how to present findings in research

Research Guide

Chapter 7 presenting your findings.

Now that you have worked so hard in your project, how to ensure that you can communicate your findings in an effective and efficient way? In this section, I will introduce a few tips that could help you prepare your slides and preparing for your final presentation.

7.1 Sections of the Presentation

When preparing your slides, you need to ensure that you have a clear roadmap. You have a limited time to explain the context of your study, your results, and the main takeaways. Thus, you need to be organized and efficient when deciding what material will be included in the slides.

You need to ensure that your presentation contains the following sections:

• Motivation : Why did you choose your topic? What is the bigger question?
• Research question : Needs to be clear and concise. Include secondary questions, if available, but be clear about what is your research question.
• Literature Review : How does your paper fit in the overall literature? What are your contributions?
• Context : Give an overview of the issue and the population/countries that you analyzed
• Study Characteristics : This section is key, as it needs to include your model, identification strategy, and introduce your data (sources, summary statistics, etc.).
• Results : In this section, you need to answer your research question(s). Include tables that are readable.
• Additional analysis : Here, include any additional information that your public needs to know. For instance, did you try different specifications? did you find an obstacle (i.e. your data is very noisy, the sample is very small, something else) that may bias your results or create some issues in your analysis? Tell your audience! No research project is perfect, but you need to be clear about the imperfections of your project.
• Conclusion : Be repetitive! What was your research question? How did you answer it? What did you find? What is next in this topic?

7.2 How to prepare your slides

When preparing your slides, remember that humans have a limited capacity to pay attention. If you want to convey your convey your message in an effective way, you need to ensure that the message is simple and that you keep your audience attention. Here are some strategies that you may want to follow:

• Have a clear roadmap at the beginning of the presentation. Tell your audience what to expect.
• Number your slides. This will help you and your audience to know where you are in your analysis.
• Ensure that each slide has a purpose
• Ensure that each slide is connected to your key point.
• Make just one argument per slide
• State the objective of each slide in the headline
• Use bullet points. Do not include more than one sentence per bullet point.
• Choose a simple background.
• If you want to direct your audience attention to a specific point, make it more attractive (using a different font color)
• Each slide needs to have a similar structure (going from the general to the particular detauls).
• Use images/graphs when possible. Ensure that the axes for the graphs are clear.
• Use a large font for your tables. Keep them as simple as possible.
• If you can say it with an image, choose it over a table.
• Have an Appendix with slides that address potential questions.

7.3 How to prepare your presentation

One of the main constraints of having simple presentations is that you cannot rely on them and read them. Instead, you need to have extra notes and memorize them to explain things beyond what is on your slides. The following are some suggestions on how to ensure you communicate effectively during your presentation.

• Practice, practice, practice!
• Keep the right volume (practice will help you with that)
• Be journalistic about your presentation. Indicate what you want to say, then say it.
• Ensure that your audience knows where you are going
• Avoid passive voice.
• Be consistent with the terms you are using. You do not want to confuse your audience, even if using synonyms.
• Face your audience and keep an eye contact.
• Do not try reading your slides
• Ensure that your audience is focused on what you are presenting and there are no other distractions that you can control.
• Do not rush your presentation. Speak calmly and controlled.
• Be comprehensive when answering questions. Avoid yes/no answers. Instead, rephrase question (to ensure you are answering the right question), then give a short answer, then develop.
• If you lose track, do not panick. Go back a little bit or ask your audience for assistance.
• Again, practice is the secret.

You have worked so hard in your final project, and the presentation is your opportunity to share that work with the rest of the world. Use this opportunity to shine and enjoy it.

Since this is the first iteration of the Guide, I expect that there are going to be multiple typos and structure issues. Please feel free to let me know, and I will correct accordingly. ↩︎

Note that you would still need to refine some of the good questions even more. ↩︎

• Chapter Seven: Presenting Your Results

This chapter serves as the culmination of the previous chapters, in that it focuses on how to present the results of one's study, regardless of the choice made among the three methods. Writing in academics has a form and style that you will want to apply not only to report your own research, but also to enhance your skills at reading original research published in academic journals. Beyond the basic academic style of report writing, there are specific, often unwritten assumptions about how quantitative, qualitative, and critical/rhetorical studies should be organized and the information they should contain. This chapter discusses how to present your results in writing, how to write accessibly, how to visualize data, and how to present your results in person.  

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 1)
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Five: Qualitative Data (Part 2)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)

Written Presentation of Results

Once you've gone through the process of doing communication research – using a quantitative, qualitative, or critical/rhetorical methodological approach – the final step is to  communicate  it.

The major style manuals (the APA Manual, the MLA Handbook, and Turabian) are very helpful in documenting the structure of writing a study, and are highly recommended for consultation. But, no matter what style manual you may use, there are some common elements to the structure of an academic communication research paper.

Title Page :

This is simple: Your Paper's Title, Your Name, Your Institutional Affiliation (e.g., University), and the Date, each on separate lines, centered on the page. Try to make your title both descriptive (i.e., it gives the reader an idea what the study is about) and interesting (i.e., it is catchy enough to get one's attention).

For example, the title, "The uncritical idealization of a compensated psychopath character in a popular book series," would not be an inaccurate title for a published study, but it is rather vague and exceedingly boring. That study's author fortunately chose the title, "A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight ," which is more precisely descriptive, and much more interesting (Merskin, 2011). The use of the colon in academic titles can help authors accomplish both objectives: a catchy but relevant phrase, followed by a more clear explanation of the article's topic.

In some instances, you might be asked to write an abstract, which is a summary of your paper that can range in length from 75 to 250 words. If it is a published paper, it is useful to include key search terms in this brief description of the paper (the title may already have a few of these terms as well). Although this may be the last thing your write, make it one of the best things you write, because this may be the first thing your audience reads about the paper (and may be the only thing read if it is written badly). Summarize the problem/research question, your methodological approach, your results and conclusions, and the significance of the paper in the abstract.

Quantitative and qualitative studies will most typically use the rest of the section titles noted below. Critical/rhetorical studies will include many of the same steps, but will often have different headings. For example, a critical/rhetorical paper will have an introduction, definition of terms, and literature review, followed by an analysis (often divided into sections by areas of investigation) and ending with a conclusion/implications section. Because critical/rhetorical research is much more descriptive, the subheadings in such a paper are often times not generic subheads like "literature review," but instead descriptive subheadings that apply to the topic at hand, as seen in the schematic below. Because many journals expect the article to follow typical research paper headings of introduction, literature review, methods, results, and discussion, we discuss these sections briefly next.

Introduction:

As you read social scientific journals (see chapter 1 for examples), you will find that they tend to get into the research question quickly and succinctly. Journal articles from the humanities tradition tend to be more descriptive in the introduction. But, in either case, it is good to begin with some kind of brief anecdote that gets the reader engaged in your work and lets the reader understand why this is an interesting topic. From that point, state your research question, define the problem (see Chapter One) with an overview of what we do and don't know, and finally state what you will do, or what you want to find out. The introduction thus builds the case for your topic, and is the beginning of building your argument, as we noted in chapter 1.

By the end of the Introduction, the reader should know what your topic is, why it is a significant communication topic, and why it is necessary that you investigate it (e.g., it could be there is gap in literature, you will conduct valuable exploratory research, or you will provide a new model for solving some professional or social problem).

Literature Review:

The literature review summarizes and organizes the relevant books, articles, and other research in this area. It sets up both quantitative and qualitative studies, showing the need for the study. For critical/rhetorical research, the literature review often incorporates the description of the historical context and heuristic vocabulary, with key terms defined in this section of the paper. For more detail on writing a literature review, see Appendix 1.

The methods of your paper are the processes that govern your research, where the researcher explains what s/he did to solve the problem. As you have seen throughout this book, in communication studies, there are a number of different types of research methods. For example, in quantitative research, one might conduct surveys, experiments, or content analysis. In qualitative research, one might instead use interviews and observations. Critical/rhetorical studies methods are more about the interpretation of texts or the study of popular culture as communication. In creative communication research, the method may be an interpretive performance studies or filmmaking. Other methods used sometimes alone, or in combination with other methods, include legal research, historical research, and political economy research.

In quantitative and qualitative research papers, the methods will be most likely described according to the APA manual standards. At the very least, the methods will include a description of participants, data collection, and data analysis, with specific details on each of these elements. For example, in an experiment, the researcher will describe the number of participants, the materials used, the design of the experiment, the procedure of the experiment, and what statistics will be used to address the hypotheses/research questions.

Critical/rhetorical researchers rarely have a specific section called "methods," as opposed to quantitative and qualitative researchers, but rather demonstrate the method they use for analysis throughout the writing of their piece.

Helping your reader understand the methods you used for your study is important not only for your own study's credibility, but also for possible replication of your study by other researchers. A good guideline to keep in mind is  transparency . You want to be as clear as possible in describing the decisions you made in designing your study, gathering and analyzing your data so that the reader can retrace your steps and understand how you came to the conclusions you formed. A research study can be very good, but if it is not clearly described so that others can see how the results were determined or obtained, then the quality of the study and its potential contributions are lost.

After you completed your study, your findings will be listed in the results section. Particularly in a quantitative study, the results section is for revisiting your hypotheses and reporting whether or not your results supported them, and the statistical significance of the results. Whether your study supported or contradicted your hypotheses, it's always helpful to fully report what your results were. The researcher usually organizes the results of his/her results section by research question or hypothesis, stating the results for each one, using statistics to show how the research question or hypothesis was answered in the study.

The qualitative results section also may be organized by research question, but usually is organized by themes which emerged from the data collected. The researcher provides rich details from her/his observations and interviews, with detailed quotations provided to illustrate the themes identified. Sometimes the results section is combined with the discussion section.

Critical/rhetorical researchers would include their analysis often with different subheadings in what would be considered a "results" section, yet not labeled specifically this way.

Discussion:

In the discussion section, the researcher gives an appraisal of the results. Here is where the researcher considers the results, particularly in light of the literature review, and explains what the findings mean. If the results confirmed or corresponded with the findings of other literature, then that should be stated. If the results didn't support the findings of previous studies, then the researcher should develop an explanation of why the study turned out this way. Sometimes, this section is called a "conclusion" by researchers.

References:

In this section, all of the literature cited in the text should have full references in alphabetical order. Appendices: Appendix material includes items like questionnaires used in the study, photographs, documents, etc. An alphabetical letter is assigned for each piece (e.g. Appendix A, Appendix B), with a second line of title describing what the appendix contains (e.g. Participant Informed Consent, or  New York Times  Speech Coverage). They should be organized consistently with the order in which they are referenced in the text of the paper. The page numbers for appendices are consecutive with the paper and reference list.

Tables/Figures:

Tables and figures are referenced in the text, but included at the end of the study and numbered consecutively. (Check with your professor; some like to have tables and figures inserted within the paper's main text.) Tables generally are data in a table format, whereas figures are diagrams (such as a pie chart) and drawings (such as a flow chart).

Accessible Writing

As you may have noticed, academic writing does have a language (e.g., words like heuristic vocabulary and hypotheses) and style (e.g., literature reviews) all its own. It is important to engage in that language and style, and understand how to use it to  communicate effectively in an academic context . Yet, it is also important to remember that your analyses and findings should also be written to be accessible. Writers should avoid excessive jargon, or—even worse—deploying jargon to mask an incomplete understanding of a topic.

The scourge of excessive jargon in academic writing was the target of a famous hoax in 1996. A New York University physics professor submitted an article, " Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity ," to a special issue of the academic journal  Social Text  devoted to science and postmodernism. The article was designed to point out how dense academic jargon can sometimes mask sloppy thinking. As the professor, Alan Sokal, had expected, the article was published. One sample sentence from the article reads:

It has thus become increasingly apparent that physical "reality", no less than social "reality", is at bottom a social and linguistic construct; that scientific "knowledge", far from being objective, reflects and encodes the dominant ideologies and power relations of the culture that produced it; that the truth claims of science are inherently theory-laden and self-referential; and consequently, that the discourse of the scientific community, for all its undeniable value, cannot assert a privileged epistemological status with respect to counter-hegemonic narratives emanating from dissident or marginalized communities. (Sokal, 1996. pp. 217-218)

According to the journal's editor, about six reviewers had read the article but didn't suspect that it was phony. A public debate ensued after Sokal revealed his hoax. Sokal said he worried that jargon and intellectual fads cause academics to lose contact with the real world and "undermine the prospect for progressive social critique" ( Scott, 1996 ). The APA Manual recommends to avoid using technical vocabulary where it is not needed or relevant or if the technical language is overused, thus becoming jargon. In short, the APA argues that "scientific jargon...grates on the reader, encumbers the communication of information, and wastes space" (American Psychological Association, 2010, p. 68).

Data Visualization

Images and words have long existed on the printed page of manuscripts, yet, until recently, relatively few researchers possessed the resources to effectively combine images combined with words (Tufte, 1990, 1983). Communication scholars are only now becoming aware of this dimension in research as computer technologies have made it possible for many people to produce and publish multimedia presentations.

Although visuals may seem to be anathema to the primacy of the written word in research, they are a legitimate way, and at times the best way, to present ideas. Visual scholar Lester Faigley et al. (2004) explains how data visualizations have become part of our daily lives:

Visualizations can shed light on research as well. London-based David McCandless specializes in visualizing interesting research questions, or in his words "the questions I wanted answering" (2009, p. 7). His images include a graph of the  peak times of the year for breakups  (based on Facebook status updates), a  radiation dosage chart , and some  experiments with the Google Ngram Viewer , which charts the appearance of keywords in millions of books over hundreds of years.

The  public domain image  below creatively maps U.S. Census data of the outflow of people from California to other states between 1995 and 2000.

Visualizing one's research is possible in multiple ways. A simple technology, for example, is to enter data into a spreadsheet such as Excel, and select  Charts  or  SmartArt  to generate graphics. A number of free web tools can also transform raw data into useful charts and graphs.  Many Eyes , an open source data visualization tool (sponsored by IBM Research), says its goal "is to 'democratize' visualization and to enable a new social kind of data analysis" (IBM, 2011). Another tool,  Soundslides , enables users to import images and audio to create a photographic slideshow, while the program handles all of the background code. Other tools, often open source and free, can help visual academic research into interactive maps; interactive, image-based timelines; interactive charts; and simple 2-D and 3-D animations. Adobe Creative Suite (which includes popular software like Photoshop) is available on most computers at universities, but open source alternatives exist as well.  Gimp  is comparable to Photoshop, and it is free and relatively easy to use.

One online performance studies journal,  Liminalities , is an excellent example of how "research" can be more than just printed words. In each issue, traditional academic essays and book reviews are often supported photographs, while other parts of an issue can include video, audio, and multimedia contributions. The journal, founded in 2005, treats performance itself as a methodology, and accepts contribution in html, mp3, Quicktime, and Flash formats.

For communication researchers, there is also a vast array of visual digital archives available online. Many of these archives are located at colleges and universities around the world, where digital librarians are spearheading a massive effort to make information—print, audio, visual, and graphic—available to the public as part of a global information commons. For example, the University of Iowa has a considerable digital archive including historical photos documenting American railroads and a database of images related to geoscience. The University of Northern Iowa has a growing Special Collections Unit that includes digital images of every UNI Yearbook between 1905 and 1923 and audio files of UNI jazz band performances. Researchers at he University of Michigan developed  OAIster , a rich database that has joined thousands of digital archives in one searchable interface. Indeed, virtually every academic library is now digitizing all types of media, not just texts, and making them available for public viewing and, when possible, for use in presenting research. In addition to academic collections, the  Library of Congress  and the  National Archives  offer an ever-expanding range of downloadable media; commercial, user-generated databases such as Flickr, Buzznet, YouTube and Google Video offer a rich resource of images that are often free of copyright constraints (see Chapter 3 about Creative Commons licenses) and nonprofit endeavors, such as the  Internet Archive , contain a formidable collection of moving images, still photographs, audio files (including concert recordings), and open source software.

Presenting your Work in Person

As Communication students, it's expected that you are not only able to communicate your research project in written form but also in person.

Before you do any oral presentation, it's good to have a brief "pitch" ready for anyone who asks you about your research. The pitch is routine in Hollywood: a screenwriter has just a few minutes to present an idea to a producer. Although your pitch will be more sophisticated than, say, " Snakes on a Plane " (which unfortunately was made into a movie), you should in just a few lines be able to explain the gist of your research to anyone who asks. Developing this concise description, you will have some practice in distilling what might be a complicated topic into one others can quickly grasp.

Oral presentation

In most oral presentations of research, whether at the end of a semester, or at a research symposium or conference, you will likely have just 10 to 20 minutes. This is probably not enough time to read the entire paper aloud, which is not what you should do anyway if you want people to really listen (although, unfortunately some make this mistake). Instead, the point of the presentation should be to present your research in an interesting manner so the listeners will want to read the whole thing. In the presentation, spend the least amount of time on the literature review (a very brief summary will suffice) and the most on your own original contribution. In fact, you may tell your audience that you are only presenting on one portion of the paper, and that you would be happy to talk more about your research and findings in the question and answer session that typically follows. Consider your presentation the beginning of a dialogue between you and the audience. Your tone shouldn't be "I have found everything important there is to find, and I will cram as much as I can into this presentation," but instead "I found some things you will find interesting, but I realize there is more to find."

Turabian (2007) has a helpful chapter on presenting research. Most important, she emphasizes, is to remember that your audience members are listeners, not readers. Thus, recall the lessons on speech making in your college oral communication class. Give an introduction, tell them what the problem is, and map out what you will present to them. Organize your findings into a few points, and don't get bogged down in minutiae. (The minutiae are for readers to find if they wish, not for listeners to struggle through.) PowerPoint slides are acceptable, but don't read them. Instead, create an outline of a few main points, and practice your presentation.

Turabian  suggests an introduction of not more than three minutes, which should include these elements:

• The research topic you will address (not more than a minute).
• Your research question (30 seconds or less)
• An answer to "so what?" – explaining the relevance of your research (30 seconds)
• Your claim, or argument (30 seconds or less)
• The map of your presentation structure (30 seconds or less)

As Turabian (2007) suggests, "Rehearse your introduction, not only to get it right, but to be able to look your audience in the eye as you give it. You can look down at notes later" (p. 125).

Poster presentation

In some symposiums and conferences, you may be asked to present at a "poster" session. Instead of presenting on a panel of 4-5 people to an audience, a poster presenter is with others in a large hall or room, and talks one-on-one with visitors who look at the visual poster display of the research. As in an oral presentation, a poster highlights just the main point of the paper. Then, if visitors have questions, the author can informally discuss her/his findings.

To attract attention, poster presentations need to be nicely designed, or in the words of an advertising professor who schedules poster sessions at conferences, "be big, bold, and brief" ( Broyles , 2011). Large type (at least 18 pt.), graphics, tables, and photos are recommended.

A poster presentation session at a conference, by David Eppstein (Own work) [CC-BY-SA-3.0 ( www.creativecommons.org/licenses/by-sa/3.0 )], via Wikimedia Commons]

The Association for Education in Journalism and Mass Communication (AEJMC) has a  template for making an effective poster presentation . Many universities, copy shops, and Internet services also have large-scale printers, to print full-color research poster designs that can be rolled up and transported in a tube.

Judging Others' Research

After taking this course, you should have a basic knowledge of research methods. There will still be some things that may mystify you as a reader of other's research. For example, you may not be able to interpret the coefficients for statistical significance, or make sense of a complex structural equation. Some specialized vocabulary may still be difficult.

