The Writing Center • University of North Carolina at Chapel Hill

Scientific Reports

What this handout is about.

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

Background and pre-writing

Why do we write research reports.

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

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

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

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

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

How do I do that?

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

  • Introduction

Methods and Materials

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

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

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

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

What should I do before drafting the lab report?

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

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

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


How do i write a strong introduction.

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

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

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

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

Not a hypothesis:

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


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

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

Justify your hypothesis

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

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

Background/previous research

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

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

Organization of this section

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

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

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

How do I write a strong Materials and Methods section?

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

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

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

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

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

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

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

Structure and style

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

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

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

How do I write a strong Results section?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How do I include figures in my report?

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

When should you use a figure?

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

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

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

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

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

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

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

How do I write a strong Discussion section?

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

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

Explain whether the data support your hypothesis

  • Acknowledge any anomalous data or deviations from what you expected

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

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

Explore the theoretical and/or practical implications of your findings

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

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

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

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

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

Acknowledge any anomalous data, or deviations from what you expected

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

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

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

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

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

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

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

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

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

Works consulted

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

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

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

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

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

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

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

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

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

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

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

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  • How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on June 13, 2023.

Structure of a research proposal

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:


Literature review.

  • Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

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Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

  • Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
  • Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

  • The proposed title of your project
  • Your supervisor’s name
  • Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

  • Introduce your topic
  • Give necessary background and context
  • Outline your  problem statement  and research questions

To guide your introduction , include information about:

  • Who could have an interest in the topic (e.g., scientists, policymakers)
  • How much is already known about the topic
  • What is missing from this current knowledge
  • What new insights your research will contribute
  • Why you believe this research is worth doing

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As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

  • Comparing and contrasting the main theories, methods, and debates
  • Examining the strengths and weaknesses of different approaches
  • Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

  • Improving best practices
  • Informing policymaking decisions
  • Strengthening a theory or model
  • Challenging popular or scientific beliefs
  • Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

  • Cost : exactly how much money do you need?
  • Justification : why is this cost necessary to complete the research?
  • Source : how did you calculate the amount?

To determine your budget, think about:

  • Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
  • Materials : do you need access to any tools or technologies?
  • Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.


  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility


  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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Research Method

Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Research Report


Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:


The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.


The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.


Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.


The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.


The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.


In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.


One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.


The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.


  • Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
  • Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
  • Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
  • Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

  • Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
  • Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
  • Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
  • Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
  • Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
  • Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

  • Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
  • Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
  • Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
  • Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
  • Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
  • Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
  • Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

  • Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
  • Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
  • Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
  • Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
  • Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

  • Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
  • Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
  • Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
  • Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
  • Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
  • Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
  • Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

  • Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
  • Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
  • Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
  • Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
  • Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

  • Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
  • Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
  • Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
  • Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
  • Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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iThenticate 2.0: Advancing research integrity with AI writing detection

Turnitin is delighted to introduce the latest iteration of iThenticate, now enhanced and more intuitive than ever before. Join us as we journey through the key features that new users of iThenticate can enjoy, starting today.

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If you’re an academic researcher, an administrator at a research institution, or an editor at an academic journal, you want your work to be void of plagiarism and beyond reproach, especially when it comes to research misconduct.

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Plagiarism is no longer a problem unique to academia. Government organizations and officials have at various times, had to deal with issues arising from using material from pre-existing sources. In this blog post, you will learn how plagiarism can occur unintentionally, the potential consequences, and how technology can protect government reputations.

Each year, iThenticate checks 14 million documents, earning its reputation as the most trusted similarity checker by the world’s top researchers, publishers, and scholars. With such an esteemed reputation comes fair expectation for a version of iThenticate that oozes quality and substance. That’s why we’ve made it a priority to measure up to the high standards of our research community—by building a tool that is not just functional, but delivers on design and user experience.

Turnitin is delighted to introduce the latest iteration of iThenticate, now enhanced and more intuitive than ever before.

With a highly anticipated, user-friendly interface, iThenticate 2.0 promises to provide new capabilities that will further promote originality within high-stakes written work, backed by world-class data security measures.

Join us as we journey through the key features that new users of iThenticate can enjoy, starting today.

Does iThenticate 2.0 support AI writing detection?

In April 2023, Turnitin’s AI writing detection capabilities launched across many of our integrity solutions—a milestone in combating the improper use of AI writing tools, such as ChatGPT.

Our continuous drive for innovation to protect against emerging threats has empowered us to introduce a version of iThenticate that includes the latest and most advanced tools to support research integrity. We are pleased to include AI writing detection as part of the iThenticate 2.0 suite of features.

We are confident that the addition of AI writing detection in iThenticate 2.0 gives researchers, publishers, and scholars the tools they need to protect themselves from this emerging form of misconduct as they embark on their research and publication journey.

In a PubCon event discussion , Fabienne Michaud, Product Manager at Crossref, shared that, “when it comes to AI, one of the biggest threats is its lack of originality, creativity, and insight. AI writing tools were not built to be correct. Instead, they are designed to provide plausible answers. This is in juxtaposition to the values that form the foundation of scholarly publishing.”

Due to the sophisticated nature of generative AI, humans are finding it increasingly difficult to manually detect AI writing. In a study by Gao et al., just 68% of AI-generated scientific abstracts and 86% of human-written abstracts were correctly identified by human reviewers ( 2022 ). Our goal is to lighten the load for researchers and publishers, minimizing the effort associated with checking for AI-generated content. Turnitin’s AI writing detection tool can check papers en masse and at speed, thus improving efficiencies across the traditionally intensive publication process.

Is iThenticate 2.0 aligned with industry standards?

At Turnitin, we believe that user experience is more than just a concept, it's a tangible feeling. We've made a concerted effort to transform every iThenticate interaction into a positive and seamless journey.

The iThenticate 2.0 experience is frustration-free

iThenticate 2.0 introduces a revitalized and contemporary interface, crafted with our research community in mind, and enabling effortless navigation from the initial onboarding stage through to the final similarity check. iThenticate’s new look is consistent and deliberate to ensure that even its newest users can get off to the best start. We take great pride in sharing that iThenticate 2.0 meets accessibility standards, thus increasing inclusivity among our education community.

Choose where your data is stored in iThenticate 2.0

We understand the concerns of our research community when it comes to data compliance, and we’re incredibly proud to share that all data transferred to us is stored on a highly secure AWS data platform. With data centers in the United States, Europe, and across the Asia-Pacific region, this assures Turnitin customers that all personal data uploaded to iThenticate 2.0 is safe, protected and being stored and processed according to the world's highest standards.

Our updated technology lays a robust foundation for delivering functionality at a faster pace. We’re confident that with state-of-the-art technology, we can now provide the latest and most advanced tools to help institutions innovate at speed; we see this as a step towards improving the effectiveness of all involved in the publishing journey.

How does the Similarity Report in iThenticate 2.0 promote research integrity?

The Similarity Report is Turnitin’s longest-standing product and is most synonymous with our brand. Comprising a myriad of functionality, the Similarity Report allows the research community to zero in on the source of text matches, pinpoint discrepancies in an academic paper, such as replaced characters or hidden text, and generate an AI writing score. The Similarity Report helps to identify specific forms of research misconduct, including text recycling, salami slicing, and redundant (or duplicate) publication .

Constant refinement and updates to the Similarity Report has made iThenticate 2.0 a valuable tool for promoting research integrity—giving researchers, publishers, and scholars the reassurance that an academic paper has been thoroughly checked for potential misconduct.

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Get insight into hidden text modification techniques with the Flags Panel

As part of the iThenticate 2.0 experience, our enhanced Similarity Report provides the same features that our education community know and love, with even greater integrity features that further improve the standard of research integrity.

Now available in iThenticate 2.0 is the Flags Panel , which highlights potential text manipulations in an academic paper. Today, there are two types of flags, including replaced characters and hidden characters. Most useful to publishers, both of these forms of text manipulations illustrate a more deliberate effort to bypass similarity algorithms.

With the new Flags Panel, publishers can be confident that the papers they publish are free from intentional forms of academic dishonesty, which if missed, stands to put their publishing reputation in jeopardy.

