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:

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:

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:

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:

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.

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:

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.

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:

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

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

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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Sample Paper in Scientific Format

Biology 151/152.

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The science research report can be segregated into three significant sections such as the process, progress of the research, results of the technical or the scientific research. There are various type of the method implemented in the scientific research study such as the qualitative, quantitative, applied, applied, basic etc. Through the usage of report , it is easy to share the information with other scientists, reviewing, showcasing the progress, and persuasion through logic.

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Table of Content

1. scientific research report template, 2. social science research report format, 3. social science research report template, 4. scientific research report declaration form, 5. science technology research project report template, 6. empirical social science research paper analysis report, 5 steps to create the science research report, faq’s, how does the science research report functions, why is it necessary to create the science research report, what are the benefits of the science research report, what are the outcome of creating science research report.

scientific research report template

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Scientific Report

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Writing up the results from an experiment can be difficult, as the nature of scientific research requires rigorous testing techniques and accurate recordings of data. The scientific report allows researchers to record their findings and publish them out into the world, expanding on the area of expertise. So, what comprises a scientific report?

Scientific Reports: Psychology

Research can be identified as primary or secondary research; whether the researcher collects the data used for analysis or uses previously published findings determines this. The different types of research produce different types of scientific reports, such as:

Primary research is data collected from the researcher, e.g., when carrying out an experiment.

For example, a laboratory produces a primary scientific psychology report.

Scientific Report, Types of data on a sheet, StudySmarter

On the other hand, secondary research is carried out using previously published research.

For example, a meta-analysis uses statistical means to combine and analyse data from similar studies.

Or, a systematic review uses a systematic approach (clearly defining variables and creating extensive inclusion and exclusion criteria to find research in databases) to gather empirical data to answer a research question.

Scientific Report: Importance

The reason why research should follow the APA recommendations for writing up psychological scientific research is that:

Scientfic Report: Writing

When conducting scientific report writing, several things must be kept in mind. A scientific report aims to help readers understand the study's procedure, findings and what this means for psychology. A scientific report should be clear and logical to make it easier to understand the research.

The American Psychological Association (APA) has created guidelines on how a scientific report should be written, including the scientific report structure and format.

APA suggests several headings for use in psychology reports. The scientific report structure and details included in the report will vary based on the researcher's experiment. However, a general framework is used as a template for research.

Scientific Report Structure

Psychology research should always start with an abstract. This section briefly summarises the whole study, typically 150-200 words. The crucial details the abstract should give include an overview of the hypothesis, sample, procedure, results, details regarding data analysis, and the conclusions drawn.

This section allows readers to read the summary and decide if the research is relevant to them.

The purpose of the introduction is to justify why the research is carried out. This is usually done by writing a literature review of relevant information to the phenomena and showing that your study will fill a gap in research.

The information described in the literature review must show how the researcher it was used to formulate and derived the hypothesis investigated.

The literature review will reflect research supporting and negating the hypothesis.

In this section, the investigated hypotheses should be reported.

The introduction should consist of a third of the psychology research report.

Scientific Report Structure: Method

The method consists of multiple subsections to ensure the report covers enough details to replicate the research. It is important to replicate investigations to identify if it is reliable. The details included in the methodology are important for peer-reviewing the quality of the study.

It allows the person peer-reviewing it to determine if the research is scientific, reliable, and valid and if it should be published in a psychological journal.

The subsections written in the methods section of a scientific report are:

State the experimental design.

State all of the (operationalised) variables investigated.

If multiple conditions are investigated, e.g., people treated for one, two, and four weeks, researchers should report it.

It is also important to note how researchers allocated participants into groups and whether they used counterbalancing methods.

The research design used, e.g., correlational research.

Counterbalancing is used to combat order effects. In some designs, participants repeat the same experiment counterbalancing techniques deal with these.

Sample/ Participants

The sampling method should be noted, e.g., opportunity.

Researchers should state the number of participants and the number of males and females participating in the study.

They should state the demographics of the participants used in the research, e.g., age (including the mean and standard deviation), ethnicity, nationality, and any other details relevant to the investigation.


This section should state all the relevant equipment used in the study, i.e., equipment/materials used to measure the variables , e.g., questionnaires (researchers should include a copy of this in the appendix).

Some research does not use this subsection if it does not use any specialised materials, e.g., researchers do not need to state if participants used pens or a stopwatch.

They should include details about standardised instruction, informed consent, and debriefing.

This section should be concise but provide enough details so it is replicable.

This section states which ethical committee reviewed and granted the research.

It should state any ethical issues that could have occurred in the research and how researchers dealt with them.

Scientific Report Conclusion and Results

The results section is where you state your findings. This section only states what you have found and does not discuss or explain it. You can present the data found through numerical values, tables, and figures. However, there are specific guidelines on reporting data per APA guidelines when reporting or adding these.

Researchers should not use the raw data collected. Instead, it should be analysed first. The results should start with descriptive data followed by inferential statistics (the type of statistical test used to identify whether a hypothesis should be accepted or rejected).

These statistics should include effect size and significance level (p).

Researchers should report data regardless of whether it is significant or not. They should report the p-value to three decimal places but everything else to two.

After the results, the scientific report conclusion should be reported; this summarises what was found in the study.

Scientific Report: Discussion

This section should discuss and conclude with the research results. The first thing researchers should write about in the discussion is whether the findings support the proposed hypothesis.

If the results support the hypothesis, researchers should compare the findings to previously published findings in the introduction that also found the same results.

You should add very little new research to the discussion section. If the hypothesis is not supported, the discussion should explain from research why this may be. Here, adding new research to present the findings is acceptable (perhaps another theory better explains it).

Critiquing this research, such as its strengths and weaknesses, how it contributed to the psychology field, and its next direction is essential. In the discussion, researchers should not add statistical values.

Scientific Report Example

An example of a scientific report includes any of those seen in studies, such as when a laboratory produces a primary scientific psychology report, or a meta-analysis which uses statistical means to combine and analyse data from similar studies.

The purpose of the reference section is to give credit to all the research used in writing the report. Researchers list this section in alphabetical order based on the author's last name – t he references listed need to be reported per the APA format.

Researchers use background information, e.g. data or theories from previous publications, to form hypotheses, support, criticise findings and learn how research should progress.

The two most common secondary sources used in scientific reports are findings from published journals or books.

Let's look at some scientific report examples of how books and journals should be referenced following APA guidelines.

Book : Author, initial (year of publication). Book title in italics. Publisher. DOI if available (digital object identifier).

Example: Comer, R. J. (2007). Abnormal psychology . New York: Worth Publishers.

Journal: Author, initial (year). Article title. Journal title in italics, volume number in italics , issue number, page range. DOI if available.

Example: Fjell, A. M., Walhovd, K. B., Fischl, B., & Reinvang, I. (2007). Cognitive function, P3a/P3b brain potentials, and cortical thickness in ageing. Human Brain Mapping, 28 (11), 1098-1116.

Scientific Report - Key takeaways

A scientific report consists of details regarding scientists reporting what their research entailed and reporting the results and conclusions drawn from the study.

Frequently Asked Questions about Scientific Report

--> how do you write a scientific report in psychology.

When psychologists carry out research, an essential part of the process involves reporting what the research entails and the results and conclusions drawn from the study. The American Psychological Association (APA) provides guidelines for the correct format researchers should use when writing psychology research reports.

--> How do you write a scientific introduction to a report?

It is usually done by writing a literature review of relevant information to the phenomena and showing that your study will fill a gap in research.

--> How do you structure a scientific report?

The structure of a scientific report should use the following subheadings: abstract, introduction, method (design, participants, materials, procedure and ethics), results, discussion, references and occasionally appendix, in this order. 

--> What is a scientific report?

A scientific report consists of details regarding scientists reporting what their research entailed and reporting the results and conclusions drawn from the study. 

--> What are the types of a scientific report?

Scientific reports can be primary or secondary. A primary scientific report is produced when the researchers conduct the research themselves. However, secondary scientific reports such as peer reviews, meta-analyses and systematic reviews are a type of scientific report that scientists produce when the researcher answers their proposed research question using previously published findings.

Final Scientific Report Quiz

What is a scientific report?

Show answer

Show question

Why is scientific research reported per APA in psychology?

How should the following book be reported per APA guidelines? The book is called Abnormal psychology, Worth Publishers published it in New York in 2007. Ronald J Comer wrote the book. 

Comer, R. J. (2007). Abnormal psychology . New York: Worth Publishers.

What structure should a scientific report follow?

The structure of a scientific report should use the following subheadings: 

What are potential subheadings we can find in the methods section of a scientific report? 

Where can readers find the hypothesis of research? 

In the abstract and introduction.

What is the purpose of the abstract?

The purpose of the abstract is to provide an overview of the research so that the reader can quickly identify if the research is relevant or of interest to them.

How long should an abstract be?

250-300 words.

Is the following reference reported in accordance with APA guidelines ‘Fjell, A. M., Walhovd, K. B., Fischl, B., & Reinvang, I. Cognitive function, P3a/P3b brain potentials, and cortical thickness in ageing. Human Brain Mapping, 28 (11), 1098-1116. doi:10.1002/hbm.20335’?

No, the publication year is missing.

Do researchers have to report insignificant data?

Yes, they need to report all data, whether significant or not.

What is the difference between the information that should be put in the results and discussion section?

In the results section, the researcher should insert the inferential data analysed, which could take the form of numerical numbers, graphs and figures. In this section, they should not discuss or explain the results. Instead, they should write it under the discussion heading. However, the data reported in the results section should not be repeated here.

What is a primary scientific report?

A primary scientific report is produced when the researchers conduct the research themselves.

What is a secondary scientific report?

Secondary scientific reports such as peer-reviews, meta-analysis and systematic reviews are a type of scientific report that scientists produce when the researcher answers their proposed research question using previously published findings.

What kind of details should be added in the discussion section?

What information should be provided in the procedure section of a scientific report?

Researchers need to add enough details of their study so that it can be .....


When referring to another study the researcher should always          the original         .

credit, author. 

Meta-analyses and systematic reports are both examples of             research.

According to APA, six main headings should be included in a report, true or false? 

According to APA, the way to reference a book and journal is the same, true or false? 

After a paper is written, what is done? 

The paper is peer-reviewed.

What does peer-reviewing ensure?

Identify if the research is scientific, reliable, and valid and if it should be published in a psychological journal. 

Can researchers refer to raw data in their scientific report? 

Should researchers refer to their statistical findings to back what they are saying? 

No, data should not be referred to in the discussion. Instead, the researcher can describe what was found and the inferences that can be made from observed trends. 

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More explanations about Research Methods in Psychology

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Research Paper Guide

Research Paper Example

Nova A.

Research Paper Example - APA and MLA Format

12 min read

Published on: Nov 27, 2017

Last updated on: Jan 26, 2023

Research Paper Example

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Do you spend time staring at the screen and thinking about how to approach a monstrous  research paper ?