But, you should understand how to critically review research. For example, imagine you have been asked to do a blind (i.e., the author's identity is concealed) "peer review" of communication research for acceptance to a conference, or publication in an academic journal. For most  conferences  and  journals , submissions are made online, where editors can manage the flow and assign reviews to papers. The evaluations reviewers make are based on the same things that we have covered in this book. For example, the conference for the AEJMC ask reviewers to consider (on a five-point scale, from Excellent to Poor) a number of familiar research dimensions, including the paper's clarity of purpose, literature review, clarity of research method, appropriateness of research method, evidence presented clearly, evidence supportive of conclusions, general writing and organization, and the significance of the contribution to the field.

Beyond academia, it is likely you will more frequently apply the lessons of research methods as a critical consumer of news, politics, and everyday life. Just because some expert cites a number or presents a conclusion doesn't mean it's automatically true. John Allen Paulos, in his book  A Mathematician reads the newspaper , suggests some basic questions we can ask. "If statistics were presented, how were they obtained? How confident can we be of them? Were they derived from a random sample or from a collection of anecdotes? Does the correlation suggest a causal relationship, or is it merely a coincidence?" (1997, p. 201).

Through the study of research methods, we have begun to build a critical vocabulary and understanding to ask good questions when others present "knowledge." For example, if Candidate X won a straw poll in Iowa, does that mean she'll get her party's nomination? If Candidate Y wins an open primary in New Hampshire, does that mean he'll be the next president? If Candidate Z sheds a tear, does it matter what the context is, or whether that candidate is a man or a woman? What we learn in research methods about validity, reliability, sampling, variables, research participants, epistemology, grounded theory, and rhetoric, we can consider whether the "knowledge" that is presented in the news is a verifiable fact, a sound argument, or just conjecture.

American Psychological Association (2010). Publication manual of the American Psychological Association (6th ed.). Washington, DC: Author.

Broyles, S. (2011). "About poster sessions." AEJMC.  http://www.aejmc.org/home/2013/01/about-poster-sessions/ .

Faigley, L., George, D., Palchik, A., Selfe, C. (2004).  Picturing texts . New York: W.W. Norton & Company.

IBM (2011). Overview of Many Eyes.  http://www.research.ibm.com/social/projects_manyeyes.shtml .

McCandless, D. (2009).  The visual miscellaneum . New York: Collins Design.

Merskin, D. (2011). A boyfriend to die for: Edward Cullen as compensated psychopath in Stephanie Meyer's  Twilight. Journal of Communication Inquiry  35: 157-178. doi:10.1177/0196859911402992

Paulos, J. A. (1997).  A mathematician reads the newspaper . New York: Anchor.

Scott, J. (1996, May 18). Postmodern gravity deconstructed, slyly.  New York Times , http://www.nytimes.com/books/98/11/15/specials/sokal-text.html .

Sokal, A. (1996). Transgressing the boundaries: towards a transformative hermeneutics of quantum gravity.  Social Text  46/47, 217-252.

Tufte, E. R. (1990).  Envisioning information . Cheshire, CT: Graphics Press.

Tufte, E. R. (1983).  The visual display of quantitative information . Cheshire, CT: Graphics Press.

Turabian, Kate L. (2007).  A manual for writers of research papers, theses, and dissertations: Chicago style guide for students and researchers  (7th ed.). Chicago: University of Chicago Press.

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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Many believe that a scientist’s most difficult job is not conducting an experiment but presenting the results in an effective and coherent way. Even when your methods and technique are sound and your notes are comprehensive, writing a report can be a challenge because organizing and communicating scientific findings requires patience and a thorough grasp of certain conventions. Having a clear understanding of the typical goals and strategies for writing an effective lab report can make the process much less troubling.

General Considerations

It is useful to note that effective scientific writing serves the same purpose that your lab report should. Good scientific writing explains:

• The goal(s) of your experiment
• How you performed the experiment
• The results you obtained
• Why these results are important

While it’s unlikely that you’re going to win the Nobel Prize for your work in an undergraduate laboratory course, tailoring your writing strategies in imitation of professional journals is easier than you might think, since they all follow a consistent pattern. However, your instructor has the final say in determining how your report should be structured and what should appear in each section. Please use the following explanations only to supplement your given writing criteria, rather than thinking of them as an indication of how all lab reports must be written.

In Practice

The Structure of a Report

The traditional experimental report is structured using the acronym “IMRAD” which stands for I ntroduction, M ethods, R esults and D iscussion. The “ A ” is sometimes used to stand for A bstract. For help writing abstracts, please see Sweetland’s resource entitled “What is an abstract, and how do I write one?”

Introduction: “What am I doing here?” The introduction should accomplish what any good introduction does: draw the reader into the paper. To simplify things, follow the “inverted pyramid” structure, which involves narrowing information from the most broad (providing context for your experiment’s place in science) to the most specific (what exactly your experiment is about). Consider the example below.

Most broad: “Caffeine is a mild stimulant that is found in many common beverages, including coffee.”

Less broad: “Common reactions to caffeine use include increased heart rate and increased respiratory rate.”

Slightly more specific (moving closer to your experiment): Previous research has shown that people who consume multiple caffeinated beverages per day are also more likely to be irritable.

Most specific (your experiment): This study examines the emotional states of college students (ages 18-22) after they have consumed three cups of coffee each day.

See how that worked? Each idea became slightly more focused, ending with a brief description of your particular experiment. Here are a couple more tips to keep in mind when writing an introduction:

• Include an overview of the topic in question, including relevant literature A good example: “In 1991, Rogers and Hammerstein concluded that drinking coffee improves alertness and mental focus (citation 1991).
• Explain what your experiment might contribute to past findings A good example: “Despite these established benefits, coffee may negatively impact mood and behavior. This study aims to investigate the emotions of college coffee drinkers during finals week.”
• Keep the introduction brief There’s no real advantage to writing a long introduction. Most people reading your paper already know what coffee is, and where it comes from, so what’s the point of giving them a detailed history of the coffee bean? A good example: “Caffeine is a psychoactive stimulant, much like nicotine.” (Appropriate information, because it gives context to caffeine—the molecule of study) A bad example: “Some of the more popular coffee drinks in America include cappuccinos, lattés, and espresso.” (Inappropriate for your introduction. This information is useless for your audience, because not only is it already familiar, but it doesn’t mention anything about caffeine or its effects, which is the reason that you’re doing the experiment.)
• Avoid giving away the detailed technique and data you gathered in your experiment A good example: “A sample of coffee-drinking college students was observed during end-of-semester exams.” ( Appropriate for an introduction ) A bad example: “25 college students were studied, and each given 10oz of premium dark roast coffee (containing 175mg caffeine/serving, except for Folgers, which has significantly lower caffeine content) three times a day through a plastic straw, with intervals of two hours, for three weeks.” ( Too detailed for an intro. More in-depth information should appear in your “Methods” or “Results” sections. )

Methods: “Where am I going to get all that coffee…?”

A “methods” section should include all the information necessary for someone else to recreate your experiment. Your experimental notes will be very useful for this section of the report. More or less, this section will resemble a recipe for your experiment. Don’t concern yourself with writing clever, engaging prose. Just say what you did, as clearly as possible. Address the types of questions listed below:

• Where did you perform the experiment? (This one is especially important in field research— work done outside the laboratory.)
• How much did you use? (Be precise.)
• Did you change anything about them? (i.e. Each 5 oz of coffee was diluted with 2 oz distilled water.)
• Did you use any special method for recording data? (i.e. After drinking coffee, students’ happiness was measured using the Walter Gumdrop Rating System, on a scale of 1-10.)
• Did you use any techniques/methods that are significant for the research? (i.e. Maybe you did a double blinded experiment with X and Y as controls. Was your control a placebo? Be specific.)
• Any unusual/unique methods for collecting data? If so, why did you use them?

After you have determined the basic content for your “methods” section, consider these other tips:

• Decide between using active or passive voice

There has been much debate over the use of passive voice in scientific writing. “Passive voice” is when the subject of a sentence is the recipient of the action.

• For example: Coffee was given to the students.

“Active voice” is when the subject of a sentence performs the action.

• For example: I gave coffee to the students.

The merits of using passive voice are obvious in some cases. For instance, scientific reports are about what is being studied, and not about YOU. Using too many personal pronouns can make your writing sound more like a narrative and less like a report. For that reason, many people recommend using passive voice to create a more objective, professional tone, emphasizing what was done TO your subject. However, active voice is becoming increasingly common in scientific writing, especially in social sciences, so the ultimate decision of passive vs. active voice is up to you (and whoever is grading your report).

• Units are important When using numbers, it is important to always list units, and keep them consistent throughout the section. There is a big difference between giving someone 150 milligrams of coffee and 150 grams of coffee—the first will keep you awake for a while, and the latter will put you to sleep indefinitely. So make sure you’re consistent in this regard.
• Don’t needlessly explain common techniques If you’re working in a chemistry lab, for example, and you want to take the melting point of caffeine, there’s no point saying “I used the “Melting point-ometer 3000” to take a melting point of caffeine. First I plugged it in…then I turned it on…” Your reader can extrapolate these techniques for him or herself, so a simple “Melting point was recorded” will work just fine.
• If it isn’t important to your results, don’t include it No one cares if you bought the coffee for your experiment on “3 dollar latte day”. The price of the coffee won’t affect the outcome of your experiment, so don’t bore your reader with it. Simply record all the things that WILL affect your results (i.e. masses, volumes, numbers of trials, etc).

Results: The only thing worth reading?

The “results” section is the place to tell your reader what you observed. However, don’t do anything more than “tell.” Things like explaining and analyzing belong in your discussion section. If you find yourself using words like “because” or “which suggests” in your results section, then STOP! You’re giving too much analysis.

A good example: “In this study, 50% of subjects exhibited symptoms of increased anger and annoyance in response to hearing Celine Dion music.” ( Appropriate for a “results” section—it doesn’t get caught up in explaining WHY they were annoyed. )

In your “results” section, you should:

• Display facts and figures in tables and graphs whenever possible. Avoid listing results like “In trial one, there were 5 students out of 10 who showed irritable behavior in response to caffeine. In trial two…” Instead, make a graph or table. Just be sure to label it so you can refer to it in your writing (i.e. “As Table 1 shows, the number of swear words spoken by students increased in proportion to the amount of coffee consumed.”) Likewise, be sure to label every axis/heading on a chart or graph (a good visual representation can be understood on its own without any textual explanation). The following example clearly shows what happened during each trial of an experiment, making the trends visually apparent, and thus saving the experimenter from having to explain each trial with words.
• Identify only the most significant trends. Don’t try to include every single bit of data in this section, because much of it won’t be relevant to your hypothesis. Just pick out the biggest trends, or what is most significant to your goals.

Discussion: “What does it all mean?”

The “discussion” section is intended to explain to your reader what your data can be interpreted to mean. As with all science, the goal for your report is simply to provide evidence that something might be true or untrue—not to prove it unequivocally. The following questions should be addressed in your “discussion” section:

• Is your hypothesis supported? If you didn’t have a specific hypothesis, then were the results consistent with what previous studies have suggested? A good example: “Consistent with caffeine’s observed effects on heart rate, students’ tendency to react strongly to the popping of a balloon strongly suggests that caffeine’s ability to heighten alertness may also increase nervousness.”
• Was there any data that surprised you? Outliers are seldom significant, and mentioning them is largely useless. However, if you see another cluster of points on a graph that establish their own trend, this is worth mentioning.
• Are the results useful? If you have no significant findings, then just say that. Don’t try to make wild claims about the meanings of your work if there is no statistical/observational basis for these claims—doing so is dishonest and unhelpful to other scientists reading your work. Similarly, try to avoid using the word “proof” or “proves.” Your work is merely suggesting evidence for new ideas. Just because things worked out one way in your trials, that doesn’t mean these results will always be repeatable or true.
• What are the implications of your work? Here are some examples of the types of questions that can begin to show how your study can be significant outside of this one particular experiment: Why should anyone care about what you’re saying? How might these findings affect coffee drinkers? Do your findings suggest that drinking coffee is more harmful than previously thought? Less harmful? How might these findings affect other fields of science? What about the effects of caffeine on people with emotional disorders? Do your findings suggest that they should or should not drink coffee?
• Any shortcomings of your work? Were there any flaws in your experimental design? How should future studies in this field accommodate for these complications. Does your research raise any new questions? What other areas of science should be explored as a result of your work?

Resources: Hogg, Alan. "Tutoring Scientific Writing." Sweetland Center for Writing. University of Michigan, Ann Arbor. 3/15/2011. Lecture. Swan, Judith A, and George D. Gopen. "The Science of Scientific Writing." American Scientist . 78. (1990): 550-558. Print. "Scientific Reports." The Writing Center . University of North Carolina, n.d. Web. 5 May 2011. http://www.unc.edu/depts/wcweb/handouts/lab_report_complete.html

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Step 8: Present Findings

Evaluating information in the research process: step 8: present findings, created by health science librarians.

• Evaluation Criteria
• Step 1: Do Initial Planning
• Step 2: Choose a Topic
• Step 3: Do Initial Search
• Step 4: Refine Topic / Refine Search
• Step 5: Identify Key Sources
• Step 6: Study Key Sources
• Step 7: Integrate Notes and Ideas
• Issues and Tips

Step 8: Write and Present Fndings

Choose a medium.

Choose the medium (paper, article, slide show, blog, website, poster, video, etc.) you will use to present your findings. 

Consider your goals, audience, and experience. If you don’t have much time, choose a medium you are familiar with. Learning to create in a new medium is a major project in itself. 

You should be familiar with software for the medium you choose. For example, word processing or page layout software for papers or posters, web design software or a blog platform for online publishing, video editing software for videos, or presentation software for stand-up presentations. 

Some design issues are related to the medium. For web design, good resources include the Yale C/AIM Web Style Guide . See also the  Web Usability Alertbox  by Jakob Nielsen at  https://www.nngroup.com/articles/alertbox-200/ . 

For posters, see Designing Effective Posters (HSL Guide) at https://guides.lib.unc.edu/posters

For papers and articles, refer to the handouts at the UNC Writing Center: http://writingcenter.unc.edu/handouts/  

Create Diagrams and Graphics

Visual thinking is extremely valuable in facilitating conceptualization and creativity. Try creating diagrams or illustrations to represent the ideas you are working with. In so doing, you may clarify your thinking and come up with new ideas. 

Mind maps and concept maps are are diagrams that represent the relationships between ideas visually. For more visual maps and aspects of visual thinking, see the HSL Introduction to Visual Literacy  guide at  https://guides.lib.unc.edu/visual-literacy .

Create an Outline or Storyboard

Put your notes and ideas in sequence by creating an outline.  An outline or other organizing tool is important for this stage. 

An outline should not make the writing process feel constrained or unnatural. Feel free to modify your outline as you go along.      Some word processing programs include an integrated outliner. This valuable feature allows you to quickly restructure your document and immediately see the results. Standalone outlining programs can also help facilitate the process.

After you have created an outline you can focus on individual pieces of content without having to worry too much about how each piece will fit in with all the other pieces. 

If you are creating a multimedia, movie, or web-based presentation, consider creating a storyboard or prototype. For example, you could create a storyboard that shows the screens the user will see, with the major headings and visual elements. For more about how to create a storyboard, see the discussion of how to create storyboards at the University of Houston Educational Uses of Digital Storytelling site:  http://digitalstorytelling.coe.uh.edu (under the "How To" tab).

Write a Draft 

DON'T worry about perfection when writing your draft! Trying to make an absolutely perfect first draft will slow you down and can cause writer's block. You can correct errors later when you edit your work.

The notes you already created make up the bulk of your first draft.  Depending on how detailed your notes are, you may also need to write passages that expand on their content.  The main task that remains is to write transitions between notes (Atchity, 1986, p. 91).

Transitions link one note to another, so that your text flows in a natural way that shows how the chunks of information and ideas are related to each other.  

Once you have completed a draft, take a break. Getting away from the work for a day or two will be refreshing and help you be more effective when you come back to edit your draft. 

Edit and Revise

Carefully review your draft and make improvements and corrections. If you know a good editor, have that person take a look at your draft as well. It helps to have another pair of eyes review your work.  

Review the content. Look for the same things you look for in evaluating a source, including credibility, accuracy, and significance. Are there errors, omissions, or weak arguments that you can improve?

Review the writing and design. Is it readable and understandable? Are there problems with grammar or spelling? Does the organization make sense? Is it wordy or redundant? Is it aesthetically appealing? 

Verify the accuracy of all quotations, and make sure all references are listed according to appropriate style guidelines. (Check with your instructor if you aren’t sure which style guidelines to use).  Have someone who belongs to your target audience look at your work. Listen to their feedback and incorporate any valuable suggestions. 

Proof your final document or product. Run a spelling check with your word processor, but keep in mind that spell check doesn't catch everything. Check for any mistakes you may have made in entering revisions.

Outcomes for Step 8: Present Findings

Diagrams or illustrations 

Outline or prototype 

Draft 

Finished product 

Satisfaction of a job well done!  

• << Previous: Step 7: Integrate Notes and Ideas
• Next: Issues and Tips >>
• Last Updated: Aug 14, 2023 9:45 AM
• URL: https://guides.lib.unc.edu/evaluating-info

• Manuscript Preparation

How to write the results section of a research paper

• 3 minute read
• 66.9K views

Table of Contents

At its core, a research paper aims to fill a gap in the research on a given topic. As a result, the results section of the paper, which describes the key findings of the study, is often considered the core of the paper. This is the section that gets the most attention from reviewers, peers, students, and any news organization reporting on your findings. Writing a clear, concise, and logical results section is, therefore, one of the most important parts of preparing your manuscript.

Difference between results and discussion

Before delving into how to write the results section, it is important to first understand the difference between the results and discussion sections. The results section needs to detail the findings of the study. The aim of this section is not to draw connections between the different findings or to compare it to previous findings in literature—that is the purview of the discussion section. Unlike the discussion section, which can touch upon the hypothetical, the results section needs to focus on the purely factual. In some cases, it may even be preferable to club these two sections together into a single section. For example, while writing  a review article, it can be worthwhile to club these two sections together, as the main results in this case are the conclusions that can be drawn from the literature.

Structure of the results section

Although the main purpose of the results section in a research paper is to report the findings, it is necessary to present an introduction and repeat the research question. This establishes a connection to the previous section of the paper and creates a smooth flow of information.

Next, the results section needs to communicate the findings of your research in a systematic manner. The section needs to be organized such that the primary research question is addressed first, then the secondary research questions. If the research addresses multiple questions, the results section must individually connect with each of the questions. This ensures clarity and minimizes confusion while reading.

Consider representing your results visually. For example, graphs, tables, and other figures can help illustrate the findings of your paper, especially if there is a large amount of data in the results.

Remember, an appealing results section can help peer reviewers better understand the merits of your research, thereby increasing your chances of publication.

Practical guidance for writing an effective results section for a research paper

• Always use simple and clear language. Avoid the use of uncertain or out-of-focus expressions.
• The findings of the study must be expressed in an objective and unbiased manner. While it is acceptable to correlate certain findings in the discussion section, it is best to avoid overinterpreting the results.
• If the research addresses more than one hypothesis, use sub-sections to describe the results. This prevents confusion and promotes understanding.
• Ensure that negative results are included in this section, even if they do not support the research hypothesis.
• Wherever possible, use illustrations like tables, figures, charts, or other visual representations to showcase the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey.
• For statistical data, it is adequate to highlight the tests and explain their results. The initial or raw data should not be mentioned in the results section of a research paper.

The results section of a research paper is usually the most impactful section because it draws the greatest attention. Regardless of the subject of your research paper, a well-written results section is capable of generating interest in your research.

For detailed information and assistance on writing the results of a research paper, refer to Elsevier Author Services.

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How to Write an Effective Results Section

Affiliation.