Improved exclusion capabilities offer a more refined similarity score

Exclusion capabilities in iThenticate are highly regarded, as researchers, publishers, and scholars seek to check only text submitted as original writing against the Turnitin database. By that, we mean discounting bibliographic content, quotes, citations, and small matches. But in having conducted intensive user research, we identified gaps in our exclusion capabilities that presented an opportunity as we looked to enhance our iThenticate offering.

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iThenticate 2.0 addresses several exclusion limitations, allowing further refinement of the Similarity Report and a more accurate similarity score:

  • Preprint Exclusions: iThenticate 2.0 can now detect matches from the world’s biggest preprint repositories and label them accordingly. Users can exclude preprint sources automatically, or exclude them on a case-by-case basis, with the opportunity to manually add additional preprint repositories. With preprint exclusions, the iThenticate 2.0 Similarity Report becomes a highly reliable source of reference, reducing the amount of manual effort usually involved in identifying this type of match.
  • Content Databases Exclusion: The iThenticate 2.0 Similarity Report allows flexible exclusion of matches from different content databases, including internet, publication, and submitted works . This added capability saves time and removes bias, allowing a more in-depth analysis of the Similarity Report, and providing the necessary tools to understand how different forms of content in the Turnitin database are influencing a paper’s similarity score in real time.

By using iThenticate 2.0’s all-new settings to dynamically discount various types of material from the Similarity Report, researchers can be confident that each paper's similarity score relies solely on the content submitted as original writing. This step also contributes to creating a level playing field for researchers and scholars submitting different types of work, such as qualitative vs. quantitative analyses.

How does iThenticate 2.0 encourage user collaboration?

For a paper to be ready for publishing, it goes through multiple rounds of peer review, editing, and even technical editing. It is an undertaking with many minds bestowing their knowledge to achieve the best outcome for all involved. It’s no surprise that without collaboration throughout the writing and publishing process, efficiency, accuracy, and even integrity have means to weaken and fall apart. In a study on ways to improve research quality, Liao identified that, “a higher intensity at which scholars are embedded in a collaboration network, results in higher research quality” ( 2010 ). One of our top priorities as a part of the iThenticate 2.0 launch is giving the research community the means to work together fluidly, removing all barriers from the research collaboration effort.

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Visibility, efficiency, and automation with User Groups

iThenticate 2.0’s new User Groups sets up collaboration workflows for vertical and horizontal teams within research and publishing organizations. Organizations can now create groups of users and give specific groups of people access to a carefully-selected set of files and folders within iThenticate 2.0. With this capability, research and publishing organizations can work together seamlessly, regardless of the formal organizational affiliation of each particular member.

iThenticate administrators also have full control over access levels and user management to streamline collaboration for a more efficient and high-quality review process at the organizational level. This includes viewing User Group statistics, and accessing User Group Similarity Reports from one, centralized location.

With User Groups, we see this as a positive step towards increased visibility, efficiency, and automation along the course of a research project, and reducing the time involved in the review process.

Is iThenticate 2.0 available to all iThenticate users?

At this time, iThenticate 2.0 is available to new customers only. We’re currently developing a tool to move existing user data to iThenticate 2.0 as smoothly as possible. This will ensure that we can minimize disruption for all involved. The tool will be in the next few months and we will keep all of our iThenticate customers updated on progress.

Conclusion: Advance research integrity with AI writing detection

Turnitin’s updated infrastructure has given us the power to deliver features and functionality to iThenticate 2.0 at speed.

Offering a solution to the sudden introduction of generative AI has been our top priority for 2023, and we are thrilled to bring AI writing detection to iThenticate 2.0 long before the year is out. But our work doesn’t stop here.

Generative AI continues to cause a wave of disruption to global education, developing at an alarming rate. But so too is the technology behind AI writing detection. At Turnitin, we recognize the needs of our education community and are hard at work on expanding our model to enable us to better detect content from other AI language models.

With best-in-class tools, including a reliable similarity checking process, plus a range of flexible collaboration tools, iThenticate 2.0 is well positioned to confront emerging threats to research integrity, supporting the research community as they seek to produce and publish original, high-stakes written work with confidence.


Writing your "Script" is the 1st step toward getting research opportunities and internships! We will guide you through the process of writing a three-sentence “script” that you will feel confident using as you approach faculty and others for research lab and internship opportunities. This program is hosted by BME but applicable to ALL Engineering Majors!

6:00-6:10 Greeting and Introduction to the Script 6:10-6:50 Guided Workshop to Write & Practice Your Script

Location: Thornton Hall A120 Classroom


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Data Brokers and the Sale of Data on U.S. Military Personnel

Risks to privacy, safety, and national security,  by: justin sherman, hayley barton, aden klein, brady kruse, and anushka srinivasan, november 2023.

The data brokerage ecosystem is a multi-billion-dollar industry comprised of companies gathering, inferring, aggregating, and then selling, licensing, and sharing data on Americans as well as providing technological services based on that data. After previously discovering that data brokers were advertising data about current and former U.S. military personnel, this study sought to understand (a) what kinds of data that data brokers were gathering and selling about military servicemembers and (b) the risk that a foreign actor, such as a foreign adversary government, could acquire the data to undermine U.S. national security. This study involved scraping hundreds of data broker websites to look for terms like “military” and “veteran,” contacting U.S. data brokers from a U.S. domain to inquire about and purchase data on the U.S. military, and contacting U.S. data brokers from a .asia domain to inquire about and purchase the same. It concludes with a discussion of the risks to U.S. military servicemembers and U.S. national security, paired with policy recommendations for the federal government to address the risks at hand.

Major Takeaways:

  • It is not difficult to obtain sensitive data about active-duty members of the military, their families, and veterans, including non-public, individually identified, and sensitive data, such as health data, financial data, and information about religious practices. The team bought this and other data from U.S. data brokers via a .org and a .asia domain for as low as $0.12 per record. Location data is also available, though the team did not purchase it.
  • Data broker methods of determining the identity of customers are inconsistent and evidence a lack of industry best-practices.
  • Currently, these inconsistent practices are highly unregulated by the U.S. government.
  • The inconsistencies of controls when purchasing sensitive, non-public, individually identified data about active-duty members of the military and veterans extends to situations in which data brokers are selling to customers who are outside of the United States.
  • Access to this data could be used by foreign and malicious actors to target active-duty military personnel, veterans, and their families and acquaintances for profiling, blackmail, targeting with information campaigns, and more.

Justin Sherman is a senior fellow at Duke University’s Sanford School of Public Policy and leads its data brokerage research project.

Hayley Barton is a Master of Public Policy (MPP) and Master of Business Administration (MBA) student at Duke University and a research assistant on Duke’s data brokerage research project.

Aden Klein is a senior at Duke University and a research assistant on Duke’s data brokerage research project.

Brady Kruse is an MPP student at Duke University and a research assistant on Duke’s data brokerage research project.

Anushka Srinivasan is a sophomore at Duke University and a former research assistant on Duke’s data brokerage research project.


The authors would like to thank the multiple reviewers for their comments on earlier versions of this study. The authors would also like to especially thank Professor David Hoffman, the Principal Investigator (PI) for the grant, and Professor Ken Rogerson for their support throughout the duration of this work.

Funding Statement:

This research was sponsored by the United States Military Academy (USMA) and was accomplished under Cooperative Agreement Number W911NF-22-2-0099. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States Military Academy or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints of the final, public research products published by Duke University for Government purposes notwithstanding any copyright notation herein.

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writing research report youtube

How To Write A YouTube Script (+ Free Template)

Roll the credits. 🎬

ICYMI: YouTube is the second largest search engine platform in the world.

And with so much content being uploaded every day, it can be hard to stand out from the crowd.

Enter: YouTube scripts. 

With the help of a YouTube script, you can create videos that'll hook your audience, keep them engaged, and get your channel noticed.

Whether you’re a small business owner or creator, we’re sharing how to write a YouTube script in this step-by-step guide, below.

