems of production and management of material wealth and/or business activities.
  • Gender -- located within the behavioral, cultural, or psychological traits typically associated with being self-identified as male, female, or other form of  gender expression.
  • Historical -- the time in which something takes place or was created and how the condition of time influences how you interpret it.
  • Interdisciplinary -- explanation of theories, concepts, ideas, or methodologies borrowed from other disciplines applied to the research problem rooted in a discipline other than the discipline where your paper resides.
  • Philosophical -- clarification of the essential nature of being or of phenomena as it relates to the research problem.
  • Physical/Spatial -- reflects the meaning of space around something and how that influences how it is understood.
  • Political -- concerns the environment in which something is produced indicating it's public purpose or agenda.
  • Social -- the environment of people that surrounds something's creation or intended audience, reflecting how the people associated with something use and interpret it.
  • Temporal -- reflects issues or events of, relating to, or limited by time. Concerns past, present, or future contextualization and not just a historical past.

Background information can also include summaries of important research studies . This can be a particularly important element of providing background information if an innovative or groundbreaking study about the research problem laid a foundation for further research or there was a key study that is essential to understanding your arguments. The priority is to summarize for the reader what is known about the research problem before you conduct the analysis of prior research. This is accomplished with a general summary of the foundational research literature [with citations] that document findings that inform your study's overall aims and objectives.

NOTE : Research studies cited as part of the background information of your introduction should not include very specific, lengthy explanations. This should be discussed in greater detail in your literature review section. If you find a study requiring lengthy explanation, consider moving it to the literature review section.

ANOTHER NOTE : In some cases, your paper's introduction only needs to introduce the research problem, explain its significance, and then describe a road map for how you are going to address the problem; the background information basically forms the introduction part of your literature review. That said, while providing background information is not required, including it in the introduction is a way to highlight important contextual information that could otherwise be hidden or overlooked by the reader if placed in the literature review section.

Background of the Problem Section: What do you Need to Consider? Anonymous. Harvard University; Hopkins, Will G. How to Write a Research Paper. SPORTSCIENCE, Perspectives/Research Resources. Department of Physiology and School of Physical Education, University of Otago, 1999; Green, L. H. How to Write the Background/Introduction Section. Physics 499 Powerpoint slides. University of Illinois; Woodall, W. Gill. Writing the Background and Significance Section. Senior Research Scientist and Professor of Communication. Center on Alcoholism, Substance Abuse, and Addictions. University of New Mexico.  

Structure and Writing Style

Providing background information in the introduction of a research paper serves as a bridge that links the reader to the research problem . Precisely how long and in-depth this bridge should be is largely dependent upon how much information you think the reader will need to know in order to fully understand the problem being discussed and to appreciate why the issues you are investigating are important.

From another perspective, the length and detail of background information also depends on the degree to which you need to demonstrate to your professor how much you understand the research problem. Keep this in mind because providing pertinent background information can be an effective way to demonstrate that you have a clear grasp of key issues, debates, and concepts related to your overall study.

The structure and writing style of your background information can vary depending upon the complexity of your research and/or the nature of the assignment. However, in most cases it should be limited to only one to two paragraphs in your introduction.

Given this, here are some questions to consider while writing this part of your introduction :

  • Are there concepts, terms, theories, or ideas that may be unfamiliar to the reader and, thus, require additional explanation?
  • Are there historical elements that need to be explored in order to provide needed context, to highlight specific people, issues, or events, or to lay a foundation for understanding the emergence of a current issue or event?
  • Are there theories, concepts, or ideas borrowed from other disciplines or academic traditions that may be unfamiliar to the reader and therefore require further explanation?
  • Is there a key study or small set of studies that set the stage for understanding the topic and frames why it is important to conduct further research on the topic?
  • Y our study uses a method of analysis never applied before;
  • Your study investigates a very esoteric or complex research problem;
  • Your study introduces new or unique variables that need to be taken into account ; or,
  • Your study relies upon analyzing unique texts or documents, such as, archival materials or primary documents like diaries or personal letters that do not represent the established body of source literature on the topic?

Almost all introductions to a research problem require some contextualizing, but the scope and breadth of background information varies depending on your assumption about the reader's level of prior knowledge . However, despite this assessment, background information should be brief and succinct and sets the stage for the elaboration of critical points or in-depth discussion of key issues in the literature review section of your paper.

Background of the Problem Section: What do you Need to Consider? Anonymous. Harvard University; Hopkins, Will G. How to Write a Research Paper. SPORTSCIENCE, Perspectives/Research Resources. Department of Physiology and School of Physical Education, University of Otago, 1999; Green, L. H. How to Write the Background/Introduction Section. Physics 499 Powerpoint slides. University of Illinois; Woodall, W. Gill. Writing the Background and Significance Section. Senior Research Scientist and Professor of Communication. Center on Alcoholism, Substance Abuse, and Addictions. University of New Mexico.

Writing Tip

Background Information vs. the Literature Review

Incorporating background information into the introduction is intended to provide the reader with critical information about the topic being studied, such as, highlighting and expanding upon foundational studies conducted in the past, describing important historical events that inform why and in what ways the research problem exists, defining key components of your study [concepts, people, places, phenomena] and/or placing the research problem within a particular context. Although introductory background information can often blend into the literature review portion of the paper, essential background information should not be considered a substitute for a comprehensive review and synthesis of relevant research literature.

Hart, Cris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage, 1998.

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How to Write an Effective Background of the Study: A Comprehensive Guide

Madalsa

Table of Contents

The background of the study in a research paper offers a clear context, highlighting why the research is essential and the problem it aims to address.

As a researcher, this foundational section is essential for you to chart the course of your study, Moreover, it allows readers to understand the importance and path of your research.

Whether in academic communities or to the general public, a well-articulated background aids in communicating the essence of the research effectively.

While it may seem straightforward, crafting an effective background requires a blend of clarity, precision, and relevance. Therefore, this article aims to be your guide, offering insights into:

  • Understanding the concept of the background of the study.
  • Learning how to craft a compelling background effectively.
  • Identifying and sidestepping common pitfalls in writing the background.
  • Exploring practical examples that bring the theory to life.
  • Enhancing both your writing and reading of academic papers.

Keeping these compelling insights in mind, let's delve deeper into the details of the empirical background of the study, exploring its definition, distinctions, and the art of writing it effectively.

What is the background of the study?

The background of the study is placed at the beginning of a research paper. It provides the context, circumstances, and history that led to the research problem or topic being explored.

It offers readers a snapshot of the existing knowledge on the topic and the reasons that spurred your current research.

When crafting the background of your study, consider the following questions.

  • What's the context of your research?
  • Which previous research will you refer to?
  • Are there any knowledge gaps in the existing relevant literature?
  • How will you justify the need for your current research?
  • Have you concisely presented the research question or problem?

In a typical research paper structure, after presenting the background, the introduction section follows. The introduction delves deeper into the specific objectives of the research and often outlines the structure or main points that the paper will cover.

Together, they create a cohesive starting point, ensuring readers are well-equipped to understand the subsequent sections of the research paper.

While the background of the study and the introduction section of the research manuscript may seem similar and sometimes even overlap, each serves a unique purpose in the research narrative.

Difference between background and introduction

A well-written background of the study and introduction are preliminary sections of a research paper and serve distinct purposes.

Here’s a detailed tabular comparison between the two of them.

What is the relevance of the background of the study?

It is necessary for you to provide your readers with the background of your research. Without this, readers may grapple with questions such as: Why was this specific research topic chosen? What led to this decision? Why is this study relevant? Is it worth their time?

Such uncertainties can deter them from fully engaging with your study, leading to the rejection of your research paper. Additionally, this can diminish its impact in the academic community, and reduce its potential for real-world application or policy influence .

To address these concerns and offer clarity, the background section plays a pivotal role in research papers.

The background of the study in research is important as it:

  • Provides context: It offers readers a clear picture of the existing knowledge, helping them understand where the current research fits in.
  • Highlights relevance: By detailing the reasons for the research, it underscores the study's significance and its potential impact.
  • Guides the narrative: The background shapes the narrative flow of the paper, ensuring a logical progression from what's known to what the research aims to uncover.
  • Enhances engagement: A well-crafted background piques the reader's interest, encouraging them to delve deeper into the research paper.
  • Aids in comprehension: By setting the scenario, it aids readers in better grasping the research objectives, methodologies, and findings.

How to write the background of the study in a research paper?

The journey of presenting a compelling argument begins with the background study. This section holds the power to either captivate or lose the reader's interest.

An effectively written background not only provides context but also sets the tone for the entire research paper. It's the bridge that connects a broad topic to a specific research question, guiding readers through the logic behind the study.

But how does one craft a background of the study that resonates, informs, and engages?

Here, we’ll discuss how to write an impactful background study, ensuring your research stands out and captures the attention it deserves.

Identify the research problem

The first step is to start pinpointing the specific issue or gap you're addressing. This should be a significant and relevant problem in your field.

A well-defined problem is specific, relevant, and significant to your field. It should resonate with both experts and readers.

Here’s more on how to write an effective research problem .

Provide context

Here, you need to provide a broader perspective, illustrating how your research aligns with or contributes to the overarching context or the wider field of study. A comprehensive context is grounded in facts, offers multiple perspectives, and is relatable.

In addition to stating facts, you should weave a story that connects key concepts from the past, present, and potential future research. For instance, consider the following approach.

  • Offer a brief history of the topic, highlighting major milestones or turning points that have shaped the current landscape.
  • Discuss contemporary developments or current trends that provide relevant information to your research problem. This could include technological advancements, policy changes, or shifts in societal attitudes.
  • Highlight the views of different stakeholders. For a topic like sustainable agriculture, this could mean discussing the perspectives of farmers, environmentalists, policymakers, and consumers.
  • If relevant, compare and contrast global trends with local conditions and circumstances. This can offer readers a more holistic understanding of the topic.

Literature review

For this step, you’ll deep dive into the existing literature on the same topic. It's where you explore what scholars, researchers, and experts have already discovered or discussed about your topic.

Conducting a thorough literature review isn't just a recap of past works. To elevate its efficacy, it's essential to analyze the methods, outcomes, and intricacies of prior research work, demonstrating a thorough engagement with the existing body of knowledge.

  • Instead of merely listing past research study, delve into their methodologies, findings, and limitations. Highlight groundbreaking studies and those that had contrasting results.
  • Try to identify patterns. Look for recurring themes or trends in the literature. Are there common conclusions or contentious points?
  • The next step would be to connect the dots. Show how different pieces of research relate to each other. This can help in understanding the evolution of thought on the topic.

By showcasing what's already known, you can better highlight the background of the study in research.

Highlight the research gap

This step involves identifying the unexplored areas or unanswered questions in the existing literature. Your research seeks to address these gaps, providing new insights or answers.

A clear research gap shows you've thoroughly engaged with existing literature and found an area that needs further exploration.

How can you efficiently highlight the research gap?

  • Find the overlooked areas. Point out topics or angles that haven't been adequately addressed.
  • Highlight questions that have emerged due to recent developments or changing circumstances.
  • Identify areas where insights from other fields might be beneficial but haven't been explored yet.

State your objectives

Here, it’s all about laying out your game plan — What do you hope to achieve with your research? You need to mention a clear objective that’s specific, actionable, and directly tied to the research gap.

How to state your objectives?

  • List the primary questions guiding your research.
  • If applicable, state any hypotheses or predictions you aim to test.
  • Specify what you hope to achieve, whether it's new insights, solutions, or methodologies.

Discuss the significance

This step describes your 'why'. Why is your research important? What broader implications does it have?

The significance of “why” should be both theoretical (adding to the existing literature) and practical (having real-world implications).

How do we effectively discuss the significance?

  • Discuss how your research adds to the existing body of knowledge.
  • Highlight how your findings could be applied in real-world scenarios, from policy changes to on-ground practices.
  • Point out how your research could pave the way for further studies or open up new areas of exploration.

Summarize your points

A concise summary acts as a bridge, smoothly transitioning readers from the background to the main body of the paper. This step is a brief recap, ensuring that readers have grasped the foundational concepts.

How to summarize your study?

  • Revisit the key points discussed, from the research problem to its significance.
  • Prepare the reader for the subsequent sections, ensuring they understand the research's direction.

Include examples for better understanding

Research and come up with real-world or hypothetical examples to clarify complex concepts or to illustrate the practical applications of your research. Relevant examples make abstract ideas tangible, aiding comprehension.

How to include an effective example of the background of the study?

  • Use past events or scenarios to explain concepts.
  • Craft potential scenarios to demonstrate the implications of your findings.
  • Use comparisons to simplify complex ideas, making them more relatable.

Crafting a compelling background of the study in research is about striking the right balance between providing essential context, showcasing your comprehensive understanding of the existing literature, and highlighting the unique value of your research .

While writing the background of the study, keep your readers at the forefront of your mind. Every piece of information, every example, and every objective should be geared toward helping them understand and appreciate your research.

How to avoid mistakes in the background of the study in research?

To write a well-crafted background of the study, you should be aware of the following potential research pitfalls .

  • Stay away from ambiguity. Always assume that your reader might not be familiar with intricate details about your topic.
  • Avoid discussing unrelated themes. Stick to what's directly relevant to your research problem.
  • Ensure your background is well-organized. Information should flow logically, making it easy for readers to follow.
  • While it's vital to provide context, avoid overwhelming the reader with excessive details that might not be directly relevant to your research problem.
  • Ensure you've covered the most significant and relevant studies i` n your field. Overlooking key pieces of literature can make your background seem incomplete.
  • Aim for a balanced presentation of facts, and avoid showing overt bias or presenting only one side of an argument.
  • While academic paper often involves specialized terms, ensure they're adequately explained or use simpler alternatives when possible.
  • Every claim or piece of information taken from existing literature should be appropriately cited. Failing to do so can lead to issues of plagiarism.
  • Avoid making the background too lengthy. While thoroughness is appreciated, it should not come at the expense of losing the reader's interest. Maybe prefer to keep it to one-two paragraphs long.
  • Especially in rapidly evolving fields, it's crucial to ensure that your literature review section is up-to-date and includes the latest research.

Example of an effective background of the study

Let's consider a topic: "The Impact of Online Learning on Student Performance." The ideal background of the study section for this topic would be as follows.

In the last decade, the rise of the internet has revolutionized many sectors, including education. Online learning platforms, once a supplementary educational tool, have now become a primary mode of instruction for many institutions worldwide. With the recent global events, such as the COVID-19 pandemic, there has been a rapid shift from traditional classroom learning to online modes, making it imperative to understand its effects on student performance.

Previous studies have explored various facets of online learning, from its accessibility to its flexibility. However, there is a growing need to assess its direct impact on student outcomes. While some educators advocate for its benefits, citing the convenience and vast resources available, others express concerns about potential drawbacks, such as reduced student engagement and the challenges of self-discipline.

This research aims to delve deeper into this debate, evaluating the true impact of online learning on student performance.

Why is this example considered as an effective background section of a research paper?

This background section example effectively sets the context by highlighting the rise of online learning and its increased relevance due to recent global events. It references prior research on the topic, indicating a foundation built on existing knowledge.

By presenting both the potential advantages and concerns of online learning, it establishes a balanced view, leading to the clear purpose of the study: to evaluate the true impact of online learning on student performance.

As we've explored, writing an effective background of the study in research requires clarity, precision, and a keen understanding of both the broader landscape and the specific details of your topic.

From identifying the research problem, providing context, reviewing existing literature to highlighting research gaps and stating objectives, each step is pivotal in shaping the narrative of your research. And while there are best practices to follow, it's equally crucial to be aware of the pitfalls to avoid.

Remember, writing or refining the background of your study is essential to engage your readers, familiarize them with the research context, and set the ground for the insights your research project will unveil.

Drawing from all the important details, insights and guidance shared, you're now in a strong position to craft a background of the study that not only informs but also engages and resonates with your readers.

Now that you've a clear understanding of what the background of the study aims to achieve, the natural progression is to delve into the next crucial component — write an effective introduction section of a research paper. Read here .

Frequently Asked Questions

The background of the study should include a clear context for the research, references to relevant previous studies, identification of knowledge gaps, justification for the current research, a concise overview of the research problem or question, and an indication of the study's significance or potential impact.

The background of the study is written to provide readers with a clear understanding of the context, significance, and rationale behind the research. It offers a snapshot of existing knowledge on the topic, highlights the relevance of the study, and sets the stage for the research questions and objectives. It ensures that readers can grasp the importance of the research and its place within the broader field of study.

The background of the study is a section in a research paper that provides context, circumstances, and history leading to the research problem or topic being explored. It presents existing knowledge on the topic and outlines the reasons that spurred the current research, helping readers understand the research's foundation and its significance in the broader academic landscape.

The number of paragraphs in the background of the study can vary based on the complexity of the topic and the depth of the context required. Typically, it might range from 3 to 5 paragraphs, but in more detailed or complex research papers, it could be longer. The key is to ensure that all relevant information is presented clearly and concisely, without unnecessary repetition.

background scientific paper

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How to Write the Background of Your Scientific Paper

Home » Writing the Manuscript » How to Write the Background of Your Scientific Paper

Backgorund #1

You can think of the background of your study as being like the story that preceded your own work. Usually, you will present this background in the introduction of your paper or thesis, although you also can elaborate on it in some cases in your discussion section. No matter where you give the background for your research, you should focus on some key goals in presenting it.

The reason you are giving your reader background is so that they understand why you asked the research question you did and how your findings add to this existing evidence. That means that as you walk the reader through the results that came before yours, you also need to show the reader where the gaps persist. One or more of these gaps is what you hope to fill with your own research.