• 1 Rothman Orthopaedics Institute, Philadelphia, PA.
• PMID: 31145152
• DOI: 10.1097/BSD.0000000000000845

Developing a well-written research paper is an important step in completing a scientific study. This paper is where the principle investigator and co-authors report the purpose, methods, findings, and conclusions of the study. A key element of writing a research paper is to clearly and objectively report the study's findings in the Results section. The Results section is where the authors inform the readers about the findings from the statistical analysis of the data collected to operationalize the study hypothesis, optimally adding novel information to the collective knowledge on the subject matter. By utilizing clear, concise, and well-organized writing techniques and visual aids in the reporting of the data, the author is able to construct a case for the research question at hand even without interpreting the data.

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Buttoning up research: How to present and visualize qualitative data

15 Minute Read

There is no doubt that data visualization is an important part of the qualitative research process. Whether you're preparing a presentation or writing up a report, effective visualizations can help make your findings clear and understandable for your audience. 

In this blog post, we'll discuss some tips for creating effective visualizations of qualitative data. 

First, let's take a closer look at what exactly qualitative data is.

What is qualitative data?

Qualitative data is information gathered through observation, questionnaires, and interviews. It's often subjective, meaning that the researcher has to interpret it to draw meaningful conclusions from it. 

The difference between qualitative data and quantitative data

When researchers use the terms qualitative and quantitative, they're referring to two different types of data. Qualitative data is subjective and descriptive, while quantitative data is objective and numerical.

Qualitative data is often used in research involving psychology or sociology. This is usually where a researcher may be trying to identify patterns or concepts related to people's behavior or attitudes. It may also be used in research involving economics or finance, where the focus is on numerical values such as price points or profit margins. 

Before we delve into how best to present and visualize qualitative data, it's important that we highlight how to be gathering this data in the first place. ‍

How best to gather qualitative data

In order to create an effective visualization of qualitative data, ensure that the right kind of information has been gathered. 

Here are six ways to gather the most accurate qualitative data:

• Define your research question: What data is being set out to collect? A qualitative research question is a definite or clear statement about a condition to be improved, a project’s area of concern, a troubling question that exists, or a difficulty to be eliminated. It not only defines who the participants will be but guides the data collection methods needed to achieve the most detailed responses.
• ‍ Determine the best data collection method(s): The data collected should be appropriate to answer the research question. Some common qualitative data collection methods include interviews, focus groups, observations, or document analysis. Consider the strengths and weaknesses of each option before deciding which one is best suited to answer the research question.  ‍
• Develop a cohesive interview guide: Creating an interview guide allows researchers to ask more specific questions and encourages thoughtful responses from participants. It’s important to design questions in such a way that they are centered around the topic of discussion and elicit meaningful insight into the issue at hand. Avoid leading or biased questions that could influence participants’ answers, and be aware of cultural nuances that may affect their answers.
• ‍ Stay neutral – let participants share their stories: The goal is to obtain useful information, not to influence the participant’s answer. Allowing participants to express themselves freely will help to gather more honest and detailed responses. It’s important to maintain a neutral tone throughout interviews and avoid judgment or opinions while they are sharing their story. 
• ‍ Work with at least one additional team member when conducting qualitative research: Participants should always feel comfortable while providing feedback on a topic, so it can be helpful to have an extra team member present during the interview process – particularly if this person is familiar with the topic being discussed. This will ensure that the atmosphere of the interview remains respectful and encourages participants to speak openly and honestly.
• ‍ Analyze your findings: Once all of the data has been collected, it’s important to analyze it in order to draw meaningful conclusions. Use tools such as qualitative coding or content analysis to identify patterns or themes in the data, then compare them with prior research or other data sources. This will help to draw more accurate and useful insights from the results. 

By following these steps, you will be well-prepared to collect and analyze qualitative data for your research project. Next, let's focus on how best to present the qualitative data that you have gathered and analyzed.

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How to visually present qualitative data.

When it comes to how to present qualitative data visually, the goal is to make research findings clear and easy to understand. To do this, use visuals that are both attractive and informative. 

Presenting qualitative data visually helps to bring the user’s attention to specific items and draw them into a more in-depth analysis. Visuals provide an efficient way to communicate complex information, making it easier for the audience to comprehend. 

Additionally, visuals can help engage an audience by making a presentation more interesting and interactive.

Here are some tips for creating effective visuals from qualitative data:

• ‍ Choose the right type of visualization: Consider which type of visual would best convey the story that is being told through the research. For example, bar charts or line graphs might be appropriate for tracking changes over time, while pie charts or word clouds could help show patterns in categorical data. 
• ‍ Include contextual information: In addition to showing the actual numbers, it's helpful to include any relevant contextual information in order to provide context for the audience. This can include details such as the sample size, any anomalies that occurred during data collection, or other environmental factors.
• ‍ Make it easy to understand: Always keep visuals simple and avoid adding too much detail or complexity. This will help ensure that viewers can quickly grasp the main points without getting overwhelmed by all of the information. 
• ‍ Use color strategically: Color can be used to draw attention to certain elements in your visual and make it easier for viewers to find the most important parts of it. Just be sure not to use too many different colors, as this could create confusion instead of clarity. 
• ‍ Use charts or whiteboards: Using charts or whiteboards can help to explain the data in more detail and get viewers engaged in a discussion. This type of visual tool can also be used to create storyboards that illustrate the data over time, helping to bring your research to life. 

Visualizing qualitative data in Notably

Notably helps researchers visualize their data on a flexible canvas, charts, and evidence based insights. As an all-in-one research platform, Notably enables researchers to collect, analyze and present qualitative data effectively.

Notably provides an intuitive interface for analyzing data from a variety of sources, including interviews, surveys, desk research, and more. Its powerful analytics engine then helps you to quickly identify insights and trends in your data . Finally, the platform makes it easy to create beautiful visuals that will help to communicate research findings with confidence. 

Research Frameworks in Analysis

The canvas in Analysis is a multi-dimensional workspace to play with your data spatially to find likeness and tension. Here, you may use a grounded theory approach to drag and drop notes into themes or patterns that emerge in your research. Utilizing the canvas tools such as shapes, lines, and images, allows researchers to build out frameworks such as journey maps, empathy maps, 2x2's, etc. to help synthesize their data.

Going one step further, you may begin to apply various lenses to this data driven canvas. For example, recoloring by sentiment shows where pain points may distributed across your customer journey. Or, recoloring by participant may reveal if one of your participants may be creating a bias towards a particular theme.

Exploring Qualitative Data through a Quantitative Lens

Once you have begun your analysis, you may visualize your qualitative data in a quantitative way through charts. You may choose between a pie chart and or a stacked bar chart to visualize your data. From here, you can segment your data to break down the ‘bar’ in your bar chart and slices in your pie chart one step further.

To segment your data, you can choose between ‘Tag group’, ‘Tag’, ‘Theme’, and ‘Participant'. Each group shows up as its own bar in the bar chart or slice in the pie chart. For example, try grouping data as ‘Participant’ to see the volume of notes assigned to each person. Or, group by ‘Tag group’ to see which of your tag groups have the most notes.

Depending on how you’ve grouped or segmented your charts will affect the options available to color your chart. Charts use colors that are a mix of sentiment, tag, theme, and default colors. Consider color as a way of assigning another layer of meaning to your data. For example, choose a red color for tags or themes that are areas of friction or pain points. Use blue for tags that represent opportunities.

AI Powered Insights and Cover Images

One of the most powerful features in Analysis is the ability to generate insights with AI. Insights combine information, inspiration, and intuition to help bridge the gap between knowledge and wisdom. Even before you have any tags or themes, you may generate an AI Insight from your entire data set. You'll be able to choose one of our AI Insight templates that are inspired by trusted design thinking frameworks to stimulate generative, and divergent thinking. With just the click of a button, you'll get an insight that captures the essence and story of your research. You may experiment with a combination of tags, themes, and different templates or, create your own custom AI template. These insights are all evidence-based, and are centered on the needs of real people. You may package these insights up to present your research by embedding videos, quotes and using AI to generate unique cover image.

You can sign up to run an end to end research project for free and receive tips on how to make the most out of your data. Want to chat about how Notably can help your team do better, faster research? Book some time here for a 1:1 demo with your whole team.

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Top tips for presenting your research effectively

Giving a short and engaging summary of your research is a vital skill for researchers. We often ask for oral presentations at our grants panels and competitions, so here the Chair of one of our grants panels shares her top tips to help you present your elevator pitch with confidence:

Be prepared

All grant panel interviews start by giving you a few minutes to introduce yourself and your project – even if you’ve not been asked to do a presentation. Knowing this and being prepared to pitch your work is key to making a strong first impression.

Know your audience

Whether you’re pitching to a grant panel or presenting at a meeting, you’re likely to be faced with a multidisciplinary audience. Adapt your pitch and make sure you can be understood by everyone present.

Get them hooked

Aim to catch the attention of everyone, not just those already interested in your research area. Start with a compelling hook that bridges the gap between something all your audience can relate to and your research.

Keep it simple

Answer the key questions: why, what and how? Doing day-to-day research can get you caught up in the fine details, but for presentations you need to be able to take a step back and communicate the essence of your research. Don’t mention technical details, caveats or contingency plans: if your audience want to know more, they’ll ask you.

Share the big picture

Show the significance of your research by putting the outcomes in context. Tell your audience how your findings could contribute to your field, and to wider society. Remember to be realistic and use positive language.

Stand out from the crowd

Research is a collaborative effort but it’s important to show what you bring to the table. Highlight what makes your research unique, and with team science efforts, clearly define who participated and what role you played.

Make it personal

Give your audience a feel for who you are. What interests you? What drives you? How does that shape your research?  

Cut the jargon

Don’t expect the audience to understand the jargon of your field. Decide which key technical terms are vital when discussing your research and take the time to explain them. Then use lay terms wherever possible to effectively communicate the rest of your message.

Simple is beautiful

Visual aids like PowerPoint can be useful to support and enhance what you’re saying, butbe sure to avoid visual clutter and redundant information so you’re not competing for the audience’s attention.

Perfect your timekeeping

Know your time limit in advance and plan accordingly. It’s better to go under time than over time, and don’t try to fit too much in: a rushed presentation is harder to understand.

Practice makes perfect

Rehearse your presentation with both peers and non-experts – your colleagues can comment on the science, and your family and friends will tell you if it’s clear and engaging. Competitions at conferences and meetings are a great way to practice your presentation skills, so don’t hesitate to take part!

Our Grants Team were speaking to Professor Marina Botto FMedSci, Chair for our Starter Grants for Clinical Lecturers Panel. Professor Botto is Professor of Rheumatology and Director of the Centre for Complement and Inflammation Research at the Imperial College London Department of Medicine.

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On October 9, 2023

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

Does your Dissertation Have the Following?

• Great Research/Sources
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If not, we can help. Our panel of experts makes sure to keep the 3 pillars of the Dissertation strong.

1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired samples correlation, paired samples test.

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

How do I report quantitative findings?

The best way to present your quantitative findings is to structure them around the  research hypothesis  or  research questions  you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the  qualitative research  results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your  research question  or  hypothesis  should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the  discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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Students Present Research at Annual Conference

Three Eastern psychology undergraduates recently presented their research findings in symposia presentations at the 2024 Rocky Mountain Psychological Association’s annual conference. 

“It is competitive,” Jillene Seiver, senior lecturer and associate chair of the psychology department, said of the selection process that led to the students’ conference participation. “Symposia needs to be interesting and meaningful to be chosen.” Seiver began taking students to this conference in 2019, and their first symposium presentation was accepted in 2020.

Eastern’s students, Ray Orthmann, Terreca Defehr and Victoria Layden, chose their own research topic, collected data and designed PowerPoint presentations to explain the significance of their findings. 

Their symposium talk, “Balancing It All and Feeling Good About It,” discussed executive functioning, which is a set of cognitive processes necessary for the control of behavior.  

Ray Orthmann studied the role of executive dysfunction as it relates to happiness. “Executive dysfunction relates to a person’s ability and skills to function in life, including initiation, task switching, focusing, and many more,” Orthmann said. “I wanted to look at individuals with a high level of executive dysfunction and a high level of happiness, then look at their personality.” 

Orthmann was interested in this research because prior studies suggest “happy” individuals have a particular behavior profile. But Orthmann hypothesized that individuals with high levels of executive dysfunction might prove an exception to these previous findings.

While Orthmann’s work related executive function to personality, Victoria Layden investigated its relation to learning comprehension. 

“I chose my research topic after noticing two key trends in online learning and video content,” she said “First, I observed the increasing utilization of video-based learning in education, especially with the rise of online courses. Second, I noticed that many platforms now offer options to adjust the playback speed of videos, giving users more control over their learning experience. These observations led me to question how the ability to adjust video playback speed impacts learning outcomes and user engagement.” 

Layden’s research began in 2023 after she received the Len Stern Student Research Award, which provided her with a grant. The grant covered part of the conference cost, in combination with funds granted by the psychology department. Layden eventually discovered that increasing the video speed by 2x “did not significantly decrease performance on a follow-up quiz.”

The contributions of Terreca Defehr, one of Eastern’s 2023-2024 McNair scholars, served as a bridge between the work of Orthmann and Layden. 

“I was interested in self-worth and work-life balance,” Defehr said. Her research found a positive correlation between personal view of paid work and individuals’ core-self evaluation results. 

“I want to thank Dr. Seiver and the McNair Scholar program for their support in introducing me to the world of research and changing the trajectory of my educational goals,” said Defehr. 

Thanks to their conference participation, each of these seniors are now equipped with the experience of presenting research at a conference with over 1,500 attendees — an experience they will use as they pursue master’s degrees. 

“Dr. Seiver was a wonderful mentor and guided me through the whole process,” Orthmann said. “The whole psychology department is ready to help you with advice or bits of knowledge.”

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Gen Z is growing up: In 2024, the generation born between 1996 to 2010 is expected to overtake Baby Boomers in the full-time workforce, according to a recent analysis by Glassdoor .

They are bringing to the office a different set of values, behaviors, and expectations than prior generations, according to research by Roberta Katz , a former senior research scholar at Stanford’s Center for Advanced Study in the Behavioral Sciences (CASBS) . Katz collaborated with a team of researchers to conduct a large, multi-year study to find out what matters to Gen Z and why – findings that culminated in a book and website .

Stanford Report sat down with Katz to talk about this research and what to expect from Gen Z in the workplace.

1. Gen Z expects change

The world Gen Zers came of age in was fundamentally different from that of their parents and even millennials, people who were born in the early 1980s to 1996.

The world of Gen Z has been defined by technological changes happening at rapid speeds that also reshaped social experiences. Disruption and impermanence have always been part of the world Gen Z experienced – for them, it’s a norm, not an exception.

“There is an expectation of constant change,” said Katz.

Growing up amid uncertainty has given Gen Z a unique set of characteristics, including being flexible and resilient. It has opened them up to new ways of thinking about the future and doing things – and questioning the ways things are done, which leads to the next trait Gen Zers will bring with them to work.

2. Gen Z is pragmatic

Gen Z has a strong sense of self-agency.

Gen Z lives in a world that has always been one search engine result away. If they want to know more about something, they readily seek the answer out for themselves ( even if it’s not always the correct one ).

They question everything and everyone – from their peers, parents, or people at work. “They don’t necessarily see elders as experts,” Katz said. “They want to understand why something is done in a certain way. They’re very pragmatic.”

They are also not afraid to challenge why things are done the way they are.

“When an older person says to them, ‘This is how you should do it,’ they want to check that out for themselves. It doesn’t mean they’re always right; it’s a different way of understanding,” Katz explained.

3. Gen Z wants to make a difference

Gen Zers not only expect change – they demand it.

They are inheriting a set of complex problems – from climate change to inequality to racial injustice, to name but a few – and want to fix it. They want to work for a place that they believe is doing good in the world.

Some Gen Zers will hold their employers accountable on the causes and issues that matter to them.

Katz warns that for some employers, it can be challenging – if not untenable – to take a position on politically charged or sensitive topics. “It is impossible for most institutions that represent lots of people and lots of identities to satisfy everybody,” Katz said.

4. Gen Z values collaboration and teamwork

For some Gen Zers, the digital world helped shape their identity: Through social media and in online groups, they found subcultures to connect and interact with.

They grew up with wikis – websites collaboratively built and edited by its users – and fandoms – enthusiastic and energetic communities centered around a shared, common interest. For example, K-pop sensation BTS has its Army , Beyonce has her Beyhive, and Taylor Swift has her Swifties.

“They’re in a posse – even with their headphones on,” Katz said.

To get things done, they value collaboration.

“There is a hope that everybody who is contributing is in it for the good of the whole,” Katz describes. “They want to have a team spirit.”

5. Gen Z wants leaders who guide by consensus

Gen Z is also less hierarchical than previous generations.

“They don’t believe in hierarchy for hierarchy’s sake,” Katz said. “They do believe in hierarchy where it is useful.”

Instead, Gen Zers prefer leadership that is dependent on expertise that is task or time specific. That could mean they favor management where team members take turns leading the group (known as a “rotating leadership” model). Another style they may prefer is “collaborative leadership,” in which people from across the organization participate in decision-making and problem-solving.

Transparency is also important.

Gen Zers value consensus and they look for leaders who are in service of the group (also called “service leadership”).

6. Gen Z cares about mental health and work-life balance

Gen Z grew up in a period that saw the blurring of the 9-to-5 work schedule and the rise of flexible work models – a mode of working that led to older generations feeling a pressure to always be “on.”

“Work and home life are all so integrated that if you don’t pay attention, you could be working all the time,” said Katz. “I think Gen Z is sensitive to that.”

Having a work-life balance and maintaining mental and physical health is also important to Gen Z.

“They’re placing a value on the human experience and recognizing that life is more than work,” Katz said.

7. Gen Z thinks differently about loyalty

Because Gen Z grew up amid so much change, Gen Z has a different perspective on loyalty.

But as Katz pointed out, “they also grew up with workplaces not being very loyal to their employees.”

Gen Zers were raised in the shadows of the global financial crisis of 2008, an event that has had long-lasting impacts on employment and the nature of work. “It used to be that people went to work for big companies thinking they’d be there for their entire career and that the company would watch out for them: providing health insurance, and so on,” Katz said.

But after the 2008 recession, and even more recently following the COVID-19 pandemic, companies have cut back labor costs and implemented other cost-saving measures, like reducing perks and benefits. Meanwhile, mass layoffs have also been rampant.

“There’s a reason that employees don’t feel the same degree of loyalty, too,” Katz said.

Meanwhile, the gig economy has also been present throughout Gen Zers’ lives, as has the rise of contract work. They are entrepreneurial, which is part of their pragmatic tendencies.

8. Gen Z looks for trust and authenticity

Gen Z also values authenticity.

“Authenticity is about trust,” Katz said. “Words and actions need to match.”

Honesty and openness are important.

For Katz, it’s all about mutually respectful communication. “My bottom line always to employers is stay open to hearing about different ways to get things done, because Gen Z has one foot in the future.”

Katz is associate vice president for strategic planning, emerita, and is currently involved in a strategic role with the Stanford Doerr School of Sustainability and the Stanford Institute for Human-Centered Artificial Intelligence . She also serves as vice chair of the board of the Center for Advanced Study in the Behavioral Sciences (CASBS).

Katz studied Gen Z as part of a multi-year CASBS research project with Sarah Ogilvie, a linguist at the University of Oxford and formerly at Stanford; Jane Shaw, a historian who is the principal of Harris Manchester College at Oxford and was previously dean for religious life at Stanford; and Linda Woodhead, a sociologist at King’s College London. The research was funded by the Knight Foundation.