Table of Contents

What is a youtube script, why should you write youtube scripts, how to write a youtube script in 5 easy steps.

A YouTube script is essentially the written framework for your video.

What’s your thing? Sticking to your script = ✍️ Improvising on the spot = 🤙 — YouTube Creators India (@YTCreatorsIndia) April 28, 2023

It will outline specific dialog, visual cues, and call-to-actions (CTAs) to hit so that you stay on-topic while also keeping viewers engaged. 

In a nutshell, YouTube scripts are a key component to creating high-quality video content.

FYI: Later’s social media management platform is trusted by over 7M social media managers, brands, and creators to plan content, analyze posts, and more. Sign up today — for free:

Come shoot day, it can be easy to get sidetracked while filming, especially if you have a ton of talking points.

With a YouTube script, you can stay on track, hit your cues, and reference important CTAs.

Writing your script ahead of time also gives on-screen talent the flexibility to play around with multiple takes when they’ve fumbled their words or need to adjust their tone to be more serious or playful. 

Plus, if you work with a video editor, it gives them more options in post-production.  

“Having a script in advance helps when planning out graphics, visual elements, and the video's pacing. It makes editing a lot more streamlined,” shares David Balista, Later’s in-house Video Editor.

"Plus, with scripts, you can reduce the amount of feedback you'll need as we'll have a document to refer back to when considering certain cuts, transitions, or where to insert b-roll," he adds.

Translation: YouTube scripts are a no-brainer when it comes to keeping teams organized during the video production process.

Ready to take your YouTube videos to the next level? Download our free YouTube video script template to keep your audience engaged and get your channel noticed. 👀

You don't need to be a professional write to create an effective YouTube script.

Here's how to do it in five easy steps (tried-and-tested by Later's social team):

Step #1: Ready, Set, Brainstorm

The topic you discuss in your video will form the base of your outline. 

So, it’s a good idea to map out a list of 5-10 topics that are relevant to your niche.

If you’re not sure where to start, try using these questions as prompts: 

What are some frequently asked questions that you could turn into a tutorial? 

What are some common myths in your industry? 

Could you add your perspective to any industry news or recent changes?

From there, establish the relevancy of those topics through keyword research and analysis.

This is key to determining the popularity and discoverability of your topic — the higher your keywords rank, the more likely your videos will perform. 

Just keep in mind, viewers can sense when you’re not genuinely interested in a topic, so it’s best to pick one that’s authentic to your brand, and not just a high-ranking keyword.  

TIP: If you're in a brainstorming rut, consider referencing your content pillars for some inspiration. Watch this 6-minute video to learn how.

Step #2: Build Your Outline 

Once you’ve nailed down your topic, it’s time to create your outline.

This is where you’ll break down your topic into sections.

If you’re basing your YouTube script off a blog post, we recommend using similar headlines to guide what your sections will be.

However, if you’re starting from scratch, you’ll want to stick to this formula:

Grab Attention: Hook your audience through a question, a pain point, an interesting fact, or a personal story.

Introduction: Introduce what you’ll be sharing in this video.

Main Headlines: Break your topic down into digestible sections with clear examples and explanations.

Throughout these sections, include visual elements that'll help illustrate your points like pop-up text, screenshots, b-roll, and transitions.

TIP: Once your video is uploaded to YouTube, you can convert these headlines into clickable timestamps in your video description (see this Later video as an example).

Conclusion: Sum up your video a short and sweet CTA, like subscribing to your channel, following you on Instagram, or clicking the link in your description.

Step #3: Write Your Script

Before you start writing, establish who will be speaking on camera. 

This will help determine the tone of the YouTube script — including how they converse naturally and any nuances that come with their personal expertise. 

Once that’s done and dusted, what software should you use to write your script? It depends!

Here at Later, our team uses Google Sheets to map out our YouTube scripts.

Why? Each line of text can be separated into its own row, making it easy to indicate where visual elements and CTAs should appear:

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Keep in mind, most teleprompter apps and software like Speakflow will require a CSV format of your text in order to automatically upload.

Alternatively, we recommend CapCut’s in-app teleprompter, which requires manual transcription to use the feature.

Once you've decided what format your script will live in, it's time to start expanding on your outline:

Keep It Short: Go into as much detail as possible using the fewest words. For your introduction, viewers want to know what they're about to learn and how you're going to help them. For your main points, avoid rambling or unnecessary filler words.

Again, Short: On the Later YouTube channel , we've found that our most engaging videos are between two to three minutes in length. However, it's worth experimenting to see if longer-form video resonates with your key audience.

TIP: To figure out how many minutes your YouTube video will be based on your script’s word count, we follow a 150 words = 1 minute of speech rule.

Step #4: Map Out Visuals 

In the outline stage, you may have started identifying where your visual elements were going to appear. 

Here, we recommend finalizing those elements along with the copy for your text pop-ups (make a note in whichever template or software you use to write your script — see step #3).

Visuals can include charts, graphics, GIFs, or screen recordings that'll help illustrate your points.

Step #5: Edit, Edit, Edit

In this step, you'll trim and simplify your YouTube script. 

Reading your script aloud will help you get a sense of the natural flow of your dialogue.

Trust us, if it’s too choppy or over-complicated, you’ll hear it.

Doing a verbal run-through of your script will also help you reduce any filler words that are taking up time.

Lastly, get a fresh set of eyes on your script by getting a colleague to review it for final approval. 

And there you have it — every step you need to write your next YouTube script. 

With so much content on YouTube, writing scripts can be your ticket to creating valuable content that'll keep your audience coming back for more.  

Make every second count. Download our YouTube script template to turn visitors into loyal subscribers today — for free. 

Chantal Hermetz

Chantal is Later’s Social Media Specialist and based in Toronto. Passionate about all things social (especially TikTok ), Chantal's experience includes creating content across multiple platforms, partnering with creators, and digital storytelling. Outside of work, you can find Chantal in new cafes, biking around the city, and perfecting her Pinterest boards.

Plan, schedule, and automatically publish your social media posts with Later.

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  • Open access
  • Published: 07 November 2023

Rapid disintegration and weakening of ice shelves in North Greenland

  • R. Millan   ORCID: 1 ,
  • E. Jager 1 ,
  • J. Mouginot   ORCID: 1 ,
  • M. H. Wood   ORCID: 2 ,
  • S. H. Larsen   ORCID: 3 ,
  • P. Mathiot   ORCID: 1 ,
  • N. C. Jourdain   ORCID: 1 &
  • A. Bjørk   ORCID: 4  

Nature Communications volume  14 , Article number:  6914 ( 2023 ) Cite this article

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  • Cryospheric science
  • Physical oceanography

The glaciers of North Greenland are hosting enough ice to raise sea level by 2.1 m, and have long considered to be stable. This part of Greenland is buttressed by the last remaining ice shelves of the ice sheet. Here, we show that since 1978, ice shelves in North Greenland have lost more than 35% of their total volume, three of them collapsing completely. For the floating ice shelves that remain we observe a widespread increase in ice shelf mass losses, that are dominated by enhanced basal melting rates. Between 2000 and 2020, there was a widespread increase in basal melt rates that closely follows a rise in the ocean temperature. These glaciers are showing a direct dynamical response to ice shelf changes with retreating grounding lines and increased ice discharge. These results suggest that, under future projections of ocean thermal forcing, basal melting rates will continue to rise or remain at high level, which may have dramatic consequences for the stability of Greenlandic glaciers.