Photo by   Drew Graham

Stay focused

Although it is tempting to begin any story at the very beginning, you need to choose the right starting point in the continuum of evidence for the story of your work. If the subject of your study is island biogeography, you do not need to begin your background information by describing the 19 th -century work of Alfred Russel Wallace. Instead, you’ll need to home in on fresher findings or more recent results that highlight persistent gaps in your field.

As you unspool the evidence that pointed the way to your own work, do not go into too much detail. Background information does not need to include every detail of previous findings, every step in a biochemical pathway, or every P value or odds ratio from clinical studies you cite. Give the main finding that’s relevant to your own work and why you pursued your research question.

Connect your ideas

For example, if your work is in cancer cell biology with a focus on a specific pathway, that pathway and the step or steps that you worked on are the theme. In presenting the background of this work, you should always use evidence that relates directly to that pathway, especially the specific steps your own research focused on. And you should avoid becoming more expansive and talking about other pathways or broader issues in cell or cancer biology.

Highlight the gaps

You asked a research question because it was an open question that needed an answer. That means that somewhere in the evidence that already existed, you found a gap. As you lay out the focused, relevant evidence that took you to your research question, be sure to point to these gaps. Do not be afraid to explicitly say that they are gaps and that your research is intended in some part to fill them.

Do not write a literature review

If you are writing a thesis, the background section is not the place for the literature review. Your background relates directly to what your work addresses and should retain a focus on that theme. A literature review is broader and can encompass anything even generally related to your work. It’s a place to take the publications you mention in the background and expand on their content and implications, giving them a fuller and more detailed treatment.

3 tips for writing your background section

1. Think: as you would for writing an introduction to a research paper, think about the direct chain of evidence that led to your own work. Make a list of the most important findings that make up that chain of evidence.

2. Organize: use only a few sentences to summarize each contribution to that chain, and then form them into a story that makes sense and stays on theme. Use connecting words and phrases, such as “then” or “after that discovery” or “following on these findings” to keep the connections obvious for the reader.

3. Delete: after you have written your first draft, go through it and delete anything that is not absolutely required for the reader to follow the chain of evidence that led to your research and the gap you’re addressing. Remember that if this is your thesis, you will be able to elaborate and add in plentiful detail in your literature review section. In fact, you can think of your background section in this situation as a sort of summary of your literature review.

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How to Write the Background of a Study

  • Research Process

The background to a study sets the scene . It lays out the “state of the art”. It tells your reader about other research done on the topic in question, via useful review papers and other summaries of the literature.

Updated on May 5, 2023

a pen by a pair of glasses and a notebook to prepare writing the background of a sutdy

The background to your study, sometimes called the ‘state of the art’ (especially in grant writing), sets the scene for a paper. This section shows readers why your research is important, relevant, and why they should continue reading. You must hook them in with a great background to your study, which is part of the overall introduction to your research paper.

In higher impact articles, such as those published in Nature or Science (which is what we are all aiming for, after all …), the study background is t he middle section of an essentially three-part introduction . This section is framed by a presentation of ‘the question’ (first part of the introduction) and a quick explanation of ‘what this paper will do’ (the third part of the introduction).

The introduction of a research paper should be “shaped” like an upside down triangle: 

Start broad. Set the scene with a large-scale general research area [e.g., why doing a PhD erases your writing skills (ha ha) or mental health in teenagers and why this is such a widespread global issue] and then focus down to the question your research addresses (e.g., how can writing skills be improved in PhD students, or brain scans and how these can be used in treatment).

Read on to learn more about framing your next research paper with a well-written and researched background section.

What is the background of a study?

The background to a study sets the scene . It lays out the “state of the art”. It tells your reader about other research done on the topic in question, via useful review papers and other summaries of the literature. 

A background is not a literature review: No one wants to read endless citations back-to-back in this section. You don’t need to list all the papers you’ve read, or all the work done in the past on this topic. 

Set the scene and frame your question in the context of the literature. Seek out review articles in particular. The aim of this section is to build on what has come before so your reader will be armed with all the information they need to understand the remainder of your article, and why - in context - the aims of your study are important.

How to write the background to your research paper

Cater to your audience.

It’s important to frame your background to the right audience.

The background of your study needs to be pitched differently depending on your target journal. A more subject-area specific journal (e.g. Journal of Brain Studies ) will be read by specialists in your field. Generally, less information to set up the paper in a wider context and less background information will be required. Your readers are already experts on the topic in question .

However, if you are aiming your paper at a more general audience (a journal like Nature or Science , for example) then you're going to need to explain more in your background. A reader of a specialized journal will know about the neocortex within the brain and where this is located, but a general reader will need you to set things up more.

Readers are always the most important people in research publishing, after all: If you want your work to be read, used, and cited (and therefore drive up your H-index as well as your institution’s ranking) you’ll need a well-pitched background of your study.

What is included in the background of a study?

Remember this section sits in the middle of the introduction. Here’s a handy template for what to include:

  • Existing research on the area of study (not everything, but a broad overview. Aim to cite review papers if you can). Start this section with preliminary data and then build it out;
  • Mention any controversies around your topic (either that you’ve identified, or that have been picked up by earlier work. Check the discussion sections of recent articles for pointers here);
  • Any gaps in existing research?, and;
  • How will your study fill these gaps? State your research methodologies. Any further research that needs to be done?

list of what's included in background of a study

Aim for one paragraph , or a series of short paragraphs within one section. The last two of the topics outlined above can be short, just one or two sentences. These are there to hook the reader in and to frame your background so that the text leads into the final section of the introduction where you explain ‘What your paper is going to do’.

Simple really.

And finally…some thoughts

I used to get really bogged down with article writing, especially the shape of the introduction.

Here’s a trick to keep in mind: Remember that the average length of an academic research paper published in a peer reviewed journal is around 4,000 - 5,000 words - not too long. 

This means that you're likely going to be aiming for an article of about this length the next time you sit down to write: Not too many words for an effective and well-structured introduction. You’ve got about 1,500 - 2,000 words maximum. And aim to keep it short (this will be enforced by word count limits, especially in higher impact journals like Nature and Science ). Editors at these journals are trained to cut down your writing to make sure your research fits in.

Less is more, in other words.

Keeping tight word count limits in mind means you can’t write an expansive, flowing background to your study that goes off in all directions and covers a huge amount of ground. Keep an eye on our tips for what to include, cite review papers, and keep your readers interested in the question your paper seeks to address.

A well written background to your study will ensure your paper gets read all the way through to the end. Can’t ask for more than that!

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How to Practice Academic Medicine and Publish from Developing Countries? pp 193–199 Cite as

How to Write the Introduction to a Scientific Paper?

  • Samiran Nundy 4 ,
  • Atul Kakar 5 &
  • Zulfiqar A. Bhutta 6  
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An Introduction to a scientific paper familiarizes the reader with the background of the issue at hand. It must reflect why the issue is topical and its current importance in the vast sea of research being done globally. It lays the foundation of biomedical writing and is the first portion of an article according to the IMRAD pattern ( I ntroduction, M ethodology, R esults, a nd D iscussion) [1].

I once had a professor tell a class that he sifted through our pile of essays, glancing at the titles and introductions, looking for something that grabbed his attention. Everything else went to the bottom of the pile to be read last, when he was tired and probably grumpy from all the marking. Don’t get put at the bottom of the pile, he said. Anonymous

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1 What is the Importance of an Introduction?

An Introduction to a scientific paper familiarizes the reader with the background of the issue at hand. It must reflect why the issue is topical and its current importance in the vast sea of research being done globally. It lays the foundation of biomedical writing and is the first portion of an article according to the IMRAD pattern ( I ntroduction, M ethodology, R esults, a nd D iscussion) [ 1 ].

It provides the flavour of the article and many authors have used phrases to describe it for example—'like a gate of the city’ [ 2 ], ‘the beginning is half of the whole’ [ 3 ], ‘an introduction is not just wrestling with words to fit the facts, but it also strongly modulated by perception of the anticipated reactions of peer colleagues’, [ 4 ] and ‘an introduction is like the trailer to a movie’. A good introduction helps captivate the reader early.

figure a

2 What Are the Principles of Writing a Good Introduction?

A good introduction will ‘sell’ an article to a journal editor, reviewer, and finally to a reader [ 3 ]. It should contain the following information [ 5 , 6 ]:

The known—The background scientific data

The unknown—Gaps in the current knowledge

Research hypothesis or question

Methodologies used for the study

The known consist of citations from a review of the literature whereas the unknown is the new work to be undertaken. This part should address how your work is the required missing piece of the puzzle.

3 What Are the Models of Writing an Introduction?

The Problem-solving model

First described by Swales et al. in 1979, in this model the writer should identify the ‘problem’ in the research, address the ‘solution’ and also write about ‘the criteria for evaluating the problem’ [ 7 , 8 ].

The CARS model that stands for C reating A R esearch S pace [ 9 , 10 ].

The two important components of this model are:

Establishing a territory (situation)

Establishing a niche (problem)

Occupying a niche (the solution)

In this popular model, one can add a fourth point, i.e., a conclusion [ 10 ].

4 What Is Establishing a Territory?

This includes: [ 9 ]

Stating the general topic and providing some background about it.

Providing a brief and relevant review of the literature related to the topic.

Adding a paragraph on the scope of the topic including the need for your study.

5 What Is Establishing a Niche?

Establishing a niche includes:

Stating the importance of the problem.

Outlining the current situation regarding the problem citing both global and national data.

Evaluating the current situation (advantages/ disadvantages).

Identifying the gaps.

Emphasizing the importance of the proposed research and how the gaps will be addressed.

Stating the research problem/ questions.

Stating the hypotheses briefly.

Figure 17.1 depicts how the introduction needs to be written. A scientific paper should have an introduction in the form of an inverted pyramid. The writer should start with the general information about the topic and subsequently narrow it down to the specific topic-related introduction.

figure 1

Flow of ideas from the general to the specific

6 What Does Occupying a Niche Mean?

This is the third portion of the introduction and defines the rationale of the research and states the research question. If this is missing the reviewers will not understand the logic for publication and is a common reason for rejection [ 11 , 12 ]. An example of this is given below:

Till date, no study has been done to see the effectiveness of a mesh alone or the effectiveness of double suturing along with a mesh in the closure of an umbilical hernia regarding the incidence of failure. So, the present study is aimed at comparing the effectiveness of a mesh alone versus the double suturing technique along with a mesh.

7 How Long Should the Introduction Be?

For a project protocol, the introduction should be about 1–2 pages long and for a thesis it should be 3–5 pages in a double-spaced typed setting. For a scientific paper it should be less than 10–15% of the total length of the manuscript [ 13 , 14 ].

8 How Many References Should an Introduction Have?

All sections in a scientific manuscript except the conclusion should contain references. It has been suggested that an introduction should have four or five or at the most one-third of the references in the whole paper [ 15 ].

9 What Are the Important Points Which Should be not Missed in an Introduction?

An introduction paves the way forward for the subsequent sections of the article. Frequently well-planned studies are rejected by journals during review because of the simple reason that the authors failed to clarify the data in this section to justify the study [ 16 , 17 ]. Thus, the existing gap in knowledge should be clearly brought out in this section (Fig. 17.2 ).

figure 2

How should the abstract, introduction, and discussion look

The following points are important to consider:

The introduction should be written in simple sentences and in the present tense.

Many of the terms will be introduced in this section for the first time and these will require abbreviations to be used later.

The references in this section should be to papers published in quality journals (e.g., having a high impact factor).

The aims, problems, and hypotheses should be clearly mentioned.

Start with a generalization on the topic and go on to specific information relevant to your research.

10 Example of an Introduction

figure b

11 Conclusions

An Introduction is a brief account of what the study is about. It should be short, crisp, and complete.

It has to move from a general to a specific research topic and must include the need for the present study.

The Introduction should include data from a literature search, i.e., what is already known about this subject and progress to what we hope to add to this knowledge.

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Nundy, S., Kakar, A., Bhutta, Z.A. (2022). How to Write the Introduction to a Scientific Paper?. In: How to Practice Academic Medicine and Publish from Developing Countries?. Springer, Singapore. https://doi.org/10.1007/978-981-16-5248-6_17

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Writing an Introduction for a Scientific Paper

Dr. michelle harris, dr. janet batzli, biocore.

This section provides guidelines on how to construct a solid introduction to a scientific paper including background information, study question , biological rationale, hypothesis , and general approach . If the Introduction is done well, there should be no question in the reader’s mind why and on what basis you have posed a specific hypothesis.

Broad Question : based on an initial observation (e.g., “I see a lot of guppies close to the shore. Do guppies like living in shallow water?”). This observation of the natural world may inspire you to investigate background literature or your observation could be based on previous research by others or your own pilot study. Broad questions are not always included in your written text, but are essential for establishing the direction of your research.

Background Information : key issues, concepts, terminology, and definitions needed to understand the biological rationale for the experiment. It often includes a summary of findings from previous, relevant studies. Remember to cite references, be concise, and only include relevant information given your audience and your experimental design. Concisely summarized background information leads to the identification of specific scientific knowledge gaps that still exist. (e.g., “No studies to date have examined whether guppies do indeed spend more time in shallow water.”)

Testable Question : these questions are much more focused than the initial broad question, are specific to the knowledge gap identified, and can be addressed with data. (e.g., “Do guppies spend different amounts of time in water <1 meter deep as compared to their time in water that is >1 meter deep?”)

Biological Rationale : describes the purpose of your experiment distilling what is known and what is not known that defines the knowledge gap that you are addressing. The “BR” provides the logic for your hypothesis and experimental approach, describing the biological mechanism and assumptions that explain why your hypothesis should be true.

The biological rationale is based on your interpretation of the scientific literature, your personal observations, and the underlying assumptions you are making about how you think the system works. If you have written your biological rationale, your reader should see your hypothesis in your introduction section and say to themselves, “Of course, this hypothesis seems very logical based on the rationale presented.”

  • A thorough rationale defines your assumptions about the system that have not been revealed in scientific literature or from previous systematic observation. These assumptions drive the direction of your specific hypothesis or general predictions.
  • Defining the rationale is probably the most critical task for a writer, as it tells your reader why your research is biologically meaningful. It may help to think about the rationale as an answer to the questions— how is this investigation related to what we know, what assumptions am I making about what we don’t yet know, AND how will this experiment add to our knowledge? *There may or may not be broader implications for your study; be careful not to overstate these (see note on social justifications below).
  • Expect to spend time and mental effort on this. You may have to do considerable digging into the scientific literature to define how your experiment fits into what is already known and why it is relevant to pursue.
  • Be open to the possibility that as you work with and think about your data, you may develop a deeper, more accurate understanding of the experimental system. You may find the original rationale needs to be revised to reflect your new, more sophisticated understanding.
  • As you progress through Biocore and upper level biology courses, your rationale should become more focused and matched with the level of study e ., cellular, biochemical, or physiological mechanisms that underlie the rationale. Achieving this type of understanding takes effort, but it will lead to better communication of your science.

***Special note on avoiding social justifications: You should not overemphasize the relevance of your experiment and the possible connections to large-scale processes. Be realistic and logical —do not overgeneralize or state grand implications that are not sensible given the structure of your experimental system. Not all science is easily applied to improving the human condition. Performing an investigation just for the sake of adding to our scientific knowledge (“pure or basic science”) is just as important as applied science. In fact, basic science often provides the foundation for applied studies.

Hypothesis / Predictions : specific prediction(s) that you will test during your experiment. For manipulative experiments, the hypothesis should include the independent variable (what you manipulate), the dependent variable(s) (what you measure), the organism or system , the direction of your results, and comparison to be made.

If you are doing a systematic observation , your hypothesis presents a variable or set of variables that you predict are important for helping you characterize the system as a whole, or predict differences between components/areas of the system that help you explain how the system functions or changes over time.

Experimental Approach : Briefly gives the reader a general sense of the experiment, the type of data it will yield, and the kind of conclusions you expect to obtain from the data. Do not confuse the experimental approach with the experimental protocol . The experimental protocol consists of the detailed step-by-step procedures and techniques used during the experiment that are to be reported in the Methods and Materials section.

Some Final Tips on Writing an Introduction

  • As you progress through the Biocore sequence, for instance, from organismal level of Biocore 301/302 to the cellular level in Biocore 303/304, we expect the contents of your “Introduction” paragraphs to reflect the level of your coursework and previous writing experience. For example, in Biocore 304 (Cell Biology Lab) biological rationale should draw upon assumptions we are making about cellular and biochemical processes.
  • Be Concise yet Specific: Remember to be concise and only include relevant information given your audience and your experimental design. As you write, keep asking, “Is this necessary information or is this irrelevant detail?” For example, if you are writing a paper claiming that a certain compound is a competitive inhibitor to the enzyme alkaline phosphatase and acts by binding to the active site, you need to explain (briefly) Michaelis-Menton kinetics and the meaning and significance of Km and Vmax. This explanation is not necessary if you are reporting the dependence of enzyme activity on pH because you do not need to measure Km and Vmax to get an estimate of enzyme activity.
  • Another example: if you are writing a paper reporting an increase in Daphnia magna heart rate upon exposure to caffeine you need not describe the reproductive cycle of magna unless it is germane to your results and discussion. Be specific and concrete, especially when making introductory or summary statements.

Where Do You Discuss Pilot Studies? Many times it is important to do pilot studies to help you get familiar with your experimental system or to improve your experimental design. If your pilot study influences your biological rationale or hypothesis, you need to describe it in your Introduction. If your pilot study simply informs the logistics or techniques, but does not influence your rationale, then the description of your pilot study belongs in the Materials and Methods section.  

How will introductions be evaluated? The following is part of the rubric we will be using to evaluate your papers.

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What is a "good" introduction?

Citing sources in the introduction, "introduction checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018..