From 2004 to 2017, Katz served under Stanford University Presidents John Hennessy and Marc Tessier-Lavigne as associate vice president for strategic planning, and in 2017 as interim chief of staff.

• Open access
• Published: 30 May 2024

Barriers and facilitators to mental health treatment access and engagement for LGBTQA+ people with psychosis: a scoping review protocol

• Cláudia C. Gonçalves   ORCID: orcid.org/0000-0001-6767-0920 1 ,
• Zoe Waters 2 ,
• Shae E. Quirk 1 ,
• Peter M. Haddad 1 , 3 ,
• Ashleigh Lin 4 ,
• Lana J. Williams 1 &
• Alison R. Yung 1 , 5  

Systematic Reviews volume  13 , Article number:  143 ( 2024 ) Cite this article

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The prevalence of psychosis has been shown to be disproportionately high amongst sexual and gender minority individuals. However, there is currently little consideration of the unique needs of this population in mental health treatment, with LGBTQA+ individuals facing barriers in accessing timely and non-stigmatising support for psychotic experiences. This issue deserves attention as delays to help-seeking and poor engagement with treatment predict worsened clinical and functional outcomes for people with psychosis. The present protocol describes the methodology for a scoping review which will aim to identify barriers and facilitators faced by LGBTQA+ individuals across the psychosis spectrum in help-seeking and accessing mental health support.

A comprehensive search strategy will be used to search Medline, PsycINFO, Embase, Scopus, LGBTQ+ Source, and grey literature. Original studies of any design, setting, and publication date will be included if they discuss barriers and facilitators to mental health treatment access and engagement for LGBTQA+ people with experiences of psychosis. Two reviewers will independently screen titles/abstracts and full-text articles for inclusion in the review. Both reviewers will then extract the relevant data according to pre-determined criteria, and study quality will be assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Key data from included studies will be synthesised in narrative form according to the Guidance on the Conduct of Narrative Synthesis in Systematic Reviews.

The results of this review will provide a comprehensive account of the current and historical barriers and facilitators to mental healthcare faced by LGBTQA+ people with psychotic symptoms and experiences. It is anticipated that the findings from this review will be relevant to clinical and community services and inform future research. Findings will be disseminated through publication in a peer-reviewed journal and presented at conferences.

Scoping review registration

This protocol is registered in Open Science Framework Registries ( https://doi.org/10.17605/OSF.IO/AT6FC ).

Peer Review reports

The prevalence of psychotic disorders in the general population has been estimated to be around 0.27–0.75% [ 1 , 2 ], with the lifetime prevalence of ever having a psychotic experience being estimated at 5.8% [ 3 ]. However, rates of psychotic symptoms and experiences are disproportionately high amongst LGBTQA+ populations, with non-heterosexual individuals estimated to be 1.99–3.75 times more likely to experience psychosis than their heterosexual peers [ 4 , 5 , 6 , 7 ]. Additionally, it has been estimated that transgender or gender non-conforming (henceforth trans) individuals are 2.46–49.7 times more likely than their cisgender peers (i.e. individuals whose gender identity is the same as their birth registered sex) to receive a psychotic disorder diagnosis [ 8 , 9 ]. The increased rates of psychotic experiences noted amongst gender and sexual minorities may be explained by evidence indicating that LGBTQA+ people are also exposed to risk factors for psychosis at a far greater rate than members of the general population, such as childhood adversity [ 10 , 11 , 12 ], minority stress [ 13 ], discrimination [ 14 ], and stigma [ 15 , 16 ]. Furthermore, there is added potential for diagnostic biases leading to over-diagnosing psychosis in gender diverse individuals, whose gender expression and dysphoria may be pathologized by mental health service providers [ 8 ].

Despite these concerning statistics, there is very little research examining the experiences of LGBTQA+ people with psychosis, and limited consideration of the unique needs these individuals may have in accessing and engaging with mental health services. While timely access to treatment has consistently been associated with better symptomatic and functional outcomes for people with psychosis [ 17 , 18 ], there are often delays to treatment initiation which are worsened for LGBTQA+ individuals [ 19 , 20 ]. These individuals face additional barriers to accessing adequate mental health support compared to cisgender/heterosexual people [ 19 ] and may need to experiment with several mental health services before finding culturally competent care [ 20 ]. This in turn may lead to longer duration of untreated psychosis. Additionally, there seems to be a lack of targeted support for this population from healthcare providers, with LGBTQA+ individuals with serious mental health concerns reporting higher rates of dissatisfaction with psychiatric services than their cisgender and heterosexual counterparts [ 7 , 14 , 21 ]. However, the extent of these differences varies across contexts [ 22 ], potentially due to improved education around stigma and LGBTQA+ issues within a subset of mental health services.

Nonetheless, stigma remains one of the highest cited barriers to help-seeking for mental health problems, particularly with regard to concerns around disclosure [ 23 ], which can be particularly challenging for people experiencing psychosis [ 24 , 25 ]. Stigma stress in young people at risk for psychosis is associated with less positive attitudes towards help-seeking regarding both psychiatric medication and psychotherapy [ 26 ], potentially partly due to fears of judgement and being treated differently by service providers [ 27 ]. This issue may be compounded for people who also belong to minoritized groups [ 23 , 28 ], particularly as LGBTQA+ individuals have reported experiencing frequent stigma and encountering uninformed staff when accessing mental healthcare [ 7 , 29 ]. Furthermore, stigma-fuelled hesitance to access services may be heightened for trans people [ 30 ] whose identities have historically been pathologized and conflated with experiences of psychosis [ 31 ].

Even when individuals manage to overcome barriers to access support, there are added challenges to maintaining adequate treatment engagement. In a large online study, half of trans and nearly one third of LGB participants reported having stopped using mental health services in the past because of negative experiences related to their gender identity or sexuality [ 20 ]. This can be particularly problematic as experiences of stigma predict poorer medication adherence in psychosis [ 32 ] which subsequently multiplies the risk for relapse and suicide [ 33 ]. While no research to date has explored non-adherence rates in people with psychosis who are LGBTQA+, concerns around suicidality are heightened for individuals who are gender and sexuality diverse [ 34 , 35 , 36 ].

Generally, there is rising demand for mental healthcare that specifically addresses the needs of gender and sexual minority individuals and promotes respect for diversity, equity, and inclusion [ 29 , 37 ]. This is particularly salient as positive relationships with staff are associated with better medication adherence for people with psychosis [ 38 ] and healthcare providers with LGBTQA+-specific mandates have demonstrated higher satisfaction rates for LGBTQA+ individuals [ 20 ]. Mental health services need to adapt treatment options to acknowledge minority stress factors for those with stigmatised identities and, perhaps more importantly, how these intersect and interact to increase inequalities in people from minoritized groups accessing and benefiting from treatment [ 37 , 39 ].

Additionally, gender affirming care needs to be recognised as an important facet of mental health treatment for many trans individuals, as it is associated with positive outcomes such as improvements in quality of life and psychological functioning [ 40 , 41 , 42 ] and reductions in psychiatric symptom severity and need for subsequent mental health treatment [ 8 , 43 ]. While there are additional barriers in access to gender affirming care for individuals with psychosis, this treatment has shown success in parallel with treatment to address psychosis symptom stabilisation [ 19 , 44 ]. The importance of affirmation is echoed by the finding that many negative experiences of LGBTQA+ participants with mental health services could be avoided simply by respecting people’s pronouns and using gender-neutral language [ 20 ].

To ensure timely access to appropriate treatment for LGBTQA+ people with psychosis, there is a need for improved understanding of the factors which challenge and facilitate help-seeking and engagement with mental health support. A preliminary search of Google Scholar, Medline, the Cochrane Database of Systematic Reviews, and PROSPERO was conducted and revealed no existing or planned reviews exploring benefits and/or obstacles to mental health treatment specific to this population. Therefore, the proposed review seeks to comprehensively search and appraise the existing literature to identify and summarise a range of barriers and facilitators to adequate mental health support faced by LGBTQA+ people with experiences of psychosis. This will allow for the mapping of the types of evidence available and identification of any knowledge gaps. Moreover, we hope to guide future decision-making in mental healthcare to improve service accessibility for LGBTQA+ individuals with psychosis and to set the foundations for future research that centres this marginalised population. Based on published guidance [ 45 , 46 , 47 ], a scoping review methodology was identified as the most appropriate approach to address these aims.

Selection criteria

This scoping review protocol has been developed in compliance with the JBI Manual for Evidence Synthesis [ 48 ] and, where relevant, the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) checklist [ 49 ] (see Additional file 1). In the event of protocol amendments, the date, justification, and description for each amendment will be provided.

Due to the limited literature around the topic of this review, any primary original study design, setting, and publication date will be considered for inclusion. Publications written in English will be included, and articles in other languages may be considered pending time and cost constraints around translation. Publications will be excluded if the full text is not available upon request from authors.

The PCC (Population, Concept, Context) framework was used to develop the inclusion criteria for this scoping review:

This review will include individuals of any age who are LGBTQA+ and have had experiences of psychosis. For the purposes of this review, ‘LGBTQA+ individuals’ will be broadly defined as any individual that is not heterosexual and/or cisgender or anyone who engages in same-gender sexual behaviour. Studies may include participants who are cisgender and heterosexual if they separately report outcomes for LGBTQA+ individuals. Within this review, the term ‘psychosis’ includes (i) any diagnosis of a psychotic disorder, such as schizophrenia spectrum disorders, mood disorders with psychotic features, delusional disorders, and drug-induced psychotic disorders, (ii) sub-threshold psychotic symptoms, such as those present in ultra-high risk (UHR), clinical high risk (CHR), or at risk mental state (ARMS) individuals, and (iii) any psychotic-like symptoms or experiences. Studies may include participants with multiple diagnoses if they separately report outcomes for individuals on the psychosis spectrum.

This review will include publications which discuss potential barriers and/or facilitators to mental health help-seeking and/or engagement with mental health treatment. ‘Barriers’ will be operationalised as any factors which may delay or prevent individuals from accessing and engaging with appropriate mental health support. These may include lack of mental health education, experienced or internalised stigma, experiences of discrimination from health services, and lack of inclusivity in health services. ‘Facilitators’ will be operationalised as any factors which may promote timely help-seeking and engagement with sources of support. These may include improved access to mental health education, positive sources of social support, and welcoming and inclusive services. Mental health help-seeking will be broadly defined as any attempt to seek and access formal or informal support to address a mental health concern related to experiences of psychosis (e.g. making an initial appointment with a service provider, seeking help from a friend). Mental health treatment engagement will be broadly defined as adherence and active participation in the treatment that is offered by a source of support (e.g. attending scheduled appointments, taking medication as prescribed, openly communicating with service providers).

This review may include research encompassing any setting in which mental healthcare is provided. This is likely to include formal healthcare settings such as community mental health teams or inpatient clinics as well as informal settings such as LGBTQA+ spaces or informal peer support. Studies will be excluded if they focus exclusively on physical health treatment.

Search strategy

Database searches will be conducted in Medline, PsycINFO, Embase, Scopus, and LGBTQ+ Source. The full search strategy for this protocol is available (see Additional file 2). This strategy has been collaboratively developed and evaluated by a scholarly services health librarian. Searches will include subject headings relevant to each database and title/abstract keywords relating to three main concepts: (i) LGBTQA+ identity, (ii) experiences of psychosis, and (iii) mental health treatment. Keywords for each concept will be combined using the Boolean operator ‘OR’, and the three concepts will be combined using ‘AND’. This search strategy was appropriately translated for each of the selected databases. There will be no limitations on language or publication date at this stage to maximise the breadth of the literature captured. Publications returned from these searches will be exported to EndNote. Searches will be re-run prior to the final analysis to capture any newly published studies.

The database searches will be supplemented by searching the grey literature as per the eligibility criteria detailed above. These may include theses and dissertations, conference proceedings, reports from mental health services, and policy documents from LGBTQA+ groups. Google and Google Scholar will be searched using a combination of clauses for psychosis (Psychosis OR psychotic OR schizophrenia OR schizoaffective), treatment (treatment or “help-seeking”), and queer identity. The latter concept will have three clauses for three separate searches, with one including broad queer identity (LGBT), one specific to non-heterosexual individuals (gay OR lesbian OR homosexual OR bisexual OR queer OR asexual), and one specific to trans individuals (transgender OR transsexual OR transexual OR “non-binary” OR “gender minority”). Additionally, reference lists and citing literature will be manually searched for each paper included in the review to capture any articles and policy documents not previously identified.

Data selection

Search results will be imported into Covidence using EndNote, and duplicates will be eliminated. Titles and abstracts will be screened by the first and second authors according to pre-defined screening criteria, which will be discussed by the authors and piloted prior to screening. These criteria will consider whether the articles included LGBTQA+ participants with experiences of psychosis (as operationalised above) in relation to mental health help-seeking and/or treatment. Full texts of relevant articles will then be obtained and screened by the first and second reviewer in accordance with the full inclusion and exclusion criteria after initial piloting to maximise inter-rater reliability. Decisions on inclusion and exclusion will be blinded and recorded on Covidence. Potential discrepancies will be resolved through discussion, and when consensus cannot be reached, these will be resolved by the supervising author. The process of study selection will be documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [ 50 ].

Data extraction

Data extraction will be performed independently by two reviewers using Covidence. Prior to beginning final extraction, both reviewers will independently pilot the extraction tool using a sample of five included studies and discuss any necessary changes. Information extracted is planned to include the following: title, author name(s), year of publication, country in which the study was conducted, study design, sample size, population of focus (i.e. sexual minorities, gender minorities, or both), sample demographics (i.e. age, gender identity, and sexual orientation), setting (e.g. early intervention service, community mental health team, etc.), psychosis characteristics (e.g. diagnoses included, severity of symptoms, etc.), type of treatment (e.g. cognitive behavioural therapy, antipsychotic medication, etc.), and any barriers and/or facilitators identified according to the aforementioned operationalised definitions. Disagreements will be resolved through discussion between the two reviewers and, when necessary, final decisions will be made by a senior supervisor. Once extracted, information will be recorded in Excel. Lead authors of papers will be contacted by the primary review author in cases where there is missing or insufficient data.

Quality assessment

Due to the expected heterogeneity in the types of studies that may be included in this review (e.g. qualitative studies, randomised controlled trials, case control studies, case reports), the relevant revised Joanna Briggs Institute (JBI) critical appraisal checklists [ 51 ] will be used to assess risk of bias and study quality for each study design. Two reviewers will independently use these checklists to assess each paper that is included following the full-text screening. If there are discrepancies in article ratings, these will be resolved through discussion between the two authors. If no consensus is reached, discrepancies will be resolved by a senior supervisor. In line with the scoping nature of this review, low-quality studies will not be excluded from the synthesis.

Evidence synthesis

Data from included studies will be synthesised using a narrative synthesis approach in accordance with the Guidance on the Conduct of Narrative Synthesis in Systematic Reviews [ 52 ]. A preliminary descriptive synthesis will be conducted by tabulating the extracted data elements from each study alongside quality assessment results and developing an initial description of the barriers and facilitators to (1) accessing and (2) engaging with mental health support that are identified in the literature. This initial synthesis will then be interrogated and refined to contextualise these barriers and facilitators in the setting, population, and methodology of each study to form the basis for an interpretative synthesis.

This review will not use a pre-existing thematic framework to categorise barriers and facilitators as it is expected that the factors identified will not neatly fit into existing criteria. Instead, these will be conceptualised according to overarching themes as interrelated factors, so that potentially complex interactions between barriers and facilitators within and across relevant studies may be explored through concept mapping. If most of the studies included are qualitative, there may also be scope for a partial meta-synthesis. To avoid oversimplifying the concept of ‘barriers and facilitators’ (see criticism by Bach-Mortensen & Verboom [ 53 ]), this data synthesis will be followed by a critical reflection of the findings through the lens of the socio-political contexts which may give rise to the barriers and facilitators identified, exploring the complexities necessary for any changes to be implemented in mental health services.

If the extracted data indicate that gender minority and sexual minority individuals experience unique or different barriers and/or facilitators to each other, these population groups will be analysed separately as opposed to findings being generalised across the LGBTQA+ spectrum. Furthermore, if there is scope to do so, analyses may be conducted to investigate how perceived barriers and facilitators for this population may have changed over time (i.e. according to publication date) as definitions of psychosis evolve and LGBTQA+ individuals gain visibility in clinical services.

The proposed review will add to the literature around mental health treatment for LGBTQA+ people with psychosis. It will provide a thorough account of the barriers and facilitators to accessing and engaging with support faced by this population and may inform future research and clinical practice.

In terms of limitations, this review will be constrained by the existing literature and may therefore not be sufficiently comprehensive in reflecting the barriers and facilitators experienced by subgroups within the broader LGBTQA+ community. Additionally, although broad inclusion criteria are necessary to capture the full breadth of research conducted in this topic, included studies are likely to be heterogeneous and varied in terms of their methodology and population which may complicate data synthesis.

Nonetheless, it is anticipated that the findings from this review will provide the most comprehensive synthesis to date of the issues driving low help-seeking and treatment engagement in people across the psychosis spectrum who are LGBTQA+. This review will likely also identify gaps in the literature which may inform avenues for future research, and the factors identified in this review will be considered in subsequent research by the authors.

Additionally, findings will be relevant to healthcare providers that offer support to people with psychosis who may have intersecting LGBTQA+ identities as well as LGBTQA+ organisations which offer support to LGBTQA+ people who may be experiencing distressing psychotic experiences. These services are likely to benefit from an increased awareness of the factors which may improve or hinder accessibility for these subsets of their target populations. Therefore, results from this review may inform decision-making around the implementation of service-wide policy changes.

The findings of this review will be disseminated through the publication of an article in a peer-reviewed journal and presented at relevant conferences in Australia and/or internationally. Additionally, the completed review will form part of the lead author’s doctoral thesis.

Availability of data and materials

Not applicable for this protocol.

Abbreviations

• At risk mental state

Clinical high risk for psychosis

Joanna Briggs Institute

Lesbian, gay, and bisexual

Lesbian, gay, bisexual, transgender, queer or questioning, asexual or aromantic, and more

Population, Concept, Context

Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols

Ultra-high risk for psychosis

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Acknowledgements

The authors would like to acknowledge the support of Ms Olivia Larobina, Scholarly Services Librarian (STEMM) at Deakin University, in the development of the search strategy.

CCG is funded by a Deakin University Postgraduate Research (DUPR) Scholarship. ZW is funded by a University of Western Australia Research Training Program (RTP) Scholarship. AL is supported by a National Health and Medical Research Council (NHMRC) Emerging Leaders Fellowship (2010063). LJW is supported by a NHMRC Emerging Leaders Fellowship (1174060). ARY is supported by a NHMRC Principal Research Fellowship (1136829). The funding providers had no role in the design and conduct of the study, or in the preparation, review, or approval of this manuscript.

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CCG is the guarantor. CCG conceptualised the review, developed the study design, and drafted the manuscript. CCG, ZW, and SQ collaborated with OL (Scholarly Services Librarian) to develop the search strategy. All authors critically reviewed the manuscript. All authors read and approved the final manuscript.

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Additional file 1. prisma-p 2015 checklist. completed prisma-p checklist for this systematic review protocol., 13643_2024_2566_moesm2_esm.docx.

Additional file 2. Search Strategy. Detailed search strategy for this systematic review, including search terms and relevant controlled vocabulary terms for each included database.

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Gonçalves, C.C., Waters, Z., Quirk, S.E. et al. Barriers and facilitators to mental health treatment access and engagement for LGBTQA+ people with psychosis: a scoping review protocol. Syst Rev 13 , 143 (2024). https://doi.org/10.1186/s13643-024-02566-5

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Vanda Pharmaceuticals Highlights Genetic Studies at SLEEP 2024

May 31, 2024 | Genes | 0 |

Research topics include sleep latency mechanisms, genetic variants related to sleep-wake phases, and genome sequencing in insomnia patients.