The Greenland ice sheet has contributed 17.3% of the observed rise in sea level in the period 2006–2018, and has thus become the second largest contributor after ocean thermal expansion 1 , 2 , 3 . During the last forty years, mass losses from the ice sheet have increased from near-balance to a loss rate of 286 ± 20 Gt/yr in 2010–2018, with 66% being attributed to glacier dynamics and 34% to increased surface melt 4 , 5 . Recent studies have shown that the intrusion of warm Atlantic water was responsible for widespread enhanced calving rates at marine terminating glaciers around Greenland 6 , 7 . Mass losses increased more or less simultaneously in the northwest, southeast and centralwest part of the ice sheet during the 80s and the 90s 4 , 8 . However, glaciers in North Greenland only started to be out of balance after 2000 9 , due to changes in the floating extension (ice shelves) of a couple of glaciers 4 , 10 , 11 . In 2018, the mass losses of these glaciers due to ice discharge remained however moderate compared to the other sectors of the Greenland ice sheet (65.4 ± 3.3 Gt/yr vs 124 ± 3.5 Gt/yr for northwest, 165 ± 6 Gt/yr for southeast in 2018) 2 , 4 .

Overall, 25% of the ice sheet area is drained through former or remaining ice shelves, which represents a sea level rise equivalent of 2.1 m 4 , 5 , 8 , 12 . If the glaciers located in North Greenland lose the buttressing provided by ice shelves, the increase in discharge 10 , 13 , 14 could rival the largest contributors to Greenland ice mass loss (e.g., southeast and northwest). Events such as the collapse of Zachariæ Isstrøm in 2003, the large calving event at Petermann in 2012 or the thinning of the 79 N ice shelf already triggered increase in dynamic mass losses 10 , 11 , 15 , 16 . Despite their fundamental buttressing role, there is to date no comprehensive overview of these ice shelves evolution, which hampers our ability to understand the processes leading to their weakening and collapse, and their relation with glacier mass changes. It is thus extremely important to define the timing and drivers of historical and current changes of ice shelves, as well as glacier response, in order to better predict the contribution of Greenland to sea level rise.

In this study, we provide a long-term and holistic view of ice shelf evolution in North Greenland. The eight ice shelves that are surveyed are the floating extensions of the following glaciers : Petermann (38 cm Sea Level Equivalent - SLE), Steensby (1.4 cm), Ryder (13 cm), Ostenfeld (3.9 cm), Hagen Bræ (6.5 cm), 79 N (60 cm), Zachariæ Isstrøm (ZI, 55 cm) and Storstrømmen/Bistrup Bræ (SB, 33 cm) (Fig.  1 ). We document the evolution of basal melting rates, calving fluxes, ice front/grounding line (GL) positions, ice shelf volume, velocity and discharge using a combination of multiple remote sensing datasets and outputs from a regional climate model (Methods, Supplementary Data  1 ). Finally, we use Conductivity Temperature Depth (CTD) measurements and an Arctic Ocean Physics Reanalysis (AOPR) to compare the ice shelves’ evolution with changes in ocean temperature 6 , 17 (Methods).

figure 1

Changes in ice shelf frontal and grounding line position between 1990 and present ( a – h ). Ice flow velocity (source: 4 ) is color coded on a linear color scale and overlaid on a shaded version of the digital elevation model from Bedmachine v3 12 ( a – h ). i Location of all ice shelves in North Greenland with their maximum extent over the study period. Partitioning is shown as bar plot for the period 2001–2021, with Surface Mass Balance (SMB) in blue, basal melting in black and calving in green. The total cumulative mass budget for each ice shelves is noted on the bar plot in Gt (negative for mass loss). FA/GR indicates positive ice shelf mass change from calving, which is typically found when floating area increase with grounding line retreat (GR) or ice front advance (FA, see Methods). Ice shelves colors correspond to the percentage of volume change since 1978. Flux gates from 6 are shown as dotted light green lines. No grounding lines are available in 1997–2010.

Ice shelf changes in North Greenland

Over the eight main ice shelves, the floating extensions of Zachariæ Isstrøm, Ostenfeld and Hagen Brae completely collapsed between 2003 and 2010 (Figs. S 1 –S 3 ). In 2003, 80% of the floating section of Ostenfeld collapsed, which translated into a volume loss of 27 ± 2 km 3  since 1978. Between 2001 and 2005, the ice shelf of Hagen Bræ started to dislocate in the shear margins (Figs. S 2 –S 4 ), and dropped from 21.8 ± 0.6 km 3 (2005) to an average of 2.7 ± 0.6 km 3 in 2009-present. The ice shelf of Zachariæ Isstrøm almost completely collapsed between 2003 and 2012 7 , decreasing from 130 ± 1 km 3 to less than 25 ± 1 km 3 (Fig.  1 ). The GL of Ostenfeld and Hagen Bræ remained stable over the entire period, while the GL of Zachariæ retreated by 7-km in 1996–2015 10 (Fig.  1 ).

Below we describe significant changes among the five remaining ice shelves. We observe a GL retreat for all of them, except Steenbsy, whose position remained stable since 1992 despite considerable terminus retreat. In August 2014, after enhanced fracturing, this ice shelf shrank to an area of 23.8 km 2 , or 34% of its area in 2000–2013. Retreat of the Petermann’s GL was reported to be 7 km between 1992 and 2021, with 5 km occurring in the last five years 18 . In 2020, the ice front readvanced close to its position prior to 2012, leading to an increase of 35% of the floating area compared to 2012–2020. At Ryder ice shelf, the western and the eastern part of the GL retreated by 2.2 and 5.7 km respectively between 1992 and 2011 (Fig.  1 ). Until 2020, the eastern part continued to retreat by an average distance of 2.6 km, for a total of 8.3 km since 1992, which is the largest retreat observed (Fig.  1 ). Ryder’s GL retreat increased the ice shelf area from 245 km 2 in 2006 to an average of 280 km 2 after 2015. At 79 N ice shelf, the GL remained stable between 1992 and 2011, and started to retreat later on between 2011 and 2016 by 2.0 km (Fig.  1 ). This was followed by another retreat event of 1.4 km in 2019–2020, for a maximum cumulative recession of 4.2 km since 1992, which translates into a 2% increase in area compared to 2009–2010. In 2020, the northern branch of 79 N broke off completely, resulting in an abrupt 5% decrease in the ice shelf area. The GL of Storstrømmen and Bistrup Bræ (which surged in 1978 and 1988) retreated by an average distance of 3.0 and 8.0 km respectively between 1996 and 2016, which is consistent with recent studies 2 (Fig.  1 ). Storstrømmen’s GL further retreated by 2.0 km in 2016–2020, and is now within 2.8 km of its pre-surge position. The GL position for Bistrup Bræ migrated upstream by 0.75 km during the same time period (Fig.  1 ). Consequently, the ice shelf area for SB increased from 80.6 km 2 in 2013 to 273.8 km 2 in 2018.

We show evidence of a consistent increase in basal melting below Petermann (Fig.  2a ). First, basal melting rates decreased from 14.1 ± 1.6 m/yr in 2001–2002 to 11.8 ± 1.1 m/yr in 2003–2004. From 2005 to 2016, width averaged GL melt rates increased by 60%, to 19.0 ± 1.7 m/yr in 2015–2016 (Fig.  2a ). After 2016, GL basal melt rates remained at high rates >17.0 ± 1.5 m/yr averaged across the ice shelf width. A similar evolution is observed for Ryder: basal melt decreased from 47.6 ± 4.2 m/yr in 2002–2003 to 38.4 ± 3.6 m/yr in 2004–2008. This was followed by an increase in GL melting rates to 48.0 ± 3.5 m/yr in 2014, or 25% higher (Fig.  2c ). While the largest increase in melting rates are usually observed close to the grounding line, where the ice shelf draft is maximum (Fig.  2a–d ), we observe for Ryder that the largest variability is measured at draft values of 300–400 m. In this area, basal melting increased from near no melting up to 25 m/yr between 2002 and 2020 (Fig.  2c ). This also suggests that the GL is isolated from the largest increase in the water column temperature. After 2014, GL melt rates remained constant, at an average value of 47.0 ± 3.1 m/yr. For Steensby, the melt rates increased from 7.0 ± 4.5 m/yr in 2002–2007 to a peak of 19.7 ± 4.6 m/yr in 2014 during the breakup. Basal melting has then remained constant at 12.5 ± 2.4 m/yr in 2016–2020, or almost twice as large as the melt rate for 2002. GL melt rates measured at 79 N averaged 21.1 ± 2.1 m/yr in 2006–2011 and increased by 37% up to 29.0 ± 2.4 m/yr in 2020 (Fig.  2 ). Spatially, the largest melt rates values are found in the center of the GL, except for Ryder, where melt rates are higher in the eastern section of the GL. Maximum GL melt rates are found for 79 N, where it often exeeds 80 m/yr, and can reach up to more than 100 m/yr in agreement with in-situ measurements 19 . Over the entire period we did not detect significant changes in melt rates for SB, which averages 4.6 ± 7.0 m/yr between 2002 and 2018 (Fig. S 6 ).

figure 2

Melt rate evolution is provided as a function of the ice shelf draft averaged across the ice shelf width all along the centerline ( a – d   panels 1 ), Fig. S 8 . Change in basal melt rate is represented as an hovmöller diagram along the ice shelf length ( a – d   panels 2 ), where basal melt rates are averaged across width. Grounding line melt rate averaged inside the black dotted line for all years is also provided, with error represented as vertical bars ( a – d   panels 3 ). The region of averaging was chosen on a case by case basis to focus on the grounding line region, where melt rates are the highest. Note the change in basal melt scale bar for each panel.