  • LITERATURE CITED
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This is where you describe briefly and clearly why you are writing the paper. The introduction supplies sufficient background information for the reader to understand and evaluate the experiment you did. It also supplies a rationale for the study.

  • Present the problem and the proposed solution
  • Presents nature and scope of the problem investigated
  • Reviews the pertinent literature to orient the reader
  • States the method of the experiment
  • State the principle results of the experiment

It is important to cite sources in the introduction section of your paper as evidence of the claims you are making. There are ways of citing sources in the text so that the reader can find the full reference in the literature cited section at the end of the paper, yet the flow of the reading is not badly interrupted. Below are some example of how this can be done:     "Smith (1983) found that N-fixing plants could be infected by several different species of Rhizobium."     "Walnut trees are known to be allelopathic (Smith 1949,  Bond et al. 1955, Jones and Green 1963)."     "Although the presence of Rhizobium normally increases the growth of legumes (Nguyen 1987), the opposite effect has been observed (Washington 1999)." Note that articles by one or two authors are always cited in the text using their last names. However, if there are more than two authors, the last name of the 1st author is given followed by the abbreviation et al. which is Latin for "and others". 

From:  https://writingcenter.gmu.edu/guides/imrad-reports-introductions

  • Indicate the field of the work, why this field is important, and what has already been done (with proper citations).
  • Indicate a gap, raise a research question, or challenge prior work in this territory.
  • Outline the purpose and announce the present research, clearly indicating what is novel and why it is significant.
  • Avoid: repeating the abstract; providing unnecessary background information; exaggerating the importance of the work; claiming novelty without a proper literature search. 
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BSCI 1510L Literature and Stats Guide: 3.2 Components of a scientific paper

  • 1 What is a scientific paper?
  • 2 Referencing and accessing papers
  • 2.1 Literature Cited
  • 2.2 Accessing Scientific Papers
  • 2.3 Traversing the web of citations
  • 2.4 Keyword Searches
  • 3 Style of scientific writing
  • 3.1 Specific details regarding scientific writing

3.2 Components of a scientific paper

  • 4 For further information
  • Appendix A: Calculation Final Concentrations
  • 1 Formulas in Excel
  • 2 Basic operations in Excel
  • 3 Measurement and Variation
  • 3.1 Describing Quantities and Their Variation
  • 3.2 Samples Versus Populations
  • 3.3 Calculating Descriptive Statistics using Excel
  • 4 Variation and differences
  • 5 Differences in Experimental Science
  • 5.1 Aside: Commuting to Nashville
  • 5.2 P and Detecting Differences in Variable Quantities
  • 5.3 Statistical significance
  • 5.4 A test for differences of sample means: 95% Confidence Intervals
  • 5.5 Error bars in figures
  • 5.6 Discussing statistics in your scientific writing
  • 6 Scatter plot, trendline, and linear regression
  • 7 The t-test of Means
  • 8 Paired t-test
  • 9 Two-Tailed and One-Tailed Tests
  • 10 Variation on t-tests: ANOVA
  • 11 Reporting the Results of a Statistical Test
  • 12 Summary of statistical tests
  • 1 Objectives
  • 2 Project timeline
  • 3 Background
  • 4 Previous work in the BSCI 111 class
  • 5 General notes about the project
  • 6 About the paper
  • 7 References

Nearly all journal articles are divided into the following major sections: abstract, introduction, methods, results, discussion, and references.  Usually the sections are labeled as such, although often the introduction (and sometimes the abstract) is not labeled.  Sometimes alternative section titles are used.  The abstract is sometimes called the "summary", the methods are sometimes called "materials and methods", and the discussion is sometimes called "conclusions".   Some journals also include the minor sections of "key words" following the abstract, and "acknowledgments" following the discussion.  In some journals, the sections may be divided into subsections that are given descriptive titles.  However, the general division into the six major sections is nearly universal.

3.2.1 Abstract

The abstract is a short summary (150-200 words or less) of the important points of the paper.  It does not generally include background information.  There may be a very brief statement of the rationale for conducting the study.  It describes what was done, but without details.  It also describes the results in a summarized way that usually includes whether or not the statistical tests were significant.  It usually concludes with a brief statement of the importance of the results.  Abstracts do not include references.  When writing a paper, the abstract is always the last part to be written.

The purpose of the abstract is to allow potential readers of a paper to find out the important points of the paper without having to actually read the paper.  It should be a self-contained unit capable of being understood without the benefit of the text of the article . It essentially serves as an "advertisement" for the paper that readers use to determine whether or not they actually want to wade through the entire paper or not.  Abstracts are generally freely available in electronic form and are often presented in the results of an electronic search.  If searchers do not have electronic access to the journal in which the article is published, the abstract is the only means that they have to decide whether to go through the effort (going to the library to look up the paper journal, requesting a reprint from the author, buying a copy of the article from a service, requesting the article by Interlibrary Loan) of acquiring the article.  Therefore it is important that the abstract accurately and succinctly presents the most important information in the article.

3.2.2 Introduction

The introduction provides the background information necessary to understand why the described experiment was conducted.  The introduction should describe previous research on the topic that has led to the unanswered questions being addressed by the experiment and should cite important previous papers that form the background for the experiment.  The introduction should also state in an organized fashion the goals of the research, i.e. the particular, specific questions that will be tested in the experiments.  There should be a one-to-one correspondence between questions raised in the introduction and points discussed in the conclusion section of the paper.  In other words, do not raise questions in the introduction unless you are going to have some kind of answer to the question that you intend to discuss at the end of the paper. 

You may have been told that every paper must have a hypothesis that can be clearly stated.  That is often true, but not always.  If your experiment involves a manipulation which tests a specific hypothesis, then you should clearly state that hypothesis.  On the other hand, if your experiment was primarily exploratory, descriptive, or measurative, then you probably did not have an a priori hypothesis, so don't pretend that you did and make one up.  (See the discussion in the introduction to Experiment 4 for more on this.)  If you state a hypothesis in the introduction, it should be a general hypothesis and not a null or alternative hypothesis for a statistical test.  If it is necessary to explain how a statistical test will help you evaluate your general hypothesis, explain that in the methods section. 

A good introduction should be fairly heavy with citations.  This indicates to the reader that the authors are informed about previous work on the topic and are not working in a vacuum.  Citations also provide jumping-off points to allow the reader to explore other tangents to the subject that are not directly addressed in the paper.  If the paper supports or refutes previous work, readers can look up the citations and make a comparison for themselves. 

"Do not get lost in reviewing background information. Remember that the Introduction is meant to introduce the reader to your research, not summarize and evaluate all past literature on the subject (which is the purpose of a review paper). Many of the other studies you may be tempted to discuss in your Introduction are better saved for the Discussion, where they become a powerful tool for comparing and interpreting your results. Include only enough background information to allow your reader to understand why you are asking the questions you are and why your hyptheses are reasonable ones. Often, a brief explanation of the theory involved is sufficient. …

Write this section in the past or present tense, never in the future. " (Steingraber et al. 1985)

3.2.3 Methods (taken verbatim from Steingraber et al. 1985)

The function of this section is to describe all experimental procedures, including controls. The description should be complete enough to enable someone else to repeat your work. If there is more than one part to the experiment, it is a good idea to describe your methods and present your results in the same order in each section. This may not be the same order in which the experiments were performed -it is up to you to decide what order of presentation will make the most sense to your reader.

1. Explain why each procedure was done, i.e., what variable were you measuring and why? Example:

Difficult to understand : First, I removed the frog muscle and then I poured Ringer’s solution on it. Next, I attached it to the kymograph.

Improved: I removed the frog muscle and poured Ringer’s solution on it to prevent it from drying out. I then attached the muscle to the kymograph in order to determine the minimum voltage required for contraction.

2. Experimental procedures and results are narrated in the past tense (what you did, what you found, etc.) whereas conclusions from your results are given in the present tense.

3. Mathematical equations and statistical tests are considered mathematical methods and should be described in this section along with the actual experimental work.

4. Use active rather than passive voice when possible.  [Note: see Section 3.1.4 for more about this.]  Always use the singular "I" rather than the plural "we" when you are the only author of the paper.  Throughout the paper, avoid contractions, e.g. did not vs. didn’t.

5. If any of your methods is fully described in a previous publication (yours or someone else’s), you can cite that instead of describing the procedure again.

Example: The chromosomes were counted at meiosis in the anthers with the standard acetocarmine technique of Snow (1955).

3.2.4 Results (with excerpts from Steingraber et al. 1985)

The function of this section is to summarize general trends in the data without comment, bias, or interpretation. The results of statistical tests applied to your data are reported in this section although conclusions about your original hypotheses are saved for the Discussion section.

Tables and figures should be used when they are a more efficient way to convey information than verbal description. They must be independent units, accompanied by explanatory captions that allow them to be understood by someone who has not read the text. Do not repeat in the text the information in tables and figures, but do cite them, with a summary statement when that is appropriate.  Example:

Incorrect: The results are given in Figure 1.

Correct: Temperature was directly proportional to metabolic rate (Fig. 1).

Please note that the entire word "Figure" is almost never written in an article.  It is nearly always abbreviated as "Fig." and capitalized.  Tables are cited in the same way, although Table is not abbreviated.

Whenever possible, use a figure instead of a table. Relationships between numbers are more readily grasped when they are presented graphically rather than as columns in a table.

Data may be presented in figures and tables, but this may not substitute for a verbal summary of the findings. The text should be understandable by someone who has not seen your figures and tables.

1. All results should be presented, including those that do not support the hypothesis.

2. Statements made in the text must be supported by the results contained in figures and tables.

3. The results of statistical tests can be presented in parentheses following a verbal description.

Example: Fruit size was significantly greater in trees growing alone (t = 3.65, df = 2, p < 0.05).

Simple results of statistical tests may be reported in the text as shown in the preceding example.  The results of multiple tests may be reported in a table if that increases clarity. (See Section 11 of the Statistics Manual for more details about reporting the results of statistical tests.)  It is not necessary to provide a citation for a simple t-test of means, paired t-test, or linear regression.  If you use other tests, you should cite the text or reference you followed to do the test.  In your materials and methods section, you should report how you did the test (e.g. using the statistical analysis package of Excel). 

It is NEVER appropriate to simply paste the results from statistical software into the results section of your paper.  The output generally reports more information than is required and it is not in an appropriate format for a paper.

3.2.4.1 Tables

  • Do not repeat information in a table that you are depicting in a graph or histogram; include a table only if it presents new information.
  • It is easier to compare numbers by reading down a column rather than across a row. Therefore, list sets of data you want your reader to compare in vertical form.
  • Provide each table with a number (Table 1, Table 2, etc.) and a title. The numbered title is placed above the table .
  • Please see Section 11 of the Excel Reference and Statistics Manual for further information on reporting the results of statistical tests.

3.2.4.2. Figures

  • These comprise graphs, histograms, and illustrations, both drawings and photographs. Provide each figure with a number (Fig. 1, Fig. 2, etc.) and a caption (or "legend") that explains what the figure shows. The numbered caption is placed below the figure .  Figure legend = Figure caption.
  • Figures submitted for publication must be "photo ready," i.e., they will appear just as you submit them, or photographically reduced. Therefore, when you graduate from student papers to publishable manuscripts, you must learn to prepare figures that will not embarrass you. At the present time, virtually all journals require manuscripts to be submitted electronically and it is generally assumed that all graphs and maps will be created using software rather than being created by hand.  Nearly all journals have specific guidelines for the file types, resolution, and physical widths required for figures.  Only in a few cases (e.g. sketched diagrams) would figures still be created by hand using ink and those figures would be scanned and labeled using graphics software.  Proportions must be the same as those of the page in the journal to which the paper will be submitted. 
  • Graphs and Histograms: Both can be used to compare two variables. However, graphs show continuous change, whereas histograms show discrete variables only.  You can compare groups of data by plotting two or even three lines on one graph, but avoid cluttered graphs that are hard to read, and do not plot unrelated trends on the same graph. For both graphs, and histograms, plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Label both axes, including units of measurement except in the few cases where variables are unitless, such as absorbance.
  • Drawings and Photographs: These are used to illustrate organisms, experimental apparatus, models of structures, cellular and subcellular structure, and results of procedures like electrophoresis. Preparing such figures well is a lot of work and can be very expensive, so each figure must add enough to justify its preparation and publication, but good figures can greatly enhance a professional article, as your reading in biological journals has already shown.

3.2.5 Discussion (taken from Steingraber et al. 1985)

The function of this section is to analyze the data and relate them to other studies. To "analyze" means to evaluate the meaning of your results in terms of the original question or hypothesis and point out their biological significance.

1. The Discussion should contain at least:

  • the relationship between the results and the original hypothesis, i.e., whether they support the hypothesis, or cause it to be rejected or modified
  • an integration of your results with those of previous studies in order to arrive at explanations for the observed phenomena
  • possible explanations for unexpected results and observations, phrased as hypotheses that can be tested by realistic experimental procedures, which you should describe

2. Trends that are not statistically significant can still be discussed if they are suggestive or interesting, but cannot be made the basis for conclusions as if they were significant.

3. Avoid redundancy between the Results and the Discussion section. Do not repeat detailed descriptions of the data and results in the Discussion. In some journals, Results and Discussions are joined in a single section, in order to permit a single integrated treatment with minimal repetition. This is more appropriate for short, simple articles than for longer, more complicated ones.

4. End the Discussion with a summary of the principal points you want the reader to remember. This is also the appropriate place to propose specific further study if that will serve some purpose, but do not end with the tired cliché that "this problem needs more study." All problems in biology need more study. Do not close on what you wish you had done, rather finish stating your conclusions and contributions.

3.2.6 Title

The title of the paper should be the last thing that you write.  That is because it should distill the essence of the paper even more than the abstract (the next to last thing that you write). 

The title should contain three elements:

1. the name of the organism studied;

2. the particular aspect or system studied;

3. the variable(s) manipulated.

Do not be afraid to be grammatically creative. Here are some variations on a theme, all suitable as titles:

THE EFFECT OF TEMPERATURE ON GERMINATION OF ZEA MAYS

DOES TEMPERATURE AFFECT GERMINATION OF ZEA MAYS?

TEMPERATURE AND ZEA MAYS GERMINATION: IMPLICATIONS FOR AGRICULTURE

Sometimes it is possible to include the principal result or conclusion in the title:

HIGH TEMPERATURES REDUCE GERMINATION OF ZEA MAYS

Note for the BSCI 1510L class: to make your paper look more like a real paper, you can list all of the other group members as co-authors.  However, if you do that, you should list you name first so that we know that you wrote it.

3.2.7 Literature Cited

Please refer to section 2.1 of this guide.

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  • Int J Sports Phys Ther
  • v.7(5); 2012 Oct

HOW TO WRITE A SCIENTIFIC ARTICLE

Barbara j. hoogenboom.

1 Grand Valley State University, Grand Rapids, MI, USA

Robert C. Manske

2 University of Wichita, Wichita, KS, USA

Successful production of a written product for submission to a peer‐reviewed scientific journal requires substantial effort. Such an effort can be maximized by following a few simple suggestions when composing/creating the product for submission. By following some suggested guidelines and avoiding common errors, the process can be streamlined and success realized for even beginning/novice authors as they negotiate the publication process. The purpose of this invited commentary is to offer practical suggestions for achieving success when writing and submitting manuscripts to The International Journal of Sports Physical Therapy and other professional journals.

INTRODUCTION

“The whole of science is nothing more than a refinement of everyday thinking” Albert Einstein

Conducting scientific and clinical research is only the beginning of the scholarship of discovery. In order for the results of research to be accessible to other professionals and have a potential effect on the greater scientific community, it must be written and published. Most clinical and scientific discovery is published in peer‐reviewed journals, which are those that utilize a process by which an author's peers, or experts in the content area, evaluate the manuscript. Following this review the manuscript is recommended for publication, revision or rejection. It is the rigor of this review process that makes scientific journals the primary source of new information that impacts clinical decision‐making and practice. 1 , 2

The task of writing a scientific paper and submitting it to a journal for publication is a time‐consuming and often daunting task. 3 , 4 Barriers to effective writing include lack of experience, poor writing habits, writing anxiety, unfamiliarity with the requirements of scholarly writing, lack of confidence in writing ability, fear of failure, and resistance to feedback. 5 However, the very process of writing can be a helpful tool for promoting the process of scientific thinking, 6 , 7 and effective writing skills allow professionals to participate in broader scientific conversations. Furthermore, peer review manuscript publication systems requiring these technical writing skills can be developed and improved with practice. 8 Having an understanding of the process and structure used to produce a peer‐reviewed publication will surely improve the likelihood that a submitted manuscript will result in a successful publication.

Clear communication of the findings of research is essential to the growth and development of science 3 and professional practice. The culmination of the publication process provides not only satisfaction for the researcher and protection of intellectual property, but also the important function of dissemination of research results, new ideas, and alternate thought; which ultimately facilitates scholarly discourse. In short, publication of scientific papers is one way to advance evidence‐based practice in many disciplines, including sports physical therapy. Failure to publish important findings significantly diminishes the potential impact that those findings may have on clinical practice. 9

BASICS OF MANUSCRIPT PREPARATION & GENERAL WRITING TIPS

To begin it might be interesting to learn why reviewers accept manuscripts! Reviewers consider the following five criteria to be the most important in decisions about whether to accept manuscripts for publication: 1) the importance, timeliness, relevance, and prevalence of the problem addressed; 2) the quality of the writing style (i.e., that it is well‐written, clear, straightforward, easy to follow, and logical); 3) the study design applied (i.e., that the design was appropriate, rigorous, and comprehensive); 4) the degree to which the literature review was thoughtful, focused, and up‐to‐date; and 5) the use of a sufficiently large sample. 10 For these statements to be true there are also reasons that reviewers reject manuscripts. The following are the top five reasons for rejecting papers: 1) inappropriate, incomplete, or insufficiently described statistics; 2) over‐interpretation of results; 3) use of inappropriate, suboptimal, or insufficiently described populations or instruments; 4) small or biased samples; and 5) text that is poorly written or difficult to follow. 10 , 11 With these reasons for acceptance or rejection in mind, it is time to review basics and general writing tips to be used when performing manuscript preparation.