Summary: Vanda Pharmaceuticals Inc announced its participation at SLEEP 2024, where it will present various research findings related to sleep disorders, including studies on sleep latency in zebrafish, genetic variants associated with sleep-wake phase disorders, and whole genome sequencing in insomnia patients. Sandra Paulina Smieszek, PhD, head of genetics, will present these findings.

Key Takeaways:

• Melanopsin and Sleep Latency: Vanda Pharmaceuticals will present research on how alterations in melanopsin affect sleep latency in zebrafish.
• Genetic Insights into Sleep-Wake Disorders: A study examining genetic variants linked to dim light melatonin onset in individuals with delayed sleep-wake phase disorder will be showcased.
• Whole Genome Sequencing in Insomnia: The company will also share findings from a whole genome sequencing study that identifies genetic variants associated with total sleep time and latency to persistent sleep in insomnia patients.

Vanda Pharmaceuticals Inc announced its participation at SLEEP 2024 , where it will present research on topics including sleep latency in zebrafish and genetic variants associated with sleep disorders.

Research to be presented at SLEEP 2024 on June 4 by Sandra Paulina Smieszek, PhD, head of genetics, is as follows: 

• Poster Presentation Session: P-17
• Poster Number: 11
• Poster Presentation Session: P-22
• Poster Number: 146
• Poster Presentation Session: Late-Breaking Abstracts (LBA); P-31
• Poster Number: 451
• Poster Number: 10

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Press Release May | 29 | 2024

Mass General Cancer Center Researchers Present Key Findings at ASCO

Leaders from the Mass General Cancer Center, a member of the Mass General Brigham healthcare system, will present research discoveries and outcomes from clinical trials in cancer at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting, held May 31-June 4, in Chicago.

ASCO brings together leading experts in clinical oncology to share the latest breakthroughs in cancer research, science and medicine. Presentations from Mass General Cancer Center investigators include a plenary session on palliative care delivery via telehealth versus in-person for patients with advanced lung cancer. Oral presentations include advances in colorectal cancer treatments, as well as those in metastatic breast cancer, meningiomas, acute myeloid leukemia and beyond.

Below are a few highlights from this year’s presentations. Times are Central Time (CT). View a full list of Mass General Cancer Center presentations here .

Updated results from ERAS-007 plus encorafenib and cetuximab (EC) in patients (pts) with EC-naïve metastatic BRAF V600E colorectal cancer (CRC) in the phase 1b/2 HERKULES-3 study When: June 3, 2024, 2:27-2:33 p.m. Who: Aparna Parikh, MD What: The RAS/MAPK pathway (including the BRAF gene) is dysregulated in a broad range of cancers, including colorectal cancer (CRC), resulting in the downstream activation of ERK1/2. Patients with metastatic CRC with a BRAF V600E mutation have dramatically poorer survival rates than those with non-BRAF V600E mutated CRC, highlighting the need for novel therapies. Currently, the combination of a BRAF plus EGFR inhibitor is approved for the treatment of patients with BRAF V600E mCRC; however, objective response occurs in only 20% of patients. ERAS-007 is a novel, potent, and orally bioavailable inhibitor of ERK. HERKULES-3 is a Phase 1b/2 study to assess the safety, tolerability, pharmacokinetics (PK), and preliminary clinical activity of ERAS-007 combinations targeting the MAPK pathway in patients with advanced GI cancers. As of November 30, 2023, 19 patients have been dosed with ERAS-007 — 100 mg twice daily-once a week (BID-QW) in combination with encorafenib and cetuximab (EC). In the efficacy evaluable patients, the objective response rate was 40% with 3 of the 4 responses confirmed. The maximum duration of response is >15 months; updated data will be presented. The data show that ERAS-007 100 mg BID-QW in combination with EC was safe and well-tolerated with preliminary evidence of promising clinical activity. These results support continued evaluation of this combination in patients with BRAF V600E CRC.

Comparative effectiveness trial of early palliative care delivered via telehealth versus in person among patients with advanced lung cancer When: June 2, 2024, 2:13-2:25 p.m. Who: Joseph Greer, PhD , and Jennifer Temel, MD, FASCO What: This plenary session and late-breaking abstract is embargoed until 7 a.m. on June 2, 2024.

Multi-site randomized trial of stepped palliative care (PC) for patients with advanced lung cancer When: June 2, 2024, 8-8:12 a.m. Who: Jennifer S. Temel, MD, FASCO What: Studies show that early palliative care (EPC), integrated with oncology care from the time of diagnosis of advanced cancer, improves patient and caregiver outcomes. However, this care model has not been widely implemented for two main reasons: the shortage of palliative care (PC) clinicians nationwide and challenges in providing PC visits throughout the course of cancer treatment, especially as novel therapeutics prolong survival. Therefore, to deliver more patient-centered and less resource-intensive PC, the team evaluated a stepped PC (SPC) model in patients with advanced lung cancer in a multi-site randomized trial of SPC versus EPC. All patients assigned to SPC started on Step 1, with an initial PC visit within four weeks of enrollment and subsequent PC visits scheduled only at the time of a change in cancer treatment or after a hospitalization. Patients on Step 1 also completed a measure of quality of life (QOL) every six weeks for up to 18 months from enrollment, and those with a greater than or equal to 10-point decrease in their score from baseline were stepped up to meet with the PC clinician every four weeks (Step 2). Patients assigned to EPC had PC visits every four weeks from enrollment. Results showed that QOL scores at week 24 for patients assigned to SPC were non-inferior to those receiving EPC. Thus, a stepped care model, with PC visits scheduled only at key points in patients’ cancer trajectories and using a decrement in QOL to trigger more intensive PC exposure, resulted in significantly fewer PC visits without sacrificing the benefits for patients’ QOL. While SPC was associated with fewer days in hospice, this novel model holds promise as a more scalable way to deliver early PC to enhance patient-reported outcomes.

Alliance A071401: Phase II trial of abemaciclib in patients with grade 2/3 meningiomas harboring somatic NF2 or CDK pathway alterations When: June 3, 2024, 8:12-8:24 a.m. Who: Priscilla Brastianos, MD What: Systemic treatments are limited for patients with meningiomas that have progressed after surgery and/or radiation. Loss of NF2and CDKN2A/B are common in higher-grade meningiomas and promote meningioma progression in preclinical models. The team evaluated the efficacy of abemaciclib, a cyclin-dependent kinase (CDK) 4/6 inhibitor, as part of Alliance umbrella trial A071401, a genomically driven phase II study in recurrent or progressive meningiomas. Eligible patients with grade 2/3 tumors and NF2 mutations or CDK pathway alterations were treated with abemaciclib — 200 mg orally twice daily until progressive disease. Of the 24 patients evaluated, 58% were female and the median age was 62 years. The observed progression-free survival rate at six months (PFS6) was 54% (13/24 patients). Abemaciclib was well-tolerated and resulted in an improved PFS6, and the overall trial endpoint was met. Abemaciclib warrants further investigation for the treatment of patients with progressive grade 2/3 meningiomas.

Multi-site randomized trial of a collaborative palliative and oncology care model for patients with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) receiving non-intensive therapy When: May 31, 2024, 5:09-5:21 p.m. Who: Areej El-Jawahri, MD What: This late-breaking abstract is embargoed until 7 a.m. on May 31, 2024.

First-line inavolisib/placebo + palbociclib + fulvestrant (Inavo/Pbo+Palbo+Fulv) in patients (pts) with PIK3CA-mutated, hormone receptor-positive, HER2 negative locally advanced/metastatic breast cancer who relapsed during/within 12 months (mo) of adjuvant endocrine therapy completion: INAVO120 Phase III randomized trial additional analyses When: June 1, 2024, 3:24-3:36 p.m. Who: Dejan Juric, MD What: NAVO120 showed significantly and meaningfully improved investigator-assessed, progression-free survival (PFS) with Inavo+Palbo+Fulv versus placebo+Palbo+Fulv, and manageable safety and tolerability. To further characterize the substantial benefit/risk of the Inavo triplet, the team assessed additional clinically relevant efficacy endpoints, detailed safety data of key adverse events (AEs) for Inavo (hyperglycemia, diarrhea, rash, stomatitis), and patient-reported outcomes (PROs). Efficacy endpoints included time from randomization to end of next-line treatment and to first chemotherapy. Patients receiving Inavo experienced a longer duration of time without worsening pain severity and maintained their day-to-day functioning and health-related quality of life. Most patients in both arms reported levels of selected symptomatic AEs from the PRO-CTCAE and overall treatment bother as moderate or less, indicating that Inavo does not contribute additional treatment burden. Thus, Inavo+Palbo+Fulv was associated with sustained benefit beyond disease progression, delaying chemotherapy administration, with manageable safety and tolerability that was reflected in PROs; hence, supporting it as a new standard of care.

About Mass General Brigham

Mass General Brigham is an integrated academic health care system, uniting great minds to solve the hardest problems in medicine for our communities and the world. Mass General Brigham connects a full continuum of care across a system of academic medical centers, community and specialty hospitals, a health insurance plan, physician networks, community health centers, home care, and long-term care services. Mass General Brigham is a nonprofit organization committed to patient care, research, teaching, and service to the community. In addition, Mass General Brigham is one of the nation’s leading biomedical research organizations with several Harvard Medical School teaching hospitals. For more information, please visit massgeneralbrigham.org .

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In this article we present a manifesto for research into the complex interplay between social media, music streaming services, and their algorithms, which are reshaping the European music industry – a sector that has transitioned from ownership to access-based models. Our focus is to assess whether the current digital economy supports a fair and sustainable development for cultural and creative industries. The manifesto is designed to pave the way for a comprehensive analysis. We begin with the context of our research by briefly examining the de-materialisation of the music industry and the critical role of proprietary algorithms in organising and ranking creative works. We then scrutinise the notion of “fairness” within digital markets, a concept that is attracting increasing policy interest in the EU. We believe that, for “fairness” to be effective, the main inquiry around this concept – especially as regards remuneration of music creators – must be necessarily interdisciplinary. This presupposes collaboration across complementary fields to address gaps and inconsistencies in the understanding of how these platforms influence music creation and consumption and whether these environments and technologies should be regulated. We outline how interdisciplinary expertise (political science, law, economics, and computer science) can enhance the current understanding of “fairness” within Europe’s cultural policies and help address policy challenges. The article details how our research plan will unfold across various disciplinary hubs of a Horizon Europe project ( Fair MusE ) that aims to explore the challenges and opportunities of today’s digital music landscape. The plan culminates in the integration of these hubs’ findings to deliver “key exploitable results”.

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1 Introduction

The exponential growth of social media and streaming services, and the fast-growing influence of their algorithms and data infrastructures, raise questions as to whether today’s digital economy will allow the cultural and creative industries (CCIs), and especially the music ecosystem, to develop in a fair and sustainable way, at least for most authors and performers. The digital revolution has done much more than just simplify content dissemination and enable content production to reach unprecedented scales. Digital technologies have broadened the notion of “creation” itself, which ranges from traditional works of composers, performers, record labels, and broadcasters to new forms of musical and music-based creativity that digital settings, social media, and artificial intelligence (AI) have enabled. These new forms and trends include the streaming of live music events and home-made creations that became even more appealing and diffused due to the COVID-19 health emergency and the ensuing long-term restrictions on the performing arts. In this scenario, the commercial power of a handful of very large tech companies increased significantly. These companies can be identified, at least in part, with the owners of the “very large online platforms” (VLOPs) Footnote 1 under Art. 33 of the DSA and with providers of core platform services according to the notion embodied in the DMA. Footnote 2 The ability of these companies to control access to unprecedented volumes of creative works and, at the same time, creators’ ability to reach and develop potential audiences raises existential questions for Europe’s policymakers and the CCIs, including players such as radio, TV broadcasters, and the market for live performance exploitations.

This paper takes the form of a manifesto to advocate a new, interdisciplinary research approach that can remedy the shortcomings of a purely doctrinal and scientifically segregated (i.e. “silo-like”) analysis of EU cultural and industrial policies in the music sector and of their effective impact in today’s platform- and algorithm-dominated economy. In our view, only a well-designed combination of distinct and complementary disciplines can test methodologically and verify empirically whether the EU’s policy changes in copyright law and recent EU regulations (Digital Services Act – DSA – and Digital Markets Act – DMA) seeking to curb the exceptional power of VLOPs are justified and suitable for today’s internet. To this end, we authored a research proposal and built an EU-wide interdisciplinary group of academics and industry partners whose consortium – Fair MusE Footnote 3 – received funding from the EC/REA’s Horizon Europe program. The group’s principal investigators are experts in the fields of law, economics, political science, and computer science and have a consolidated leadership in developing projects of international relevance and solid connections with policymakers and industry.

The predominantly academic character of this consortium Footnote 4 aims at guaranteeing the highest quality and independence of the proposed research. The consortium composition seeks to prevent conflicts of interests which would inevitably arise in our view if, due to the project’s mission, the consortium incorporated industry partners (such as a major record publisher or label, an online music service provider, or a social media platform owner) that would pursue their own corporate interests. This could hinder, or even distort, the results of the empirical research concerning data and confidential information Fair MusE has envisaged. To prevent this risk while still being able to engage in “co-creation” of tools for policymakers and the music industry together with CCIs, our consortium incorporates industry partners which have an interest in promoting fairness in music ecosystems: (i) an Italian composers’ collecting society (SIAE), which is broadly representative of Italian composers and whose repertoire is strong at the local level but not mainstream at the international level; Footnote 5 and (ii) a UK-based company (Verifi Media Ltd) that is currently leading the market development of rights data management services for the music industry, including data collaboration and sharing, which are a prerequisite for market transparency for both creators and exploiters of digital music. Footnote 6

Our manifesto is based on Fair MusE’s main research proposal and puts forward a novel approach to address the European idea of a “fair” digital society and of fair digital markets in the music sector in an extensive and integrated manner. Such a necessity is even more compelling at the European level if we consider that the notion of “fairness” is currently being used in several policy areas. Footnote 7 Considering that fairness is designed to support cultural creation in today’s fast-changing, very broad, and increasingly AI-dominated music ecosystems, independent research should give this concept a more tangible and measurable dimension. Our manifesto and its potential outcomes aim at pursuing this goal and making policymakers, stakeholders, and the general public more aware of the risks that creators’ lack of appropriate remuneration as well as platforms’ algorithm-based and non-transparent exploitations of creative works pose to music’s sustainability and diversity.

The manifesto is organised as follows. Section 2 briefly summarises how the music industry has progressively de-materialised over the past three decades and gone from ownership-based to access-centred business models where streaming services and social media platforms organise and rank sound recordings on the grounds of their (secret) algorithms. Section 3 lists complementary disciplines and methods that are necessary to perform effective and independent research activities focused on whether music platforms can function fairly, especially for music creators. This section identifies gaps in the literature and shows how interdisciplinary research can go beyond the state of the art and help resolve persisting policy dilemmas in this field. Section 4 describes the main contents and purposes of our manifesto while drawing on the emerging notion of fairness in EU music policymaking and other policy fields. Section 5 details how we see our ideas being put into practice in Fair MusE’s research proposal and concrete set of activities.

2 Evolution of the Music Industry and Its Current Dependence on Platforms

The music industry, more than other sectors, has gone through radical changes in the past two decades. These have been even more difficult to face because of the extreme fragmentation of the rights, business interests, and artistic prerogatives that characterise the related creative communities. When the internet first emerged in the mid-1990s, the end-to-end architecture of this new medium and the fast development of file-sharing software enabled internet users to access and exchange large amounts of recorded music without intermediation. Free and uncompensated file sharing threatened the survival of the music industry for almost a decade, given that it had the potential to replace physical formats like CDs, which were the core business of the industry. Footnote 8 Since the early 2000s, proprietary online platforms have dramatically changed content distribution models and made music materials ubiquitous in the online environment. Although unauthorised file-sharing continued, becoming even more efficient and sophisticated, an unstoppable evolution of the internet infrastructure in terms of bandwidth and connectivity enabled companies to launch on-demand music stores, such as iTunes, which Apple released in 2001. iTunes was the first service that made digital music marketable by successfully creating its own ecosystem based on proprietary technologies for computers and portable devices. Streaming services like Spotify and Deezer as well as social media platforms like YouTube emerged at a later stage, which consolidated both a trajectory of music consumption from an ownership to an access model as well as a process of online re-intermediation for the whole internet and, even more so, for digital music distribution. This platform-centred environment has allowed music right-holders to start licensing their works and earn remuneration from the technology companies that exploited their music. Despite this evolution, music right-holders’ communities claim not only that the value of their works has been disrupted by a platform-dominated economy but also that a “value gap” exists between the remuneration they earn from music streaming services and social media platforms. Footnote 9

Our interdisciplinary research agenda seeks to understand and illustrate, in an autonomous and evidence-based way, the consequences that the various business models deployed by the largest digital music platforms have had as far as music production, distribution, and consumption processes are concerned. These complex environments are deeply influencing the economic and social value of this art form, in ways which are often contradictory from a public policy perspective. On the one hand, platforms have effectively enabled new forms of music production and home-made creations that empower amateur, early career, or disenfranchised categories of authors (“professionalising amateurs”) to gain online exposure and eventually establish themselves as music professionals. Footnote 10 On the other hand, these algorithm-dominated businesses seem to have induced a significant impoverishment of creators, especially those of niche or marginal repertoires that are penalised by the logic of filter bubbles and recommender systems.

The above-mentioned scenario has led to significant reforms of legal and regulatory frameworks that aim to govern and shape European music ecosystems. The most significant among these adaptations are embodied in Directive 2019/790: Footnote 11

This directive seeks to protect the commercial value of copyright works – in particular recorded music – by making providers of online sharing content services directly liable for works their users make available. Footnote 12 This policy change represents a turn away from the legal principle of platform neutrality that EU lawmakers maintained for nearly two decades to stimulate the growth of a robust internet infrastructure. In reversing this principle, the legal provision aimed at obliging social media companies to obtain licences and to implement content identification technologies that can either restrict access to unauthorised works or help copyright holders to be remunerated for online exploitation of their works.

A second, potentially very impactful change is condensed into Chapter 3 of the directive, where the law codifies a principle of fair and proportionate remuneration for authors and performers, in particular with regard to online music exploitations, Footnote 13 and a right to receive – on a regular basis – timely, accurate, relevant, and comprehensive information on modes of exploitation of their works, direct and indirect revenues generated, as well as any remuneration due. Footnote 14

We believe that both these policy changes constitute a turning point or even a “big bang” in the European history of copyright and artists’ rights, whose real effects are yet to be evaluated in a non-doctrinal and evidence-based way. This has not happened yet because of the very slow transposition of these provisions into national laws and an approach to academic research on these reforms that we find incomplete, too abstract and ideological, and discipline-segregated (“silo-like”).

3 Advancing Complementary Disciplinary Expertise to Go Beyond the State of the Art

Our research presupposes the identification of disciplines that can eventually enable independent scholars to fully understand the consequences of market-driven and legislative changes in Europe’s music ecosystem, going beyond the state of the art in measuring and enhancing the impact of the main EU policymaking initiatives in this field. While the music industry has been analysed from an economic perspective, Footnote 15 we believe that these analyses should be strongly connected to political, legal, and technical investigations and a thorough empirical exploration of the societal impact of music platforms on European music creators and audiences. In the following subsections we seek to identify gaps in the literature and illustrate how research can produce new knowledge to the benefit of policymakers, stakeholders, and society at large.