Overall, the volume of ice shelves in North Greenland decreased from 957.0 ± 8.5 km 3 in 2000 to 704.0 ± 7.8 km 3 in 2012, equivalent to a loss of 26%. Between 2013 and 2022, the total volume stabilized and slightly increased to 750 ± 7.7 km 3 , because of widespread GL retreat and the frontal advance of Petermann, which increased the ice shelf area. Using an historical DEM 20 , we calculate a total volume of 1149.5 ± 55.1 km 3 in 1978. We conclude that ice shelves of North Greenland have lost 35% of their volume during the last 45 years (Fig. S 16 ). Similarly, the total area of floating ice dropped from 5386.6 km 2 in 1978, down to 3305.8 km 2 in 2013–2022, hence losing more than one-third of its original extent (Fig. S 16 ).

Partitioning of ice shelf mass losses

Overall in the period 2001-2021 (Fig.  3 ), ice shelves mass losses due to basal melting total 331.3 ± 52.8 Gt vs 222.8 ± 55.8 Gt from calving fluxes and 38.4 ± 6.5 Gt from SMB (Fig.  3 ). Specifically, over this period, mass losses due to basal melting dominate over increased calving with 152.2 ± 27.0 Gt (melt) vs 73.0 ± 29.0 Gt (calving) for Petermann, and 46.2 ± 10.0 Gt vs 4.0 ± 12.0 Gt for Ryder (Fig. S 26 ). For Steensby, mass losses from calving overwhelm basal melting, with a total loss of 2.7 ± 1.7 Gt from basal melting and 6.6 ± 2.3 Gt from calving (Fig. S 28 ). This is mainly due to the large breakups that occurred between 2012 and 2014 (Fig. S 3 ). For the case of 79 N, basal melting totals 126.7 ± 43.0 Gt and 184.0 ± 45.4 Gt from calving. We note that for this glacier, the share of mass losses owing to basal melting has increased after 2012 from 35% to 39% in 2021 (Fig. S 28 ). For SB, in the period 2001–2013, the calving totals 7.4 ± 5.0 Gt/yr against 2.7 ± 2.8 Gt/yr for basal melting. After 2013, the ice shelf gained mass, which is mainly attributed to the grounding line retreat that occured in this time period, rather than a real ice shelf frontal advance (which remained stable) and which is a limitation of our approach for the specific case of SB (see Methods).

figure 3

a – d Changes in ice discharge is represented in % relative to the average of 1970–2000, and color-coded from purple to brown. Ice shelf melt rate evolution is represented as black dashed line. Calving event and observed dates of GL retreat are noted as vertical red and green bars respectively. Changes in ocean temperature between 200 m and 450 m is plotted as blue solid line for North East Greenland (NEG) and Western North Greenland (WNG) regions. Error on potential temperature is plotted as a shaded area. The area where potential temperatures are calculated are shown in Fig. S 15 ). e Cumulative mass loss changes owing to basal melting, calving and SMB for the five remaining ice shelves (cf Fig. 1 ), with errors plotted as a shaded area.

Glacier dynamical response

Ice shelf changes were followed by important glacier dynamical responses. After the partial collapse of Steensby in 2014, the GL velocity increased by more than 60% to 451.5 ± 43.0 m/yr in 2020 (Fig. S 12 ). Similarly, the 2012 calving event of Petermann was followed by a speed increase of 10–15% 18 . Our dense time series shows that the surface flow velocity of Ryder increased from 467.6 ± 17.0 m/yr in 2000–2013 to 590.1 ± 49.1 m/yr in 2018 (or 26%) before slowing down to 543.5 ± 39.0 m/yr in 2020 (Fig. S 13 ). Finally, the ice velocity of 79 N consistently increased from 1500.0 ± 100.0 m/yr in 2000 up to more than 2100.0 ± 41.0 m/yr in 2020, or by 40% (Fig. S 14 ). For Zachariæ Isstrøm, we expand on previous studies 10 and show that the glacier continued to accelerate from 1200 m/yr in 2000 to 2900 m/yr in 2019 (Fig. S 10 ). No changes in ice dynamics are observed for the other glaciers (Figs. S 9 –S 15 ).

These dynamical changes are also reflected in the yearly discharge estimates and are consistent with the observed evolution of basal melt close to the GL (Fig.  3 ). The discharge of Steensby increased by 28% between 2000 and present. Discharge rates continued to rise while basal melting stabilized after 2015 (Fig.  3 ). For Petermann, the ice discharge started to increase in 2010, and reached a maximum in 2018 at 11.7 ± 1.2 Gt/yr, two years later from the peak in basal melt (Figs.  2 , 3 ). Interestingly, for Petermann and Ryder, the slowdown in ice discharge observed after 2018 is coincident with stabilized GL basal melt (Fig.  3 ). The GL discharge of 79 N increased by 14% from 11.6 ± 0.8 Gt/yr in 2000 to 13.2 ± 0.7 Gt/yr in 2022 (Fig.  3 ). These results suggests the strong control of basal melt rates on glacier dynamics.

Changes in ocean conditions

The Norwegian Atlantic Current advects warmer water northward. A branch of this current flows east of Svalbard across Fram Strait and directly toward NEG 21 . In contrast, the waters in WNG (Fig. S 17 ) are branching from the Arctic Transpolar current down south into Naires Straits and along the North coast of Greenland. Analysis of CTD and AOPR highlights different thermal regimes across the entire North Greenlandic region. In WNG, we note that the ocean temperature at depth (250-450 m), modestly increased by 0.1 °C in the period 1965–2000, from −0.1 °C to 0.0 °C (Fig. S 17 ). Between 2000 and 2015, we found a larger increase in temperature, from 0.0 °C to 0.25 °C. In NEG (Fig.  3 ), CTD measurements show that the average temperature at depth is 0.8 °C higher than WNG (Fig.  3 ). In this region, the increase in water temperature was more important and started earlier than WNG: between 1980 and 1990 the temperature increased from 0.1 °C to 0.4 °C (Fig. S 17d ). In the period 1990–2020, the change was more than twice larger and increased from 0.4 °C to 1.2 °C. For NEG, we observe a high peak in water temperature in 2010, with a magnitude similar to the one reached in 2020. For WNG, the highest temperature peak was reached in 2015, and ocean temperature decreased since then down to 0.1 °C (Fig. S 17 ).