“Begin with the end in mind” . When you begin writing about your research, begin with a specific target journal in mind. 12 Every scientific journal should have specific lists of manuscript categories that are preferred for their readership. The IJSPT seeks to provide readership with current information to enhance the practice of sports physical therapy. Therefore the manuscript categories accepted by IJSPT include: Original research; Systematic reviews of literature; Clinical commentary and Current concept reviews; Case reports; Clinical suggestions and unique practice techniques; and Technical notes. Once a decision has been made to write a manuscript, compose an outline that complies with the requirements of the target submission journal and has each of the suggested sections. This means carefully checking the submission criteria and preparing your paper in the exact format of the journal to which you intend to submit. Be thoughtful about the distinction between content (what you are reporting) and structure (where it goes in the manuscript). Poor placement of content confuses the reader (reviewer) and may cause misinterpretation of content. 3 , 5

It may be helpful to follow the IMRaD format for writing scientific manuscripts. This acronym stands for the sections contained within the article: Introduction, Methods, Results, and Discussion. Each of these areas of the manuscript will be addressed in this commentary.

Many accomplished authors write their results first, followed by an introduction and discussion, in an attempt to “stay true” to their results and not stray into additional areas. Typically the last two portions to be written are the conclusion and the abstract.

The ability to accurately describe ideas, protocols/procedures, and outcomes are the pillars of scientific writing . Accurate and clear expression of your thoughts and research information should be the primary goal of scientific writing. 12 Remember that accuracy and clarity are even more important when trying to get complicated ideas across. Contain your literature review, ideas, and discussions to your topic, theme, model, review, commentary, or case. Avoid vague terminology and too much prose. Use short rather than long sentences. If jargon has to be utilized keep it to a minimum and explain the terms you do use clearly. 13

Write with a measure of formality, using scientific language and avoiding conjunctions, slang, and discipline or regionally specific nomenclature or terms (e.g. exercise nicknames). For example, replace the term “Monster walks” with “closed‐chain hip abduction with elastic resistance around the thighs”. You may later refer to the exercise as “also known as Monster walks” if you desire.

Avoid first person language and instead write using third person language. Some journals do not ascribe to this requirement, and allow first person references, however, IJSPT prefers use of third person. For example, replace “We determined that…” with “The authors determined that….”.

For novice writers, it is really helpful to seek a reading mentor that will help you pre‐read your submission. Problems such as improper use of grammar, tense, and spelling are often a cause of rejection by reviewers. Despite the content of the study these easily fixed errors suggest that the authors created the manuscript with less thought leading reviewers to think that the manuscript may also potentially have erroneous findings as well. A review from a second set of trained eyes will often catch these errors missed by the original authors. If English is not your first language, the editorial staff at IJSPT suggests that you consult with someone with the relevant expertise to give you guidance on English writing conventions, verb tense, and grammar. Excellent writing in English is hard, even for those of us for whom it is our first language!

Use figures and graphics to your advantage . ‐ Consider the use of graphic/figure representation of data and important procedures or exercises. Tables should be able to stand alone and be completely understandable at a quick glance. Understanding a table should not require careful review of the manuscript! Figures dramatically enhance the graphic appeal of a scientific paper. Many formats for graphic presentation are acceptable, including graphs, charts, tables, and pictures or videos. Photographs should be clear, free of clutter or extraneous background distractions and be taken with models wearing simple clothing. Color photographs are preferred. Digital figures (Scans or existing files as well as new photographs) must be at least 300dpi. All photographs should be provided as separate files (jpeg or tif preferred) and not be embedded in the paper. Quality and clarity of figures are essential for reproduction purposes and should be considered before taking images for the manuscript.

A video of an exercise or procedure speaks a thousand words. Please consider using short video clips as descriptive additions to your paper. They will be placed on the IJSPT website and accompany your paper. The video clips must be submitted in MPEG‐1, MPEG‐2, Quicktime (.mov), or Audio/Video Interface (.avi) formats. Maximum cumulative length of videos is 5 minutes. Each video segment may not exceed 50 MB, and each video clip must be saved as a separate file and clearly identified. Formulate descriptive figure/video and Table/chart/graph titles and place them on a figure legend document. Carefully consider placement of, naming of, and location of figures. It makes the job of the editors much easier!

Avoid Plagiarism and inadvertent lack of citations. Finally, use citations to your benefit. Cite frequently in order to avoid any plagiarism. The bottom line: If it is not your original idea, give credit where credit is due . When using direct quotations, provide not only the number of the citation, but the page where the quote was found. All citations should appear in text as a superscripted number followed by punctuation. It is the authors' responsibility to fully ensure all references are cited in completed form, in an accurate location. Please carefully follow the instructions for citations and check that all references in your reference list are cited in the paper and that all citations in the paper appear correctly in the reference list. Please go to IJSPT submission guidelines for full information on the format for citations.

Sometimes written as an afterthought, the abstract is of extreme importance as in many instances this section is what is initially previewed by readership to determine if the remainder of the article is worth reading. This is the authors opportunity to draw the reader into the study and entice them to read the rest of the article. The abstract is a summary of the article or study written in 3 rd person allowing the readers to get a quick glance of what the contents of the article include. Writing an abstract is rather challenging as being brief, accurate and concise are requisite. The headings and structure for an abstract are usually provided in the instructions for authors. In some instances, the abstract may change slightly pending content revisions required during the peer review process. Therefore it often works well to complete this portion of the manuscript last. Remember the abstract should be able to stand alone and should be as succinct as possible. 14

Introduction and Review of Literature

The introduction is one of the more difficult portions of the manuscript to write. Past studies are used to set the stage or provide the reader with information regarding the necessity of the represented project. For an introduction to work properly, the reader must feel that the research question is clear, concise, and worthy of study.

A competent introduction should include at least four key concepts: 1) significance of the topic, 2) the information gap in the available literature associated with the topic, 3) a literature review in support of the key questions, 4) subsequently developed purposes/objectives and hypotheses. 9

When constructing a review of the literature, be attentive to “sticking” or “staying true” to your topic at hand. Don't reach or include too broad of a literature review. For example, do not include extraneous information about performance or prevention if your research does not actually address those things. The literature review of a scientific paper is not an exhaustive review of all available knowledge in a given field of study. That type of thorough review should be left to review articles or textbook chapters. Throughout the introduction (and later in the discussion!) remind yourself that a paper, existing evidence, or results of a paper cannot draw conclusions, demonstrate, describe, or make judgments, only PEOPLE (authors) can. “The evidence demonstrates that” should be stated, “Smith and Jones, demonstrated that….”

Conclude your introduction with a solid statement of your purpose(s) and your hypothesis(es), as appropriate. The purpose and objectives should clearly relate to the information gap associated with the given manuscript topic discussed earlier in the introduction section. This may seem repetitive, but it actually is helpful to ensure the reader clearly sees the evolution, importance, and critical aspects of the study at hand See Table 1 for examples of well‐stated purposes.

Examples of well-stated purposes by submission type.

The methods section should clearly describe the specific design of the study and provide clear and concise description of the procedures that were performed. The purpose of sufficient detail in the methods section is so that an appropriately trained person would be able to replicate your experiments. 15 There should be complete transparency when describing the study. To assist in writing and manuscript preparation there are several checklists or guidelines that are available on the IJSPT website. The CONSORT guidelines can be used when developing and reporting a randomized controlled trial. 16 The STARD checklist was developed for designing a diagnostic accuracy study. 17 The PRISMA checklist was developed for use when performing a meta‐analyses or systematic review. 18 A clear methods section should contain the following information: 1) the population and equipment used in the study, 2) how the population and equipment were prepared and what was done during the study, 3) the protocol used, 4) the outcomes and how they were measured, 5) the methods used for data analysis. Initially a brief paragraph should explain the overall procedures and study design. Within this first paragraph there is generally a description of inclusion and exclusion criteria which help the reader understand the population used. Paragraphs that follow should describe in more detail the procedures followed for the study. A clear description of how data was gathered is also helpful. For example were data gathered prospectively or retrospectively? Who if anyone was blinded, and where and when was the actual data collected?

Although it is a good idea for the authors to have justification and a rationale for their procedures, these should be saved for inclusion into the discussion section, not to be discussed in the methods section. However, occasionally studies supporting components of the methods section such as reliability of tests, or validation of outcome measures may be included in the methods section.

The final portion of the methods section will include the statistical methods used to analyze the data. 19 This does not mean that the actual results should be discussed in the methods section, as they have an entire section of their own!

Most scientific journals support the need for all projects involving humans or animals to have up‐to‐date documentation of ethical approval. 20 The methods section should include a clear statement that the researchers have obtained approval from an appropriate institutional review board.

Results, Discussion, and Conclusions

In most journals the results section is separate from the discussion section. It is important that you clearly distinguish your results from your discussion. The results section should describe the results only. The discussion section should put those results into a broader context. Report your results neutrally, as you “found them”. Again, be thoughtful about content and structure. Think carefully about where content is placed in the overall structure of your paper. It is not appropriate to bring up additional results, not discussed in the results section, in the discussion. All results must first be described/presented and then discussed. Thus, the discussion should not simply be a repeat of the results section. Carefully discuss where your information is similar or different from other published evidence and why this might be so. What was different in methods or analysis, what was similar?

As previously stated, stick to your topic at hand, and do not overstretch your discussion! One of the major pitfalls in writing the discussion section is overstating the significance of your findings 4 or making very strong statements. For example, it is better to say: “Findings of the current study support….” or “these findings suggest…” than, “Findings of the current study prove that…” or “this means that….”. Maintain a sense of humbleness, as nothing is without question in the outcomes of any type of research, in any discipline! Use words like “possibly”, “likely” or “suggests” to soften findings. 12

Do not discuss extraneous ideas, concepts, or information not covered by your topic/paper/commentary. Be sure to carefully address all relevant results, not just the statistically significant ones or the ones that support your hypotheses. When you must resort to speculation or opinion, be certain to state that up front using phrases such as “we therefore speculate” or “in the authors' opinion”.

Remember, just as in the introduction and literature review, evidence or results cannot draw conclusions, just as previously stated, only people, scientists, researchers, and authors can!

Finish with a concise, 3‐5 sentence conclusion paragraph. This is not just a restatement of your results, rather is comprised of some final, summative statements that reflect the flow and outcomes of the entire paper. Do not include speculative statements or additional material; however, based upon your findings a statement about potential changes in clinical practice or future research opportunities can be provided here.

CONCLUSIONS

Writing for publication can be a challenging yet satisfying endeavor. The ability to examine, relate, and interlink evidence, as well as to provide a peer‐reviewed, disseminated product of your research labors can be rewarding. A few suggestions have been offered in this commentary that may assist the novice or the developing writer to attempt, polish, and perfect their approach to scholarly writing.

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Writing a Scientific Paper

Writing a scientific paper is very similar to writing a lab report. The structure of each is primarily the same, but the purpose of each is different. Lab reports are meant to reflect understanding of the material and learn something new, while scientific papers are meant to contribute knowledge to a field of study.  A scientific paper is broken down into eight sections: title, abstract, introduction, methods, results, discussion, conclusion, and references. 

  • Ex: "Determining the Free Chlorine Content of Pool Water"
  • Abstracts are a summary of the research as a whole and should familiarize the reader with the purpose of the research. 
  • Abstracts will always be written last, even though they are the first paragraph of a scientific paper. 
  • Unlike a lab report, all scientific papers will have an abstract.
  • Why was the research done?
  • What problem is being addressed?
  • What results were found?
  • What are the meaning of the results?
  • How is the problem better understood now than before, if at all?

Introduction

  • The introduction of a scientific paper discusses the problem being studied and other theory that is relevant to understanding the findings. 
  • The hypothesis of the experiment and the motivation for the research are stated in this section. 
  • Write the introduction in your own words. Try not to copy from a lab manual or other guidelines. Instead, show comprehension of the research by briefly explaining the problem.

Methods and Materials

  • Ex: pipette, graduated cylinder, 1.13mg of Na, 0.67mg Ag
  • List the steps taken as they actually happened during the experiment, not as they were supposed to happen. 
  • If written correctly, another researcher should be able to duplicate the experiment and get the same or very similar results. 
  • In a scientific paper, most often the steps taken during the research are discussed more in length and with more detail than they are in lab reports. 
  • The results show the data that was collected or found during the research. 
  • Explain in words the data that was collected.
  • Tables should be labeled numerically, as "Table 1", "Table 2", etc. Other figures should be labeled numerically as "Figure 1", "Figure 2", etc. 
  • Calculations to understand the data can also be presented in the results. 
  • The discussion section is one of the most important parts of a scientific paper. It analyzes the results of the research and is a discussion of the data. 
  • If any results are unexpected, explain why they are unexpected and how they did or did not effect the data obtained. 
  • Analyze the strengths and weaknesses of the design of the research and compare your results to similar research.
  • If there are any experimental errors, analyze them.
  • Explain your results and discuss them using relevant terms and theories.
  • What do the results indicate?
  • What is the significance of the results?
  • Are there any gaps in knowledge?
  • Are there any new questions that have been raised?
  • The conclusion is a summation of the experiment. It should clearly and concisely state what was learned and its importance.
  • If there is future work that needs to be done, it can be explained in the conclusion.
  • When any outside sources to support a claim or explain background information, those sources must be cited in the references section of the lab report. 
  • Scientific papers will always use outside references. 

Other Useful Sources

  • Guidelines for Writing Scientific Papers
  • How to Write a Scientific Article
  • Writing a Scientific Research Article
  • How to Write a Good Scientific Paper
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Parts of a Scientific & Scholarly Paper

Introduction.

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Different sections are needed in different types of scientific papers (lab reports, literature reviews, systematic reviews, methods papers, research papers, etc.). Projects that overlap with the social sciences or humanities may have different requirements. Generally, however, you'll need to include:

INTRODUCTION (Background)

METHODS SECTION (Materials and Methods)

What is a title

Titles have two functions: to identify the main topic or the message of the paper and to attract readers.

The title will be read by many people. Only a few will read the entire paper, therefore all words in the title should be chosen with care. Too short a title is not helpful to the potential reader. Too long a title can sometimes be even less meaningful. Remember a title is not an abstract. Neither is a title a sentence.

What makes a good title?

A good title is accurate, complete, and specific. Imagine searching for your paper in PubMed. What words would you use?

  • Use the fewest possible words that describe the contents of the paper.
  • Avoid waste words like "Studies on", or "Investigations on".
  • Use specific terms rather than general.
  • Use the same key terms in the title as the paper.
  • Watch your word order and syntax.

The abstract is a miniature version of your paper. It should present the main story and a few essential details of the paper for readers who only look at the abstract and should serve as a clear preview for readers who read your whole paper. They are usually short (250 words or less).

The goal is to communicate:

  •  What was done?
  •  Why was it done?
  •  How was it done?
  •  What was found?

A good abstract is specific and selective. Try summarizing each of the sections of your paper in a sentence two. Do the abstract last, so you know exactly what you want to write.

  • Use 1 or more well developed paragraphs.
  • Use introduction/body/conclusion structure.
  • Present purpose, results, conclusions and recommendations in that order.
  • Make it understandable to a wide audience.
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The Writing Center • University of North Carolina at Chapel Hill

Scientific Reports

What this handout is about.

This handout provides a general guide to writing reports about scientific research you’ve performed. In addition to describing the conventional rules about the format and content of a lab report, we’ll also attempt to convey why these rules exist, so you’ll get a clearer, more dependable idea of how to approach this writing situation. Readers of this handout may also find our handout on writing in the sciences useful.

Background and pre-writing

Why do we write research reports.

You did an experiment or study for your science class, and now you have to write it up for your teacher to review. You feel that you understood the background sufficiently, designed and completed the study effectively, obtained useful data, and can use those data to draw conclusions about a scientific process or principle. But how exactly do you write all that? What is your teacher expecting to see?

To take some of the guesswork out of answering these questions, try to think beyond the classroom setting. In fact, you and your teacher are both part of a scientific community, and the people who participate in this community tend to share the same values. As long as you understand and respect these values, your writing will likely meet the expectations of your audience—including your teacher.

So why are you writing this research report? The practical answer is “Because the teacher assigned it,” but that’s classroom thinking. Generally speaking, people investigating some scientific hypothesis have a responsibility to the rest of the scientific world to report their findings, particularly if these findings add to or contradict previous ideas. The people reading such reports have two primary goals:

  • They want to gather the information presented.
  • They want to know that the findings are legitimate.

Your job as a writer, then, is to fulfill these two goals.

How do I do that?

Good question. Here is the basic format scientists have designed for research reports:

  • Introduction

Methods and Materials

This format, sometimes called “IMRAD,” may take slightly different shapes depending on the discipline or audience; some ask you to include an abstract or separate section for the hypothesis, or call the Discussion section “Conclusions,” or change the order of the sections (some professional and academic journals require the Methods section to appear last). Overall, however, the IMRAD format was devised to represent a textual version of the scientific method.

The scientific method, you’ll probably recall, involves developing a hypothesis, testing it, and deciding whether your findings support the hypothesis. In essence, the format for a research report in the sciences mirrors the scientific method but fleshes out the process a little. Below, you’ll find a table that shows how each written section fits into the scientific method and what additional information it offers the reader.