3.1 Politics: The EU as a Policymaker in the Music Industry

Despite a series of thoughtful studies on EU cultural-media policies, Footnote 16 there has so far been no comprehensive attempt to examine and critically assess the ways in which EU policy and law have sought to cope with the notion and the goal of fairness in the music sector, the values underpinning the policy instruments introduced (market vs. non-market values), and the objectives pursued. We believe that the first pillar of an effective research agenda in this field should be a comprehensive policy analysis of different EU initiatives that relate to the music sector. We need such an exhaustive analysis to understand the origin, nature, breadth, and degree of policy changes towards the governance of online platforms in Europe and the implications for the music ecosystem. This endeavour shall consist in scrutinising several policy instruments, proposals, and reports, including key documents related to “Music Moves Europe”, that the EU has issued in the past three decades. Footnote 17

Our analysis will focus predominantly on three issues that have dominated debates on online platforms and EU music governance in the past few years: (i) the availability and prominence of local and national music content online; (ii) the rights for creators in relation to the use of their music works by online service providers; and (iii) a fair and proportionate remuneration of music creators. Our team will engage in a historical analysis covering a span of 30 years of EU policy initiatives in this sector to understand the nature and breadth of policy changes towards the governance of music streaming and social media platforms, including the latest tweaks that specifically regard fairness and transparency. This analysis will also help us address the way EU governance rules seek to promote fairness in an economy where platforms’ dominance was exacerbated by the COVID-19 pandemic. We believe that an in-depth understanding of these changes is essential for policymakers as well as key digital industry players and music associations to assess the pros and cons of an increasingly pervasive dimension of EU law where copyright, contract law, and various forms of platform regulation are used to govern the extended landscape of business models and music professionals that characterises the platform economy. This unprecedented policy analysis can produce, in our view, new knowledge on the impact of online platforms and of phenomena such as the COVID-19 pandemic on music production and dissemination and thus contribute to finding solutions with a clear potential to bolster fairness.

3.2 Law: Copyright, Contract Law, and Platform Liability

Despite the adoption of the DSA and its broad attempt to introduce new obligations for VLOPs, the most important form of regulation aimed at helping music right-holders exercise their rights in the social media landscape is Art. 17 of the 2019 Copyright Directive. Footnote 18 This provision aims at setting a new standard of copyright liability applicable to social media platforms and at excluding the (previously uncertain) application of liability exemptions embodied in Directive 2000/31 (e-Commerce Directive). Footnote 19 Since it was included (as Art. 13) in the EU Commission’s directive proposal in September 2016, this provision has been the target of an endless number of academic articles, studies, parliament interrogations, open letters, popular petitions, and other initiatives that aimed at flagging the “negatives” of the complex legal mechanism it incorporates, especially for the protection of freedom of expression and “internet freedom”. Footnote 20 The volume and the strength of this critical movement increased, and became even more apparent, as soon as the EU Member States started transposing this provision in a rather inhomogeneous, scattered, and (mostly) untimely manner. Footnote 21 Such a broadly shared and vehement attack on this provision found its point of sublimation in the appeal brought by the Republic of Poland against Art. 17 before the European Court of Justice (ECJ), which the Court eventually rejected. Footnote 22

Our research agenda, while duly considering the controversial aspects of this provision, as reflected in an exceptionally abundant literature, aims mostly at identifying its “positives”. We believe that only a fairness-centred reading and an evidence-based analysis of Art. 17 and its national implementations can tell whether this legislative reform strikes a suitable balance between antagonistic interests. A literature review shows that, from a constitutional perspective, many European legal scholars tend to place copyright and the rights of authors at a level that is lower than that of other fundamental rights. Several scholars write as if internet users’ freedom of expression and the tech companies’ freedom to run their online businesses should systematically prevail over the authors’ expectation to enforce their rights and to receive fair remuneration for the exploitations of their work. Footnote 23 Despite the relevance of these remarks, this conclusion cannot be justified on the grounds of the European human rights framework if we consider that even the European Court of Human Rights (ECtHR), in several judgments, held that copyright, as a form of “property”, prevailed over other fundamental rights. Footnote 24 This conclusion is even clearer and stronger under EU law, considering the constitutionalisation of the EU Charter of Fundamental Rights. As recently held by the ECJ on the grounds of the Charter, the complex provision of Art. 17 of the 2019 Copyright Directive can be viewed as a proportionate and legitimate attempt to ensure a fair balance between the protection of users’ and online intermediaries’ interests, on the one hand, and creators’ rights on the other. Footnote 25 In the social media industry, the ECJ’s reasoning in Poland v. Parliament and Council emphasised that, although not inviolable and absolute, the right to intellectual property embodied in Art. 17(2) of the EU Charter on Fundamental Rights is a human right whose high level of protection justifies the complex regulation embodied in Art. 17 of the 2019 Copyright Directive and supports its adoption and EU-wide enforcement. Footnote 26 This opinion is perfectly consistent with the continental European approach to copyright and authors’ rights as personality rights and human rights that give rise to moral and economic prerogatives. Footnote 27

Currently, the implementation of a principle of “fair balance” based on the EU Charter of Fundamental Rights clearly shows that (i) the protection of authors’ rights can prevail over other fundamental rights, and (ii) the resolution of disputes in this field, especially in online environments, requires the ECJ to engage in a case-by-case assessment of the various interests at stake. Footnote 28 Our research seeks to provide more than just a doctrinal analysis of the effective impact of the 2019 Copyright Directive in the European music sector. In doing so, we intend to embrace an evidence-based and neutral approach to Art. 17, which only a minority of European scholars seem to have pursued, at least in the literature available in English. Footnote 29 To fill this gap, we will involve stakeholders and experts in empirical investigations to ascertain whether platform obligations, on the one hand, and copyright exceptions and the remedies embodied in Art. 17 to protect media and artistic freedoms, on the other hand, are being effectively implemented across EU Member States. Moreover, to assess more objectively the impact of content filtering measures, we will scrutinise music licensing practices, the use of content-recognition technologies, and other forms of content moderation before and after the entry into force of Art. 17’s national transpositions. This is relevant, in our view, also to understand whether these practices are well-established policies of social media services even in jurisdictions where a provision like Art. 17 and a brand-new legal infrastructure such as the DSA do not exist.

An equally relevant research gap in legal scholarship exists regarding the interplay of Art. 17 with other principles, rights, and obligations embodied in Chapter 3 of the 2019 Copyright Directive. Our research project assumes that, without empirical investigations, it is impossible to assess the effects of these joint measures on the businesses of legacy music producers and new generations of music creators. As things stand, the above-mentioned legal principles of fairness, proportionate remuneration, and transparency are likely to remain empty promises without the development of a new, data-driven approach to creators’ rights. This approach can only be based on the availability of large volumes of data enabling music creators, their representatives, and online exploiters to negotiate and conclude licensing agreements in a smooth, nuanced, machine-readable, transparent, and thus fair manner. Footnote 30 Our research proposal assumes that, in data-analytics businesses like digital platforms, even subscription-based services that choose and curate their repertoires (negotiating and paying royalties to creators) cannot ensure fair and proportionate remuneration without using reliable, standardised, and unequivocal copyright ownership and management information coming from the music sector. Footnote 31 The research we advocate in this field goes beyond the state of the art by providing a cross-country empirical analysis of the impact of recent copyright and contract law provisions embodied in the 2019 Copyright Directive and, at an earlier stage, Directive 2014/26 on the collective management of copyright on the music industry, broadly defined. Footnote 32 Our research includes an evaluation of how EU competition law and EU regulations (including the DSA, DMA, and upcoming legislation such as the EU Artificial Intelligence Act Footnote 33 and the EU Data Act Footnote 34 ) can apply and have an impact in the domain of online music platforms. This will allow us not only to produce evidence-based policy recommendations, but also to formulate a law-data-and-technology concept – built on the grounds of “co-creation” with stakeholders – to identify and rank solutions to the problem of information asymmetry across online platforms in Europe.

3.3 Economics and Business: Music Professionals and Value Networks

The music industry has been at the forefront of CCIs when it comes to the impact of technological advancements and related business model innovations. Currently, streaming platforms and social media are dominating the market, relying on their crucial position as intermediaries Footnote 35 and benefiting from winner-takes-all effects. Footnote 36 Their new business models, favouring access over ownership Footnote 37 and relying on the availability of vast amounts of (real-time) data, are accused of altering the value of content, particularly music. The music industry and its business models have constantly evolved with digitalisation and the growing domination of platforms. Footnote 38 Economists can contribute to interdisciplinary research by integrating the latest advancements in their analysis of value networks, of music professionals’ perspectives, and of innovative business models and by offering a longitudinal perspective on ecosystems, extensive surveys, and the use of quick-scan analysis to map large numbers of companies’ business models. Footnote 39 This will notably allow the integration of the role of “professionalising amateurs”, Footnote 40 a new category of content creators who act as YouTube, TikTok, or other social media’s partners, with growing economic and cultural relevance. After YouTube’s launch of its creator partnerships and programmatic advertising in 2006, these social media platforms started signing creators for the purpose of maximising value from their content and communities. More generally, the economic and business analysis of the music industry will consider the role played by data. A major disruption emerged from the availability of vast amounts of (real-time) data for music platforms. By translating data on user’s music consumption into relevant metrics, some authors argue, the business model of the industry was reshaped from music as a product to music as a service. Footnote 41 This is the case for services relying on advertising (content-sharing services like YouTube and Spotify’s free service) since data allow for the personalisation of advertising. This is, however, also the case with licensed services. For example, Spotify’s freemium model has been strongly supported by the platform’s focus on personalised content, which has been key in converting users to premium subscriptions. Footnote 42 Curated user-specific playlists are part of their product offering and perceived value. Footnote 43

The economic analysis we advocate addresses the notion of fairness notably in relation to value networks. While there is an increasing policy interest in ensuring that music streaming platforms are fair, there is a research gap regarding the industry’s and music professionals’ perspectives on fairness in the music platform market. Footnote 44 Since online platforms have become major enablers of music content flow, with unparalleled gatekeeping powers, Footnote 45 the remuneration of creators deeply depends on monetisation practices of platforms and on the ways through which algorithms expose information and cultural content. Footnote 46 However, to properly define this notion, there is an empirical gap regarding the industry’s and music professionals’ understandings of fairness in the music platform economy at both the European and national levels. Footnote 47 This task is even more complex if we consider that the impact of COVID-19 on culture and the performing arts has led to re-evaluations of the power of these platforms, paving the way for in-depth research into how industry representatives from the tech and music sectors conceptualise the fairness of music streaming platforms and social media. Footnote 48 The dramatic consequences of the recent pandemic for the performing arts encouraged several countries to start public inquiries into the power of global platforms, whose consequences are yet to be seen. Footnote 49

3.4 Computer Science: Influence of Algorithms on Music Consumption

Despite being presented as easing consumer choice, Footnote 50 platforms’ recommender algorithms are accused of lacking transparency, Footnote 51 threatening the exposure of content diversity and thereby challenging democracies Footnote 52 as well as violating consumers’ rights and citizens’ freedom of expression. Footnote 53 Algorithms have been accused of bias, Footnote 54 reinforcing discrimination in the real world, notably linked to race and gender, Footnote 55 and further increasing the popularity of superstars, blockbusters, and best-sellers at the expense of minority perspectives, local content, and emerging artists. Footnote 56 Our research project will highlight the effective influence of algorithms and aim to understand the way algorithms are being designed and implemented by different platforms. Footnote 57 Data are part of the algorithmic systems (especially recommender systems) that build this crucial personalisation process. Technological and economic developments have led to the availability of overwhelming quantities of digital content, notably music. Footnote 58 While some physical limitations have disappeared (for instance: space for storing, time for scheduling), others remain, notably users’ attention and what can be displayed to users (for instance: what a Spotify or YouTube user sees when connecting to the platform). Because of “overchoice”, Footnote 59 item selection can become cumbersome and complicated. Footnote 60 This makes it crucial, especially for media content providers, to incorporate algorithms that allow for a flexible and immediate response and adjustment to personal preferences of consumers. Such algorithms automatically filter, rank, and recommend content. Footnote 61 They influence the display or recommendation of content. Hence, algorithms are not neutral, and they raise questions as to how they are designed and implemented, who decides such matters, and on which basis. Beyond platform providers, all stakeholders in the music industry develop strategies and business models to cope with algorithms and adapt them to their own objectives.

Our research aims to produce new knowledge on the way platforms are affecting music diversity across the consortium members’ countries. In extending the work by Snickars and Mähler to detect and map patterns in algorithmic auditing by Spotify’s recommendation service, Footnote 62 we will account for a shortcoming of their work: access to data. Instead of using fictitious, stereotypical bots acting as users, we believe that this research would be more meaningful and fit for its purpose with the recruitment of a sufficiently broad and diverse number of real users (for instance: +1000). These users can donate their playlist data on the grounds of their right to access personal data collected and stored by streaming services and social media companies under Art. 20 of the General Data Protection Regulation (GDPR). Footnote 63 Although the recruitment of users as personal data donors can be difficult, their involvement can be spurred by a data donation campaign across EU countries to fund symbolic or little monetary rewards, so that users have both a financial and an ethical motivation to participate.

User data are very useful for measuring the influence of algorithms on music consumption because patterns in personal playlists can be compared against one another and with curated playlists obtained from several radio broadcast channels in each of the countries where the investigation takes place. Moreover, in-depth qualitative interviews on music habits, perception of bias, diversity, and serendipity with 100 users can add a qualitative dimension to the interpretation of the playlist data. An important contribution here can be the development of fairness indicators for online platforms’ algorithmic systems based on the analysis of the data collected. To do this analysis, Fair MusE’s data scientists can rely on the use of Human-Num Footnote 64 and Dataiku, Footnote 65 a free software platform to analyse machine-learning algorithms, predictive models, and big data. Indeed, with the data and related statistics, this research can lead to an in-depth data analysis of the way platforms and their algorithms function and influence consumers. With that input, this new research can go further than previous research Footnote 66 by addressing the concept of fairness in a broader way through the development of indicators related to several dimensions.

4 Our Ethos

Disciplinary expertise is core to our work; its interdisciplinary deployment is what makes our research and its empirical investigations meaningful and promising. We believe that to address a multi-faceted concept such as fairness and to use it as an effective and desirable policy and legal instrument in the music sector, the approach shall necessarily be interdisciplinary. New criteria, methodologies, and tools are required.

4.1 The Concept of “Fairness” and Its Special Function in the CCIs

Recent developments in EU law and policymaking clearly show a strong and fast-growing policy interest in the notion of “fairness” in digital markets and ecosystems. Although this notion has various, conflicting facets, EU policy and legislative initiatives through which the European Commission is currently exploring the function of “fairness” clearly aim to promote awareness of how certain structural factors can radically reduce economic output and social welfare in several industries. Footnote 67 Especially in the CCIs, the principle of fairness is expected to reduce financial losses for content creators, whose work is significant not only in terms of economic growth but also in terms of the sustainability of Europe’s cultural and linguistic diversity. Footnote 68 From this angle, the music sector is exceptionally relevant and complex considering its vastness as a cultural and commercial phenomenon and the fact that music is created and enjoyed everywhere, including low-income areas and communities where more expensive and complex types of creative works cannot be produced.

Our research seeks to shed light on the economic, cultural, societal, and technical context of EU music ecosystems, where a great variety of composers, performers, record labels, and platform artists target very different audiences in terms of size and geographical scope without knowing how the main digital music gatekeepers treat, promote, and commercially exploit their works. In this regard, the notion of fairness stands not only as a prerequisite for the pursuit of goals such as sustainability and competitiveness of an entire industry but also as a guarantee of consistency and compliance with the EU’s constitutional obligation to preserve and promote the cultural diversity of artistic productions. An important assumption of our research agenda is that EU lawmakers believe that a genuinely diverse music ecosystem can thrive only on the grounds of contractual and economic fairness. This presupposes much greater transparency in collective rights management, data collection, and proportionate remuneration of individual authors and performers. Yet these values, which have been recently embodied in EU legislative measures, are far from materialising in either market realities or in the day-to-day activities of music creators and their commercial and cultural partners.

4.2 Interdisciplinary Effort to Elaborate New Criteria, Methodologies and Tools

Our investigation entails considering a broad variety of online and offline environments where music professionals are involved, and assessing contemporary uncertainties around music’s economic and societal value and how they challenge creators’ opportunities to thrive and make a living. We believe that, notwithstanding the exceptional challenges that platformisation poses to a more transparent, competitive, and sustainable music sector in Europe, the current state of digitalisation holds the potential to help a great variety of music creators gain recognition beyond local or national borders and to overcome physical limitations. To investigate the impact of platforms on CCIs, a truly interdisciplinary team and approach are needed to connect media production, dissemination, and use on the one hand, and the legal conditions that are expected to achieve public policy goals on the other. Where our research seeks to innovate the most concerns tackling “fairness” from a conceptual perspective, considering it as a complex concept that requires interdisciplinarity and the analysis of several stakeholders’ perspectives and points of view. In a digital media economy where the largest gatekeepers are data-analytics businesses, appealing content such as music (in both audio and audiovisual formats) is used to attract and keep users active on the gatekeepers’ platforms for as long as possible.

Our approach to the notion of fairness from policy, legal, economic, and technical perspectives considers the various challenges raised by the advent and domination of platforms such as YouTube, Spotify, and, more recently, TikTok. Our research project is designed to unveil how today’s music industry can significantly improve and evolve in terms of transparency and access to relevant data. So far, the digital music sector has been dominated by trade secrecy, which has made it very difficult for policymakers to intervene by developing appropriate policy measures. Footnote 69 Our assumption is that greater transparency in the music sector and broader societal participation can help fight some phenomena that systematically penalise the majority of performing artists, music composers, and content producers. These phenomena include the implementation of unfair algorithmic systems and a race to the bottom that leads to the degradation of the commercial value of professionally created music and unfair remuneration. Our research also assumes that there is an exceptionally complex problem of data asymmetry across different stakeholders in the value chains, insofar as online platforms treat data about artists’ and content producers’ compensation and modes of content supply, exploitation, and consumption as a trade secret, claiming they need to protect data from industrial competition. The restricted access to data raises major issues in terms of accountability and of establishing a level playing field in the music sector. Lack of transparency also prevents the development of policy measures to promote fairness and diversity in a post-COVID-19 context.

In Fair MusE, we aim to investigate whether and how platforms have effectively enabled new forms of music production and home-made creations that empower amateur, early career, or disenfranchised categories of authors (“professionalising amateurs”) to gain online exposure, build and curate new audiences, and eventually become well-established music professionals. Footnote 70 At the same time, this type of analysis will enable the consortium to assess whether content platforms have induced a significant impoverishment of creators of niche or marginal repertoires that seem to be penalised by the logic and functioning of algorithms. Footnote 71

4.3 Our Agenda’s Major Obstacles

In designing our research project and building on the experience of the consortium partners, we have tried to identify potential challenges, the biggest of which is certainly the secrecy of the data our research is expected to collect and draw upon. Our project deals with issues that are very sensitive – commercially and technically – for major economic and political stakeholders at the European and global levels. We are aware of the difficulties this might raise, especially when liaising with the tech companies that own very large platforms and music services. For this reason, our research plan relies on multiple data collection sources and seeks to take advantage of duties of data disclosure that, under certain conditions, EU law imposes on data controllers and processors.