While we measure large changes in glaciers and ice shelves, the timing of events are heterogeneous. The earlier observed ice shelf collapse was recorded at Ostenfeld in 2003. CTD and model reanalysis only show a modest increase in ocean temperature in that sector (Fig. S 17 ). The analysis of optical imagery shows that the ice shelf had no lateral contact with the fjord margins since 1978 (Figs.  1 , S 1 ). We also note the absence of ice mélange after 1978–1992, which may have buttressed the ice shelf prior to collapse 22 (Fig. S 1 ). The absence of dynamical response (Fig. S 9 ) further confirms the minor role of Ostenfeld in providing buttressing to the glacier. The collapse of Hagen Bræ corresponds with a previously reported surge in 2007–2015 23 (Fig. S 11 ). In the southern section of the shelf we measure large basal melting rates, which combined with the important strain rates, have made the floating section prone to collapse (Figs. S 4 , S 5 , S 7 ). Earlier observations of changes in ice thicknesses and basal melting in the 1990–2000 would be needed to further detail the processes and exact timing of events that have led to the collapse of these ice shelves.

For the remaining ice shelves, basal melting rivals the highest rates observed in the Amundsen sea Embayment of Antarctica 24 , 25 , 26 . The observed increase in melting coincides with a distinct rise in ocean potential temperature, suggesting a strong oceanic control on ice shelves changes. Calculated correlation coefficients between basal melt rates and ocean temperatures exceed 0.9 for Petermann, Ryder and Steensby, and 0.5 for 79 N (see Methods). The general increase in water temperature for the period 2005–2016 corresponds to the calculated rise in basal melting at the GL of Petermann, Steensby and Ryder (Fig.  3a–c ). Peaks in melt observed in 2014–2016 at Petermann, Steensby and Ryder are consistent with the highest ocean temperature in that sector during the same period (Fig.  3a, c ). Similarly, constant or decreasing melt rates in the period 2000–2005 and 2016–2019 match periods of decreasing ocean temperatures (Fig.  3b, c ). While ocean thermal forcing has slightly decreased in WNG, it continued to rise at the front of the 79 N, with consistently increasing melt rates in 2006-2021 (Fig.  3 ). Overall, the warmest ocean temperatures are observed in NEG (>1 °C in 2020, Fig.  3d ) and correspond to the maximum melt rates values, which are observed at 79 N (Figs.  3 , S 8 and S 17 ). We note that the largest GL retreats are observed where basal melt rates are the highest, i.e., the central section of Petermann and 79 N’s GL, and the eastern side of Ryder’s GL (Fig. S 8 ). For Steensby the large calving event of 2014 is consistent with the highest measured basal melt value of the entire time series (Fig.  3 ).

Regional atmospheric climate model outputs show a similar trend in the surface runoff for all drainage basins. The runoff increased between 1990 and 2010, after which it stabilized at rates of 4.5 Gt/yr, 1.5 Gt/yr and 2.8 Gt/yr for Petermann, Steensby and Ryder respectively, with a large inter-annual variability after 2010 (Fig. S 26 ). For 79 N, runoff increased from 2.1 Gt/yr in 2000 and stabilized at 5 Gt/yr around 2007 (Figs. S 26 , S27 ). After the runoff stabilization, basal melting rates continued to increase, hence suggesting that runoff played a minor role in its evolution (Figs. S 26 , S27 ). Using the parametrization from 26 over Petermann and neglecting the role of subglacial water discharge, we show a good agreement between modeled and observed GL melt rates (Fig. S 29 ), which further supports the interpretation that changes in runoff had a negligible influence on basal melt rates.

In the case of Ryder, Petermann and 79 N, increased basal melting is accompanied by GL retreat and followed by an increase in ice discharge (Fig.  3b–c ). The lack of grounding line data, specifically during the period of 1997–2010 when basal melting significantly increased, is a limiting factor in accurately assessing the precise timing relationships between these two processes. The comparison of the spatial variability of basal melting with shear strain rates and crevasse formations over Petermann shows a close spatial correlation between subglacial melt channels and recently formed fractures in 2015 18 (Fig. S 30 ). This suggests basal melting may be playing a complex and crucial role in thinning the ice shelf from below, and modulating the GL position and glacier dynamics, hence making it prone to enhanced fracturing. For Steensby, the changes in ice discharge observed after 2014 suggests that the glacier responded to a loss in ice shelf buttressing, with a strong interplay between enhanced basal melt rates and the large calving event.

Subglacial bedrock topography can also exert a strong control on the retreat rate of glaciers 12 . Currently, the eastern section of the GL of Ryder stabilized on a prograde bedslope grounded at 700 m below sea level (Fig. S 18 ). The western part is however sitting on top of a deep retrograde bed at −400 m, which deepens over the next 6 km to −740 m. For 79 N, the central part of the GL is at −520 m and sitting on top of a downsloping bedrock that goes down to −640 m over a distance of 4 km (Fig. S 18 ). A similar setting has recently been reported for Petermann 18 , which could face a retreat of another 8 km before the GL stabilizes (Fig. S 18 ).

Continued ocean and satellite observations are key to provide insights on how these ice shelves will respond to future climate forcing. High resolution ocean models and bathymetry mapping should be used, together with CTD deployments, to provide insights into warm water intrusions in fjords and ice shelves cavities 27 . Basal melting is a complex process, and one of the main sources of uncertainties in future projections of the ice sheets contribution to SLR 3 , 28 . We provide observations of basal melt rate at an unprecedented level of resolution which opens the door to a higher degree of understanding of ice shelf processes. This allows reanalysis data to validate coupled ice-ocean models and to better estimate parametrization of ice-ocean interaction processes. This will ultimately provide insight into the future of these glaciers as well as the fate of larger ice shelves in Antarctica 28 .

Our results document a holistic overview of glacier-climate-ocean interaction in North Greenland. We are able to identify a widespread ongoing phase of weakening for the last remaining ice shelves of this sector. The GL are exposed to the warmest water layers and currently sitting on retrograde bed slopes. This makes them extremely vulnerable to unstable retreat and ice shelf collapse if ocean thermal forcing continues to rise, which is likely to be the case in the coming century 29 , 30 . A loss in the buttressing provided by ice shelves in this sector will likely trigger an increase in the discharge 13 , 14 that could rival the largest contributors to Greenland ice mass loss. This could have dramatic consequences in terms of SLR, as it is the sector in Greenland with the greatest SLR potential (2.1 m) 3 , 4 .

Ice surface elevation and volume change

In this study we make an extensive use of all available surface elevation data to reconstruct a comprehensive yearly history of ice shelf thickness changes and basal melt rates between 2000 and present. Airborne altimetry measurements from NASA’s Operation Icebridge Airborne Topographic Mapper (ATM) and LVIS (Land, Vegetation and Ice Sensors) were used to document ice shelf thickness changes from 1993 to 2016 31 , 32 . Satellite altimetry from NASA’s Ice, Cloud and land Elevation Satellite missions (ICESat-1) were also used to document the evolution of ice shelf thickness between 2003 and 2009, after which the satellite was retired due to a laser failure. Between 2018 and present, we used satellite altimetry measurements from the recent ICESat-2 mission. Quarterly digital Elevation models (i.e temporally averaged) derived using Digital Globe imagery as part of the Greenland Ice Mapping Project were used between 2012 and 2016 33 , 34 . We also used DEMs derived from Synthetic Aperture Radar using NASA’s GLacier and Land Ice Surface Topography Interferometer airborne (GLISTIN-A) which measured surface elevations in 2016-2019 around the periphery of the Greenland Ice Sheet using Ka-Band (8.4 mm wavelength) single-pass interferometry 35 . Note that the GIMP and GLISTIN-A DEM are co-registered to airborne altimetry data (see below).

DEMs from ASTER imagery are generated at 30 m resolution between 2000 and present with the MMASTER processing chain, which is based on the MicMac photogrammetry software 36 . These observations are aligned horizontally and vertically following the classical scheme from 37 , and using yearly mosaics of satellite altimeters as ground control points on land and ice 38 (see Fig. S 39 for examples). The reference datasets were assembled using all altimetry data on stable ground (without ice) and elevation measurements from the same year as the DEMs on grounded and floating ice 24 . When all ASTER DEMs are generated and coregistered, we filter the elevation map with respect to correlation score (ranging between 0–100) 36 . We typically remove all pixel with correlation score below 85, and stack all elevation maps within a year into a composite mosaic. After stacking, the resulting DEM is again aligned horizontally and vertically to the relative reference surface elevation. Before stacking, we find an average standard deviation of the difference with satellite altimetry that is ranging between 6 and 9 m. We calculate the root mean square error between all ASTER DEMs and the latest version of the GIMP digital elevation model 39 . We removed outlier pixels with differences exceeding 200 m compared to the latest version of the GIMP v2 34 . For elevations above 1000 m (the accumulation area), we additionally filtered pixels with differences over 75 m from the GIMP v2 DEM, considering the anticipated lower dh/dt values in this region. These threshold values were chosen arbitrarily through multiple rounds of iterative filtering tests.