Thinking of your research report as based on the scientific method, but elaborated in the ways described above, may help you to meet your audience’s expectations successfully. We’re going to proceed by explicitly connecting each section of the lab report to the scientific method, then explaining why and how you need to elaborate that section.

Although this handout takes each section in the order in which it should be presented in the final report, you may for practical reasons decide to compose sections in another order. For example, many writers find that composing their Methods and Results before the other sections helps to clarify their idea of the experiment or study as a whole. You might consider using each assignment to practice different approaches to drafting the report, to find the order that works best for you.

What should I do before drafting the lab report?

The best way to prepare to write the lab report is to make sure that you fully understand everything you need to about the experiment. Obviously, if you don’t quite know what went on during the lab, you’re going to find it difficult to explain the lab satisfactorily to someone else. To make sure you know enough to write the report, complete the following steps:

  • What are we going to do in this lab? (That is, what’s the procedure?)
  • Why are we going to do it that way?
  • What are we hoping to learn from this experiment?
  • Why would we benefit from this knowledge?
  • Consult your lab supervisor as you perform the lab. If you don’t know how to answer one of the questions above, for example, your lab supervisor will probably be able to explain it to you (or, at least, help you figure it out).
  • Plan the steps of the experiment carefully with your lab partners. The less you rush, the more likely it is that you’ll perform the experiment correctly and record your findings accurately. Also, take some time to think about the best way to organize the data before you have to start putting numbers down. If you can design a table to account for the data, that will tend to work much better than jotting results down hurriedly on a scrap piece of paper.
  • Record the data carefully so you get them right. You won’t be able to trust your conclusions if you have the wrong data, and your readers will know you messed up if the other three people in your group have “97 degrees” and you have “87.”
  • Consult with your lab partners about everything you do. Lab groups often make one of two mistakes: two people do all the work while two have a nice chat, or everybody works together until the group finishes gathering the raw data, then scrams outta there. Collaborate with your partners, even when the experiment is “over.” What trends did you observe? Was the hypothesis supported? Did you all get the same results? What kind of figure should you use to represent your findings? The whole group can work together to answer these questions.
  • Consider your audience. You may believe that audience is a non-issue: it’s your lab TA, right? Well, yes—but again, think beyond the classroom. If you write with only your lab instructor in mind, you may omit material that is crucial to a complete understanding of your experiment, because you assume the instructor knows all that stuff already. As a result, you may receive a lower grade, since your TA won’t be sure that you understand all the principles at work. Try to write towards a student in the same course but a different lab section. That student will have a fair degree of scientific expertise but won’t know much about your experiment particularly. Alternatively, you could envision yourself five years from now, after the reading and lectures for this course have faded a bit. What would you remember, and what would you need explained more clearly (as a refresher)?

Once you’ve completed these steps as you perform the experiment, you’ll be in a good position to draft an effective lab report.

Introductions

How do i write a strong introduction.

For the purposes of this handout, we’ll consider the Introduction to contain four basic elements: the purpose, the scientific literature relevant to the subject, the hypothesis, and the reasons you believed your hypothesis viable. Let’s start by going through each element of the Introduction to clarify what it covers and why it’s important. Then we can formulate a logical organizational strategy for the section.

The inclusion of the purpose (sometimes called the objective) of the experiment often confuses writers. The biggest misconception is that the purpose is the same as the hypothesis. Not quite. We’ll get to hypotheses in a minute, but basically they provide some indication of what you expect the experiment to show. The purpose is broader, and deals more with what you expect to gain through the experiment. In a professional setting, the hypothesis might have something to do with how cells react to a certain kind of genetic manipulation, but the purpose of the experiment is to learn more about potential cancer treatments. Undergraduate reports don’t often have this wide-ranging a goal, but you should still try to maintain the distinction between your hypothesis and your purpose. In a solubility experiment, for example, your hypothesis might talk about the relationship between temperature and the rate of solubility, but the purpose is probably to learn more about some specific scientific principle underlying the process of solubility.

For starters, most people say that you should write out your working hypothesis before you perform the experiment or study. Many beginning science students neglect to do so and find themselves struggling to remember precisely which variables were involved in the process or in what way the researchers felt that they were related. Write your hypothesis down as you develop it—you’ll be glad you did.

As for the form a hypothesis should take, it’s best not to be too fancy or complicated; an inventive style isn’t nearly so important as clarity here. There’s nothing wrong with beginning your hypothesis with the phrase, “It was hypothesized that . . .” Be as specific as you can about the relationship between the different objects of your study. In other words, explain that when term A changes, term B changes in this particular way. Readers of scientific writing are rarely content with the idea that a relationship between two terms exists—they want to know what that relationship entails.

Not a hypothesis:

“It was hypothesized that there is a significant relationship between the temperature of a solvent and the rate at which a solute dissolves.”

Hypothesis:

“It was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases.”

Put more technically, most hypotheses contain both an independent and a dependent variable. The independent variable is what you manipulate to test the reaction; the dependent variable is what changes as a result of your manipulation. In the example above, the independent variable is the temperature of the solvent, and the dependent variable is the rate of solubility. Be sure that your hypothesis includes both variables.

Justify your hypothesis

You need to do more than tell your readers what your hypothesis is; you also need to assure them that this hypothesis was reasonable, given the circumstances. In other words, use the Introduction to explain that you didn’t just pluck your hypothesis out of thin air. (If you did pluck it out of thin air, your problems with your report will probably extend beyond using the appropriate format.) If you posit that a particular relationship exists between the independent and the dependent variable, what led you to believe your “guess” might be supported by evidence?

Scientists often refer to this type of justification as “motivating” the hypothesis, in the sense that something propelled them to make that prediction. Often, motivation includes what we already know—or rather, what scientists generally accept as true (see “Background/previous research” below). But you can also motivate your hypothesis by relying on logic or on your own observations. If you’re trying to decide which solutes will dissolve more rapidly in a solvent at increased temperatures, you might remember that some solids are meant to dissolve in hot water (e.g., bouillon cubes) and some are used for a function precisely because they withstand higher temperatures (they make saucepans out of something). Or you can think about whether you’ve noticed sugar dissolving more rapidly in your glass of iced tea or in your cup of coffee. Even such basic, outside-the-lab observations can help you justify your hypothesis as reasonable.

Background/previous research

This part of the Introduction demonstrates to the reader your awareness of how you’re building on other scientists’ work. If you think of the scientific community as engaging in a series of conversations about various topics, then you’ll recognize that the relevant background material will alert the reader to which conversation you want to enter.

Generally speaking, authors writing journal articles use the background for slightly different purposes than do students completing assignments. Because readers of academic journals tend to be professionals in the field, authors explain the background in order to permit readers to evaluate the study’s pertinence for their own work. You, on the other hand, write toward a much narrower audience—your peers in the course or your lab instructor—and so you must demonstrate that you understand the context for the (presumably assigned) experiment or study you’ve completed. For example, if your professor has been talking about polarity during lectures, and you’re doing a solubility experiment, you might try to connect the polarity of a solid to its relative solubility in certain solvents. In any event, both professional researchers and undergraduates need to connect the background material overtly to their own work.

Organization of this section

Most of the time, writers begin by stating the purpose or objectives of their own work, which establishes for the reader’s benefit the “nature and scope of the problem investigated” (Day 1994). Once you have expressed your purpose, you should then find it easier to move from the general purpose, to relevant material on the subject, to your hypothesis. In abbreviated form, an Introduction section might look like this:

“The purpose of the experiment was to test conventional ideas about solubility in the laboratory [purpose] . . . According to Whitecoat and Labrat (1999), at higher temperatures the molecules of solvents move more quickly . . . We know from the class lecture that molecules moving at higher rates of speed collide with one another more often and thus break down more easily [background material/motivation] . . . Thus, it was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases [hypothesis].”

Again—these are guidelines, not commandments. Some writers and readers prefer different structures for the Introduction. The one above merely illustrates a common approach to organizing material.

How do I write a strong Materials and Methods section?

As with any piece of writing, your Methods section will succeed only if it fulfills its readers’ expectations, so you need to be clear in your own mind about the purpose of this section. Let’s review the purpose as we described it above: in this section, you want to describe in detail how you tested the hypothesis you developed and also to clarify the rationale for your procedure. In science, it’s not sufficient merely to design and carry out an experiment. Ultimately, others must be able to verify your findings, so your experiment must be reproducible, to the extent that other researchers can follow the same procedure and obtain the same (or similar) results.

Here’s a real-world example of the importance of reproducibility. In 1989, physicists Stanley Pons and Martin Fleischman announced that they had discovered “cold fusion,” a way of producing excess heat and power without the nuclear radiation that accompanies “hot fusion.” Such a discovery could have great ramifications for the industrial production of energy, so these findings created a great deal of interest. When other scientists tried to duplicate the experiment, however, they didn’t achieve the same results, and as a result many wrote off the conclusions as unjustified (or worse, a hoax). To this day, the viability of cold fusion is debated within the scientific community, even though an increasing number of researchers believe it possible. So when you write your Methods section, keep in mind that you need to describe your experiment well enough to allow others to replicate it exactly.

With these goals in mind, let’s consider how to write an effective Methods section in terms of content, structure, and style.

Sometimes the hardest thing about writing this section isn’t what you should talk about, but what you shouldn’t talk about. Writers often want to include the results of their experiment, because they measured and recorded the results during the course of the experiment. But such data should be reserved for the Results section. In the Methods section, you can write that you recorded the results, or how you recorded the results (e.g., in a table), but you shouldn’t write what the results were—not yet. Here, you’re merely stating exactly how you went about testing your hypothesis. As you draft your Methods section, ask yourself the following questions:

  • How much detail? Be precise in providing details, but stay relevant. Ask yourself, “Would it make any difference if this piece were a different size or made from a different material?” If not, you probably don’t need to get too specific. If so, you should give as many details as necessary to prevent this experiment from going awry if someone else tries to carry it out. Probably the most crucial detail is measurement; you should always quantify anything you can, such as time elapsed, temperature, mass, volume, etc.
  • Rationale: Be sure that as you’re relating your actions during the experiment, you explain your rationale for the protocol you developed. If you capped a test tube immediately after adding a solute to a solvent, why did you do that? (That’s really two questions: why did you cap it, and why did you cap it immediately?) In a professional setting, writers provide their rationale as a way to explain their thinking to potential critics. On one hand, of course, that’s your motivation for talking about protocol, too. On the other hand, since in practical terms you’re also writing to your teacher (who’s seeking to evaluate how well you comprehend the principles of the experiment), explaining the rationale indicates that you understand the reasons for conducting the experiment in that way, and that you’re not just following orders. Critical thinking is crucial—robots don’t make good scientists.
  • Control: Most experiments will include a control, which is a means of comparing experimental results. (Sometimes you’ll need to have more than one control, depending on the number of hypotheses you want to test.) The control is exactly the same as the other items you’re testing, except that you don’t manipulate the independent variable-the condition you’re altering to check the effect on the dependent variable. For example, if you’re testing solubility rates at increased temperatures, your control would be a solution that you didn’t heat at all; that way, you’ll see how quickly the solute dissolves “naturally” (i.e., without manipulation), and you’ll have a point of reference against which to compare the solutions you did heat.

Describe the control in the Methods section. Two things are especially important in writing about the control: identify the control as a control, and explain what you’re controlling for. Here is an example:

“As a control for the temperature change, we placed the same amount of solute in the same amount of solvent, and let the solution stand for five minutes without heating it.”

Structure and style

Organization is especially important in the Methods section of a lab report because readers must understand your experimental procedure completely. Many writers are surprised by the difficulty of conveying what they did during the experiment, since after all they’re only reporting an event, but it’s often tricky to present this information in a coherent way. There’s a fairly standard structure you can use to guide you, and following the conventions for style can help clarify your points.

  • Subsections: Occasionally, researchers use subsections to report their procedure when the following circumstances apply: 1) if they’ve used a great many materials; 2) if the procedure is unusually complicated; 3) if they’ve developed a procedure that won’t be familiar to many of their readers. Because these conditions rarely apply to the experiments you’ll perform in class, most undergraduate lab reports won’t require you to use subsections. In fact, many guides to writing lab reports suggest that you try to limit your Methods section to a single paragraph.
  • Narrative structure: Think of this section as telling a story about a group of people and the experiment they performed. Describe what you did in the order in which you did it. You may have heard the old joke centered on the line, “Disconnect the red wire, but only after disconnecting the green wire,” where the person reading the directions blows everything to kingdom come because the directions weren’t in order. We’re used to reading about events chronologically, and so your readers will generally understand what you did if you present that information in the same way. Also, since the Methods section does generally appear as a narrative (story), you want to avoid the “recipe” approach: “First, take a clean, dry 100 ml test tube from the rack. Next, add 50 ml of distilled water.” You should be reporting what did happen, not telling the reader how to perform the experiment: “50 ml of distilled water was poured into a clean, dry 100 ml test tube.” Hint: most of the time, the recipe approach comes from copying down the steps of the procedure from your lab manual, so you may want to draft the Methods section initially without consulting your manual. Later, of course, you can go back and fill in any part of the procedure you inadvertently overlooked.
  • Past tense: Remember that you’re describing what happened, so you should use past tense to refer to everything you did during the experiment. Writers are often tempted to use the imperative (“Add 5 g of the solid to the solution”) because that’s how their lab manuals are worded; less frequently, they use present tense (“5 g of the solid are added to the solution”). Instead, remember that you’re talking about an event which happened at a particular time in the past, and which has already ended by the time you start writing, so simple past tense will be appropriate in this section (“5 g of the solid were added to the solution” or “We added 5 g of the solid to the solution”).
  • Active: We heated the solution to 80°C. (The subject, “we,” performs the action, heating.)
  • Passive: The solution was heated to 80°C. (The subject, “solution,” doesn’t do the heating–it is acted upon, not acting.)

Increasingly, especially in the social sciences, using first person and active voice is acceptable in scientific reports. Most readers find that this style of writing conveys information more clearly and concisely. This rhetorical choice thus brings two scientific values into conflict: objectivity versus clarity. Since the scientific community hasn’t reached a consensus about which style it prefers, you may want to ask your lab instructor.

How do I write a strong Results section?

Here’s a paradox for you. The Results section is often both the shortest (yay!) and most important (uh-oh!) part of your report. Your Materials and Methods section shows how you obtained the results, and your Discussion section explores the significance of the results, so clearly the Results section forms the backbone of the lab report. This section provides the most critical information about your experiment: the data that allow you to discuss how your hypothesis was or wasn’t supported. But it doesn’t provide anything else, which explains why this section is generally shorter than the others.

Before you write this section, look at all the data you collected to figure out what relates significantly to your hypothesis. You’ll want to highlight this material in your Results section. Resist the urge to include every bit of data you collected, since perhaps not all are relevant. Also, don’t try to draw conclusions about the results—save them for the Discussion section. In this section, you’re reporting facts. Nothing your readers can dispute should appear in the Results section.

Most Results sections feature three distinct parts: text, tables, and figures. Let’s consider each part one at a time.

This should be a short paragraph, generally just a few lines, that describes the results you obtained from your experiment. In a relatively simple experiment, one that doesn’t produce a lot of data for you to repeat, the text can represent the entire Results section. Don’t feel that you need to include lots of extraneous detail to compensate for a short (but effective) text; your readers appreciate discrimination more than your ability to recite facts. In a more complex experiment, you may want to use tables and/or figures to help guide your readers toward the most important information you gathered. In that event, you’ll need to refer to each table or figure directly, where appropriate:

“Table 1 lists the rates of solubility for each substance”

“Solubility increased as the temperature of the solution increased (see Figure 1).”

If you do use tables or figures, make sure that you don’t present the same material in both the text and the tables/figures, since in essence you’ll just repeat yourself, probably annoying your readers with the redundancy of your statements.

Feel free to describe trends that emerge as you examine the data. Although identifying trends requires some judgment on your part and so may not feel like factual reporting, no one can deny that these trends do exist, and so they properly belong in the Results section. Example:

“Heating the solution increased the rate of solubility of polar solids by 45% but had no effect on the rate of solubility in solutions containing non-polar solids.”

This point isn’t debatable—you’re just pointing out what the data show.

As in the Materials and Methods section, you want to refer to your data in the past tense, because the events you recorded have already occurred and have finished occurring. In the example above, note the use of “increased” and “had,” rather than “increases” and “has.” (You don’t know from your experiment that heating always increases the solubility of polar solids, but it did that time.)

You shouldn’t put information in the table that also appears in the text. You also shouldn’t use a table to present irrelevant data, just to show you did collect these data during the experiment. Tables are good for some purposes and situations, but not others, so whether and how you’ll use tables depends upon what you need them to accomplish.

Tables are useful ways to show variation in data, but not to present a great deal of unchanging measurements. If you’re dealing with a scientific phenomenon that occurs only within a certain range of temperatures, for example, you don’t need to use a table to show that the phenomenon didn’t occur at any of the other temperatures. How useful is this table?

A table labeled Effect of Temperature on Rate of Solubility with temperature of solvent values in 10-degree increments from -20 degrees Celsius to 80 degrees Celsius that does not show a corresponding rate of solubility value until 50 degrees Celsius.

As you can probably see, no solubility was observed until the trial temperature reached 50°C, a fact that the text part of the Results section could easily convey. The table could then be limited to what happened at 50°C and higher, thus better illustrating the differences in solubility rates when solubility did occur.

As a rule, try not to use a table to describe any experimental event you can cover in one sentence of text. Here’s an example of an unnecessary table from How to Write and Publish a Scientific Paper , by Robert A. Day:

A table labeled Oxygen requirements of various species of Streptomyces showing the names of organisms and two columns that indicate growth under aerobic conditions and growth under anaerobic conditions with a plus or minus symbol for each organism in the growth columns to indicate value.