If yes, you are not alone.

Research papers are no less than a curse for high school and college students.

It takes time, effort, and expertise to craft a striking research paper.

Every other person craves to master the magic of producing impressive research papers.

Continue with the guide to investigate the mysterious nature of different types of research through examples.

Research Paper Example for Different Formats

An academic paper doesn't have to be boring. You can use an anecdote, a provocative question, or a quote to begin the introduction.

Learning from introductions written in professional college papers is the best strategy.

Have a look at the expertise of the writer in the following example.

Social Media and Social Media Marketing: A Literature Review

APA Research Paper Example

While writing research papers, you must pay attention to the required format.

Follow the example when the instructor mentions the  APA format .

Effects of Food Deprivation of Concentration and Preserverance

Research Paper Example APA 7th Edition

Research Paper Example MLA

Once you are done with APA format, let’s practice the art of writing quality MLA papers.

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We have provided you with a top-notch research paper example in  MLA format  here.

Research Paper Example Chicago

Chicago style  is not very common, but it is important to learn. Few institutions require this style for research papers, but it is essential to learn. The content and citations in the research paper are formatted like this example.

Chicago Research Paper Sample

Research Paper Example Harvard

To learn how a research paper is written using the  Harvard citation style , carefully examine this example. Note the structure of the cover page and other pages.

Harvard Research Paper Sample

Examples for Different Research Paper Parts

A research paper has different parts. Each part is important for the overall success of the paper. Chapters in a research paper must be written correctly, using a certain format and structure.

The following are examples of how different sections of the research paper can be written.

Example of Research Proposal

What is the first step to starting a research paper?

Submitting the research proposal!

It involves several sections that take a toll on beginners.

Here is a detailed guide to help you  write a research proposal .

Are you a beginner or do you lack experience? Don’t worry.

The following example of a research paper is the perfect place to get started.

View Research Proposal Example Here

Research Paper Example Abstract

After submitting the research proposal, prepare to write a seasoned  abstract  section.

The abstract delivers the bigger picture by revealing the purpose of the research.

A common mistake students make is writing it the same way a summary is written.

It is not merely a summary but an analysis of the whole research project. Still confused?

Read the abstract mentioned in the following research to get a better idea.

Affirmative Action: What Do We Know? - Abstract Example

Literature Review Research Paper Example

What if a novice person reads your research paper?

He will never understand the critical elements involved in the research paper.

To enlighten him, focus on the  literature review  section. This section offers an extensive analysis of the past research conducted on the paper topics.

It is relatively easier than other sections of the paper.

Take a closer look at the paper below to find out.

Methods Section of Research Paper Example

While writing research papers, excellent papers focus a great deal on the methodology.

Yes, the research sample and methodology define the fate of the papers.

Are you facing trouble going through the methodology section?

Relax and let comprehensive sample research papers clear your doubts.

View Methods Section of Research Paper Here

Research Paper Conclusion Example

The conclusion leaves the last impression on the reader.

“Who cares for the last impression? It’s always the first.”

Don’t be fooled!

The conclusion sets the tone of the whole research paper properly.

A key list of elements must be present in conclusion to make it crisp and remarkable.

The Conclusion: Your Paper's Final Impression

View the sample paper and identify the points you thought were never a part of the conclusion.

Research Paper Examples for Different Fields

Research papers can be about any subject that needs a detailed study. The following examples show how research papers are written for different subjects.

History Research Paper Sample

Many Faces of Generalisimo Fransisco Franco

Sociology Research Paper Sample

A Descriptive Statistical Analysis within the State of Virginia

Science Fair Research Paper Sample

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Psychology Research Paper Sample

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Art History Research Paper Sample

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Scientific Research Paper Example

We have discussed several elements of research papers through examples.

Research Proposal!

Introduction in Research Paper!

Read on to move towards advanced versions of information.

Scientific research paper

Let's have a look at the template and an example to elaborate on concepts.

It includes:

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Example of Methodology in Research Paper

The words methodology, procedure, and approach are the same. They indicate the approach pursued by the researcher while conducting research to accomplish the goal through research.

The methodology is the bloodline of the research paper.

A practical or assumed procedure is used to conduct the methodology.

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See the way the researcher has shared participants and limits in the methodology section of the example.

Research Paper Example for Different Levels

The process of writing a research paper is based on a set of steps. The process will seem daunting if you are unaware of the basic steps. Start writing your research paper by taking the following steps:

You will find writing a research paper much easier once you have a plan.

No matter which level you are writing at, your research paper needs to be well structured.

Research Paper Example Outline

Before you plan on writing a well-researched paper, make a rough draft.

Brainstorm again and again!

Pour all of your ideas into the basket of the outline.

What will it include?

A standard is not set but follow the  research paper outline  example below:

View Research Paper Outline Example Here

This example outlines the following elements:

Utilize this standard of outline in your research papers to polish your paper. Here is a step-by-step guide that will help you write a research paper according to this format.

Good Research Paper Examples for Students

Theoretically, good research paper examples will meet the objectives of the research.

Always remember! The first goal of the research paper is to explain ideas, goals, and theory as clearly as water.

Yes, leave no room for confusion of any sort.

Fiscal Research Center - Action Plan

Qualitative Research Paper Example

Research Paper Example Introduction

How to Write a Research Paper Example?

Research Paper Example for High School

When the professor reads such a professional research paper, he will be delighted.

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Research Paper Conclusion

“Who cares for the last impression? It's always the first.”

Don't be fooled!

A key list of elements must be present in the conclusion to make it crisp and remarkable.

Critical Research Paper

To write a research paper remarkably, include the following ingredients in it:

How to Write the Methods Section of a Research Paper

Theoretical Framework Examples

The theoretical framework is the key to establish credibility in research papers.

Read the purpose of the theoretical framework before following it in the research paper.

The researcher offers a guide through a theoretical framework.

An in-depth analysis of theoretical framework examples research paper is underlined in the sample below.

View Theoretical Framework Example Here

Now that you have explored the research paper examples, you can start working on your research project.

Hopefully, these examples will help you understand the writing process for a research paper. You can hire an essay writer online if you still require help writing your paper. You can buy well-written yet cheap research papers by contacting our expert and professional writers.

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An research paper examples on science is a prosaic composition of a small volume and free composition, expressing individual impressions and thoughts on a specific occasion or issue and obviously not claiming a definitive or exhaustive interpretation of the subject.

Some signs of science research paper:

The goal of an research paper in science is to develop such skills as independent creative thinking and writing out your own thoughts.

Writing an research paper is extremely useful, because it allows the author to learn to clearly and correctly formulate thoughts, structure information, use basic concepts, highlight causal relationships, illustrate experience with relevant examples, and substantiate his conclusions.

Examples List on Science Research Paper

Science Research Paper

scientific research report sample

View sample science research paper. Browse other  research paper examples and check the list of history research paper topics for more inspiration. If you need a history research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our custom writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Like all effective knowledge systems, science is based on induction: careful empirical observation followed by attempts to generalize. Like all knowledge systems, it is subject to falsification, to the sudden appearance of new realities, or to new forms of information that may overturn established certainties. What distinguishes modern science from earlier knowledge systems is the size of the intellectual arena within which its ideas are generated and tested.

The English word science derives from the Latin scire, “to know.” In many languages, the word science or its equivalents can be used broadly to mean “a systematic body of knowledge that guides our relations with the world.” This is the sense that is present in phrases such as “the social sciences.” There have existed many different knowledge systems of this type. All animals with brains have, and make use of, structured knowledge of the external world, so in principle we could claim that even animals depend on some form of science.

More Science Research Papers:

Used in a narrower sense, the word science refers to the distinctive body of systematic knowledge about the material world that emerged in Europe within the last five hundred years and that underpinned the technological achievements of modern societies. Many societies have had complex technologies, and many have had rich and rigorous systems of religious and philosophical thought, but what is distinctive about modern science is that its theories have been used to generate extraordinarily powerful and effective technologies. As a recent study puts it, “Modern science is not just a thought-construction among others—it entails both an intellectual and an operative mastery of nature. Whereas empirical technology is a feature of every major civilization, the systematic application of scientific insights to change our natural environment (‘to conquer Nature by obeying her’, as Francis Bacon phrased it) is a creation of Europe alone” (Cohen 1994, 4). Conceived in this sense, science is a distinctively modern way of understanding the world. So, to understand the modern world, we have to understand science.

The idea of a “scientific revolution”—a fundamental transformation in ways of thinking about the world—is central to this view of the role of science in world history. Though it is generally accepted that the roots of modern science can be traced to classical Greece and Mesopotamia (although anticipations of modern scientific thought can be found in many different societies, from China to Mesoamerica, and even in some aspects of Paleolithic thought), it is widely assumed that modern science appeared during the scientific revolution of the sixteenth and seventeenth centuries, and its appearance marked a fundamental intellectual shift. As one survey puts it, “The Scientific Revolution represents a turning point in world history. By 1700 European scientists had overthrown the science and worldviews of Aristotle and Ptolemy. Europeans in 1700—and everyone else not long afterwards—lived in a vastly different intellectual world than that experienced by their predecessors in, say, 1500” (McClellan and Dorn 1999, 203). Over the next few centuries that revolution transformed human attitudes and human relations with the material world.

But the notion of science as a revolutionary new form of knowledge raises some complex problems. Was modern science really that different from earlier systems of knowledge? Why has it given modern societies such astonishing leverage over the material world? And is it really true, as some have claimed, that modern science offers a fundamentally superior way of describing reality?

What Is Different about Modern Science?

Answering these questions is not easy. It has proved particularly difficult to show that science offers a more accurate description of the world than earlier systems of knowledge.

Some of the earliest attempts to explain the efficacy of modern science claimed that its defining feature was careful, objective observation of the material world. Whereas most earlier systems of thought relied heavily on religious revelation or on the traditional authority of earlier writers and thinkers, so these claims go, scientists tried to put aside all preconceived notions and observe the world directly and without bias. To ensure the objectivity and precision of their observations, they devised rigorous and sometimes complex experimental methods. Then, using the results of their observations, they came up with general hypotheses about the nature of reality, using the logical method of induction.

In this view, scientific theories work because they are based on meticulous observation and rigorous logic, which explains why they offer exceptionally accurate and useful descriptions of the world. Galileo Galilei (1564–1642) is often thought to have exemplified the new experimental methods in his observations of the sun and planets through the recently invented telescope and in his experiments rolling balls down sloping planes to study the effects of gravity, while the achievement of Isaac Newton (1642–1727) in formulating general laws of motion is often taken as a paradigm example of the possibilities for radical generalization on the basis of information derived from careful observation. The seventeenth-century English natural philosopher (the contemporary term; now we would say scientist) Francis Bacon (1561–1626) was probably the first to describe the method of induction systematically, but similar arguments about the nature of modern science are still widely held today. Here, for example, is a modern definition of how science works: “Scientists propose theories and assess those theories in the light of observational and experimental evidence; what distinguishes science is the careful and systematic way in which its claims are based on evidence” (Worrall 1998, 573).

