scientific report introduction example

Writing an Introduction for a Scientific Paper

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

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

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

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

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

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

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

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

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

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

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

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

Some Final Tips on Writing an Introduction

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

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

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How to Write a Scientific Report | Step-by-Step Guide

Got to document an experiment but don't know how? In this post, we'll guide you step-by-step through how to write a scientific report and provide you with an example.

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Is your teacher expecting you to write an experimental report for every class experiment? Are you still unsure about how to write a scientific report properly? Don’t fear! We will guide you through all the parts of a scientific report, step-by-step.

How to write a scientific report:

• What is a scientific report
• General rules to write Scientific reports
• Syllabus dot point 
• Introduction/Background information
• Risk assessment

What is a scientific report?

A scientific report documents all aspects of an experimental investigation. This includes:

• The aim of the experiment
• The hypothesis
• An introduction to the relevant background theory
• The methods used
• The results
• A discussion of the results
• The conclusion

Scientific reports allow their readers to understand the experiment without doing it themselves. In addition, scientific reports give others the opportunity to check the methodology of the experiment to ensure the validity of the results.

A scientific report is written in several stages. We write the introduction, aim, and hypothesis before performing the experiment, record the results during the experiment, and complete the discussion and conclusions after the experiment.

But, before we delve deeper into how to write a scientific report, we need to have a science experiment to write about! Read our 7 Simple Experiments You Can Do At Home article and see which one you want to do.

General rules about writing scientific reports

Learning how to write a scientific report is different from writing English essays or speeches!

You have to use:

• Passive voice (which you should avoid when writing for other subjects like English!)
• Past-tense language
• Headings and subheadings
• A pencil to draw scientific diagrams and graphs
• Simple and clear lines for scientific diagrams
• Tables and graphs where necessary

Structure of scientific reports:

Now that you know the general rules on how to write scientific reports, let’s look at the conventions for their structure!

The title should simply introduce what your experiment is about.

The Role of Light in Photosynthesis

2. Introduction/Background information

Write a paragraph that gives your readers background information to understand your experiment.

This includes explaining scientific theories, processes and other related knowledge.

Photosynthesis is a vital process for life. It occurs when plants intake carbon dioxide, water, and light, and results in the production of glucose and water. The light required for photosynthesis is absorbed by chlorophyll, the green pigment of plants, which is contained in the chloroplasts.

The glucose produced through photosynthesis is stored as starch, which is used as an energy source for the plant and its consumers.

The presence of starch in the leaves of a plant indicates that photosynthesis has occurred.

The aim identifies what is going to be tested in the experiment. This should be short, concise and clear.

The aim of the experiment is to test whether light is required for photosynthesis to occur.

4. Hypothesis

The hypothesis is a prediction of the outcome of the experiment. You have to use background information to make an educated prediction.

It is predicted that photosynthesis will occur only in leaves that are exposed to light and not in leaves that are not exposed to light. This will be indicated by the presence or absence of starch in the leaves.

5. Risk assessment

Identify the hazards associated with the experiment and provide a method to prevent or minimise the risks. A hazard is something that can cause harm, and the risk is the likelihood that harm will occur from the hazard.

A table is an excellent way to present your risk assessment.

Remember, you have to specify the  type of harm that can occur because of the hazard. It is not enough to simply identify the hazard.

• Do not write:  “Scissors are sharp”
• Instead, you have to write:  “Scissors are sharp and can cause injury”

The method has 3 parts:

• A list of every material used
• Steps of what you did in the experiment
• A scientific diagram of the experimental apparatus

Let’s break down what you need to do for each section.

6a. Materials

This must list every piece of equipment and material you used in the experiment.

Remember, you need to also specify the amount of each material you used.

• 1 geranium plant
• Aluminium foil
• 2 test tubes
• 1 test tube rack
• 1 pair of scissors
• 1 250 mL beaker
• 1 pair of forceps
• 1 10 mL measuring cylinder
• Iodine solution (5 mL)
• Methylated spirit (50ml)
• Boiling water
• 2 Petri dishes

The rule of thumb is that you should write the method in a clear way so that readers are able to repeat the experiment and get similar results.

Using a numbered list for the steps of your experimental procedure is much clearer than writing a whole paragraph of text.  The steps should:

• Be written in a sequential order, based on when they were performed.
• Specify any equipment that was used.
• Specify the quantity of any materials that were used.

You also need to use past tense and passive voice when you are writing your method. Scientific reports are supposed to show the readers what you did in the experiment, not what you will do.