For the GIMP, GLISTIN and ASTER DEM, we additionally calculate the yearly difference with the corresponding altimetry data on the ice shelf: if the average difference exceeds 1 m, the DEMs are shifted vertically, in order to be centered on zero. Overall, we find a mean standard deviation of the difference on ice shelves of 2 m on ASTER DEM (after stacking) and GLISTIN-A, and 0.8 m for GIMP (Figs. S 31 , S 38 ). For Petermann, Ryder and Steensby, ASTER DEMs from 2000-2001 didn’t have altimetry data on the ice for coregistration, hence we used the closest reference DEM in time to align DEMs. Uncertainties on the calculated melt rates might therefore be higher for these dates (see Fig.  2 ). For 79 N, we were not able to find ASTER DEM with a satisfying signal to noise ratio between 2000 and 2006.

Finally, mosaics of surface elevation for each ice shelf are assembled by merging all available elevation data from all available sensors, and by assigning higher priorities  to the best vertical accuracy (e.g, altimetry). This ensures to have the most comprehensive spatial coverage and highest vertical accuracy on all ice shelves around Greenland. Ice shelf volume changes are calculated by converting the surface elevation of the ice shelf into ice thickness using the hydrostatic equilibrium equation within an ice shelf mask, with an ice density of 0.917 g/cm 3 , 40 ,and a water density of 1.028 g/cm 3 . Grounding line and ice front mapping determination is described below. Uncertainties in the ice volume are calculated by assuming an error in ice shelf area of 1 pixel at the ice front 41 , and the corresponding error in ice thickness using the error on the surface elevation (see above). We also consider an additional uncertainty of 1 m, for changes in thickness due to firn air content, as described in ref. 40 . Mean firn air content in this region is typically <1 m, with change rates of less than 1 cm/yr 33 .

Grounding line and ice front mapping

We use Interferometric Synthetic Aperture Radar (InSAR) data from ESA’s Earth Remote Sensing radar satellite (ERS-1) acquired in 1992 with a 3-day revisit time, in 1995/1996 from the ERS-1/2 tandem mission, and in 2011 from a 3-day revisit time before the end of ERS-2 mission. Data are downloaded via the ESA Online Dissemination Service as single look complex (SLC) scenes and processed using the GAMMA Software 42 . We measure the tide-induced vertical motion of ice using a Quadruple Differential SAR Interferometry approach (QDInSAR) 43 . Phase coherence is maintained in fast flowing regions by coregistering SLC data using speckle tracking 44 , 45 . We calculate interferograms with the phase difference between the co-registered SLCs. The grounding line position is obtained by differentiating two interferograms spanning the same time interval, after correcting for topography 43 . We use the GIMP v1 DEM time-tagged in 2007 to remove the topographic signal 34 , assuming that no changes in surface ice elevation has occurred in the time interval between the DEM and SAR data. For 2014–2021, we use Sentinel-1 (S1) data with a repeat cycle of 6 to 12 days. Phase jumps at burst boundaries were accounted for using the TOPS coregistration method 44 . We use the GIMP DEM v2 time-tagged in 2014 to correct for the topographic phase for S1 34 . We map the inward limit of detection of vertical motion, where the glacier first lifts off its bed 43 . We processed around 1000 interferograms for Petermann, 600 for Ryder, 520 for Steensby and 500 for 79 N, which provide us with a good idea of the grounding zone (>80% is from Sentinel-1 imagery). We measure the distances of maximum grounding line retreat or advance relative to the earliest most retreated grounding line observation date. Example of grounding lines and interferograms are provided in Fig. S 39 .

We manually digitized yearly ice shelf frontal positions between 1990 and 2021 using summer imagery from NASA’s Landsat-1-8 optical satellite, ESA’s Sentinel-1/2 satellite. We used the Google Earth Engine Digitisation Tool (GEEDiT v1.012) developed by James M Lea at the University of Liverpool 46 .

Ice velocity

We monitor the evolution of the glacier dynamic state velocity by calculating the surface displacement from three different satellite sensors from images collected between 2013 and 2021. Two of them, ESA’s Sentinel-2 (S2) and NASA’s Landsat-8 (L8), are optical imagers and one, ESA’s Sentinel-1 (S1), is a synthetic aperture radar operating in C-band. We use persistent surface features or speckle to map ice displacements between two consecutive images. We calculate the normalized cross-correlations between the reference and search image chips using repeat cycles shorter than 30 days for Landsat-7/8 and Sentinel-2, and 12 days for Sentinel-1 47 , 48 . Between 1999 and 2012 we supplemented our Landsat-7 ice velocity record with repeat cycles ranging from 336 to 400 days. For L8, S2, and S1, sub-images of 32 × 32, 32 × 32, and 192 × 48 pixels are used, respectively. We calibrate our displacement maps by taking advantage of the ice velocity products from prior surveys in 47 . The final calibrated maps are resampled to 150 m posting in the north polar stereographic projection (EPSG:3413). The time series established is completed by historical measurements made from ERS-1/2, RADARSAT-1, ALOS/PALSAR, ENVISAT/ASAR, Landsat 4 to 7 and TerraSAR-X 4 , 33 , 47 , 48 , 49 , 50 .

In order to monitor changes in rates of ice deformation, we derive the evolution of the shear strain rate for 2000 and 2019 using annual ice velocity mosaics 2 . The annual mosaics are assembled from the same observations as described earlier, using a strict procedure of spatial and temporal filtering. These data provides the most accurate ice velocities values with the best signal to noise ratio and lowest uncertainties in ice flow direction 47 . Strain rates were retrieved using the same methodology as described in 18 , 51 .

Ice shelf basal melt rates

Changes in ice shelf thickness can be caused by (1) the rapid advection of ice, (2) surface mass balance, (3) firn air content, and (4) basal melting, can be summarized through Eq. ( 1 ):

Where \(\frac{{{{{{\rm{Dh}}}}}}}{{{{{{\rm{Dt}}}}}}}\) is the total Lagrangian thickness change, H the ice thickness, \(\nabla .{{{{{\rm{v}}}}}}\) the velocity divergence, \({{{{{{\rm{M}}}}}}}_{{{{{{\rm{s}}}}}}}\) the surface mass balance, \(\frac{{{{{{\rm{D}}}}}}{{{{{{\rm{h}}}}}}}_{{{{{{\rm{air}}}}}}}}{{{{{{\rm{Dt}}}}}}}\) the change in firn air content and \({w}_{b}\) the basal melt rate. The ice density is taken as 917 kg m −3 and the ocean water density as 1.028 kg m −3 .

Changes in ice thicknesses are determined using a Lagrangian framework, i.e., we track the evolution of every pixel in a given DEM to its downstream location in another DEM acquired later in time. We follow the work of ref. 38 and calculate the trajectory of every pixel along flow paths calculated from yearly surface flow velocity fields, (time step of 15 days), to appropriately account for changes in ice dynamics 38 . To avoid artifacts in ice velocity mosaic we smooth the ice flow observation using a 2.5 km rolling median filter 38 . Because the shear margin of glaciers is a region where surface flow velocity errors are the highest, we manually filter glacier boundaries to avoid artefacts in the thickness changes and ice flow divergence derivation (see below).