There is much truth in the inductivist view of modern science. Though examples of careful, empirical observation can be found in all human societies, never before had so many scientific observations been conducted so systematically and with such care and precision, and never before had natural philosophers tried so rigorously to build from them universal theories about the nature of reality. Unfortunately, though, the method of induction cannot guarantee the truth of scientific theories. In the first place, it is now clear that our minds shape and reorganize information as they receive it; so we can never separate observation from theorization in the neat way presupposed in the simplest models of inductive logic.

But the most fundamental problem is logical. Induction leads us from particular observations about the world to general theories about the world. Yet no observations can embrace all of reality, so induction involves a leap of faith that the small sample of reality that we can observe directly is characteristic of the whole of reality. Though it makes sense to rely on theories based on a large body of empirical evidence, induction can never yield conclusions whose truth is certain. (Bertrand Russell’s famous example was the inductivist turkey, who observed carefully how, each day, her bipedal servants provided food at a particular time; unfortunately, in mid-December, just as the turkey was about to formulate the general hypothesis that food would always appear at the same time, her servants killed her and cooked her for Christmas.) As a result, conclusions based on induction are always subject to modifications, sometimes of the most fundamental kind, as new observations become available. Thus, by carefully observing the position and motion of distant galaxies, using work on variable stars by Henrietta Leavitt (1868–1921), Edwin Hubble (1889–1953) showed that the universe, far from being stable and eternal, is in fact expanding.

Early in the twentieth century, the British-Austrian philosopher Karl Popper (1902–1994) proposed what he hoped was a more reliable apology for science. He argued that science advances through a process of “falsification.” As he pointed out, even if it is impossible to prove the truth of any theory reached by induction, it is possible to prove that some theories are wrong. So Popper argued that science should be trusted not because its conclusions are true in any absolute sense, but because it consisted of theories that had been tested rigorously and had not yet been proved wrong. The best known example of a falsifi- able idea is perhaps the claim put forward by Albert Einstein (1879–1955) that gravity affected light, a claim he suggested could be tested by seeing if the light from distant stars was bent as it passed behind the sun. The claim was successfully tested in 1919 during a solar eclipse, but what interested Popper was that Einstein’s claim was risky: it could have been proved false. Popper argued that ideologies such as Marxism and disciplines such as history did not count as sciences because they did not generate hypotheses that were precise enough to be falsified. Marxism was simply too rubbery: when it was pointed out that the socialist revolution predicted by Marx had failed to materialize, Marxists simply shifted their ground and changed the anticipated date of the revolution.

Unfortunately, even Popper’s attempts to distinguish science from other forms of knowledge were shown to be inadequate as historians of science became aware of the extent to which scientists, too, could cling to outdated theories or tweak their theories to avoid falsification. Despairing of finding any decisive proof of the truth of scientific theories, some philosophers of science gave up. The historian Thomas Kuhn (1922–1996), impressed by the subjectivity and partisanship of real science, argued that the main defining feature of modern science was simply that scientists within each scientific discipline seemed to agree about the discipline’s core ideas. Sciences, he argued, were organized around paradigms, or core ideas, such as Newton’s laws of motion, or the theory of natural selection. Once firmly established these were rarely subjected to the rigorous testing procedures Popper had taken for granted; on the contrary, there was a powerful element of faith in the work of most scientists most of the time. Paradoxically, Kuhn argued that it was this faith in a core idea that explained the effectiveness of scientific research. Unlike historians, who cannot agree about the fundamental laws by which their discipline works, scientists commit to a certain body of theory and this, he argued, explains why they conduct research in a more coordinated and more effective way than historians. For example, biologists, working within the paradigm of natural selection, know that any observation appearing to threaten the fundamental principle of natural selection is important, so such problems attract many researchers, and eventually their work can lead to new insights that usually support the core paradigm.

But not always. In extreme cases, he conceded, the accumulation of new data and new ideas may lead to the overthrow of an existing paradigm. In the late nineteenth century, most physicists assumed the existence of “ether,” a universal medium within which all physical processes took place. Unfortunately, experiments on the speed of light by the U.S. researchers Albert Michelson (1852–1931) and Edward Morley (1838–1923), seemed to show that the ether did not exist—the speed of light was uniform in all directions, whereas the existence of an ether ought to have slowed light beams traveling against the ether’s flow. It was these anomalies that led Einstein to suggest that the Newtonian paradigm had to be revised. So Kuhn distinguished between normal science, the slow, sometimes plodding process by which scientists flesh out the implications of a well-established paradigm, and scientific revolutions, or periods when an established paradigm breaks down and is replaced with a new one.

Though Kuhn’s ideas may have offered a more realistic portrayal of how science actually works, they provided weak support for its truth claims and failed to account for its explanatory power, for it was easy to point to other knowledge systems, including most forms of religion, in which there existed a core body of ideas that were taken on trust but were sometimes violently overthrown. To some, it began to seem that all we could say about science was that it was better at solving the sorts of problems that need to be solved in modern societies. Instrumentalist theories of science argue that it does not really matter whether or not scientific theories are true—all that matters is whether they work. Science is best thought of not as a more or less accurate description of reality, but rather as a tool—the mental equivalent of a stone axe or a computer. Or, to adopt a more precise analogy, it is like a map of reality. As Michael Polanyi has written: “all theory may be regarded as a kind of map extended over space and time.” Similarly, Thomas Kuhn has argued that scientific theory “provides a map whose details are elucidated by mature scientific research. And since nature is too complex and varied to be explored at random, that map is as essential as observation and experiment to science’s continuing development” (Kuhn 1970, 109). Like all knowledge systems, science offers simplified and partial maps of some aspects of the real world. But it is not the same as reality.

A last-ditch attempt to preserve the idea that science can provide an accurate account of reality is the delightful no-miracles argument advanced by the philosopher Hilary Putnam (b. 1926). Putnam argued that if a theory works, then the simplest explanation of that fact is to assume that the theory provides a good description of the real world. On this argument, it is the success of modern science that justifies its claims to provide accurate descriptions of reality. As Putnam puts it, “The positive argument for realism [the doctrine that science provides an accurate description of the real world] is that it is the only philosophy that does not make the success of science a miracle” (Psillos 1999, 71).

The apparent impossibility of finding any rigorous way of defining what is distinctive about modern science suggests that science may not be as different from other systematic forms of knowledge as is often supposed. All knowledge systems, even those of animals, offer maps of reality that provide more or less accurate guides to material reality. Perhaps, as the historian Steven Shapin has argued, the scientific revolution does not mark as clear an epistemological break as was once assumed. Most seventeenth-century scientists were well aware of the continuities between their ideas and those of the medieval and ancient worlds. Indeed, Newton, like many other scientists of his epoch, continued to study alchemy even as he was laying the foundations of what many think of today as true science. Even the notion of a scientific revolution is a modern idea; the phrase was first coined in 1939, by the philosophical historian Alexandre Koyre (1892–1964).

Developments in the twentieth century have done even more to blur the distinction between modern science and other systematic forms of knowledge. Quantum physics and chaos theory have shown that reality itself is fuzzier than was once supposed, a conclusion that has forced scientists to abandon the nineteenth-century hope of attaining a mechanically perfect description of reality. As a result, the differences between the sciences and the social sciences appear much less clear-cut than they once did. This is particularly true of historical scientific disciplines, such as cosmology or biology. Insofar as they try to describe changes in the past, specialists in these fields face the same dilemmas as historians; far from basing conclusions on repeatable laboratory experiments, they try, like historians, to reconstruct a vanished past from fragments left randomly to the present.

As the borders between the sciences and other modern disciplines have blurred, the idea of science as a quite distinct form of knowledge has become harder to defend. Careful observation leading to technological innovation is a feature of most human societies, while general theories about the nature of reality are offered in most forms of religion. Inductivist and falsificationist arguments cannot prove the truth of science; at best they highlight the pragmatic fact that scientific theories work because they are based on a larger body of observational evidence than any earlier knowledge systems and are also subject to exceptionally rigorous truth tests.

That line of argument suggests that we examine modern science’s place in human life historically, seeing modern science as one of many different human knowledge systems that have evolved in the course of world history. From this perspective, it is striking how, over time, human knowledge systems have had to incorporate more and more information, and how the task of distilling that information into coherent theories has required ever more stringent testing of ideas and yielded theories that were increasingly universal and abstract in their form though increasingly elaborate in their details. Perhaps, then, the main distinguishing feature of modern science is its scale.

As Andrew Sherratt (1995) puts it: “‘Intellectual Evolution’ . . . consists principally in the emergence of modes of thinking appropriate for larger and larger human groupings . . . This transferability has been manifested in the last five hundred years in the growth of science, with its striving for culture-free criteria of acceptance . . .” Because it is the first truly global knowledge system, modern science tries to explain a far greater volume and variety of information, and it subjects that information to far more stringent truth tests than any earlier knowledge system.

This approach may help explain the two other distinctive features of modern science: its astonishing capacity to help us manipulate our surroundings and rigorous avoidance of anthropomorphic explanations. For most of human history, knowledge systems were closely linked to particular communities, and as long as they provided adequate explanations of the problems faced by those communities, their credibility was unlikely to be challenged. But their limitations could be exposed all too easily by the sudden appearance of new problems, new ideas, or new threats. This was what happened throughout the Americas, for example, after the arrival of European conquerors, whose ideas undermined existing knowledge systems as effectively as their diseases and military technologies undermined existing power structures. As the scale of human information networks widened, attempts to integrate knowledge into coherent systems required the elimination of culture-specific explanations and encouraged reliance on abstract universals that could embrace larger and more diverse bodies of information and that could appeal to more diverse audiences. As the sociologist Norbert Elias (1897–1990) wrote in an elegant account of changing concepts of time, “The double movement towards larger and larger units of social integration and longer and longer chains of social interdependencies . . . had close connections with specific cognitive changes, among them the ascent to higher levels of conceptual synthesis” (Elias 1998, 179). The change can be seen clearly in the history of religions. As religious systems embraced larger and larger areas, local gods were increasingly supplanted by universal gods claiming broader and more general powers and behaving in more law-like and predictable ways than the local gods they displaced. Eventually, the gods themselves began to be displaced by abstract, impersonal forces such as gravity that seemed to work in all societies, irrespective of local religious or cultural beliefs.

The Emergence and Evolution of Science

The knowledge systems of the animal world are individualistic; each individual has to construct its own maps of reality, with minimal guidance from other members of its species. Humans construct their knowledge systems collectively because they can swap information so much more effectively than other animals. As a result, all human knowledge systems distill the knowledge of many individuals over many generations, and this is one reason why they are so much more effective and more general in their application than those of animals.