• Aluminium foil was used to fully cover a leaf of the geranium plant. The plant was left in the sun for three days.
• On the third day, the covered leaf and 1 non-covered leaf were collected from the plant. The foil was removed from the covered leaf, and a 1 cm square was cut from each leaf using a pair of scissors.
• 150 mL of water was boiled in a kettle and poured into a 250 mL beaker.
• Using forceps, the 1 cm square of covered leaf was placed into the beaker of boiling water for 2 minutes. It was then placed in a test tube labelled “dark”.
• The water in the beaker was discarded and replaced with 150 mL of freshly boiled water.
• Using forceps, the 1 cm square non-covered leaf was placed into the beaker of boiling water for 2 minutes. It was then placed in a test tube labelled “light”
• 5 mL of methylated spirit was measured with a measuring cylinder and poured into each test tube so that the leaves were fully covered.
• The water in the beaker was replaced with 150 mL of freshly boiled water and both the “light” and “dark” test tubes were immersed in the beaker of boiling water for 5 minutes.
• The leaves were collected from each test tube with forceps, rinsed under cold running water, and placed onto separate labelled Petri dishes.
• 3 drops of iodine solution were added to each leaf.
• Both Petri dishes were placed side by side and observations were recorded.
• The experiment was repeated 5 times, and results were compared between different groups.

6c. Diagram

After you finish your steps, it is time to draw your scientific diagrams! Here are some rules for drawing scientific diagrams:

• Always use a pencil to draw your scientific diagrams.
• Use simple, sharp, 2D lines and shapes to draw your diagram. Don’t draw 3D shapes or use shading.
• Label everything in your diagram.
• Use thin, straight lines to label your diagram. Do not use arrows.
• Ensure that the label lines touch the outline of the equipment you are labelling and not cross over it or stop short of it
• The label lines should never cross over each other.
• Use a ruler for any straight lines in your diagram.
• Draw a sufficiently large diagram so all components can be seen clearly.

This is where you document the results of your experiment. The data that you record for your experiment will generally be qualitative and/or quantitative.

Qualitative data is data that relates to qualities and is based on observations (qualitative – quality). This type of data is descriptive and is recorded in words. For example, the colour changed from green to orange, or the liquid became hot.

Quantitative data refers to numerical data (quantitative – quantity). This type of data is recorded using numbers and is either measured or counted. For example, the plant grew 5.2 cm, or there were 5 frogs.

You also need to record your results in an appropriate way. Most of the time, a table is the best way to do this.

Here are some rules to using tables

• Use a pencil and a ruler to draw your table
• Draw neat and straight lines
• Ensure that the table is closed (connect all your lines)
• Don’t cross your lines (erase any lines that stick out of the table)
• Use appropriate columns and rows
• Properly name each column and row (including the units of measurement in brackets)
• Do not write your units in the body of your table (units belong in the header)
• Always include a title

Note : If your results require calculations, clearly write each step.

Observations of the effects of light on the amount of starch in plant leaves.

If quantitative data was recorded, the data is often also plotted on a graph.

8. Discussion

The discussion is where you analyse and interpret your results, and identify any experimental errors or possible areas of improvements.

You should divide your discussion as follows.

1. Trend in the results

Describe the ‘trend’ in your results. That is, the relationship you observed between your independent and dependent variables.

The independent variable is the variable that you are changing in the experiment. In this experiment, it is the amount of light that the leaves are exposed to.

The dependent variable is the variable that you are measuring in the experiment, In this experiment, it is the presence of starch in the leaves.

Explain how a particular result is achieved by referring to scientific knowledge, theories and any other scientific resources you find. 2. Scientific explanation: 

The presence of starch is indicated when the addition of iodine causes the leaf to turn dark purple. The results show that starch was present in the leaves that were exposed to light, while the leaves that were not exposed to light did not contain starch.

2. Scientific explanation:

Provide an explanation of the results using scientific knowledge, theories and any other scientific resources you find.

As starch is produced during photosynthesis, these results show that light plays a key role in photosynthesis.

3. Validity 

Validity refers to whether or not your results are valid. This can be done by examining your variables.

VA lidity =  VA riables

Identify the independent, dependent, controlled variables and the control experiment (if you have one).

The controlled variables are the variables that you keep the same across all tests e.g. the size of the leaf sample.

The control experiment is where you don’t apply an independent variable. It is untouched for the whole experiment.

Ensure that you never change more than one variable at a time!

The independent variable of the experiment was amount of light that the leaves were exposed to (the covered and uncovered geranium leaf), while the dependent variable was the presence of starch. The controlled variables were the size of the leaf sample, the duration of the experiment, the amount of time the solutions were heated, and the amount of iodine solution used.

4. Reliability 

Identify how you ensured the reliability of the results.

RE liability = RE petition

Show that you repeated your experiments, cross-checked your results with other groups or collated your results with the class.