As Day notes, all the information in this table can be summarized in one sentence: “S. griseus, S. coelicolor, S. everycolor, and S. rainbowenski grew under aerobic conditions, whereas S. nocolor and S. greenicus required anaerobic conditions.” Most readers won’t find the table clearer than that one sentence.

When you do have reason to tabulate material, pay attention to the clarity and readability of the format you use. Here are a few tips:

  • Number your table. Then, when you refer to the table in the text, use that number to tell your readers which table they can review to clarify the material.
  • Give your table a title. This title should be descriptive enough to communicate the contents of the table, but not so long that it becomes difficult to follow. The titles in the sample tables above are acceptable.
  • Arrange your table so that readers read vertically, not horizontally. For the most part, this rule means that you should construct your table so that like elements read down, not across. Think about what you want your readers to compare, and put that information in the column (up and down) rather than in the row (across). Usually, the point of comparison will be the numerical data you collect, so especially make sure you have columns of numbers, not rows.Here’s an example of how drastically this decision affects the readability of your table (from A Short Guide to Writing about Chemistry , by Herbert Beall and John Trimbur). Look at this table, which presents the relevant data in horizontal rows:

A table labeled Boyle's Law Experiment: Measuring Volume as a Function of Pressure that presents the trial number, length of air sample in millimeters, and height difference in inches of mercury, each of which is presented in rows horizontally.

It’s a little tough to see the trends that the author presumably wants to present in this table. Compare this table, in which the data appear vertically:

A table labeled Boyle's Law Experiment: Measuring Volume as a Function of Pressure that presents the trial number, length of air sample in millimeters, and height difference in inches of mercury, each of which is presented in columns vertically.

The second table shows how putting like elements in a vertical column makes for easier reading. In this case, the like elements are the measurements of length and height, over five trials–not, as in the first table, the length and height measurements for each trial.

  • Make sure to include units of measurement in the tables. Readers might be able to guess that you measured something in millimeters, but don’t make them try.
  • Don’t use vertical lines as part of the format for your table. This convention exists because journals prefer not to have to reproduce these lines because the tables then become more expensive to print. Even though it’s fairly unlikely that you’ll be sending your Biology 11 lab report to Science for publication, your readers still have this expectation. Consequently, if you use the table-drawing option in your word-processing software, choose the option that doesn’t rely on a “grid” format (which includes vertical lines).

How do I include figures in my report?

Although tables can be useful ways of showing trends in the results you obtained, figures (i.e., illustrations) can do an even better job of emphasizing such trends. Lab report writers often use graphic representations of the data they collected to provide their readers with a literal picture of how the experiment went.

When should you use a figure?

Remember the circumstances under which you don’t need a table: when you don’t have a great deal of data or when the data you have don’t vary a lot. Under the same conditions, you would probably forgo the figure as well, since the figure would be unlikely to provide your readers with an additional perspective. Scientists really don’t like their time wasted, so they tend not to respond favorably to redundancy.

If you’re trying to decide between using a table and creating a figure to present your material, consider the following a rule of thumb. The strength of a table lies in its ability to supply large amounts of exact data, whereas the strength of a figure is its dramatic illustration of important trends within the experiment. If you feel that your readers won’t get the full impact of the results you obtained just by looking at the numbers, then a figure might be appropriate.

Of course, an undergraduate class may expect you to create a figure for your lab experiment, if only to make sure that you can do so effectively. If this is the case, then don’t worry about whether to use figures or not—concentrate instead on how best to accomplish your task.

Figures can include maps, photographs, pen-and-ink drawings, flow charts, bar graphs, and section graphs (“pie charts”). But the most common figure by far, especially for undergraduates, is the line graph, so we’ll focus on that type in this handout.

At the undergraduate level, you can often draw and label your graphs by hand, provided that the result is clear, legible, and drawn to scale. Computer technology has, however, made creating line graphs a lot easier. Most word-processing software has a number of functions for transferring data into graph form; many scientists have found Microsoft Excel, for example, a helpful tool in graphing results. If you plan on pursuing a career in the sciences, it may be well worth your while to learn to use a similar program.

Computers can’t, however, decide for you how your graph really works; you have to know how to design your graph to meet your readers’ expectations. Here are some of these expectations:

  • Keep it as simple as possible. You may be tempted to signal the complexity of the information you gathered by trying to design a graph that accounts for that complexity. But remember the purpose of your graph: to dramatize your results in a manner that’s easy to see and grasp. Try not to make the reader stare at the graph for a half hour to find the important line among the mass of other lines. For maximum effectiveness, limit yourself to three to five lines per graph; if you have more data to demonstrate, use a set of graphs to account for it, rather than trying to cram it all into a single figure.
  • Plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Remember that the independent variable is the condition that you manipulated during the experiment and the dependent variable is the condition that you measured to see if it changed along with the independent variable. Placing the variables along their respective axes is mostly just a convention, but since your readers are accustomed to viewing graphs in this way, you’re better off not challenging the convention in your report.
  • Label each axis carefully, and be especially careful to include units of measure. You need to make sure that your readers understand perfectly well what your graph indicates.
  • Number and title your graphs. As with tables, the title of the graph should be informative but concise, and you should refer to your graph by number in the text (e.g., “Figure 1 shows the increase in the solubility rate as a function of temperature”).
  • Many editors of professional scientific journals prefer that writers distinguish the lines in their graphs by attaching a symbol to them, usually a geometric shape (triangle, square, etc.), and using that symbol throughout the curve of the line. Generally, readers have a hard time distinguishing dotted lines from dot-dash lines from straight lines, so you should consider staying away from this system. Editors don’t usually like different-colored lines within a graph because colors are difficult and expensive to reproduce; colors may, however, be great for your purposes, as long as you’re not planning to submit your paper to Nature. Use your discretion—try to employ whichever technique dramatizes the results most effectively.
  • Try to gather data at regular intervals, so the plot points on your graph aren’t too far apart. You can’t be sure of the arc you should draw between the plot points if the points are located at the far corners of the graph; over a fifteen-minute interval, perhaps the change occurred in the first or last thirty seconds of that period (in which case your straight-line connection between the points is misleading).
  • If you’re worried that you didn’t collect data at sufficiently regular intervals during your experiment, go ahead and connect the points with a straight line, but you may want to examine this problem as part of your Discussion section.
  • Make your graph large enough so that everything is legible and clearly demarcated, but not so large that it either overwhelms the rest of the Results section or provides a far greater range than you need to illustrate your point. If, for example, the seedlings of your plant grew only 15 mm during the trial, you don’t need to construct a graph that accounts for 100 mm of growth. The lines in your graph should more or less fill the space created by the axes; if you see that your data is confined to the lower left portion of the graph, you should probably re-adjust your scale.
  • If you create a set of graphs, make them the same size and format, including all the verbal and visual codes (captions, symbols, scale, etc.). You want to be as consistent as possible in your illustrations, so that your readers can easily make the comparisons you’re trying to get them to see.

How do I write a strong Discussion section?

The discussion section is probably the least formalized part of the report, in that you can’t really apply the same structure to every type of experiment. In simple terms, here you tell your readers what to make of the Results you obtained. If you have done the Results part well, your readers should already recognize the trends in the data and have a fairly clear idea of whether your hypothesis was supported. Because the Results can seem so self-explanatory, many students find it difficult to know what material to add in this last section.

Basically, the Discussion contains several parts, in no particular order, but roughly moving from specific (i.e., related to your experiment only) to general (how your findings fit in the larger scientific community). In this section, you will, as a rule, need to:

Explain whether the data support your hypothesis

  • Acknowledge any anomalous data or deviations from what you expected

Derive conclusions, based on your findings, about the process you’re studying

  • Relate your findings to earlier work in the same area (if you can)

Explore the theoretical and/or practical implications of your findings

Let’s look at some dos and don’ts for each of these objectives.

This statement is usually a good way to begin the Discussion, since you can’t effectively speak about the larger scientific value of your study until you’ve figured out the particulars of this experiment. You might begin this part of the Discussion by explicitly stating the relationships or correlations your data indicate between the independent and dependent variables. Then you can show more clearly why you believe your hypothesis was or was not supported. For example, if you tested solubility at various temperatures, you could start this section by noting that the rates of solubility increased as the temperature increased. If your initial hypothesis surmised that temperature change would not affect solubility, you would then say something like,

“The hypothesis that temperature change would not affect solubility was not supported by the data.”

Note: Students tend to view labs as practical tests of undeniable scientific truths. As a result, you may want to say that the hypothesis was “proved” or “disproved” or that it was “correct” or “incorrect.” These terms, however, reflect a degree of certainty that you as a scientist aren’t supposed to have. Remember, you’re testing a theory with a procedure that lasts only a few hours and relies on only a few trials, which severely compromises your ability to be sure about the “truth” you see. Words like “supported,” “indicated,” and “suggested” are more acceptable ways to evaluate your hypothesis.

Also, recognize that saying whether the data supported your hypothesis or not involves making a claim to be defended. As such, you need to show the readers that this claim is warranted by the evidence. Make sure that you’re very explicit about the relationship between the evidence and the conclusions you draw from it. This process is difficult for many writers because we don’t often justify conclusions in our regular lives. For example, you might nudge your friend at a party and whisper, “That guy’s drunk,” and once your friend lays eyes on the person in question, she might readily agree. In a scientific paper, by contrast, you would need to defend your claim more thoroughly by pointing to data such as slurred words, unsteady gait, and the lampshade-as-hat. In addition to pointing out these details, you would also need to show how (according to previous studies) these signs are consistent with inebriation, especially if they occur in conjunction with one another. To put it another way, tell your readers exactly how you got from point A (was the hypothesis supported?) to point B (yes/no).

Acknowledge any anomalous data, or deviations from what you expected

You need to take these exceptions and divergences into account, so that you qualify your conclusions sufficiently. For obvious reasons, your readers will doubt your authority if you (deliberately or inadvertently) overlook a key piece of data that doesn’t square with your perspective on what occurred. In a more philosophical sense, once you’ve ignored evidence that contradicts your claims, you’ve departed from the scientific method. The urge to “tidy up” the experiment is often strong, but if you give in to it you’re no longer performing good science.

Sometimes after you’ve performed a study or experiment, you realize that some part of the methods you used to test your hypothesis was flawed. In that case, it’s OK to suggest that if you had the chance to conduct your test again, you might change the design in this or that specific way in order to avoid such and such a problem. The key to making this approach work, though, is to be very precise about the weakness in your experiment, why and how you think that weakness might have affected your data, and how you would alter your protocol to eliminate—or limit the effects of—that weakness. Often, inexperienced researchers and writers feel the need to account for “wrong” data (remember, there’s no such animal), and so they speculate wildly about what might have screwed things up. These speculations include such factors as the unusually hot temperature in the room, or the possibility that their lab partners read the meters wrong, or the potentially defective equipment. These explanations are what scientists call “cop-outs,” or “lame”; don’t indicate that the experiment had a weakness unless you’re fairly certain that a) it really occurred and b) you can explain reasonably well how that weakness affected your results.

If, for example, your hypothesis dealt with the changes in solubility at different temperatures, then try to figure out what you can rationally say about the process of solubility more generally. If you’re doing an undergraduate lab, chances are that the lab will connect in some way to the material you’ve been covering either in lecture or in your reading, so you might choose to return to these resources as a way to help you think clearly about the process as a whole.

This part of the Discussion section is another place where you need to make sure that you’re not overreaching. Again, nothing you’ve found in one study would remotely allow you to claim that you now “know” something, or that something isn’t “true,” or that your experiment “confirmed” some principle or other. Hesitate before you go out on a limb—it’s dangerous! Use less absolutely conclusive language, including such words as “suggest,” “indicate,” “correspond,” “possibly,” “challenge,” etc.

Relate your findings to previous work in the field (if possible)

We’ve been talking about how to show that you belong in a particular community (such as biologists or anthropologists) by writing within conventions that they recognize and accept. Another is to try to identify a conversation going on among members of that community, and use your work to contribute to that conversation. In a larger philosophical sense, scientists can’t fully understand the value of their research unless they have some sense of the context that provoked and nourished it. That is, you have to recognize what’s new about your project (potentially, anyway) and how it benefits the wider body of scientific knowledge. On a more pragmatic level, especially for undergraduates, connecting your lab work to previous research will demonstrate to the TA that you see the big picture. You have an opportunity, in the Discussion section, to distinguish yourself from the students in your class who aren’t thinking beyond the barest facts of the study. Capitalize on this opportunity by putting your own work in context.

If you’re just beginning to work in the natural sciences (as a first-year biology or chemistry student, say), most likely the work you’ll be doing has already been performed and re-performed to a satisfactory degree. Hence, you could probably point to a similar experiment or study and compare/contrast your results and conclusions. More advanced work may deal with an issue that is somewhat less “resolved,” and so previous research may take the form of an ongoing debate, and you can use your own work to weigh in on that debate. If, for example, researchers are hotly disputing the value of herbal remedies for the common cold, and the results of your study suggest that Echinacea diminishes the symptoms but not the actual presence of the cold, then you might want to take some time in the Discussion section to recapitulate the specifics of the dispute as it relates to Echinacea as an herbal remedy. (Consider that you have probably already written in the Introduction about this debate as background research.)

This information is often the best way to end your Discussion (and, for all intents and purposes, the report). In argumentative writing generally, you want to use your closing words to convey the main point of your writing. This main point can be primarily theoretical (“Now that you understand this information, you’re in a better position to understand this larger issue”) or primarily practical (“You can use this information to take such and such an action”). In either case, the concluding statements help the reader to comprehend the significance of your project and your decision to write about it.

Since a lab report is argumentative—after all, you’re investigating a claim, and judging the legitimacy of that claim by generating and collecting evidence—it’s often a good idea to end your report with the same technique for establishing your main point. If you want to go the theoretical route, you might talk about the consequences your study has for the field or phenomenon you’re investigating. To return to the examples regarding solubility, you could end by reflecting on what your work on solubility as a function of temperature tells us (potentially) about solubility in general. (Some folks consider this type of exploration “pure” as opposed to “applied” science, although these labels can be problematic.) If you want to go the practical route, you could end by speculating about the medical, institutional, or commercial implications of your findings—in other words, answer the question, “What can this study help people to do?” In either case, you’re going to make your readers’ experience more satisfying, by helping them see why they spent their time learning what you had to teach them.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.

Beall, Herbert, and John Trimbur. 2001. A Short Guide to Writing About Chemistry , 2nd ed. New York: Longman.

Blum, Deborah, and Mary Knudson. 1997. A Field Guide for Science Writers: The Official Guide of the National Association of Science Writers . New York: Oxford University Press.

Booth, Wayne C., Gregory G. Colomb, Joseph M. Williams, Joseph Bizup, and William T. FitzGerald. 2016. The Craft of Research , 4th ed. Chicago: University of Chicago Press.

Briscoe, Mary Helen. 1996. Preparing Scientific Illustrations: A Guide to Better Posters, Presentations, and Publications , 2nd ed. New York: Springer-Verlag.

Council of Science Editors. 2014. Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers , 8th ed. Chicago & London: University of Chicago Press.

Davis, Martha. 2012. Scientific Papers and Presentations , 3rd ed. London: Academic Press.

Day, Robert A. 1994. How to Write and Publish a Scientific Paper , 4th ed. Phoenix: Oryx Press.

Porush, David. 1995. A Short Guide to Writing About Science . New York: Longman.

Williams, Joseph, and Joseph Bizup. 2017. Style: Lessons in Clarity and Grace , 12th ed. Boston: Pearson.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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  • Published: 21 February 2024

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions

  • Katy E. Trinkley   ORCID: orcid.org/0000-0003-2041-7404 1 , 2 , 3 , 4 ,
  • Ruopeng An 5 ,
  • Anna M. Maw 2 , 6 ,
  • Russell E. Glasgow 1 , 2 &
  • Ross C. Brownson 7 , 8  

Implementation Science volume  19 , Article number:  17 ( 2024 ) Cite this article

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The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods.

This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of “why” the field of implementation science should consider artificial intelligence, for “what” (the purpose and methods), and the “what” (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.

Conclusions

Artificial intelligence holds promise to advance implementation science methods (“why”) and accelerate its goals of closing the evidence-to-practice gap (“purpose”). However, evaluation of artificial intelligence’s potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

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Contributions to the literature

Artificial intelligence may be integrated into implementation science research and practice to enhance speed, sustainability, equity, and generalizability as well as the ability to assess context, context-outcome relationships, and causality. We highlight ways artificial intelligence can complement implementation science methods and provide examples.

Using artificial intelligence with implementation science methods can also present new challenges and unintended consequences. We describe the potential pitfalls of using artificial intelligence, along with examples.

We offer recommendations and resources on how to begin to responsibly integrate artificial intelligence into implementation science methods, including transdisciplinary collaboration and proactive monitoring for and mitigation of potential unintended consequences.

Healthy People 2030 vision calls for a “society in which all people can achieve their full potential for health and well-being” [ 1 ]. This vision is aspirational and leaves much to be done. Among high-income countries, the average number of annual deaths that could be avoided altogether with preventive or treatment strategies ranges from 130 to more than 330 per 100,000 people [ 2 ]. Although the lag time from knowledge generation to translation varies by situation, recent estimates suggest the average time is 15 years, which is a modest improvement from prior estimates [ 3 , 4 ]. To move the needle and make real progress toward this vision, we need to do better faster, which includes producing and sustaining equitable results. We need more rapid knowledge generation and translation done in replicable, equitable, sustainable, locally relevant, and externally valid ways [ 5 , 6 ]. Implementation science (IS) can play a key role in translating evidence into practice and policy.