This means that even the most ancient of human knowledge systems possessed in some degree the qualities of generality and abstraction that are often seen as distinguishing marks of modern science. Frequently, it seems, the knowledge systems of foragers relied on the hypothesis that reality was full of conscious and purposeful beings of many different kinds, whose sometimes eccentric behavior explained the unpredictability of the real world. Animism seems to have been widespread, and perhaps universal, in small-scale foraging communities, and it is not unreasonable to treat the core ideas of animism as an attempt to generalize about the nature of reality. But foraging (Paleolithic) era knowledge systems shared more than this quality with modern science. There are good a priori reasons to suppose that foraging communities had plenty of well-founded empirical knowledge about their environment, based on careful and sustained observations over long periods of time. And modern anthropological studies of foraging communities have demonstrated the remarkable range of precise knowledge that foragers may have of those aspects of their environment that are most significant to them, such as the habits and potential uses of particular species of animals and plants. Archaeological evidence has also yielded hints of more systematic attempts to generalize about reality. In Ukraine and eastern Europe engraved bones dating to as early as thirty thousand years ago have been found that appear to record astronomical observations. All in all, the knowledge systems of foraging societies possessed many of the theoretical and practical qualities we commonly associate with modern science. Nevertheless, it remains true that the science of foragers lacked the explanatory power and the universality of modern science—hardly surprising given the limited amount of information that could accumulate within small communities and the small scale of the truth markets within which such ideas were tested.

With the appearance of agricultural technologies that could support larger, denser, and more varied communities, information and ideas began to be exchanged within networks incorporating millions rather than hundreds of individuals, and a much greater diversity of experiences and ideas. By the time the first urban civilizations appeared, in Mesopotamia and Egypt late in the fourth millennium BCE, networks of commercial and intellectual exchange already extended over large and diverse regions. Mesopotamia and Egypt probably had contacts of some kind with networks that extended from the Western Mediterranean shores (and perhaps Neolithic Europe) to Sudan, northern India, and Central Asia, in what some authors have described as the first world system.

Calendrical knowledge was particularly important to coordinate the agricultural activities, markets, and public rituals of large and diverse populations. The earliest calendars distilled a single system of time reckoning from many diverse local systems, and they did so by basing time reckoning on universals such as the movements of the heavenly bodies. This may be why evidence of careful astronomical observations appears in developed Neolithic societies in Mesopotamia, China, Mesoamerica (whose calendars may have been the most accurate of all in the agrarian era), and even in more remote environments such as England (as evidenced by Stonehenge) or Easter Island. The development of mathematics represents a similar search for universally valid principles of calculation. It was stimulated in part by the building of complex irrigation systems and large monumental structures such as pyramids, as well as by the need to keep accurate records of stored goods. In Mesopotamia, a sexagesimal system of calculation was developed that allowed complex mathematical manipulations including the generation of squares and reciprocals.

In the third and second millennia BCE, Eurasian networks of commercial and information exchanges reached further than ever before. By 2000 BCE, there existed trading cities in Central Asia that had contacts with Mesopotamia, northern India, and China, linking vast areas of Eurasia into loose networks of exchange. Late in the first millennium BCE, goods and ideas began traveling regularly from the Mediterranean to China and vice versa along what came to be known as the Silk Roads. The scale of these exchange networks may help explain the universalistic claims of religions of this era, such as Zoroastrianism, Buddhism, and Christianity.

The impact of these developments on knowledge systems is easiest to see in the intellectual history of classical Greece. Here, perhaps for the first time in human history, knowledge systems acquired a new degree of theoretical generality, as philosophers tried to construct general laws to describe the real world. As the writings of the historian Herodotus suggest, the Greeks were exposed to and interested in a colossal variety of different ideas and influences, from North Africa, Egypt, Persia, India, and the pastoralist societies of the steppes. The volume and variety of ideas to which Greek societies were exposed reflected their geographical position and the role of Greek traders, explorers, and emigrants forced, partly by overpopulation, to explore and settle around the many different shores of the Mediterranean and the Black Sea. Faced with a mass of new information, Greek philosophers set about the task of eliminating the particular and local and isolating those ideas that remained true in general. Thales of Miletus (c. 625–547 BCE), often regarded as the first of the Greek natural philosophers, offered explanations of phenomena such as earthquakes and floods that are universal in their claims and entirely free of the notion that reality is controlled by conscious entities.

At its best, Greek natural philosophy tried to capture not just this or that aspect of reality, but reality’s distilled essence. This project is most apparent in Greek mathematics and in Plato’s conviction that it is possible to attain knowledge of a perfect real world beneath the imperfections of the existing world. Greek philosophers were particularly interested in the testing of new ideas, a trait that is perhaps inevitable in societies faced with a sudden influx of new forms of knowledge. The rigor with which ideas were tested is apparent in the dialogues of Socrates, in which ideas are repeatedly subjected to Socrates’ corrosive logic (in an ancient anticipation of the notion of falsification), with only the most powerful surviving. Many other societies developed sophisticated methods of mathematical calculation and astronomical observation, and some, such as Song China (960–1279), developed metallurgical, hydraulic, and financial technologies that were unsurpassed until the twentieth century. But few showed as much openness to new ideas or as much interest in the testing of new ideas and theories as the Greeks.

Other societies have responded in similar ways to the exposure to new and more varied ideas. Perhaps Mesopotamia and Egypt, both with relatively easy access to Africa, India and the Mediterranean, count as early pioneers of scientific ideas for similar reasons. And perhaps it is the extensive contacts of medieval Islam that explain the fundamental role of Islam both in exchanging ideas (such as the mathematical concept of zero) between India and the Mediterranean worlds and in preserving and developing the insights of Greek and Hellenic science. Even in the Americas, it may have been the size of Mesoamerican populations and their exposure to many different regional cultures that led to the development of sophisticated calendrical systems from perhaps as early as the second millennium BCE.

Europe in the era of the scientific revolution certainly fits this model. Medieval European societies showed a remarkable openness to new ideas and an exploratory spirit that was similar to that of classical Greece. By the late medieval ages, European contacts reached from Greenland in the west to China in the east. Then, as European seafarers established close links with Southeast Asia in the east and the Americas in the west, Europe suddenly found itself at the center of the first global network of informational exchanges. The unification of the world in the sixteenth century constituted the most revolutionary extension of commercial and intellectual exchange networks in the entire history of humanity. Ideas about navigation and astronomy, about new types of human societies and new gods, about exotic crops and animal species, began to be exchanged on an unprecedented scale. Because Europe suddenly found itself at the center of these huge and varied information networks, it was the first region of the world to face the task of integrating information on a global scale into coherent knowledge systems. In the sixteenth century, European philosophers struggled to make sense of the torrent of new information that descended upon them, much of which undermined existing certainties. Like the Greeks, European thinkers faced the challenge of sorting the ephemeral from the durable, and to do that they had to devise new methods of observing and testing information and theories. It was this project that yielded the observational and experimental techniques later regarded as the essence of scientific method.

Thinkers in the era of the scientific revolution not only developed new ways of studying the world, they also created a new vision of the universe. The new vision was based on the work of three astronomers: Nicholas Copernicus (1473–1543), Tycho Brahe (1546–1601), and Johannes Kepler (1571–1630). Copernicus was the first modern astronomer to suggest that the earth might be orbiting the sun; Brahe’s careful astronomical observations provided the empirical base for Copernicus’s theories, and Kepler’s calculations showed that the new model of the universe worked much better if it was assumed that heavenly bodies traveled in ellipses rather than circles. Galileo used the newly invented telescope to show that heavenly bodies were as scarred and blemished as the earth, an observation that raised the intriguing possibility that the heavens might be subject to the same laws as the earth. Newton clinched this powerful unifying idea by showing that both the earth and the heavens—the very small and the very large— were subject to the same basic laws of motion. And this suggested the possibility that the universe as a whole might run according to general, abstract laws rather than according to the dictates of divine beings. Galileo’s discovery of millions of new stars also suggested that the universe might be much larger than had been supposed, while Anthony van Leeuwenhoek (1632–1723), the pioneer of modern microscopy, showed that at small scales there was also more to reality than had been imagined. Taken together, the theories of the sixteenth and seventeenth centuries transformed traditional views of the universe in ways that threatened to decenter human beings and throw into question God’s role in managing the universe. It was no wonder, then, that many feared that the new science might undermine religious faith.

Since the seventeenth century, the global information exchanges that stimulated the scientific breakthroughs of the scientific revolution have accelerated and affected more and more of the world. The prestige of the new sciences was particularly high in the era of the Enlightenment (seventeenth and eighteenth centuries), and encouraged more and more investigators to study the world using the techniques and assumptions of the scientific revolution. In the eighteenth and nineteenth centuries, scientific investigations yielded powerful new theories in fields as diverse as medicine (the germ theory), chemistry (the atomic theory and the periodic table), the study of electromagnetism (the unified theory of electromagnetism), energetics (theories of thermodynamics), geology, and biology (natural selection).

Scientific research was supported by the creation of scientific societies and journals, the introduction of science courses in universities, and the creation of research laboratories by businesses. The last two developments were both pioneered in Germany. The word scientist was first used in the 1840s. Meanwhile, the spread of scientific approaches to the study of reality and the increasing scope of scientific theory began to yield significant technological innovations in health care, manufacturing, and warfare. Particularly important were innovations in transportation and communications, such as the invention of trains and planes and the introduction of postal services, the telegraph, the telephone, and eventually the Internet, because these innovations expanded the scale and quickened the pace of information exchanges.

In the twentieth century, a series of new scientific theories appeared that refined the orthodoxies of eighteenth- and nineteenth-century science. Einstein’s theory of relativity demonstrated that space and time were not absolute frames of reference, while the quantum theory showed that, at the very smallest scales, reality itself does not behave in the predictable, mechanical ways assumed by earlier theories. Big bang cosmology, which has dominated cosmological thought since the 1960s, demonstrated that the universe, far from being eternal and infinite, had a history, beginning many billions of years ago, while the theory of plate tectonics, which appeared at about the same time, provided the foundations for a unified theory of geology and a detailed history of the formation and evolution of the earth. In biology, Francis Crick (1916–2004) and James Watson (b. 1928) described the structure of DNA in 1953; their work laid the foundations for modern evolutionary theory and modern genetic technologies. Meanwhile, the scale of scientific research itself expanded as governments and corporations began to fund special research facilities, sometimes to fulfill national objectives, as was the case with the Manhattan Project, which designed the first atomic weapons.

Recent scholarship suggests that it is a mistake to see modern science as fundamentally different from all other knowledge systems. Like all effective knowledge systems, it is based on induction: on careful empirical observation followed by attempts to generalize. Like all knowledge systems, it is subject to falsification, to the sudden appearance of new realities or new forms of information that may overturn established certainties. What really distinguishes modern science from earlier knowledge systems is the size of the intellectual arena within which its ideas are generated and tested. Its explanatory power and its qualities of abstraction and universality reflect the volume and diversity of the information it tries to distil, and the rigor of the truth tests to which its claims are subjected in a global truth market.