Changes in ice volume Dh/Dt are determined using yearly DEMs described above and covering the years 2000–2020. The ice shelf area dynamically evolves every year with changing ice front and grounding lines. We calculate a dense time series of basal melt rates using all possible combinations of DEMs since 2000, with time difference between the DEM sources spanning from 2 year (low Signal to Noise Ratio-SNR) to 6 years (best SNR). Several tests were conducted with DEMs separated by 1 year, but the SNR  was insufficient for use in long-term melt rate trend interpretations. Prior to the calculation of the Dh/Dt term, we corrected every DEM over vertically induced tidal motion using the pyTMD toolbox, which reads outputs from the AOTIM-5 inverse tide model 52 . To account for changes in thicknesses due to surface melting, we use the monthly version of the Modele Atmospheric Regional (MAR) at a resolution of 1 km 53 . Changes in surface mass balance are also calculated in a Lagrangian Framework using annual values of SMB. Mean firn air content in this region is negligible, hence we did not account for it in the melt rate calculation 40 (see “Ice surface elevation and volume change section”).

We calculate changes in ice thickness due to the advection using the ice flow divergence based on yearly maps of surface flow velocity as described previously. Finally basal melt rate maps are smoothed using a rolling median of 450 m. For each pair of DEMs, we use the central date to reference the basal melt map. For all ice shelves, we stacked all available melt maps within a year to produce  most accurate time series of basal melting. The evolution of the melt history is plotted using an hovmöller diagram (Fig.  2 ), which is the width-averaged basal melting along an ice shelf flowline (Fig. S 8 ). Uncertainties in basal melting rates are calculated using standard error propagation methods and assuming a conservative error of 15% on the SMB 54 . This was calculated considering Fig. 5 from ref. 54 and the SMB values <−3 mWE which are the ranges with the largest discrepancies between observations and regional climate models. An arbitrary and conservative error of 100% is applied on the ice flow divergence. We use the related yearly errors on the DEMs that are used for calculating the changes in ice thickness (see Section “Ice surface elevation and volume change”)

Grounding line ice discharge

Due to the likely high uncertainties in bedrock elevation at the grounding line 7 , 12 , that can reach hundreds of meters, we decided to use estimates from 4 for the period 1990–2017 and an updated version of the calculation of 8 . For 2017–2022, we evaluate changes in ice discharge based on the PROMICE solid ice discharge data product, calculating ice flux through gates located approximately 5 km upstream of the grounding line (Fig. S 18 ). The flux gates discharge obtained are generally higher than the estimate of 4 as these are not corrected for the surface mass balance downstream of the flux gate. We therefore scale our new discharge estimates, based on the mean bias on the overlapping periods of measurements, so that they match the median values of ref. 4 .

Calving fluxes

Yearly calving fluxes were calculated for the still-standing ice shelves (Steensby, Petermann, Ryder, 79 N, Bistrup/Storstrømmen) using an input-output approach 55 . We estimated this flux over the entire studied period using the calculated mass changes from the ice shelf volume variations, basal melt rates, grounding line discharge and changes in surface mass balance. Positive calving flux values are typically found when the ice shelf area increased, due to a frontal advance or large grounding line retreat. The drawback of this methodology lies in the case of positive calving values, as it doesn’t allow for discrimination between an increase in volume due to retreat of the grounding line or advancement of the front. For missing values of basal melt rates, time series were extrapolated to obtain a comprehensive time series over the period 2001–2021. The melt rate value of 2002 was used for 2001 in the case of the Steensby ice shelf and we used the 2005 basal melt values for 79 N over the period 2002-2005, which might bias the mass losses owing to basal melting. Uncertainties in calving fluxes are calculated with the square root of the sum of squared errors on the grounding line discharge, the ice volume, and the basal melt rates. Due to the quiescent phase of Bistrup/Storstrømmen, the grounding line discharge is often less than 0.5 Gt/yr over the entire study period. Futhermore, the calculated basal melt rates are <10 m/yr, and we only observe moderate changes in SMB, which averages −0.25 Gt/yr in 2019–2021 over the shelf. These changes are moderate, or non-existing, and our approach does not allow to highlight a clear environmental trigger on the large increase in ice mass attributed to the grounding line retreat observed after 2013.

Ocean conditions

In order to monitor the evolution of ocean thermal forcing, we use conductivity temperature and depth measurements from the Hadley centre ( spanning 1960 and 2019, combined with CTD from NASA’s Ocean melting Greenland campaign from 2016 to 2021 17 . In addition to the in-situ observation, we also document the spatial variation of ocean thermal forcing using the Arctic ocean physics reanalysis (AOPR, ) produced by the Nansen Environmental and Remote Sensing Center, Norway, and distributed by the Copernicus Marine Environment Monitoring Service. The AOPR captures the trends in the CTD in-situ measurements and allows us to interpret changes in ocean temperature with a finer temporal scale. The ocean and sea-ice model assimilate CTD profiles as well as remotely sensed data such as sea surface temperature, sea-ice concentration and sea surface height, for the North Atlantic Ocean and Arctic between 1992 and 2020 56 . The reanalysis is provided on a horizontal grid cell size of 12.5 km × 12.5 km and 40 vertical levels, and monthly outputs are used to calculate yearly ocean thermal forcing (potential temperature minus freezing temperature). In North Greenland, grounding line depth, sub-ice shelf, and fjord bathymetry are highly uncertain and results from rough interpolation (Fig. S 18 ). Furthermore, the availability of CTD measurements in the fjords is sparse, and specially over the study period (Fig. S 18 ). In order to maximize our confidence in the trends of ocean temperature, we decided to analyze regional values for ocean potential temperature in the western North Greenland and north east Greenland (see Fig. S 18 ). For each box (Fig. S 17c ), we extract average temperature below depth 200 m and 550 m. These temperature ranges were chosen with respect to CTD profiles for each region (Fig. S 17 ). Within each region we evaluated the bias between in-situ data and reanalysis model and found a significant mean bias of 1.1 °C for NEG. We adjusted the depth averaged temperature value accordingly and found afterwards average differences with CTD measurements of 0.06 ± 0.02 °C for WNG and 0.05 ± 0.28 °C for NEG (Fig.  3 ). We use these statistics as uncertainty measurements in the reanalysis temperature trend in Fig.  3 . We use the temporal evolution of ocean temperatures to compare them to the evolution of basal melt between 2000 and 2021. Given the temporal and spatial uncertainty of the datasets, we decided to smooth the time series with a moving average of 4 years (the average baseline used for melt rates). In this way the correlation coefficient is calculated on general trends over the whole study period. Indeed, uncertainties can be caused by the temporal resolution of our basal melting time series, which averages different temporal baselines, and the large spatial averaging on the AOPR. Additionally, AOPR may not fully capture the ocean dynamic and specifically in 2000-2015, where few in-situ measurements exist (Fig. S 17 ).

Surface mass balance

We investigate changes in surface mass balance using a simulation from the Modèle Atmosphérique Régional (MAR-v3.12 53 ) forced by the ERA5 reanalysis 57 . The simulations were run over Greenland at a resolution of 11 km, then statistically downscaled at a resolution of 1 km.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The grounding line, ice front positions, surface flow velocity, basal melt and calving rates have been deposited in the Zenodo database and can be accessed at . Outputs from the Modèle Atmosphérique Régional are available at . This study has been conducted using E.U. Copernicus Marine Service Information 58 ; . CTD measurements are freely available at and .

Code availability

Codes used to produce the figures of this paper can be accessed at .

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This work was supported by the French National Research Agency, grant no. ANR-19-CE01-0011-01 (R.M, E.J, J.M), and grant no. and ANR-19-CE01-0015 (P.M). This work was also supported by the Villum Young Investigator grant no. 29456 (R.M, A.B). S.H.L. was funded by the PROMICE project ( ). N.C.J. was funded by EU-H2020 grant no 101003536 (ESM2025). M.H.W. was supported by awards from the NASA Cryospheric Sciences Program (NNH20ZDA001N-CRYO) and the NASA Physical Oceanography Program (NNH22ZDA001N-PO). This work is dedicated to Jeremie Mouginot, leader of the ANR SOSice project, and who tragically passed away in September 2022.

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