IS methods and approaches can drive improvements in equity, sustainability, and the balance between local relevance and external validity needed to support translational science. When used by learning health systems or, more generally, in healthcare or public health settings, IS can iteratively support the continuum of knowledge generation to translation in many ways [ 7 ]. IS specifically focuses on feasibility and relevance to the local context while also considering principles of designing for dissemination, sustainability, and equity [ 8 ]. Importantly, equity is considered at each step in the continuum of knowledge generation to translation and promoted through the representation of partner perspectives and representativeness of outcomes [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. However, IS has several limitations and challenges, notably the time and resources required to apply its methods and approaches. Such constraints can lead to reduced frequency, sample sizes, or representation of partner engagement and hamper other methods commonly used to assess context and outcomes [ 17 ]. Such limitations can dampen the potential for IS to enhance reach, equity, sustainability, and generalizability and ultimately impede its ability to close the evidence-to-practice gap.

As artificial intelligence (AI) gains prominence in the public health and healthcare sectors, it provides avenues to address some of the challenges to IS. AI algorithms such as machine learning (ML), deep learning (DL), and reinforcement learning (RL) serve as the foundation. Domain applications of these algorithms, such as natural language processing (NLP), are increasingly acknowledged as essential tools in the health sciences landscape [ 18 ]. See Table  1 for a description of key AI terms used in this paper. Their diverse applications range from predicting disease outbreaks, enhancing medical imaging, and refining patient communication via tools like chatbots to influencing behavior changes at patient, staff, organizational, or even community levels. Over the last decade, there has been a significant increase in the volume of scientific literature integrating AI into health research [ 19 , 20 ]. This research incorporates a broad spectrum of AI models—from shallow ML algorithms, such as decision trees and k-means clustering, to deep neural networks. These AI models are applied to various data sources and types, such as clinical and observational data and data formats, including tabular, text, and images. The growth of large-scale, diverse health data, coupled with the emergence of new AI techniques, has led to significant change in the healthcare sector, improving our capabilities in diagnosis, disease prediction, patient care, and behavior modification [ 19 , 20 , 21 , 22 , 23 ]. AI technologies also afford opportunities to automate aspects of care delivery, quality improvement, and health services research processes that previously required human labor and thereby can increase speed and efficiency, including for implementation research and practice [ 24 ].

The potential of AI to enhance IS is evident, but there are also cautions to consider , including AI’s potential to exacerbate inequities if unchecked [ 25 , 26 , 27 , 28 ]. This paper aims to elucidate how AI can address current IS challenges while also shedding light on its potential pitfalls. We further provide specific examples from global health systems, public health, and precision health to illustrate both the advantages and precautions when integrating AI with IS. We conclude by providing recommendations and selected resources for implementation researchers and practitioners to leverage AI in their work. While there are extant primers on AI in healthcare and research and papers describing how IS can enhance AI [ 29 , 30 , 31 ], this paper focuses on ways AI can address challenges specific to IS and offers tangible guidance tailored to the IS community on how to apply AI to their work while being cognizant of and mitigating potential unintended consequences. We also discuss intellectual property rights related to the use of AI.

2A. Opportunities for integration of AI to optimize IS methods

Here, we outline “why” AI should be used in the field of IS by describing some of the key challenges facing IS as well as tangible examples of how AI can help overcome these challenges. The specific IS challenges addressed are (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Table  2 summarizes these IS challenges and AI solutions. Table  3 provides examples from health systems and public health settings describing how AI can address the limitations of IS.

2A1. Speed as an IS challenge

Improving and measuring the speed of its methods and translation is a critical issue for IS [ 17 , 40 , 41 ]. Despite great promise and evolving methods to improve the speed of certain activities, IS methods require time, including time to conduct partner engagement, test implementation strategies, evaluate outcomes, and collect and analyze mixed methods data. The time required to carry out traditional IS approaches can slow the speed of knowledge generation and translation, which can be expedited with AI. For example, AI-enabled chatbots can be trained to lead or moderate qualitative interviews or focus groups, allowing for multiple sessions to be completed in parallel without the usual constraints of personnel available. There are examples of such chatbots that are already being used in the business sector for job candidate interviews [ 42 ], which could be adapted for IS applications. NLP and more advanced forms of AI can also be used to collect and analyze data inductively or deductively, including the collection of unstructured data that typically requires manual analysis of qualitative data that is traditionally time-consuming and slow [ 43 ]. The use of AI to conduct qualitative analyses is becoming more common, either as a standalone method or in a “human-assisted” method where researchers iteratively review the AI outputs and provide redirection as needed [ 44 , 45 , 46 , 47 ]. Newer, rapid approaches to qualitative analysis in IS have already sped up this step [ 48 ], but these newer analysis methods could also be augmented with AI to expedite or supplant person time by an order of magnitude. Notably, AI-enabled software is readily available to assist with transcription [ 49 ]. Table  3 summarizes a study that compared NLP to traditional qualitative methods and found NLP was effective at identifying major themes but was not as precise at more granular interpretations [ 33 ]. Chatbots can also be used to automate the creation and testing of tailored messaging as educational implementation strategies (e.g., behavioral nudges) for different target groups of patients, staff, or settings based on their unique characteristics to increase the speed of identifying contextually appropriate and effective strategies [ 50 ]. Most examples of leveraging AI to accelerate speed are currently outside the field of IS [ 42 , 44 , 45 , 46 , 47 ].

2A2. Sustainability as an IS challenge

Sustainability is a central tenant of IS and ideally requires iterative, ongoing progress assessments to identify intervention components and implementation strategies needing adaptation [ 51 , 52 , 53 , 54 ]. However, these ongoing evaluation methods can tax available resources, particularly human capital. By automating iterative evaluation cycles, AI may reduce the demand for human resources, which is often a bottleneck to sustainability methods. A study conducted by the Regional Social Health Agency in Italy (Table  3 ) demonstrates how AI can improve the efficiency of using health information and promote the sustainability of healthcare systems [ 35 ]. There are other avenues in which AI can contribute to sustainability. For instance, AI algorithms can be configured to continuously monitor for and work in tandem with chatbots and NLP tools to detect subtle changes in outcomes that are difficult for traditional quantitative approaches to detect within complex and big data sets used within healthcare. These algorithms can provide partners with real-time insights through integration with platforms like dashboards [ 55 , 56 , 57 ]. To date, there are examples of dashboards being used to make such sustainability methods feasible for IS projects [ 58 ], but there are few examples of AI-enabled approaches in the field of IS. Such use of AI with dashboards can be particularly useful when rapid decisions are required or when partners need to identify and understand complex or subtle patterns in data over time.

AI’s predictive analytics could also be employed to simulate or forecast the sustainability of certain initiatives and estimate the long-term viability of a project or implementation strategy [ 59 ]. For example, if an intervention is implemented in a healthcare setting, AI could analyze data on adherence rates, participant feedback, and other relevant metrics to project the likelihood of its continued success. IS frameworks could guide the systematic assessment of the complex and multilevel contextual factors (e.g., culture, strategic priorities, burnout rates, turnover) that influence sustainability which could be categorized into themes and used as input or predictor variables within the AI model. Such predictive capabilities could allow for proactive and iterative adjustments throughout the life of a project to maximize sustainability. This approach could also assist in identifying the optimal allocation of limited resources by knowing in advance which areas might falter or by potentially supplanting the need for a costly or time-consuming trial that is predicted to be unsustainable. The use of AI to predict sustainability is a potential future direction for the field of IS.

2A3. Equity as an IS challenge

IS aims to promote equity, but some equity-enhancing activities can be challenging within resource constraints. Resources are often not available to (1) disband language barriers to participation in partner engagement activities and implementation studies; (2) create culturally appropriate implementation strategies that address issues such as mistrust; or (3) offer data that represent the spectrum of perspectives beyond the usual, including that of persons who have historically been marginalized and experienced disparities [ 60 , 61 ].

IS can benefit from integrating AI to promote equity amidst resource constraints. AI-driven translation tools render text in different languages and can capture the essence and nuances across dialects and regional variations. Further, speech-to-text systems can convert spoken language into written form, facilitating participation for those who might be literate in their native tongue but not in the primary language of a study. For immediate interactions, real-time AI-enhanced software interpretation allows non-native speakers to understand and contribute actively. AI chatbots, tailored using user data and historical contexts, can resonate with local customs and beliefs, offering a culturally attuned interaction [ 62 , 63 ]. Additionally, these AI systems can be trained to transparently provide resources that resonate with targeted communities and can be employed for cultural awareness training, ensuring researchers approach communities with heightened sensitivity [ 62 , 63 ]. In terms of data, AI algorithms can increase diverse representation by pulling from a range of sources, inclusive of historically marginalized voices that are often omitted from traditional datasets because the data are in unstructured formats and/or too large and complex for traditional analytic methods [ 9 ].

In Table  3 , we present an example of proactively using AI to identify clinical trials for patients from historically underrepresented populations [ 37 ]. These AI tools can also be configured to detect and rectify inherent biases in datasets and present complex data visually, aiding in identifying and correcting disparities. For recruitment, AI’s ability to analyze complex population data from diverse sources means that underrepresented groups can be pinpointed for more inclusive outreach [ 64 ]. Data sources could include social media platforms, online community forums, or leverage crowdsourcing techniques. AI-driven tools such as voice assistants and adaptive interfaces could also be used to make research platforms more navigable for those with disabilities or language barriers [ 65 ]. Finally, AI’s feedback mechanisms enable real-time adjustments to implementation strategies based on participant input, and sentiment analysis tools can gauge the emotional underpinnings of this feedback, illuminating areas of potential mistrust or dissatisfaction [ 66 ]. In harnessing these AI capabilities, IS can promote equity more effectively, ensuring historically marginalized communities are actively engaged in research and its applications. The use of AI to promote equity remains largely untapped, with most examples outside the field of IS [ 60 , 61 , 64 , 65 , 66 ].

2A4. Generalizability as an IS challenge

Although IS prioritizes generalizability and transportability [ 67 ], limitations of data and human resources to conduct partner and participant engagement and collect data can threaten generalizability. Generalizability decreases if the breadth of perspectives considered is limited when designing, implementing, or evaluating a study [ 68 ]. While AI’s role in easing resource demands through chatbots and NLP has been acknowledged, AI’s potential to enhance generalizability stretches beyond that. Because AI can sift through large amounts of complex data, it can incorporate insights from non-traditional sources. For example, social media platforms, with user-generated content, can provide rich insights into public sentiment, behavior, and preferences, which increases the representation of perspectives and assists in generalizing findings across diverse populations [ 69 ]. The United Kingdom has applied AI to Twitter and Facebook forums to evaluate adverse reactions and understand public sentiment toward the COVID-19 vaccination and found that common and rare adverse effects were discussed with relatively equal frequency and that vaccine perceptions were largely positive over time (Table  3 ) [ 70 ]. Additionally, AI can use crowdsourcing to increase the representation of diverse perspectives [ 71 , 72 ]. Crowdsourcing has the potential to capture diverse insights from global audiences. AI has been used to coordinate and process data from large, crowdsourced projects, ensuring that perspectives are drawn from a cross-section of diverse individuals [ 73 ]. This means that studies can encompass views from varied geographical locations, socio-economic statuses, and cultural backgrounds while operating within existing resource confines [ 74 ]. As is also the case with traditional data sources, the selection of an appropriate social media or crowdsourcing data source must be aligned with the target population or issue at hand to ensure relevance, and inherent data biases, including misinformation and missingness must be considered.

Moreover, the dynamic nature of the world means that generalizability is not static. Populations evolve, cultures shift, and societal priorities change. Here, AI can assist by automating continuous assessments of generalizability. Similar to approaches described above related to sustainability, AI can monitor for changes that might impact the external relevance or transportability of study findings and provide alerts or updates when shifts are detected. Such iterative assessments, automated by AI, can be used to strategically guide adaptations such that the work and findings remain generalizable throughout all stages of a study and over time [ 57 , 60 , 61 ].

2A5. Assessing context and context-outcome relationships as an IS challenge

Traditional IS approaches to assessing context and outcomes are often limited to the “stated” or simple interpretations of the “realized.” While the stated (explicit declarations) often come from qualitative methods like partner engagement sessions or surveys with limited samples, the realized is generally garnered from quantitative data necessitating a predefined hypothesis or signal [ 75 ]. Although emergent configurational analysis techniques delve deeper into intricate relationships between context and outcomes [ 76 ], IS and traditional quantitative approaches often fall short of capturing the intricate relationships of non-linear interactions. AI algorithms present new opportunities to address these challenges that are often inherent in complex data. AI can assimilate large and complex data repositories to discern non-linear relationships and detect patterns or context—implementation strategy—outcome relationships even without predefined signals [ 77 , 78 , 79 ]. One study leveraged AI and electronic health record data to understand reasons for gaps in clinician prescribing for a clinical scenario that had already been well studied using traditional mixed methods [ 75 ]. This study identified a variety of contextual determinants, including some that were previously unrecognized and were used to inform the design of an ongoing IS trial (Table  3 ) [ 75 ]. The versatility of AI means that these algorithms are not only static tools, but that they can be optimized to constantly operate in the background, evolving with the data they encounter. This becomes particularly crucial in dynamic landscapes such as healthcare, where relationships between context and outcomes can change rapidly. As AI iteratively processes this information, it can provide a pulse on any emerging shifts, ensuring that IS remains responsive and adaptive to the changing context. While there are limited examples of AI being used to assess context for IS studies [ 75 ], there are additional avenues in which to explore how AI can be leveraged to assess changes in context, strategies, and outcomes.

2A6. Assessing causality and mechanism as an IS challenge

In IS, ascertaining causality and mechanism is difficult. While traditional quantitative tests for causality could assist [ 80 , 81 ], as could more qualitative approaches such as mechanism mapping [ 82 ], deciphering the direct causal connections, rather than mere associative links, between interventions, implementation strategies, and outcomes, is difficult due to the complex interplay of confounding variables in real-world settings. Modern AI-driven causal inference and discovery mechanisms offer a path forward for IS [ 83 , 84 , 85 ]. Leveraging structured graphical models, techniques such as causal Bayesian networks adeptly delineate explicit cause-and-effect relationships, duly accounting for latent confounders. Consider, for example, a healthcare scenario aimed at curtailing hospital readmissions. Whereas traditional analytical frameworks might predominantly identify an associative link between an intervention and reduced readmissions, AI causal tools probe deeper, scrutinizing whether the intervention itself was the direct catalyst or if obscured variables intervened. In Table  3 , we provide a precision health example of using causal AI methods to generate treatment predictions for patients with dementia [ 39 ]. Further enriching the AI toolkit are counterfactual neural networks [ 86 , 87 ], which could be used by IS practitioners to simulate hypothetical outcomes that would have occurred without specific interventions. Another notable advancement is AI's deployment in analyzing potential or simulated outcomes, which elucidates the individual treatment effect, thereby shedding light on the distinct impact of interventions or implementation strategies on specific demographic or clinical subgroups. Such AI-based simulation models have the potential to save unnecessary resource expenditure (time, money) if a trial is predicted to produce a null effect. Through this AI-driven lens, IS could better assess causality and mechanism with heightened precision, fostering the design and deployment of increasingly effective and equitable programs. The use of AI to assess causality and mechanism is a largely unexplored methodologic area for the field of IS.

2B. Potential consequences of using AI in IS

The consequences of using AI can be both positive and negative. Thus far we have focused on those positive consequences that can be anticipated, but there are likely others that are unanticipated. For example, it is not yet known what the true potential of AI is, and AI-generated innovations could create solutions with benefits we cannot begin to predict. However, when using AI, important considerations and potential adverse unintended consequences need to be monitored and minimized [ 25 , 26 , 27 , 28 ]. Here we highlight potential cautions of using AI with examples of how AI has caused harm or gone awry. In Table  2 , we explicitly relate these AI concerns with the AI solutions proposed above to address IS challenges. Across all of these counterarguments, proactive vigilance is required to identify and mitigate issues at each stage of the AI lifecycle , which includes (1) data creation, (2) data acquisition, (3) model development, (4) model evaluation, and (5) model deployment [ 88 ]. We provide examples showing how AI can lead to erroneous conclusions, inequities, biases, or harmful behaviors.

Unmonitored AI applications (e.g., AI algorithms, chatbots, NLP) can lead to erroneous messages or results. AI is restricted to the available data inputs and subject to all the biases of the data collection process, often referred to as “garbage in, garbage out.” For example, it is known that clinician diagnoses are biased by gender and race [ 25 , 26 ], and models using such data will capture these biases. AI’s sentiment analysis can also incorrectly interpret data or be influenced more by counts or frequencies than a manual human-only process would be, and such errors can significantly influence results [ 43 ]. These issues can be hidden or exacerbated when using “black box” AI models that result in an effect or outcome but do not allow for explainability of the processes that produced the effect [ 89 ].

In one study, an AI algorithm was applied to 14,199 patients with pneumonia across 78 hospitals to risk-stratify the probability of death [ 90 ]. The model recommended that patients with asthma were at lower risk than those without asthma. This recommendation contrasted with existing evidence, thus triggering the researchers to investigate further. The researchers discovered that the data inputs biased this finding. Specifically, the data inputs did not capture the fact that patients with asthma and pneumonia were commonly directly admitted for treatment and thus had better treatment outcomes compared to patients who had pneumonia without asthma.

AI also has the potential to exacerbate or create new inequities. Reliance on data that underrepresent the population or that are subject to inherent biases stemming from sexism, racism, classism, or mistrust leads to inaccurate predictions or evaluations and could perpetuate inequities or misguide decision-making [ 26 , 27 , 28 ]. Misguided decision-making can be particularly apparent when AI is used to inform recommendations for tools such as clinical decision support within electronic health records.