During the past two centuries, science has spread beyond the European heartland to Russia, China, Japan, India, and the Americas. Today it is a global enterprise, and its accounts of reality shape the outlook of educated people throughout the world. Far from diminishing, the flow of new information that stimulated the original scientific revolution has kept expanding as the pace of change has accelerated and the world as a whole has become more integrated. Early in the twenty-first century, the power of science to generate new ways of manipulating the material world, for better or worse, shows no sign of diminishing. Science has given our species unprecedented control over the world; how wisely we use that control remains to be seen.



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“The scientific man does not aim at an immediate result. He does not expect that his advanced ideas will be readily taken up. His work is like that of the planter – for the future. His duty is to lay the foundation for those who are to come, and point the way.” These words from Nikola Tesla make sense in a way today’s scientists should be competent and reliable in their scientific research studies as their works will significantly help the succeeding generations of new scientists in their scientific discovery for the future.

Scientific Review Report

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If you are a person who aspires to be a scientist, one of the things you need to consider is being able to create an effective scientific review report . In this article, we will discuss beneficial steps in writing your literature review for a scientific research paper, plus several downloadable templates for you to use. Keep on reading!

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A scientific review report is a useful piece of professional research paper that contains a well-detailed analytical summary of the process, development, and/or results of a specific scientific research, a brief examination concerning the condition of a scientific research problem, or a crucial account about a scientific journal that has been published by certified researchers. It comprises several analyses, suggestions, opinions, and conclusions of the scientific research project.

Creating a clear and well-detailed literature review for scientific reports is helpful in a wide array of research studies. It is integral that you use explicit words and definite sentences while interpreting your analysis of a particular lab study. 

In this matter, we suggest that you follow the steps below while freely using one of our scientific review report templates in this article:

One of the core aspects that you need to consider in creating a scientific review report is writing the abstract of your scientific review. So, include a summary of your main thesis and the scientific studies you analyze in your review. 

In order to design an effective report for your scientific review work , it is important that you develop an effective introduction which outlines what you are going to discuss throughout the scientific review. Then, explain to your reader the reason why it is fundamental that you reviewed the literature based on your subject area.

The next step you must do to have a successful scientific review report is to create the overall content or body. Preparing the body of your report can take different types of forms and structures based on your scientific study. After that, break the content into sessions, especially if you are performing various methodologies on your scientific research work. Remember to always check every little detail of your literature review and keep it in an orderly manner. 

Lastly, you should restate your thesis and polish off your scientific review. So, draw everything together with a comprehensive discussion and ascertain that your conclusions about your field of study are comprehensible according to the scientific research studies you read and evaluated.

Some common sources of scientific research are dissertations, interviews, patents, conference papers, a study published from a journal article, a published survey, scientific laboratory notebooks, and technical reports.

The main elements that should be included in a scientific report are in the following:

Write the methods section in a past tense. Avoid a narrative style of writing, as well as creating a list of supplies or materials used for the experiment similar to a recipe. Include a well-detailed description of the experimental treatments and sample sizes for each trial that you conducted.

The types of scientific research reports are case reports, original articles, scientific reviews, technical notes, pictorial essays, commentaries, and editorials.

Therefore, you need to have the right skills and expertise in demonstrating and interpreting your literature analysis for your scientific research project in biology, chemistry , isotopic sciences, time-varying geospatial data management, psychology, geography, nanotechnology, or other subject areas that involve research.

Despite having several circumstances where you are grasping at straws while conducting and analyzing various scientific experiments, you can be confident in writing the best scientific review report. You just need to follow the aforementioned steps in this article. So, we encourage you to choose from our diverse selection of review report templates here and get a scientific report sample today! 

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Label-free photothermal optical coherence microscopy to locate desired regions of interest in multiphoton imaging of volumetric specimens

Scientific Reports volume  13 , Article number:  3625 ( 2023 ) Cite this article

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Biochip-based research is currently evolving into a three-dimensional and large-scale basis similar to the in vivo microenvironment. For the long-term live and high-resolution imaging in these specimens, nonlinear microscopy capable of label-free and multiscale imaging is becoming increasingly important. Combination with non-destructive contrast imaging will be useful for effectively locating regions of interest (ROI) in large specimens and consequently minimizing photodamage. In this study, a label-free photothermal optical coherence microscopy (OCM) serves as a new approach to locate the desired ROI within biological samples which are under investigation by multiphoton microscopy (MPM). The weak photothermal perturbation in sample by the MPM laser with reduced power was detected at the endogenous photothermal particles within the ROI using the highly sensitive phase-differentiated photothermal (PD–PT) OCM. By monitoring the temporal change of the photothermal response signal of the PD–PT OCM, the hotspot generated within the sample focused by the MPM laser was located on the ROI. Combined with automated sample movement in the x–y axis, the focal plane of MPM could be effectively navigated to the desired portion of a volumetric sample for high-resolution targeted MPM imaging. We demonstrated the feasibility of the proposed method in second harmonic generation microscopy using two phantom samples and a biological sample, a fixed insect on microscope slide, with dimensions of 4 mm wide, 4 mm long, and 1 mm thick.


Multiphoton microscopy (MPM) allows for high-resolution analyses in various biological research fields. In recent years, the use of MPM has continued to gain attraction in biomedical imaging, especially in areas of in vivo and live deep neuron imaging in small animals 1 , 2 , 3 , 4 , 5 , early detection of tumors and its characterization 6 , 7 , 8 , 9 , vascular network and organoid imaging in its microenvironment in bio-chips 10 , 11 , 12 . There have been many approaches to increase the imaging depth, larger field of view (FOV), and shorten the volumetric scanning time by adopting appropriate fluorophores 13 , adaptive optics 14 , multi-beam and multi-focal mechanisms 15 , 16 , in addition to other similar modifications in the optical setup which has given variations of MPM imaging systems adoptable as per imaging needs and application scenarios 5 , 16 , 17 , 18 , 19 , 20 , 21 .

Photodamage and photobleaching are critical factors to consider in multiphoton imaging. Prolonged illumination accompanied with high laser power necessary for label-free multiphoton imaging, there is photomechanism of tissue damage that results in enhanced fluorescence causing cell death, which is widely referred to as photodamage 22 , 23 , 24 . To avoid photodamage occurrence in living cells, care must be taken to ensure the imaging parameters are well below the photodamage threshold like peak intensity, repetition rate, and prolonged exposure (dwell) times 24 . Most of these parameters can be controlled by the source and detection systems. Multiple research groups studied and proposed various approaches to suppress the effects of photobleaching and photodamage. The simplest and most widely adopted method is to reduce the total illumination power of a light source 4 , 25 , 26 . However, this, in turn, reduces the overall sensitivity of the system due to the reduced signal-to-noise ratio. Alternative approaches reported to mitigate this effect involve a controlled light-exposure method that spatially controls the light exposed onto the sample, the use of a passive pulse splitter that redistributes the illumination laser pulse into sub-pulses with equal energies, and a fast optical scanning mechanism that reduces photodamage by controlling the illumination and collection of emission from specimens 27 , 28 , 29 , 30 . In addition, using the light-sheet illumination method, only the focal plane of the detection objective is effectively illuminated 31 . Although these methods are useful for reducing photodamage and photobleaching effects, long-term high-power illumination is required when searching for region of interest (ROI) in large samples with small FOVs and long volumetric scanning times, thus resulting in photodamage and photobleaching.

In recent decades, multiple research groups reported the advantages of incorporating both MPM and optical coherence tomography (OCT) as a single multimodal imaging platform, where their advantages compensate for each other modalities limitations 32 , 33 , 34 , 35 . In particular, MPM and OCT imaging techniques have different characteristics in terms of resolution, FOV, imaging depth, and imaging speed. For example, MPM uses a nonlinear mechanism that requires a tightly focused volume of the laser beam in the sample with a high numerical aperture (NA) objective that produces high lateral and axial resolutions but a small FOV. Moreover, OCT is a linear imaging system that provides increased flexibility in choosing the resolution and FOV that suits your application. In terms of imaging depth, MPM typically provides imaging depths of several hundred micrometers in biological samples where high scattering and aberrations exist, whereas OCT utilizes the higher penetrating power of near-infrared light sources to achieve imaging depths of 1–2 mm in biological samples. Generating volumetric images of samples using an MPM system requires the use of a z-axis stage with two-dimensional scanning in the transverse direction to move the sample axially. In contrast, OCT can acquire volumetric images using only raster x–y scanning without moving the sample axially. The combined MPM-OCT system can serve as an effective multiscale-imaging platform for biological investigations by the complementary use of the superiority of each system in imaging.

Since the introduction of OCT, various research groups investigated and utilized the different functional properties of the laser source and detection mechanism to establish new methods with respect to imaging requirements. Several examples include Doppler OCT for flow measurements in samples 36 , polarization-sensitive OCT to study birefringence properties in the sample 37 , 38 , optical coherence elastography to measure the stress and tensile strength of structures within samples 37 , 38 , spectroscopic OCT to investigate the wavelength-dependent absorption and scattering of light of structures in samples 37 , 38 , and photothermal OCT (PT-OCT) to analyze thermal fluctuations within samples 39 , 40 , 41 . Among these technologies, PT-OCT has attracted significant research attention for the evaluation of the thermal fluctuations of samples and the resulting changes in their physical and optical properties. In PT-OCT systems, nanoparticles, photothermal responsive exogenous contrast agents, and absorbers have been utilized to generate and measure the photothermal effects within the sample 42 , 43 , 44 , 45 . To date, studies using endogenous contrast agents instead of external contrast agents have been limited due to the lack of usefulness and the difficulty in measuring weak photothermal response signals. Milner et al. proposed a differential phase measurement for the depth-resolved detection of photothermal response in tissue, without the use of external contrast or absorber material 46 , 47 . Although the proposed photothermal response detection technique can be applied to samples without the need for contrast or absorbers, it requires multiple dedicated detection channels and a rapid scanning optical delay to match the two different interferometric paths in the sample scanning introduced by the birefringent material. Furthermore, the required illumination laser power is typically 80–100 mW for biological samples, to measure the photothermal response with reasonable signal-to-noise ratio 47 . Recent advances in PT-OCT techniques have enabled effective photothermal response measurements using endogenous contrast using a single detection channel and with reduced laser illumination power of 4–10 mW 39 , 48 , 49 , 50 .