Another difficulty for research dealing with exceptionally large corporate interests such as those that exist in the music sector and, even more so, in the tech industry is that of developing normative recommendations on the EU policy and legal frameworks towards creators, business strategies, or large media environments while facing the risk of capture and lack of neutrality, which could weigh upon each research or communication initiative. Research that takes copyright and creators’ rights as one of its main pillars is subject to a lot of – not necessarily justified – criticism. We know that scientists cannot avoid being drawn into the controversies they are investigating. Footnote 72 In any case, while acknowledging that it can be difficult, especially for social scientists, to ensure neutrality and objectivity when investigating issues that touch upon their values, groups, and cultures, Footnote 73 our objective is to take a balanced approach that relies on critical thinking without ever transforming it into activism.

Another set of challenges comes from the strongly interdisciplinary nature of our research. Public research funding agencies promote and identify interdisciplinarity, but organisational constraints can restrict their capacity to fully embrace novel ways of interdisciplinary collaboration and investigation. Footnote 74 More generally, researchers from different disciplines and different countries work in different contexts, share different objectives, and may simply differ in terms of vocabulary used. Regarding the context, Friedman argues that institutional structures and funding patterns (among other things) make interdisciplinary research difficult. Footnote 75 One could simply add that researchers working in the social sciences in labs or under remote working arrangements (by necessity or by choice) have a totally different experience from their fellows working in biological labs. Moreover, different objectives can be illustrated by the fact that while there are “few more familiar aphorisms in the academic community than ‘publish or perish’”, Footnote 76 the length, the type of outlet (e.g. journal vs. conference proceedings or monographs), the usual number of authors, etc. can vary greatly from one discipline to another. As regards different vocabularies, they are at the core of our work on the multi-faceted notion of fairness. More generally, this challenge relates to the fact that sector-specific differences in methodologies can quickly emerge during interdisciplinary research efforts. Footnote 77 Rogers et al. even suggest that interdisciplinary research can be difficult to achieve due to incommensurable positions adopted by different disciplines. Footnote 78 Cultural differences – as one may find in large European research projects – may add to the difficulty to understand each other. Arguably, some of the interdisciplinary collaborations envisaged in Fair MusE are more common than others (for instance: between law and economics), but our mix is more peculiar. Finally, one challenge could be that interdisciplinary research potentially detracts from researchers’ expertise. While learning from others, researchers may end up spending less time developing their disciplinary expertise. This is largely because interdisciplinary research involves negotiating conflicts. Footnote 79 Sanz-Menéndez therefore finds that interdisciplinary research can lead to both specialisation and fragmentation, depending on the research area. Footnote 80

5 Putting Our Research Agenda into Practice

From a methodological perspective, we believe that a two-phase structure can allow us to pursue our research agenda and put our idea of integrating different disciplinary elements into practice.

5.1 Phase 1

Phase 1 (M1–M24, where “M” stands for “Month”) is designed essentially as a two-year mapping exercise in which four research hubs (which include industry partners) will split into two groups: (i) Law and Political Science, on the one hand, and (ii) Economics and Computer/Data Science on the other. The former focuses on the role of EU regulation, assessing the impact of new or recent policy or lawmaking initiatives targeting online platforms in the existing law and policy scenario (as detailed in Section 5.1.1 ). The latter analyses the complexities of music platforms from the perspectives of music professionals and their business models (see Section 5.1.2 ) and of consumers, where our computer scientists analyse the influence of algorithms on music diversity (Section 5.1.3 ).

5.1.1 Assessing the Role of Regulation

A. Analysis of the normative and policy framework

Our project explores, among others, the domain of music policy and lawmaking through an in-depth critical analysis of EU instruments, reports, and proposals. Footnote 81 The consortium will pay special attention to the 2019 Copyright Directive and to the overarching framework for the EU Commission’s actions in support of the European music sector: “Music Moves Europe”. Footnote 82 Both instruments are exceptionally important pillars of the EU music sector policy, seeking to address key concerns of this industry and professionals in terms of financial aid, intellectual property rights regulation and subsidies. Considering that fairness has been a key driver for rethinking the sector-specific objectives of EU policy initiatives, Footnote 83 it is crucial for our project to explore the role of policymaking over the past few decades and to understand the evolution of this field and how (and when) “fairness” became a priority.

B. Music creators’ rights under EU law

This part of our work focuses mainly on the rights and other prerogatives originating from the implementation of Directive 2001/29 (the so-called “Information Society” Directive), Footnote 84 the 2014 Collective Rights Management (CRM) Directive, and the 2019 Copyright Directive. We will investigate the practical implications of authors’ and performers’ rights for transparency, fair remuneration, and contractual adjustments (and, possibly, revocation) of their copyright transfers, as laid down in Chapter 3 of the 2019 Copyright Directive. This will be done by analysing the standard “Terms of Service” of each of the aforementioned platforms because they play an essential function from a copyright point of view, granting social media companies a free, global, perpetual, and non-exclusive licence which covers the original work each user-creator uploads. This analytical exercise will have long-term utility, as the DSA imposes more stringent obligations on VLOPs. Footnote 85

C. Copyright liability of social media platforms

This section focuses on the scope and implications of Art. 17 of the 2019 Copyright Directive and of its national transpositions. Footnote 86 We will verify how social media companies seek to obtain licences for all works uploaded by their users and how they eventually restrict access to unauthorised works without infringing on users’ fundamental rights and freedoms. For this task, academics and experts from the consortium’s industry partners, authors’ collecting societies, and music right-holders’ representatives who are members of Fair MusE’s Advisory Board will cooperate closely. Footnote 87

D. Collective rights management in Europe

One of our research assumptions is that the global reach of social media and their multi-territorial distribution of music has been at odds with collective rights management, which has traditionally been fragmented from a territorial perspective, ultimately on the grounds of copyright’s territoriality. Footnote 88 Fair MusE aims to analyse the governance and licensing practices of EU collecting societies, especially for digital uses, as a result of the implementations of the CRM Directive. This analysis is essential to evaluate whether EU law has paved the way for an adequate music metadata infrastructure and the emergence of music data collection standards. Footnote 89 From a music licensing perspective, our main goal is that of ascertaining whether the EU has succeeded in reducing the very high transaction costs that, until the adoption of this directive in 2014, made fair remuneration of various music right-holders very difficult if not impossible. Footnote 90

E. EU competition law

We believe that traditional competition law remedies and the European Commission’s investigations in this field have a significant role to play in targeting potentially anticompetitive practices of dominant music platforms and social media. Footnote 91 This work includes a comparative analysis of the US and EU legal and music market scenarios. For several reasons, US federal antitrust law seems unfit (at least until recently) to remedy the extreme corporate power that the largest platform owners have acquired. Footnote 92 This situation sharply contrasts with that of the EU, where competition law has been widely used against tech companies’ abuses of their dominant position and where policymakers are trying to prevent these abuses through ex ante regulation.

F. Platform regulation and soft law instruments

Fair MusE’s team will consider the interplay between copyright-specific rules in the 2019 Copyright Directive and general obligations of digital platforms arising from regulations such as the DSA and the DMA. Considering that some of the largest online music platforms qualify, under the above-mentioned regulations, as “very large online platforms” and/or “gatekeepers”, we will map and evaluate how data access rights and protection mechanisms enshrined in these regulations impact on music right-holders’ effective participation and business on platforms. This work presupposes an analysis of automated decision-making procedures and music platforms’ content moderation policies, also to understand how many of these activities rely on standardisation, certification procedures, or human review. Our analysis includes soft law instruments, such as codes of conduct and best practices, which might prove essential to promote fairness towards music creators by enhancing data transparency and facilitating fair and proportionate remuneration.

5.1.2 Platforms, Business Models, and Professionals in the Music Industry

A. From value networks in the music industry to new music ecosystems

Our research project analyses evolutions in the music industry considering the implications of dematerialisation, of the dominance of platforms and their increasing reliance on algorithmic systems to filter and recommend content. To do so, based on a methodology applied in previous research, Footnote 93 we will map “value networks” and the inter-relations between actors. To this end, our researchers will identify: (i) the value chains and related activities; (ii) the different stages in the value chains that compose the value networks (including content creation, content production, distribution and placement, support environment, and support industries); (iii) the different actors (both generic names and actual examples of key players) in a process of stakeholder mapping. At the same time, our researchers will analyse relations between the different actors and possible schematic relations with other value networks, mapping inter-relations among, and multi-directional flows of value between, the actors and the process of value creation.

Our research will go beyond the deployment of a “value network” analysis by incorporating business perspectives that are targeted at platform-centred and platform-led networks and ecosystems. The added value of also applying “ecosystem” theories Footnote 94 consists in being able to address a wider range of factors (including regulation, music education, live performances, etc.) that determine how value is being created in the music industry.

The above-mentioned analysis will allow us to observe the impact of online music platforms beyond online streaming consumption. This impact is primarily in the online realm, between uses on different platforms (for instance: how the use of a track excerpt on TikTok can lead to an increase in this track’s exposure on streaming platforms), but also in the interactions between online and more traditional offline uses, such as the cross-effects between live performances and online consumption. Our analysis will finally address fairness from an economic perspective, especially in relation to the “value gap” debates, and more generally issues of creators’ remuneration, Footnote 95 in close connection with the project’s legal analysis (see Section 5.1.1 . supra ).

B. Conflictual and consensual aspects of fairness that digital industry and music professionals consider relevant for platforms

Our project will investigate what “fairness” actually is, not only for music professionals but also for the online platform providers themselves. At the European level, the focus will be on six key European associations: DIGITALEUROPE, DOT Europe, European live music association, European Music Council, European Composer and Songwriter Alliance, and IMPALA. Footnote 96 Data collection will draw on desk research (notably grey literature documents coming from the six associations) and will further be gathered by conducting semi-structured interviews.

At the Member State level, the goal is to explore (i) whether fairness is related to the remuneration of music composers and the rights for authors in relation to the use of their works by platforms, and (ii) whether fairness is perceived as connected with additional aspects, such as the role of online platforms in fostering cultural diversity, the creation of a level playing field for independent digital distribution platforms, etc. We will place special emphasis on the perception and use of algorithms (for instance: recommender systems) by authors and music professionals, seeking to explore how they understand algorithms’ influence and whether they adapt their works to fit the platforms’ expectations. Data collection will draw on an online panel survey involving participants from the digital industry and music associations in Fair MusE’s eight countries of investigation (Portugal, Austria, Belgium, Denmark, Estonia, France, Greece, and Italy). Potential differences between Member States deriving from the size of the music market and their different systems of subsidies to the music sector will meaningfully enrich the analysis.

C. Online music platforms from a business model angle

Our analysis will finally map business models, combining research methods including desk research, expert interviews, and case studies. Our framework for mapping innovative business models will be based to a large extent on the Business Model Matrix Footnote 97 and the Business Model Canvas. Footnote 98 Based on the main types of actors identified previously, this work will produce a two-step business model analysis. First, based on a quick-scan analysis, Footnote 99 we will map all the main business model features of all the main types of stakeholders. It is expected that these main stakeholder categories are authors, distributors, and (playlist) curators. Second, we will conduct an in-depth analysis of at least six platforms with innovative models that are active in the EU. While online platforms have already been largely defined and researched, an in-depth analysis of online music platforms from a business model angle is still missing. We will conduct semi-structured interviews with selected organisations and companies to produce in-depth case studies.

5.1.3 Consumers, Platforms, and Music Diversity

A. In-depth assessment of the influence of algorithms on music consumption

Finally, Phase 1 of our research will include the consumer side of platforms, trying to analyse how these platforms and their algorithms impact consumers and, conversely, the strategies end-users may deploy to access, discover, and remain informed about music thanks to, or despite, platforms. This is also crucial for EU policymakers to effectively promote a fair and sustainable ecosystem. This work will help us make a synthesis of the various issues that have been encountered in research so far, especially as regards the practical effects of algorithms’ design (including recommender systems and playlists) on internet users.

B. Quantitative approach and data analysis

Our team will examine the effective influence of algorithms in the context of music recommender systems by using a quantitative approach and data analysis. We will rely on existing methods in the analysis of recommender systems, Footnote 100 extending Snickars and Mähler’s Footnote 101 analysis of algorithms beyond Spotify. Footnote 102 We will apply a broader and innovative approach to the collection of playlist data by replacing stereotypical fictitious users with +1000 real users who will donate their platform-derived data. Footnote 103 We will compare the +1000 anonymised playlists against each other and against playlists from 80 broadcast radio channels (i.e. ten from each of the eight EU countries within the consortium). This way we will be able to map playlist patterns; characterise diversity and bias in personalised playlists – which represents actual listening – with the curated playlists coming from broadcast radios. Qualitative in-depth interviews on music habits, perceptions of bias, Footnote 104 diversity, and serendipity with approximately 100 users (selected among those who donate their historical playlist data) will add a qualitative dimension to the interpretation of the playlist data. Interviews with broadcast editors responsible for playlists, curation, editorial profile, and rotation policies, and with representatives of online music platforms, will add an interpretative dimension to the analysis of broadcast music programming.

C. Fairness indicators

Finally, based on our previous work, our research team will produce fairness indicators in terms of platform transparency Footnote 105 and bias in recommender systems – as in Htun Footnote 106 and Mehrotra Footnote 107 – regarding algorithmic systems that are currently being used by the online platforms under scrutiny. By characterising the mechanics of the music recommender system algorithms as well the programming policies of many broadcast channels, our research team will highlight effective variables that indicate whether a given platform is fair and gives rise to a sustainable music business, while further suggesting a predictive model that can mitigate the adverse effects of these algorithms from a music diversity perspective.

5.2 Phase 2

In Phase 2 (M25–M36) we envisage the delivery of research outcomes to policymakers and stakeholders (Sections. 5.2.1 , 5.2.2 , and 5.2.3 ) alongside a comprehensive set of policy recommendations embedded in a White Paper on fairness in Europe’s music ecosystems (Section 5.2.4 ).

5.2.1 Music Copyright Infrastructure

In order to be fair, the increasingly platform-dominated music ecosystem needs to address the current lack of adequate data infrastructures through standardisation and sharing of content identifiers and music repertoire information, without which online music exploitations cannot be rewarded in a fair and proportionate way. To this end, we intend to develop a pilot named “Music Copyright Infrastructure”, the main goal of which is to help stakeholders target and solve the problem of information asymmetry across online platforms and right-holders – an asymmetry that is detrimental to all parties, including consumers interested in the diversity of music. We know that online music exploiters have turned data into their main asset (namely: massive, real-time data about their users, music consumption, and hence online music revenues). Considering prior efforts to solve these data asymmetries and their failures, due to participant concerns about the control of data and costs, we will provide a model agreement (and a set of guidelines) to help right-holders and licensees such as online platforms conclude music data-sharing agreements. In our view, these model agreements can help prioritise disclosure over enclosure (or secrecy) and can be directly tested by Fair MusE’s industry partners during the last year of project development.

5.2.2 Music Data Dashboard

The consortium will develop a demo of a Music Data Dashboard of statistical indicators for the European music sector to serve the information needs of policymakers, music professionals, and other stakeholders in this sector. This Dashboard will enable users to get a better understanding of evolutions related to the digitalisation and platformisation of the European music sectors by proposing or identifying indicators and data collection methods. Moreover, the Dashboard will incorporate a link to national statistical institutes, where appropriate. In short, we will (i) review current statistical sources of data on music at the EU and national levels, thus analysing statistical shortcomings in current sources, particularly regarding online music consumption and revenues; (ii) validate the data identified as well as the structure and the objectives of this tool during a “co-creation” policy workshop that involves policymakers; and, eventually; and (iii) deliver a demo for the Dashboard.

5.2.3 Fairness Score

The consortium partners will use the result of the business models analysis and of platform algorithms to set up a tool to assess music services and social media. A “Fairness Score” can become an effective tool to evaluate how online music platforms concretely deal with the criteria and goals EU policymakers intend to foster in the digital media environment. Each criterion, in its definition and assessment, will rely on the work performed in Phase 1 and will be reflected in the White Paper’s recommendations.

The Fairness Score will include the following indicative list of criteria: (i) governance in platform/social media; Footnote 108 (ii) market/non-market values; Footnote 109 (iii) local and national music in content online; Footnote 110 (iv) rights for creators, including access to data regarding their works and the exploitation thereof; Footnote 111 (v) fair and proportionate remuneration; Footnote 112 (vi) business model of the platform/social media; Footnote 113 (vii) gender equality; Footnote 114 (viii) small and medium-size producers vis-à-vis “superstars”; Footnote 115 and (ix) promotion of diversity in the algorithm. Footnote 116

Our Score will either be shaped as an industry-led solution or – on the grounds of data disclosure obligations that arise under EU law (cf. GDPR, DSA, DMA) Footnote 117 – as a soft-law policy instrument or a proper legislative instrument. We assume that this instrument could help EU policymakers influence platform/social media’s practices and conduct at various levels: legal (for instance, in terms of compliance with EU artists’ rights and copyright contract law); economic (for example, as regards fair and transparent remuneration); and social (promotion of cultural and gender diversity); and technical (algorithmic transparency).

5.2.4 Policy Recommendations: White Paper on Fairness in the Music Sector

Our policy recommendations will draw upon the above-mentioned research results, especially the in-depth analysis of new EU law measures aimed at promoting fairness and transparency towards music creators. On the grounds of an interdisciplinary analysis of the consequences of recent EU legislative measures, and of the related national transpositions, our Policy Recommendations will detail tools and actions to facilitate the exercise of creators’ rights through adequate data infrastructures. More precisely, we will include recommendations on the main objectives of Fair MusE: (i) whether and how today’s music industry can significantly improve and evolve in terms of transparency and fairness; (ii) whether and how, from both a legal and technological standpoint, the music sector can develop reliable, standardised, and unequivocal rights ownership information to be able to remunerate individual creators in a fair and proportionate way; and (iii) how legislative or industry-led solutions can reduce or minimise risks created by the enhanced dominance of the largest online music platforms.

6 Conclusion

In this manifesto , we advocate a new, interdisciplinary research approach that can remedy the shortcomings of a purely “silo-like” analysis of EU cultural and industrial policies in the music sector and of their effective impact in today’s platform- and algorithm-dominated economy. The music industry is an interesting case to apply this approach to, as it has gone through radical changes in the past two decades because of the extreme fragmentation of the rights, business interests, and artistic prerogatives that characterise the related creative communities. This has led to significant reforms of the legal and regulatory frameworks governing and shaping European music ecosystems, particularly those embodied in the 2019 Copyright Directive. This directive constitutes a “big bang” in the European history of copyright and artists’ rights, whose real effects are yet to be evaluated in a non-doctrinal and evidence-based way.

Approaching such changes, and in particular the multi-faceted concept of fairness, requires interdisciplinary expertise. This should include policy, legal, economic, and computer science perspectives. In Fair MusE, we analyse the EU as a policymaker in the music industry; we examine the legal framework regarding copyright, contract law, and platform liability; we study music professionals and how value networks have evolved; we assess how algorithms influence music consumption. We involve the music industry, notably via industry partners, members of our Advisory Board and other experts representing the tech and music industries, as well as the community of independent legal practitioners in several European countries. This does not go without challenges: overcoming data secrecy; dealing with opposing interests that govern strategic decisions in the music sector; and ensuring a harmonious collaboration between the diverse disciplines combined in Fair MusE. The last section describes briefly how we will do it, with a quick overview of the tasks and the main expected outcomes.

One point we are especially interested in is the EU’s policy responses. The 2019 Copyright Directive, with its provisions on the copyright liability of social media platforms (Art. 17), the fair and proportionate remuneration of authors and performers (Art. 18), and the codification of a right to transparency and access to data on the earnings generated by creative works (Art. 19), has an exceptional potential to strengthen the bargaining power of individual right-holders and their respective collecting societies in digital markets. The above-mentioned policy changes can become even more effective if we consider the entry into force of other instruments embodied in EU regulations, such as the DSA and the DMA, which are designed to significantly increase the level of responsiveness, internal risk assessment, and accountability of VLOPs and gatekeepers. This new array of EU law provisions targeted at the platform economy can certainly help address some of the existential questions raised by the largest online intermediaries’ ability to control consumers’ access to music repertoires and, at the same time, creators’ content distribution strategies and remuneration opportunities.