Authors of another paper provide a use case of AI algorithms scheduling medical appointments to improve scheduling efficiency to illustrate how such AI algorithms can yield racially biased outcomes [ 91 ]. Such algorithms consider many factors, such as characteristics of patients that arrive late to appointments or “no show.” However, historically, Black patients have a higher likelihood of “no shows”; thus, the algorithm scheduled these patients into less desirable appointment times.

AI is beholden to the data inputs. Beyond inherent biases of how data are collected, data inputs are also subject to data drift (e.g., temporal changes in how and where data is documented) and can also lead to biased interpretations if the sample sizes are not representative or sufficiently large [ 92 ]. Traditional IS data sources have limited sample sizes, and data drift is common in rapidly changing environments where public health and healthcare happen.

In 2009 it was announced that by using AI applications and publicly available data from Google search engine queries for “flu-like symptoms,” researchers could predict regional flu trends 1-2 weeks earlier than the Centers for Disease Control and Prevention [ 93 ]. Later, it was discovered that the prediction was no longer accurate, in part because of changes to search engines that prompted or suggested certain search terms to users, which changed the data inputs [ 94 ].

AI can tailor messages or nudges for specific populations in ways that prompt and facilitate good decision-making [ 95 , 96 ]. However, such AI applications can also inadvertently promote harmful behaviors. For example, AI has been leveraged to create tailored messages or nudges to increase consumer uptake of unhealthy food and beverages [ 97 ]. AI can learn and adapt its messaging over time, thus posing the potential for messages originally well-intended to encourage inadvertent harm. Other ethical implications of nudges include situations in which certain options are forbidden and autonomy in decision-making is impaired [ 98 , 99 , 100 ].

2c. Intellectual property rights of using AI for IS

Intellectual property issues present unique challenges and opportunities when AI is used in IS for design, data analysis, or reporting [ 101 ]. Questions arise about property rights of the knowledge generated from AI models, especially when the knowledge generated stems from data in which it is unclear who owns the data. For example, AI models could leverage data sourced from public datasets or collaborative efforts in which ownership of the data is unclear. Furthermore, as AI aids in creating or optimizing interventions, discerning the boundaries between human-generated property and machine-augmented contributions can become ambiguous. It is imperative for researchers and practitioners to proactively navigate these complexities, ensuring that while AI propels IS forward, it does so in a manner that respects and delineates intellectual property rights and contributions.

If relying on existing AI applications, it is important to identify and understand any potential intellectual property rights, which could require fees for use or restrictions on how the AI can be used or disseminated. On the flip side, if creating de novo AI applications, it may be prudent to consider establishing intellectual property rights to enforce responsible use and avoid the potential consequences outlined above. Intellectual property rights apply to any invention, such as EHR-based tools and decision aids, but in the case of AI, intentional use to promote responsible AI use may be novel and important to consider. While there are clear implications of intellectual property rights for AI applications themselves, there is less clarity regarding the property rights of AI-generated products [ 102 , 103 ]. The latter is a new and developing area that is currently handled on a country-by-country basis. Although allowable under the laws of some countries such as the UK, the US stance is that AI-generated products are prohibited from intellectual property rights [ 103 ]. The fundamental question that served as the basis for the US’s decision was “How can a thing (not a human) own property?” The inability to predict or anticipate AI-generated products confounded by limited means of regulation is cause for increased caution and monitoring.

Discussion and future directions

IS and AI can complement each other and have the potential to work together to increase the speed of sustainable and equitable knowledge generation and translation to enhance healthcare and population health. We have focused on how AI can augment specific bottlenecks faced by the field of IS, while others have called attention to ways in which IS can augment AI, which includes making AI more relevant to local settings, scalable, and sustainable [ 29 , 30 , 31 ]. In summary, key ways AI can help address IS-specific challenges include: increasing the speed with which data can be collected, analyzed, and acted on; automating and reducing the workforce required to conduct partner engagement and other IS methods; expanding the size and heterogeneity of available data sources and participant recruitment; and increasing access to new methods to assist in discovering contextual influences and complex interactions between context, implementation strategies and outcomes. Focusing solely on AI’s potential to automate many of IS’s traditional methods and processes, AI provides a path to help IS researchers and practitioners become more rapid and achieve goals of sustainability, equity, and generalizability.

AI presents new opportunities for IS and many potential AI applications remain largely unexplored or untapped by the IS community. As AI use assuredly increases, it needs to be monitored and used responsibly to avoid unintended consequences, especially in the face of limited regulations on AI. It is also important to note that the benefits and pitfalls of AI may not equally apply to all types of AI and it is beyond the scope of this paper to address each separately, but the reader should keep in mind that there are differences based on the specific AI model and application. Among AI’s potential to cause harm, inequities have received much attention and may be one of the most challenging issues to monitor and mitigate. Inequities can surface over time, and multiple root causes include biased data inputs or data that do not represent the spectrum of cultures or perspectives. In this paper, we describe ways AI can optimize equity, but every use of AI also requires careful and ongoing vigilance for potential effects on inequities. Other potential pitfalls of AI discussed above include inaccurate predictions, recommendations, or interpretations of data. Another key challenge of AI is its algorithms’ reproducibility or “brittleness” across settings and over time [ 104 ]. There is a need for the regulations, frameworks, and guidance currently being developed for AI [ 105 , 106 ] to include policies and procedures for systematic and proactive monitoring of unintended consequences and careful consideration of “black box” models [ 89 ]. With the widespread update and examination of ChatGPT (and other AI online and related tools), there is increasing awareness about AI’s potential for errors [ 107 , 108 , 109 ]. The full potential of using AI to enhance IS is not yet known, nor is its potential for errors and harm, which makes the development of regulations even more challenging [ 101 ].

IS should take full advantage of AI’s benefits while being mindful of its pitfalls. To do so, a transdisciplinary team science approach is optimal. Team science certainly extends beyond AI and IS partnering, but we focus on these two fields here. Historically, the fields of AI and IS have had limited collaboration with different foci (e.g., heavily quantitative and causal versus mixed methods and pragmatic effectiveness). Now as each becomes essential to the vision of precision public health and learning health systems [ 7 , 110 , 111 , 112 , 113 , 114 , 115 ], they are progressively realizing the value of each other. Given both are rapidly evolving fields and that it is hard to anticipate what is new or next, close collaboration or perhaps a new generation of cross-trained scientists is needed. Such cross-trained scientists may be particularly adept at keeping pace with the latest discoveries related to AI’s potential and monitoring for and mitigating unanticipated consequences. To foster this budding partnership or cross-training between IS and AI, accessibility of expertise and resources is important. In Table  4 , we provide a select sample of resources and tools to facilitate the use of AI especially relevant for IS.

We call for increased uptake of innovations in AI through transdisciplinary collaboration to overcome challenges to IS methods and to enhance public health and healthcare while remaining vigilant of potential unintended consequences. We acknowledge that our paper is “first generation” in that it is one of the first to describe intersections between AI and IS—in doing so, we hope to spark future debate, scholarship, and enhancement of the concepts we have introduced. In Table  5 , we provide concrete summary suggestions of how to begin to responsibly and optimally use AI, including building a representative team. Application of AI is complex and uncertain, but has the potential to make IS more efficient and can facilitate more in-depth and iterative contextual assessment, which in turn can lead to more rapid, sustainable, equitable, and generalizable generation and translation of knowledge into real-world settings.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed.

Abbreviations

  • Artificial intelligence

Deep learning

Dissemination and implementation

Evidence-based intervention

  • Implementation science

Machine learning

Natural language processing

Reinforcement learning

Theories, models, and frameworks

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Acknowledgements

Not applicable.

The findings and conclusions in this paper are those of the authors and do not necessarily represent the official positions of the National Institutes of Health or the Centers for Disease Control and Prevention.

This work was supported in part by the National Cancer Institute (numbers P50CA244688, P50 CA244431), the National Institute of Diabetes and Digestive and Kidney Diseases at the National Institutes of Health (numbers P30DK092950, P30DK056341, R25DK123008), the Centers for Disease Control and Prevention (number U48DP006395), and the Foundation for Barnes-Jewish Hospital. Dr. Trinkley’s time was supported in part by the NHLBI (number 1K23HL161352).

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Katy E. Trinkley, Anna M. Maw & Russell E. Glasgow

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KET conceptualized the original ideas and wrote the first draft of the outline and paper. RA, AMM, REG, and RCB provided input on the original outline, contributed text to the draft manuscript, and provided intellectual content to the paper. All authors provided critical edits to the paper and approved the final version.

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Trinkley, K.E., An, R., Maw, A.M. et al. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implementation Sci 19 , 17 (2024). https://doi.org/10.1186/s13012-024-01346-y

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background scientific paper

Our next-generation model: Gemini 1.5

Feb 15, 2024

The model delivers dramatically enhanced performance, with a breakthrough in long-context understanding across modalities.

SundarPichai_2x.jpg

A note from Google and Alphabet CEO Sundar Pichai:

Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced . Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI .

Our teams continue pushing the frontiers of our latest models with safety at the core. They are making rapid progress. In fact, we’re ready to introduce the next generation: Gemini 1.5. It shows dramatic improvements across a number of dimensions and 1.5 Pro achieves comparable quality to 1.0 Ultra, while using less compute.

This new generation also delivers a breakthrough in long-context understanding. We’ve been able to significantly increase the amount of information our models can process — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model yet.

Longer context windows show us the promise of what is possible. They will enable entirely new capabilities and help developers build much more useful models and applications. We’re excited to offer a limited preview of this experimental feature to developers and enterprise customers. Demis shares more on capabilities, safety and availability below.

Introducing Gemini 1.5

By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team

This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0 , we’ve been testing, refining and enhancing its capabilities.

Today, we’re announcing our next-generation model: Gemini 1.5.

Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.

The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra , our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.

Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.

As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.

These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI.

Context lengths of leading foundation models

Highly efficient architecture

Gemini 1.5 is built upon our leading research on Transformer and MoE architecture. While a traditional Transformer functions as one large neural network, MoE models are divided into smaller "expert” neural networks.

Depending on the type of input given, MoE models learn to selectively activate only the most relevant expert pathways in its neural network. This specialization massively enhances the model’s efficiency. Google has been an early adopter and pioneer of the MoE technique for deep learning through research such as Sparsely-Gated MoE , GShard-Transformer , Switch-Transformer, M4 and more.

Our latest innovations in model architecture allow Gemini 1.5 to learn complex tasks more quickly and maintain quality, while being more efficient to train and serve. These efficiencies are helping our teams iterate, train and deliver more advanced versions of Gemini faster than ever before, and we’re working on further optimizations.

Greater context, more helpful capabilities

An AI model’s “context window” is made up of tokens, which are the building blocks used for processing information. Tokens can be entire parts or subsections of words, images, videos, audio or code. The bigger a model’s context window, the more information it can take in and process in a given prompt — making its output more consistent, relevant and useful.

Through a series of machine learning innovations, we’ve increased 1.5 Pro’s context window capacity far beyond the original 32,000 tokens for Gemini 1.0. We can now run up to 1 million tokens in production.

This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.

Complex reasoning about vast amounts of information

1.5 Pro can seamlessly analyze, classify and summarize large amounts of content within a given prompt. For example, when given the 402-page transcripts from Apollo 11’s mission to the moon, it can reason about conversations, events and details found across the document.

Reasoning across a 402-page transcript: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can understand, reason about and identify curious details in the 402-page transcripts from Apollo 11’s mission to the moon.

Better understanding and reasoning across modalities

1.5 Pro can perform highly-sophisticated understanding and reasoning tasks for different modalities, including video. For instance, when given a 44-minute silent Buster Keaton movie , the model can accurately analyze various plot points and events, and even reason about small details in the movie that could easily be missed.

Multimodal prompting with a 44-minute movie: Gemini 1.5 Pro Demo

Gemini 1.5 Pro can identify a scene in a 44-minute silent Buster Keaton movie when given a simple line drawing as reference material for a real-life object.

Relevant problem-solving with longer blocks of code

1.5 Pro can perform more relevant problem-solving tasks across longer blocks of code. When given a prompt with more than 100,000 lines of code, it can better reason across examples, suggest helpful modifications and give explanations about how different parts of the code works.

Problem solving across 100,633 lines of code | Gemini 1.5 Pro Demo

Gemini 1.5 Pro can reason across 100,000 lines of code giving helpful solutions, modifications and explanations.

Enhanced performance

When tested on a comprehensive panel of text, code, image, audio and video evaluations, 1.5 Pro outperforms 1.0 Pro on 87% of the benchmarks used for developing our large language models (LLMs). And when compared to 1.0 Ultra on the same benchmarks, it performs at a broadly similar level.

Gemini 1.5 Pro maintains high levels of performance even as its context window increases. In the Needle In A Haystack (NIAH) evaluation, where a small piece of text containing a particular fact or statement is purposely placed within a long block of text, 1.5 Pro found the embedded text 99% of the time, in blocks of data as long as 1 million tokens.

Gemini 1.5 Pro also shows impressive “in-context learning” skills, meaning that it can learn a new skill from information given in a long prompt, without needing additional fine-tuning. We tested this skill on the Machine Translation from One Book (MTOB) benchmark, which shows how well the model learns from information it’s never seen before. When given a grammar manual for Kalamang , a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person learning from the same content.

As 1.5 Pro’s long context window is the first of its kind among large-scale models, we’re continuously developing new evaluations and benchmarks for testing its novel capabilities.

For more details, see our Gemini 1.5 Pro technical report .

Extensive ethics and safety testing

In line with our AI Principles and robust safety policies, we’re ensuring our models undergo extensive ethics and safety tests. We then integrate these research learnings into our governance processes and model development and evaluations to continuously improve our AI systems.

Since introducing 1.0 Ultra in December, our teams have continued refining the model, making it safer for a wider release. We’ve also conducted novel research on safety risks and developed red-teaming techniques to test for a range of potential harms.

In advance of releasing 1.5 Pro, we've taken the same approach to responsible deployment as we did for our Gemini 1.0 models, conducting extensive evaluations across areas including content safety and representational harms, and will continue to expand this testing. Beyond this, we’re developing further tests that account for the novel long-context capabilities of 1.5 Pro.

Build and experiment with Gemini models

We’re committed to bringing each new generation of Gemini models to billions of people, developers and enterprises around the world responsibly.

Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI . Read more about this on our Google for Developers blog and Google Cloud blog .

We’ll introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model.

Early testers can try the 1 million token context window at no cost during the testing period, though they should expect longer latency times with this experimental feature. Significant improvements in speed are also on the horizon.

Developers interested in testing 1.5 Pro can sign up now in AI Studio, while enterprise customers can reach out to their Vertex AI account team.

Learn more about Gemini’s capabilities and see how it works .

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    If you are preparing a laboratory write-up, refer to your textbook and laboratory manual for background information. For a research article, perform a thorough literature search on a credible search engine (e.g., Web of Science, Google Scholar). ... If you are interested in publishing a scientific paper, academic journal websites also provide ...

  17. How to Write a Scientific Paper: Practical Guidelines

    A scientific paper is the formal lasting record of a research process. It is meant to document research protocols, methods, results and conclusions derived from an initial working hypothesis. ... is best suited. Poorly defined background information and problem setting are the 2 most common weaknesses encountered in introductions. They stem ...

  18. 3.2 Components of a scientific paper

    3.2.1 Abstract. The abstract is a short summary (150-200 words or less) of the important points of the paper. It does not generally include background information. There may be a very brief statement of the rationale for conducting the study. It describes what was done, but without details.

  19. How to Write a Good Background Section for Your Scientific Paper

    The background section of a scientific paper is where you provide the context and rationale for your research question, objectives, and hypotheses. It is also where you review the relevant...

  20. HOW TO WRITE A SCIENTIFIC ARTICLE

    Reviewers consider the following five criteria to be the most important in decisions about whether to accept manuscripts for publication: 1) the importance, timeliness, relevance, and prevalence of the problem addressed; 2) the quality of the writing style (i.e., that it is well‐written, clear, straightforward, easy to follow, and logical); 3) t...

  21. Library Research Guides: STEM: How To Write A Scientific Paper

    A scientific paper is broken down into eight sections: title, abstract, introduction, methods, results, discussion, conclusion, and references. Title The title of the lab report should be descriptive of the experiment and reflect what the experiment analyzed. Ex: "Determining the Free Chlorine Content of Pool Water" Abstract

  22. Parts of the paper

    INTRODUCTION (Background) METHODS SECTION (Materials and Methods) RESULTS DISCUSSION Title What is a title Titles have two functions: to identify the main topic or the message of the paper and to attract readers. The title will be read by many people.

  23. Scientific Reports

    Background and pre-writing Why do we write research reports? You did an experiment or study for your science class, and now you have to write it up for your teacher to review. ... How to Write and Publish a Scientific Paper, 4th ed. Phoenix: Oryx Press. Porush, David. 1995. A Short Guide to Writing About Science. New York: Longman.

  24. Leveraging artificial intelligence to advance implementation science

    Background The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including ...

  25. Introducing Gemini 1.5, Google's next-generation AI model

    A note from Google and Alphabet CEO Sundar Pichai: Last week, we rolled out our most capable model, Gemini 1.0 Ultra, and took a significant step forward in making Google products more helpful, starting with Gemini Advanced.Today, developers and Cloud customers can begin building with 1.0 Ultra too — with our Gemini API in AI Studio and in Vertex AI.

  26. White Papers

    White Paper, "Enhancing Agricultural Resilience, Enabling Scalable Sustainability, and Ensuring Food Security through Space-based Earth Observations," prepared by the Climate and Societal Benefits Subcommittee. This is a position paper supporting the recommendations of the Climate and Societal Benefits Subcommittee.