This study serves as a precursor to the direction of a new ROI location mechanism along three axes within the sample volume to reduce the overall photodamage and photobleaching effects and improve user convenience in multiphoton imaging of volumetric samples. The desired ROIs were selected within the complete observable depth and lateral range of Optical coherence Microscopy (OCM). The changes in the optical properties of the sample due to the photothermal effects illuminated by the MPM laser source were measured at the endogenous photothermal particles within the ROI using the highly sensitive phase-differentiated photothermal (PD–PT) OCM. By monitoring the temporal change of the photothermal response signal of the PD–PT OCM, the focus position of the excitation laser (hotspot) was placed on the ROI. Combined with automated sample movement in the x–y axis, the focal plane of MPM could be effectively navigated to the desired portion of a volumetric sample for high-resolution targeted MPM imaging. The ROI navigation was attained using a low-powered (lower than conventional power by a factor of less than 10) MPM laser source. The PD–PT OCM guiding method can be implemented for any nonlinear imaging technique that requires high-power laser illumination, which leads to photobleaching and photodamage in samples, e.g., two-photon excited fluorescence and coherent anti-Stokes Raman scattering microscopy.

When a high-energy laser beam irradiates a sample, the internal temperature and optical properties of the internal sample structure changes 51 . In the OCT imaging technique, the detected interference signal in the Fourier domain OCT can be expressed as Eq. (1) in complex form:

where z is the path length difference between the reference mirror and interface within the sample. The \({I}_{R}\) and \({I}_{S}\) are the backreflected light intensities from the reference mirror and the sample interface, respectively. The \(\Phi \left(z,t\right)\) is the phase term of the interference signal, which can be represented as an integral function of the depth-dependent refractive index in space at a certain point in time, as expressed by Eq. ( 2 ):

where ω is the central angular frequency of the light source, and c is the speed of light in vacuum. The sample is assumed to be located on the positive side in the space domain, and \(n\left(z,t\right)\) represents the depth and time-dependent refractive index within the sample. When a temperature change occurs within the sample, the thermal response optically observed in the sample is a phase change due to the thermoelastic and thermorefractive effects. Thus, Eq. ( 2 ) can be extended considering the thermal responses, as expressed by Eq. ( 3 ) (Ref. 47 ).

where dn / dT represents the change in the refractive index with temperature, \(\Delta T\left(z,t\right)\) represents the temperature change in the sample tissues, and \(\beta\) is the thermal expansion coefficient 47 . To measure the phase changes in the sample due to the thermal response when illuminated by the MPM laser source, complex interference signals were obtained as digitized values with PD–PT OCM. The obtained phase term is the accumulated phase along the depth. Therefore, it first requires differentiation to resolve the accumulated phase 46 , 47 , 49 , as expressed by Eq. ( 4 ).

where \(m\) is the depth index of A-scans, and \(n\) denotes the designated sequential incremental indexes from the first to the last A-scan signal with time interval \(\Delta t\) . The total number N of A-scans was obtained for the same lateral position on the sample to increase the thermal response sensitivity. It takes a time period of T which equals \(\left(N-1\right)\) times the interval \(\Delta t\) . The N of A-scans is referred to as a frame. Successive A-scans are subtracted to measure the phase change over time, as expressed by Eq. ( 5 ).

where \(\varphi (m,n)\) is the random phase noise generated in the system within the time interval \(\Delta t\) for the spatial inde× m and temporal index n . In this study, 801 A-scans were obtained for the time period to achieve 800 differentiated A-scans. The differentiated phases beyond the range of −π and π were wrapped with −2π to avoid possible errors near the bounds –π and π. A specific depth \(d\) of interest was selected among the depths of the A-scans, and 800 differential phase values at the depth were achieved. d corresponds to the product of \({m}_{s}\) and \(\Delta z\) , where \({m}_{s}\) is the selected depth index and \(\Delta z\) is the depth interval. We averaged those differential phase values into an effective phase difference that occurred within the time period to suppress random phase errors, thus improving the thermal response detection sensitivity of the system. Phase noise mainly results from ambient thermal changes and mechanical vibrations in the system. The averaged differential phase \({\Delta \Phi }_{avg}(d)\) was then calculated using Eq. ( 6 ).

where \(\Delta ={\Delta }_{t}{\Delta }_{z}\) and \(\Delta \Phi ({m}_{s},n)\) denote the differential phase values at a specific depth d in the \({n}\) th differentiated A-scan signal, respectively. This process was sequentially repeated for the desired number of times \(Fn\) (frame number). In here, the term frame is not to be confused with the conventional denotation for B-scan, which is usually used in OCT imaging. The \(Fn\) is the accumulated 801 A-scans from a single point. The term "frame" is used here to have the readers contemplate and correlate the sense of time it takes to have a PD–PT-OCM signal when being able to relate to a B-scan to understand the high-speed measurement capability of the proposed method. To perturb the photothermal response in the sample and generate the resultant phase change with the MPM laser source, a mechanical shutter was incorporated in the MPM illumination beam path, and was then controlled remotely to open and close at specified times within the duration of \(Fn\) , as per-requisite. Throughout this study, the maximum value used for \(Fn\) was 50 unless mentioned otherwise. The measurement of 50 frames is referred to as the reading of the photothermal response to the perturbation and corresponds to an illumination cycle. The shutter was set to open and close near the center of each illumination cycle. This was to ensure that there was sufficient time for the sample to cool down to its natural temperature. The implementation of the shutter action for a single reading of the photothermal response and typical phase change over time is shown in Fig.  1 .

figure 1

Relationship between frame number, shutter action, and photothermal response during an illumination cycle.

Optical endogenous absorption agents such as lipids, water, melanin, and hemoglobin etc. are widely present in biological tissues 52 . After selecting an ROI within the volumetric OCM image, an absorption agent within the ROI that exhibited an appropriate photothermal response were selected as an observation point for the photothermal response. The closer the focal position of the excitation light of the MPM to the observation point, the greater photothermal response. We measured multiple photothermal responses while varying the distance between the observation point and focal position of the illumination beam, to determine the point at which cthe photothermal response was maximized. It should be noted that the focal plane of the MPM excitation light was placed on the ROI, and the MPM image could be obtained immediately without wandering around to find the ROI. A schematic representation of the aforementioned overall workflow is shown in Fig.  2 . Combined with automated sample movement in the x–y axis, the depth localization search of the ROI has been extended to be explored in three dimensions.

figure 2

Workflow for photothermal response detection with the label-free PD–PT OCM. Step-by-step figurative description of the process involved in calculating the photothermal signal using the PD–PT OCM.

Two different phantom samples were fabricated and used in this experimental study to demonstrate and characterize the proposed PD–PT OCM-guided MPM. The first was a simple phantom sample used to evaluate the concept and analyze the detected photothermal signal. An additional phantom sample with a multilayered structure was used to validate the robustness of the proposed system applicability to a complex sample such as the blood vessel network of tissue structures. Finally, we applied it to an insect ( Ixodes dammini ) sample with dimensions of 4 × 4 × 1 mm, embedded in a microscope slide, to evaluate the practical applicability of the system for biological samples. Detailed information on the samples used is provided in the “ Materials and methods ” section.

Proof of concept using a simple phantom sample

A simple phantom sample (ultrasound gel) was used to characterize and analyze the photothermal response using the proposed method. The experimental protocol was similar for all experiments conducted in this study (refer to the “ Materials and methods ” section for the Simple and complex network phantom sample fabrication and experimental protocol ). The photothermal response was measured as shown in Fig.  3 . Backscattering particles, such as air bubbles or dust, located in the middle region of the gel medium were selected in the cross-sectional OCM image as an observation point for the photothermal response. The focal position of the MPM objective was moved up along the optical axis of the OCM, from under the coverslip and across the ultrasonic gel area of the phantom sample, as shown in Fig.  3 a. The photothermal response was measured for each 10 µm movement of the MPM objective up to 500 µm of the total travel distance corresponding to 50 readings. The total time required for 51 readings was ~ 73 s. The time spent moving manual translations was not included. As shown in Fig.  3 b, the photothermal response signal at intervals of 100 µm from the total readings is indicated by the dotted rectangular box area. It can be seen that the maximum photothermal response detected at each reading exhibited a steady increase and decrease as the MPM objective focus position approached and moved away from the mid-region of the sample, respectively. As expected, the photothermal response was maximized when it passed through the middle region of the gel. Figure  3 c,d are the zoomed-in graphs shown in Fig.  3 b, which indicates the photothermal response signals observed when the focal point of the MPM objective was near the middle of the gel region and the detailed characteristics of the photothermal response, respectively. As can be seen throughout Fig.  3 b-d, the detected photothermal response at all readings exhibited two peaks representing the phase difference change corresponding to the maximum temperature perturbation. The temperature in the sample changed most rapidly right after when the shutter was open and closed. When the shutter was open, the MPM laser beam was focused on the sample, thus inducing photothermal effects due to changes in the internal temperature of the sample, which was considered as the first temperature perturbation (temperature rise) region, as shown in Fig.  3 c. When the temperature reached its maximum and stabilized, the change in phase difference returned to a value close to that at the reference temperature in the absence of illumination. When the shutter closed, the accumulated thermal energy in the sample began to decrease. In other words, when the cause of the increase in temperature (MPM laser beam) is no longer exposed to the sample the overall sample temperature to decrease and settles to its initial natural temperature. This can be seen as the second temperature perturbation region (temperature decrease), as shown in Fig.  3 d.

figure 3

PD–PT OCM signal analysis using a simple phantom sample. ( a ) is the figurative description of MPM focus position movement over varied depth. ( b ) is the complete photothermal response observed in the simple phantom sample within a depth range of 500 µm. ( c ) is the enlarged graph of the photothermal response observed in the middle region of the sample. ( d ) is the peak photothermal response observable within the sample. ( e ) is the maximum mean phase values plotted against the depth range of 500 µm in the sample. ( f ) is the graph plotted from the absolute difference between successive values in ( b ). Figure ( a ) is not drawn to scale.

To further analyze the amount of the photothermal response according to the change of the focal position of the objective lens, the maximum values in the photothermal signals are plotted with respect to different focal positions of the MPM objective in depth as shown in Fig.  3 e. This allows for the visualization of the overall thermal response of the sample. The red dashed line in Fig.  3 e indicates the selected observation point of the OCM for the photothermal response, given that the MPM objective was translated throughout the sample depth. The three intensity peaks (black) are the detected SHG signals corresponding to the first surface of the coverslip, second surface of the coverslip, and first surface of the glass slide, respectively. The observation point for the photothermal response coincides with the middle of the gel area between the two SHG signals representing the second surface of the coverslip and the first surface of the glass slide. By applying fifth-order polynomial fitting (to compensate for induced random phase noise) to the photothermal signal curve, a smooth transitional trend of the curve was obtained. Additionally, a plot representing the absolute difference between successive values after polynomial fitting is shown in Fig.  3 f. This allows for the clear visualization of the instance at which the focus of the MPM objective coincided with the fixed observation point.