We argue that a proper evaluation of these recent developments in EU law should be supported by clear evidence. Such evidence can be built only through interdisciplinary efforts by independent researchers. We know that, to be effective and desirable as a policy instrument, the multi-faceted – and somehow open-ended – notion of “fairness” (used in key EU law provisions, and in many judgments of the ECJ in the copyright law sphere) needs to be dissected and analysed from a legal, policy, economic, and technological perspective, embracing a simultaneously balanced and multi-stakeholder viewpoint. That is the main reason why we promoted the creation of a consortium like Fair MusE, and why we intend to involve several categories of music professionals as well as representatives of industry and civil society in the co-creation of the project’s outcomes. Beyond the music-specific character of our interdisciplinary analysis, we are confident that our research results can also be very useful for other creative industries and media environments – including the news publishing sector – where data-driven exploitations and artificial intelligence have become pervasive and are inevitably changing the processes of content value creation and control and re-shaping ecosystems.

Change history

26 february 2024.

A Correction to this paper has been published: https://doi.org/10.1007/s40319-024-01435-x

Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC [2022] OJ L277/1 (“DSA”). See Chapter 3, Section 5.

Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 [2022] OJ L265/1 (“DMA”): see Art. 2 and Art. 3.

Promoting Fairness of the Music Ecosystem in a Platform-Dominated and Post-Pandemic Europe (“Fair MusE”), Grant agreement ID: 101095088, https://cordis.europa.eu/project/id/101095088 , accessed on 2 November 2023.

The academic members of the consortium are: Universidade Católica Portuguesa (UCP); Vrije Universiteit Brussel (VUB); Aalborg Universitet (AAU); Université de Lille (ULILLE); Université de Liège (ULIEGE); Hellenic Foundation for European and Foreign Policy (ELIAMEP); Tartu Ülikool (UTARTU); Central European University Gmbh (CEU).

https://www.siae.it .

https://www.verifi.media .

The European Commission’s recent legislative initiatives in the areas of standard essential patents, artificial intelligence, platform-to-business trading practices, as well as competition law all rely on fairness as one of their objectives, namely: Proposal for a Regulation of the European Parliament and of the Council on standard essential patents and amending Regulation (EU) 2017/1001 [2023] COM(2023) 232 final; Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence and amending certain Union legislative acts [2021] COM/2021/206 final (“Draft Artificial Intelligence Act”); Regulation (EU) 2019/1150 of the European Parliament and of the Council of 20 June 2019 on promoting fairness and transparency for business users of online intermediation services [2019] OJ L186/57 (“Platform-to-Business Regulation”). Regulation (EU) 2022/1925 ( supra note 2).

Statistics evidenced a dramatic fall of the music business between 1999 and 2014, when global revenues from physical and digital music sales declined by 42%, from $25.2 to 14.6 billion. See IFPI, “Global Music Report 2018: Annual State of the Industry” https://www.ifpi.org/ifpi-global-music-report-2018/ , accessed 2 November 2023.

“Value gap” is an expression used for the first time by representatives of the music industry in Brussels to describe the impoverishment of their sector as a consequence of widely uncompensated uses of copyright works across online platforms and a sharp difference between the licensing fees paid by social media and the fees paid by music streaming services: see , for instance, Smith, Desbrosses and Moore ( 2016 ).

Cunningham and Craig ( 2019 ), pp. 11–14.

Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC [2019] OJ L130/92 (“2019 Copyright Directive”).

2019 Copyright Directive, Art. 17.

2019 Copyright Directive, Art. 18.

2019 Copyright Directive, Art. 19.

Wikström ( 2020 ), p. 367.

Laing ( 1999 ), p. 31; Sarikakis ( 2007 ); Littoz-Monnet ( 2007 ); Iosifidis ( 2011 ); Donders et al. ( 2014 ).

See European Commission, “Music Moves Europe”: https://culture.ec.europa.eu/cultural-and-creative-sectors/music/music-moves-europe , accessed 2 November 2023.

The complex infrastructure of the DSA is designed not to interfere, but rather to be complementary with the copyright-specific mechanism of Art. 17: see on this topic, Quintais and Schwemer ( 2022 ), p. 191; Rosati ( 2021 ).

Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market [2000] OJ L178/1 (“e-Commerce Directive”).

A non-exhaustive list of these initiatives includes the following ones: Sophie Stalla-Bourdillon et al (40 academics), Open Letter to the European Commission – On the Importance of Preserving the Consistency and Integrity of the EU Acquis Relating to Content Monitoring within the Information Society, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2850483 , accessed on 2 November 2023; European Copyright Society, “General Opinion on the EU Copyright Reform Package”, 2017, available at https://europeancopyrightsocietydotorg.files.wordpress.com/2015/12/ecs-opinion-on-eu-copyright-reform-def.pdf , accessed on 2 November 2023; Max Planck Institute for Innovation and Competition (2017), Position Statement on the Proposed Modernization of European Copyright Rules: Art. 13, available at: https://www.ip.mpg.de/fileadmin/ipmpg/content/stellungnahmen/MPI_Position_Statement_PART_G_incl_Annex-2017_03_01.pdf , accessed on 2 November 2023. See also Cory Doctorow, “Four million Europeans’ signatures opposing Article 13 have been delivered to the European Parliament” (EFF, 10 December 2018) https://www.eff.org/deeplinks/2018/12/four-million-europeans-signatures-opposing-article-13-have-been-delivered-european , accessed on 2 November 2023. Among the academic contributions following the adoption of the directive, see Dusollier ( 2020 ), p. 979, who describes Art. 17 as a “monster provision” considering its size and “hazardousness”. At an earlier stage, very critical scholars included Frosio ( 2017 ), p. 565; and Senftleben et al. ( 2018 ).

Rosati ( 2022 ), p. 397.

Although this initiative was consistent with Poland’s dissenting vote at the time the EU Council adopted Directive 2019/790, this case suddenly transformed the Polish government, a notorious antagonist (at least until very recently) of EU institutions vis-à-vis the affirmation of human rights and the rule of law, into a noble and tireless paladin of freedom of expression: see C-401/19 Poland v. Parliament and Council , ECLI:EU:C:2022:297. It is worth recalling that the Polish rule-of-law crisis culminated in infringement proceedings launched by the European Commission against Poland, alleging a failure to fulfil its obligations under Art. 19(1)(2) of the Treaty of the European Union (TEU) and Art. 47 of the Charter of the Fundamental Rights of the European Union. In the subsequent appeal, the ECJ ruled that Poland indeed infringed the principle of judicial independence under Art. 19(1)(2) TEU when lowering the retirement age of Supreme Court judges: see case C-619/18 European Commission v. Republic of Poland , ECLI:EU:C:2019:531.

A good example of scholars’ focus on the importance of safeguarding users’ freedom of expression and information in the online environment when implementing Art. 17 of the 2019 Copyright Directive is provided by Quintais et al. ( 2019 ), pp. 277–282. In a similar way, Geiger and Jütte claim that Art. 17 fails to properly address the need to strike a fair balance between competing interests, emphasising the negative effect of filtering mechanisms on users’ fundamental rights: see Geiger and Jütte ( 2021 ), pp. 532–534. Other contributions emphasise how Art. 17 can negatively impact on the platforms’ freedom to conduct business: see , for instance, Reda et al. ( 2020 ), at pp. 42–49, claiming that the provisions of Art. 17 are not capable of achieving a fair balance between the fundamental right to conduct a business and other rights, as they place a significant economic burden on online content-sharing service providers. See also Geiger and Jütte, mentioned above, p. 542, maintaining that Art. 17 imposes immense obligations on social media platforms, restricting their freedom to conduct a business.

Among the most recent judgments, see , for instance, ECtHR Fredrik Neij and Peter Sunde Kolmisoppi (The Pirate Bay) v. Sweden , 40397/12, where the Court stressed that intellectual property – more specifically the “rights of the copyright-holders” – is a form of “property” that benefits from the protection afforded by Art. 1 of Protocol No. 1 to the ECHR against unauthorised dissemination of protected works through file-sharing technologies. At an earlier stage, ECtHR Case Ashby Donald et autres v. France , 36769/08 founded the protection of the copyright of fashion houses in their own creations (against unauthorised photographers invoking their right to freedom of expression) again on the grounds of the constitutional protection of “property” under Art. 1 of Protocol No. 1 of the ECHR. For a detailed review of the ECtHR case law on intellectual property rights, see Geiger and Izyumenko ( 2018 ), p. 9.

In C-401/19 Poland v. Parliament and Council , the ECJ provides an analysis of the principle of proportionality under paras. 63–69 and explicitly states, in para. 82, that “in the context of the review of proportionality referred to in Article 52(1) of the Charter, it must be noted, first of all, that the limitation on the exercise of the right to freedom of expression and information of users of online content-sharing services, referred to in paragraph 69 above, meets the need to protect the rights and freedoms of others within the meaning of Article 52(1) of the Charter, that is, in this case, the need to protect intellectual property guaranteed in Article 17(2) of the Charter.”

See C-401/19 Poland v. Parliament and Council , paras. 92–99.

See Strowel ( 2020 ), pp. 40–46, who emphasises how the constant, explicit reference to intellectual property as a fundamental right in the case law of the ECJ has played a central role in strengthening the protection and enforcement of copyright, especially in digital settings. As argued by this author, this explicit recognition under EU law provides an even stronger foundation for the qualification of authors’ rights as human rights. This is consistent with Art. 27(2) of the 1948 Universal Declaration of Human Rights at the international level, which protects the moral and material interests of authors resulting from their scientific, literary, or artistic productions. It is worth recalling that while the concept of authors’ rights as moral rights is eminently European, it is gaining traction because of technological challenges even in systems – like the United States – that have historically neglected this concept: see , for instance, Sundara Rajan ( 2019 ), pp. 257–258.

As stressed by Strowel ( 2020 ), pp. 40–52, the recent case law of the ECJ reveals a careful approach in the examination of copyright disputes in the digital environment. The author stresses how, in several cases, the principle of fair balance made copyright claims prevail over defences based on freedom of expression and other fundamental rights (such as the right to privacy) because of the necessity to guarantee a high level of protection to intellectual property rights, as embodied in the EU legislation and as requested under Art. 17(2) of the EU Charter of Fundamental Rights. See , for instance: C-275/06 Promusicae v. Telefonica , ECLI:EU:C:2008:54; C-160/15 GS Media v. Sanoma et al. , ECLI:EU:C:2016:644; Case C-161/17 Land Nordrhein-Westfalen v. Dirk Renckoff , ECLI: EU:C:2018:634; C-476/17 Pelham GmbH and Others v. Ralf Hütter and Florian Schneider-Esleben , ECLI:EU:C: 2019:624.

For a more positive view on Art. 17’s impact on fundamental rights, see , for instance, Cabay ( 2020 ).

Mazziotti ( 2021 ).

The fact that prior attempts to improve rights information through standard tools such as the Global Repertoire Database (GRD) have largely failed can help solve a data-sharing dilemma that has only grown worse with the exponential increase in the availability of content on access-based platforms. On the failure of the GRD see , for instance, Milosic ( 2015 ).

Directive 2014/26/EU of the European Parliament and of the Council of 26 February 2014 on collective management of copyright and related rights and multi-territorial licensing of rights in musical works for online use in the internal market [2014] OJ L84/72 (“CRM Directive”).

Draft Artificial Intelligence Act ( supra note 7).

Proposal for a Regulation of the European Parliament and of the Council on harmonised rules on fair access to and use of data [2022] COM(2022) 68 final (“Draft Data Act”).

Rochet and Tirole ( 2002 ), p. 549; Poell et al. ( 2019 ), p. 1; Evans et al. ( 2005 ), p. 189.

Rochet and Tirole ( 2006 ), p. 645.

Luck ( 2016 ), p. 46.

Poell et al. ( 2019 ); Vlassis et al. ( 2020 ).

Van Audenhove et al. ( 2016 ).

Croll ( 2015 ).

Kastrenakes ( 2019 ).

Iqbal ( 2023 ).

Ferraro et al. ( 2021 ).

Vlassis et al. ( 2020 ).

Mazziotti ( 2020 ), p. 1027.

Flew and Gillett ( 2021 ), p. 231.

Castells et al. ( 2015 ).

Zarsky ( 2016 ), p. 118.

Bozdag and Van Den Hoven ( 2015 ), p. 249.

Helberger ( 2012 ), p. 65.

Bozdag ( 2013 ), p. 209.

Noble ( 2018 ).

Nechushtai and Lewis ( 2019 ), p. 298.

Chen et al. ( 2020 ).

Masnick and Ho ( 2014 ).

Gourville and Soman ( 2005 ), p. 382.

Kunaver and Požrl ( 2017 ), p. 154.

Haim et al. ( 2018 ), p. 330.

Snickars ( 2017 ), p. 184; Snickars and Mähler ( 2018 ).

Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC [2016] OJ L119/1 (“GDPR”).

Human-Num is a French infrastructure that aims at supporting research communities by providing services, assessments, and tools for digital research data. See https://www.huma-num.fr/ , accessed on 2 November 2023.

See https://github.com/dataiku , accessed on 2 November 2023.

Melchiorre et al. ( 2021 ) p. 1.

See various legislative initiatives of the European Commission, cited above ( supra note 7).

See the Treaty on the Functioning of the European Union (TFEU), Art. 167, para. 4.

Johansson et al. ( 2018 ), p. 165.

Mazziotti ( 2021 ), pp. 214–215.

Scott et al. ( 1990 ), pp. 474–494.

D’Agostino ( 1995 ), pp. 396–405.

König and Gorman ( 2017 ).

Friedman ( 2013 ).

De Rond and Miller ( 2005 ), p. 321.

Lach ( 2014 ), pp. 88–93.

Rogers et al. ( 2005 ).

Villeneuve et al. ( 2020 ), p. 197.

Sanz-Menéndez et al. ( 2001 ), pp. 47–58.

Relevant instruments and reports include: Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market (2000) OJ L178/1 (“e-Commerce Directive”); Directive 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society (2001) OJ L167/10; Regulatory framework for electronic communications and services (2003); Commission, “A Digital Agenda for Europe” (Communication) COM (2010) 245 final; Commission, “A Digital Single Market Strategy for Europe” (Communication) COM (2015) 0192 final; Commission, “The AB Music Working Group Report” (2016) Publications Office of the European Union https://op.europa.eu/en/publication-detail/-/publication/f5479d95-2fca-11e7-9412-01aa75ed71a1 , accessed on 2 November 2023; Commission, “New European Agenda for Culture” (Communication) COM (2018) 267 final; Commission, “Proposal for a Regulation establishing the New Creative Europe programme” COM (2018) 366 final; Council Conclusions on the Work Plan for Culture 2019-2022 [2018] OJ C460/12; Commission, “Music Moves Europe – First Dialogue Meeting-Report” (2019) https://culture.ec.europa.eu/sites/default/files/library/mme-conference-report-web.pdf , accessed on 2 November 2023; 2019 Copyright Directive; Proposal for a Regulation of the European Parliament and of the Council on contestable and fair markets in the digital sector (Digital Markets Act) COM (2020) 842 final; Proposal for a Regulation of the European Parliament and of the Council on a Single Market For Digital Services (Digital Services Act) and amending Directive 2000/31/EC COM (2020) 825 final; Commission, ‘Report from the Conference ‘Diversity and Competitiveness of the European Music Sector’ with EU Member States Experts” (2021) https://culture.ec.europa.eu/document/report-conference-diversity-and-competitiveness-european-music-sector-eu-member-states-experts , accessed on 2 November 2023.

Commission, “Music Moves Europe – First Dialogue Meeting-Report” (2019) https://culture.ec.europa.eu/sites/default/files/library/mme-conference-report-web.pdf , accessed on 2 November 2023.

See various legislative initiatives of the European Commission, cited above ( supra note7).

Directive 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society [2001] OJ L167/10 (“Information Society Directive”).

Arts. 33–43 DSA.

On this front, our analysis will be comparative in nature. Namely, it will compare the copyright treatment of user-generated content platforms under EU and US law, in particular the case law based upon the US Digital Millennium Copyright Act (DMCA) 1998, which amended the US Copyright Act (17 US Code), Section 512(c).

For the full list of the Advisory Board’s members, see the Fair Muse’s website at https://fairmuse.eu/team/ , accessed on 2 November 2023.

Cunningham and Craig ( 2019 ), p. 15, where the authors emphasise that social media entertainment has, from the beginning, a global dimension because its content is not primarily based on intellectual property’s territorial control (as it is, instead, in the film and TV broadcasting sectors); Mazziotti ( 2021 ).

CRM Directive ( supra note 32).

Ranaivoson et al. ( 2013 ), p. 665.

The project also aims to consider the recent actions of the French, German, and Italian competition authorities, which have been particularly active in enforcing competition rules against large online platforms. See , for instance, as regards the French Competition Authority: Decision 21-D-11 of June 07, 2021, against Google regarding practices implemented in the online advertising sector; Decision 22-D-12 of June 16, 2022, against Meta regarding practices implemented in the online advertising sector. As regards the German Competition Authority, see Decision B6-22/16 of 6 February 2019 against Facebook for data handling practices; Decision V-43/20 of 21 December 2022 against Google for data handling practices in the case of Google News Showcases. In Italy, see the proceedings launched in April 2023 by the Italian Competition Authority against Meta for abuse of economic dependence towards SIAE, available at https://www.agcm.it/dotcmsdoc/allegati-news/A559%20avvio%20e%20caut.pdf , accessed on 2 November 2023.

See , for instance, Wu ( 2018 ), p. 132.

De Voldere et al. ( 2017 ).

Kostovska et al. ( 2021 ), pp. 6–26.

Negus ( 2019 ), p. 367.

DIGITALEUROPE is an organisation that represents the digital technology sector in Europe. See https://www.digitaleurope.org/ , accessed on 2 November 2023.

DOT Europe is an association of the main internet companies active in Europe, including leading social media and streaming platforms. See https://doteurope.eu/ , accessed on 2 November 2023.

European live music association is a non-profit organisation that supports the European live music industries. See https://www.elmnet.org/ , accessed on 2 November 2023.

European Music Council is a non-profit organisation whose mission is to develop and promote music of all genres and types. See https://www.emc-imc.org/ , accessed on 2 November 2023.

European Composer and Songwriter Alliance (ECSA) focuses on protecting and advancing the rights of composers and songwriters. See https://composeralliance.org/ , accessed on 2 November 2023.

IMPALA is the European organisation for independent music companies and national associations. See https://www.impalamusic.org/ , accessed on 2 November 2023.

Ballon ( 2007 ), p. 6.

Osterwalder and Pigneur ( 2010 ).

Loecherbach and Trilling ( 2020 ), p. 53.

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Based on the research conducted by the politics research hub of Fair MusE, as elaborated in Section 3.1 .

See the discussion in Section 3.1 above.

See an overview of the relevant issues in Section 4.2 above.

From both legal and economic perspectives, as elaborated in Sections 5.1.1 B, E, F and 5.1.2 A.

See Section 5.1.2 above.

As laid down in the Fair MusE’s Gender Action Plan.

See the discussion in Section 4.2 concerning the impact of algorithms on the discoverability of niche or marginal repertoires.

See Section 5.1.3 above.

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Mazziotti, G., Ranaivoson, H. Can Online Music Platforms Be Fair? An Interdisciplinary Research Manifesto. IIC 55 , 249–279 (2024). https://doi.org/10.1007/s40319-023-01420-w

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