Validation of robustness of ROI selection for nonlinear imaging using PD–PT OCM

To evaluate the ROI selection for nonlinear imaging using PD–PT OCM, large-area multilayered composite phantom samples that mimic the tissue structure of blood vessels or neuronal networks were used in the experiment. The sample contained a multilayered lens cleaning tissue with a thickness of approximately ~ 300 µm that was immersed in distilled water (refer to “ Materials and methods ” section for further details). It is utilized to validate the effectiveness of the ROI selection method using the real-time cross-sectional and en face imaging capabilities of OCM, which has a wider FOV than the limited FOV of MPM. This is shown in Fig. 4 a-c,e,f. Figure  4 a presents the volumetric three-dimensional (3D) OCM image of the sample with an FOV of 3.0 × 3.5 × 0.3 mm along the horizontal, vertical, and depth axes. The 3D volume image is highlighted by two rectangular ROI regions: the initial ROI (orange) and the moved ROI (blue) at a desired locations for MPM imaging. Figure  4 b,e present the en face OCM images with an FOV of 1.0 × 1.5 mm obtained at the ROI regions. Figure  4 c,f present enlarged images of the ROIs shown within the rectangular blue and yellow box areas in Fig. 4 b,e, respectively, which were used for comparison with SHG images. The first observation point within the initial ROI was selected at a depth of 100 μm, where it was close to the coverslip. The focal plane of the MPM objective was positioned using the ROI selection mechanism. Figure  4 e-g were obtained after moving the sample to 1000 μm in the lateral directions of X and Y, respectively, and 100 μm in the depth direction towards the glass slide (Z-axis); to simulate the navigation of different ROIs (refer to “ Materials and methods ” section for further details). Before sample movement, the selected region of the tissue structure for MPM imaging was close to the coverslip; and after sample movement, the region of tissue structures to be imaged was relocated closer to the first surface of the glass slide. Figure  4 d,g present the MPM SHG images obtained before and after the sample-stage movement. The SHG images were stacked images with a depth range of 70 µm within the represented orange and blue rectangular boxes in the OCM volumetric image, and this is done to better match and correlate the MPM en face images with the OCM en face images. The hollow crosses in the red, orange, and blue regions indicated correlates to matched positions in the respective OCM and MPM en face images.

figure 4

Evaluating the robustness of the PD–PT OCM-guided MPM with complex network phantom sample. ( a ) is the 3D volumetric OCM image of the sample. ( b , c , e , f ) are OCM en face images of the complex network phantom sample. ( c , f ) are the enlarged regions indicated by dashed squares in ( b ) and ( e ), respectively. ( d , g ) are the stacked SHG en face images of the same location scanned in ( c ) and ( f ). Additionally, in ( a ), the rectangular boxes in orange and blue indicate the stacking depth range for SHG images at the initial and moved ROIs at the desired locations, respectively.

Employing the three-axes positioning of MPM focus point in a biological large-volume sample with guidance of PD–PT OCM

To validate the usefulness of PD–PT OCM guidance to ROIs in a biological large-volume sample for MPM imaging, a biological specimen (female Ixodes dammini ) embedded in a microscope slide was used. Prior to photothermal imaging, the specimen was imaged with the maximum FOV of the OCM and post-processed using a volume-rendering software. A volume rendered OCM 3D image is shown in Fig.  5 a, and an en face image of the volume image at a depth of 65 µm is shown in Fig.  5 b. Using the real-time cross-sectional and en face OCM images, multiple different sections of the biological specimen were targeted using the ROI selection mechanism based on the photothermal response measurement with the PD–PT OCM. A total area 4 × 4 mm (Vertical × horizontal) is used for ROI selection. In particular, four regions were selected and targeted, which were located at the uppermost surface of the Scutum/shield, top edge of the left palp, tip of the hypostome, and top edge of the right palp. Among these parts, the hypostome, left and right edge of palps were chosen as these parts of the ticks are more useful in studies of tick feeding habitats relating to Lyme disease 53 , and scutum covers the superior portion of the dorsal surface. Almost all parts of the specimen’s body offered a good observable PD–PT-OCM signal, and it is noteworthy that the photothermal signal response reduces with higher thickness. This may be due to the composition and optical and thermal properties of the specimen. The SHG images of the targeted regions are shown in Fig. 5 c-f, respectively. The total readings of the observed photothermal responses in the hypostome region according to the depth-varying MPM objective focal positions are shown in the graph plotted in Fig.  5 g. The step interval between two depth positions used here is 20 µm. A total of 11 readings were used for covering the entire hypostome region. As can be seen from the graph of a representative photothermal response, as shown in Fig.  5 h, the detected maximum phase-differentiated peaks were not inversely proportional. This can be attributed to the thermoelastic expansion of the biological structures of the specimen.

figure 5

Verification of applicability of the proposed PD–PT OCM-guided MPM for a biological sample. ( a , b ) are the respective OCM volume-rendered image and en face image of the insect sample. Figures ( c to f ) are the MPM en face images of different regions in the sample obtained using the PD–PT OCM guidance. ( g ) is the representative photothermal response observed by varying depth position of MPM objective in the hypostome region of the sample, and ( h ) is the representative graph of one PD–PT OCM photothermal response signal.

The use of MPM has progressively gained in popularity in diverse biomedical imaging scenarios. One such traction where MPM has grown in applicability is biochip-based research investigations. Biochip-based research is currently evolving from two-dimensional (2D) to 3D platforms, similar to the in vivo microenvironment. In particular, it is necessary to establish large-scale biological complexity to create artificial organs for future drug screening or eventual organ replacements. To achieve this, research is currently being conducted on 3D printing and organoid-based self-organization methods 54 , 55 . For the long-term live imaging and multiscale analysis of the large-scale specimens, nonlinear microscopy capable of deep tissue imaging and label-free imaging is becoming increasingly important 56 , 57 . When conducting nonlinear microscopy, care must be taken to avoid photodamage. The proposed method uses endogenous particles within the sample for ROI location in 3D in label-free multiphoton imaging. The proposed method can minimize photodamage by accurately identifying an ROI, where high-resolution 3D molecular imaging is required ( supplementary information ). This concept is similar to that of phase-contrast microscopy, which is commonly used in fluorescence microscopes to minimize photobleaching, but these conventional guiding mechanism offers only a 2D topological image of sample. OCT can be used as a modality for guided imaging of large-scale, non-transparent, and thick biological tissues in 3D, just as phase-contrast microscopy which is used as a guide imaging tool for transparent specimens.

In the current study, we obtained the photothermal response curve while manually changing the focal point of the MPM objective which is moved towards the observation point (OCM objective). The photothermal response curve has the form of a unimodal function with one maximum value in the measurement area. In future, by incorporating a well-established efficient search method such as the golden-section search 58 , Fibonacci search 59 , or curve-fitting search 60 (with an automated axial focus motor incorporated with objective lens) instead of a linear search of 50 attempts at intervals of 10 µm; the desired point can be found with the same accuracy in less than 10 attempts. In addition, the search time can be significantly reduced when the high-speed search algorithm is combined with a high-speed motorized device for axial scanning of the focal plane of the MPM objective. In this study, ROI locating in a relatively large biological sample with dimensions of 4 × 4 mm (Vertical × horizontal), a total of 11 readings with 20 µm step interval in depth direction was used. The step interval and total readings can be adaptively chosen depending on the sample composition and thickness.

The probability of error occurrence for ROI location (in depth) for the MPM objective guidance depends on the axial resolution of the OCM system, and the phase sensitivity. The axial resolution of the used OCM system is ~ 5 µm. Hence, the accuracy of MPM objective guidance is same with axial resolution of the OCM. The proposed multimodal imaging system is configured in a way that the OCM and MPM is co-axially aligned and in opposite sides of the sample. The demonstrated method measures the absolute position of the hotspot of the MPM objective within the sample, hence even if a sample with complex structures and sample interfaces is used the performance of the PD–PT-OCM detection is not subject to any erroneous measurements. The FOV of the PD–PT-OCM system can be adaptively changed as per imaging requirements by changing the objective lenses in the sample and reference arm setup. However, the FOV and the lateral resolution are inversely related. In general, wider FOV objectives will have lower lateral resolution and vice versa. The depth-resolved imaging capability of OCM reduces in thick samples. This is due to the limitations of light penetration in thick tissues. Also, in thick samples with densely packed complex structures, the detectable multiphoton generated signals reduce in deeper tissues. Thus the heat generated by the hotspot of MPM will also degrade in deeper tissues. The usability of proposed methods for ROI selection using OCM is not just limited with the phantom sample thickness reported here. The ROI selection within the sample is possible along the entire optical depth range of OCM and is only dependent on sample composition and the optics used for the OCM system. The photothermal signal generated within the sample can be effectively detected along the entire observable depth range of the PD–PT-OCM. The photothermal signal dissipation and degradation of detection efficiency will depend on the sample thickness, composition, and its optical and thermal properties 51 .

Conventionally, photothermal OCT imaging is used to image blood vessels using the photothermal effect of blood cells, which are endogenous substances, or to measure the location and hot spots of exogenous photoreactive particles such as polymers and gold nanorods in living tissues. The highly sensitive PD–PT OCM can also be used if endogenous or exogenous factors are present within the observable range of the OCM image. Using the proposed method, we can carefully target the excitation illumination for photothermal therapy with exogenous substances and locally restrict it to the desired location, thus avoiding unnecessary heating of normal tissues.

In summary, this paper serves as a preliminary report to present the potential usability of employing PD–PT OCM as a guiding tool for nonlinear microscopy to effectively and accurately navigate ROIs in a large-volume samples. The ROIs were selected by utilizing the large FOV and observable depth range in en face and cross-sectional OCM images. To achieve targeted MPM imaging within the ROIs in 3D, hotspots occurring within the sample focused with the MPM laser at a weak power were detected using the highly sensitive PD–PT OCM. We then focused the MPM laser on an observation point selected from the endogenous absorption agents in the ROI. The PD–PT OCM allows the focal position of the MPM objective to be placed on the ROI within the observable depth range of the OCM cross-sectional image. Effectively finding and targeting ROIs requiring high-resolution MPM imaging can reduce the overall exposure time of high-power lasers, especially for large samples, significantly mitigating photobleaching, and photodamage. The high-sensitivity PD–PT OCM system has potential applications for photothermal reaction analysis because it can detect in real-time even temperature changes due to weak photothermal perturbation occurring within three-dimensional biological samples. Furthermore, it is to be noted that the utility of the proposed PD–PT OCM-based guiding method can be implemented for any nonlinear imaging technique that requires high-power laser illumination (which may lead to photobleaching and photodamage in samples) such as two-photon excited fluorescence and coherent anti-Stokes Raman scattering microscopy. This reduces the overall acquisition time required for the ROI location in nonlinear imaging systems.

Materials and methods

Multimodal imaging system design and specifications.

The overall configuration of the multimodal imaging system is illustrated in Fig.  6 . The multimodal imaging platform had two independent light sources. The OCM system was powered by a super-luminescent broadband light source (SLD-37-HP3, Superlum Diodes Ltd., Carrigtwohill, Ireland) with a maximum power output of 18 mW centered at 840 nm. The output from the source was directly connected to the entrance port of an optical circulator (850-H7-L-15-FA, OF-LINK Communications Co., Ltd. Shenzhen China). The output port of the circulator was connected to a doublet collimator (F240APC-850, Thorlabs, Inc., NJ, USA) with a beam size 2.4 mm. The collimated beam was raster-scanned along the horizontal and vertical axes using a dual-axis galvanometer scanning mirror (GVSM002-JP, Thorlabs, Inc., NJ, USA). Subsequently, the scanning beam was directed towards a combination of scan and tube lenses with focal lengths of 54 mm (LSM04-BB, Thorlabs, Inc., NJ, USA) and 200 mm (TTL200-B, Thorlabs, Inc., NJ, USA), respectively. With this configuration, the optical beam size (from the collimator) was magnified by a factor of 3.7. The enlarged beam was then split by a rectangular beam splitter (BSW11, Thorlabs, Inc., NJ, USA) at a 50:50 ratio. The split beams were directed to the reference arm and the sample arm setup. The beam towards the reference path passed through a combination of optical systems such as a continuous variable attenuator (NDC-50C-4 M, Thorlabs, Inc., NJ, USA) and a 4 × objective lens (UPlanFL N 4.0x, Olympus, Tokyo, Japan) with a focal length of 45 mm, followed by a high-reflective broadband mirror (PF10-03-P01P, Thorlabs, Inc., NJ, USA). Similarly, in the sample path, the incident split beam was directed to an objective lens that matched the specifications of the objective lens used in the reference path. The back-reflected laser beam from the sample surface interfered with the back-reflected reference beam in the beam splitter. The backscattered interference signal was directed via a circulator to a spectrometer containing a line-scan camera (Sprint spl4096-140k, Basler AG, Ahrensburg, Germany) with 4096 pixels. In the current configuration, the spectrum covered only the middle part of the camera sensor, and only 2048 pixels in the line scan camera were used. The detected signals were processed on a computer to display the cross-sectional and en face OCM images in real-time. The built OCM system had axial and lateral resolutions of ~ 5 µm, and a maximum FOV of 4 × 4 × 1 mm along the horizontal, vertical, and depth axes, respectively. A set of 250 lateral positions (A-line) was scanned successively to construct one 2D cross-sectional OCM image, and 250 horizontal positions were scanned (2D images) to obtain one volume image set. A specific depth was selected from the volume image set (as required) to obtain an en face image in real-time. The OCM system had an average speed of 90 frames per second.

figure 6

Schematic of the multimodal imaging system. Optical design schematic with the optical components used along with the MPM and OCM sources, detection components, and two-axes micrometer translational sample stage. Figure not drawn to scale.

The MPM system had a high-power femtosecond fiber laser source (ELMO HP, Menlo Systems, Inc., NJ, USA) with a maximum output power of 180 mW, and a pulse width of < 70 fs (typically 45 fs), and a repetition rate of 100 MHz. The laser source was centered at 1560 nm, with a spectral bandwidth of 30 nm. The laser beam was collimated using an optical triplet collimator (TC18APC-1550, Thorlabs, Inc., NJ, USA) with a beam size of approximately ~ 3 mm. The laser power was controlled as required using a combination of optical attenuators. The propagating beam then passed through an electronically controlled mechanical shutter (SHB025, Thorlabs, Inc., NJ, USA). The computer controlled the mechanical shutter to obtain optical illumination in the sample region during a specific operation time, as required. The subsequent laser beam after the shutter was raster-scanned along the horizontal and vertical axes using a dual-axis galvanometer scanning mirror (GVSM002-JP, Thorlabs, Inc., NJ, USA). The laser beam was then passed through a combination of scan lens (SL50-2P2, Thorlabs, Inc., NJ, USA) and tube lenses (TTL200MP, Thorlabs, Inc., NJ, USA) with focal lengths of 50 mm and 200 mm, respectively. This optical combination magnified the laser beam by a factor of 4. The collimated and magnified laser beam propagated through a dichroic mirror (FF801-Di02-25 × 36, IDEX Health & Science, LLC, New York, USA). The dichroic mirror was placed at an inclination angle of 45° with respect to the incident laser beam. The redirected MPM laser beam was then focused on the sample using a 30 × aspheric objective lens (5723-C-H 30x, Newport Corporation, CA, USA) with a focal length of 6.2 mm. The focused MPM laser beam on the sample generated the SHG signals from molecules of samples, which were backscattered and collected by the objective lens. The backscattered SHG signals were then transmitted through the dichroic mirror, which was directed to a tube lens (TTL200MP, Thorlabs, Inc., NJ, USA) with a focal length of 200 mm, bandpass filter (FF01-794/32-25, IDEX Health & Science, LLC, New York, USA), and achromatic doublet lens (AC254-050-B, Thorlabs, Inc., NJ, USA) with a focal length of 50 mm. This beam was transmitted to a photomultiplier tube (H7422-50, Hamamatsu Photonics, Shizuoka, Japan). The electrically converted signals were pre-amplified and then transmitted to a computer for further processing, to obtain the sample's en face MPM SHG images. The MPM system had a resolution of approximately ~ 1 µm. With the stated optical configuration, a maximum FOV of 300 × 300 µm was achieved.

The ROI selection from the OCM image and MPM targeting on the ROI

The OCM and MPM imaging systems were mounted onto a microscope (OLYMPUS-IX73, Olympus, Tokyo, Japan) from the top and bottom directions, respectively, and the OCM and MPM laser beams were coaxially aligned. The x- and y-coordinates of the ROI for the high-resolution MPM were selected from the real-time en face OCM image. We selected the depth coordinate of the ROI from the correlated cross-sectional OCM image, which was the depth position for observing the photothermal response. The lateral targeting of the selected ROI for the MPM was implemented by the translational movement of a micro-positioning (X and Y) stage (MCL-MOTNZ, Mad City Labs Inc., WI, USA). The MPM excitation light was then focused on the depth position of the ROI by observing the changes in the photothermal response while changing the focal position of the MPM with respect to the in-built axial motion of the microscope body. The sample translational movement was automated using the LabVIEW program, whereas the axial movement was controlled manually.

Simple and complex network phantom sample fabrication and experimental protocol

To evaluate and analyze the methodology and robustness of the proposed multimodal PD–PT OCM-guided MPM imaging system, two phantom samples were fabricated. One of the samples was prepared by placing a commercially available ultrasonic gel on a 1 mm glass slide, and a 170 ± 5 µm thick coverslip was stacked on the ultrasonic gel, as shown in Fig.  7 a. Here ultrasonic gel is used as its thermal properties are more stable and it thermal conductivity is close to biological tissues 61 . The overall thickness of the fabricated sample was ~ 1500 µm, and the thickness of the introduced ultrasound gel was ~ 330 µm. Similarly, we developed a multilayered complex network phantom sample to mimic complex network structures in biological tissues, as shown in Fig.  7 b. Multilayered lens tissues with an overall thickness of ~ 300 µm and distilled water were used instead of the ultrasound gel, and the total sample thickness was ~ 1470 µm. A rapid set epoxy was used to seal the area surrounding the coverslip in both samples to prevent the evaporation of water. The sample was mounted with a coverslip oriented towards the MPM objective, while the glass slide was oriented towards the OCM, as shown in Fig. 7 a,b. To demonstrate the usefulness of PD–PT OCM guidance to ROIs in a biological large-volume sample for MPM imaging, a biological specimen ( Ixodes dammini (Deer Tick) Female, w.m. Microscope Slide, Carolina Biological Supply, NC, USA) embedded in a microscope slide was used which was 3 mm in length and width (excluding some parts of legs). To obtain the PD–PT OCM photothermal responses of the phantom samples and the biological specimen, the initial MPM objective focus position was placed 30 µm below the coverslip, and the MPM objective lens was moved axially towards the sample in increments of 10 µm step interval, as shown in Fig.  7 c. Upon each movement of the MPM objective, the mechanical shutter was set to open for the desired time period (350 ms, unless mentioned otherwise) for each PD–PT OCM signal acquisition. The observation points of the OCM, where the photothermal responses were measured, were selected from the points within the ROI that provided the appropriate photothermal signal. Principally, the step interval specified here is not an absolute value that must be strictly adhered to. For instance, a 20 µm step interval was employed to generate the photothermal response of the biological specimen (female Ixodes dammini ) depicted in Fig.  5 . The step interval and total readings can be selected adaptively based on the composition and thickness of the sample.

figure 7

Schematic of phantom samples and the PD–PT OCM experimental protocol. ( a ) is the schematic cross-sectional representation of the simple phantom sample fabricated with ultrasound gel. ( b ) is a schematic cross-sectional representation of the complex network phantom sample fabricated with multilayered lens tissue. c is the figurative depiction of MPM objective positioning for the PD–PT OCM based methodology. Figures not drawn to scale.

Data availability

All the necessary data required to analyze and reproduce the results and conclusions presented in the manuscript are provided in relevant sections in the manuscript. Additional details and data related to this study are available from the corresponding author upon reasonable request.

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This work was supported by the Korea Basic Science Institute [grant number D300300, and C330210], and the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korean government (MSIT) [No. 1711152793].

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Naresh Kumar Ravichandran, Hwan Hur, Hyemi Kim, Sangwon Hyun, Ji Yong Bae, Dong Uk Kim, I Jong Kim, Ki-Hwan Nam, Ki Soo Chang & Kye-Sung Lee

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K.S.L. developed the design concept and co-developed the imaging system. R.N.K. co-developed the system, performed experiments, and processed data. R.N.K. wrote the manuscript draft. K.S.L. and K.S.C critically revised the manuscript. H. K. fabricated the biochip sample used in supplementary information . All authors were involved in discussions and contributed to the manuscript drafting and revision.

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Ravichandran, N.K., Hur, H., Kim, H. et al. Label-free photothermal optical coherence microscopy to locate desired regions of interest in multiphoton imaging of volumetric specimens. Sci Rep 13 , 3625 (2023).

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  1. How to Write a Research Paper

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  1. Functions of research report

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  5. Finding Sources for the Research Report



  1. 13+ SAMPLE Scientific Research Report in PDF

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    A science research report is a clear and comprehensive document which is composed of collected, analyzed, and interpreted data performed by research scientists as it clearly communicates key messages about why certain scientific findings are valuable.

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    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.

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    According to the facts and figures from the UNESCO Science Report towards 2030, a record of over 7.8 million full-time scientific researchers is active during 2013, with a growth rate of 21 percent since 2017. For a bigger picture, researchers represent 0.1 percent of the total population. Types of Scientific Research

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    Research reports usually follow a standard five-part format: (1) introduction, (2) methods, (3) results (4) discussion of results, and (5) conclusions and recommendations. Introduction. Here you explain briefly the purpose of your investigation. What problem did you address? Why did you address it?

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