The reliability of the results was ensured by repeating the experiment 5 times and comparing results with other groups. Since other groups obtained comparable results, the results are reliable.

5. Accuracy

Accuracy should be discussed if your results are in the form of quantitative data, and there is an accepted value for the result.

Accuracy would not be discussed for our example photosynthesis experiment as qualitative data was collected, however it would if we were measuring gravity using a pendulum:

The measured value of gravity was 9.8 m/s 2 , which is in agreement with the accepted value of 9.8 m/s 2 .

6. Possible improvements 

Identify any errors or risks found in the experiment and provide a method to improve it.

If there are none, then suggest new ways to improve the experimental design, and/or minimise error and risks.

Possible improvements could be made by including control experiments. For example, testing whether the iodine solution turns dark purple when added to water or methylated spirits. This would help to ensure that the purple colour observed in the experiments is due to the presence of starch in the leaves rather than impurities.

9. Conclusion

State whether the aim was achieved, and if your hypothesis was supported.

The aim of the investigation was achieved, and it was found that light is required for photosynthesis to occur. This was evidenced by the presence of starch in leaves that had been exposed to light, and the absence of starch in leaves that had been unexposed. These results support the proposed hypothesis.

Written by Matrix Science Team

© Matrix Education and www.matrix.edu.au, 2023. Unauthorised use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Matrix Education and www.matrix.edu.au with appropriate and specific direction to the original content.

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Scientific Reports

What this handout is about.

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

Background and pre-writing

Why do we write research reports.

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

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

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

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

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

How do I do that?

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

• Introduction

Methods and Materials

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

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

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

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

What should I do before drafting the lab report?

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

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

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

Introductions

How do i write a strong introduction.

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

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

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

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

Not a hypothesis:

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

Hypothesis:

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

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

Justify your hypothesis

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

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

Background/previous research

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

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

Organization of this section

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

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

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

How do I write a strong Materials and Methods section?

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

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

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

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

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

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

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

Structure and style

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

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

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

How do I write a strong Results section?

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

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

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

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

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

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

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

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

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

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

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

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

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

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:

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

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

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

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:

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

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

How do I include figures in my report?

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

When should you use a figure?

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

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

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

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

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

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

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

How do I write a strong Discussion section?

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

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

Explain whether the data support your hypothesis

• Acknowledge any anomalous data or deviations from what you expected

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

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

Explore the theoretical and/or practical implications of your findings

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

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

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

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

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

Acknowledge any anomalous data, or deviations from what you expected

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

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

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

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

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

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

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

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

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

Works consulted

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

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

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

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

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

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

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

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

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

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

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

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

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Writing a scientific paper.

• Writing a lab report

What is a "good" introduction?

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

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Peer Review
• Presentations
• Lab Report Writing Guides on the Web

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

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

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

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

• Indicate the field of the work, why this field is important, and what has already been done (with proper citations).
• Indicate a gap, raise a research question, or challenge prior work in this territory.
• Outline the purpose and announce the present research, clearly indicating what is novel and why it is significant.
• Avoid: repeating the abstract; providing unnecessary background information; exaggerating the importance of the work; claiming novelty without a proper literature search. 
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How To Write A Lab Report | Step-by-Step Guide & Examples

Published on May 20, 2021 by Pritha Bhandari . Revised on July 23, 2023.

A lab report conveys the aim, methods, results, and conclusions of a scientific experiment. The main purpose of a lab report is to demonstrate your understanding of the scientific method by performing and evaluating a hands-on lab experiment. This type of assignment is usually shorter than a research paper .

Lab reports are commonly used in science, technology, engineering, and mathematics (STEM) fields. This article focuses on how to structure and write a lab report.

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Table of contents

Structuring a lab report, introduction, other interesting articles, frequently asked questions about lab reports.

The sections of a lab report can vary between scientific fields and course requirements, but they usually contain the purpose, methods, and findings of a lab experiment .

Each section of a lab report has its own purpose.

• Title: expresses the topic of your study
• Abstract : summarizes your research aims, methods, results, and conclusions
• Introduction: establishes the context needed to understand the topic
• Method: describes the materials and procedures used in the experiment
• Results: reports all descriptive and inferential statistical analyses
• Discussion: interprets and evaluates results and identifies limitations
• Conclusion: sums up the main findings of your experiment
• References: list of all sources cited using a specific style (e.g. APA )
• Appendices : contains lengthy materials, procedures, tables or figures

Although most lab reports contain these sections, some sections can be omitted or combined with others. For example, some lab reports contain a brief section on research aims instead of an introduction, and a separate conclusion is not always required.

If you’re not sure, it’s best to check your lab report requirements with your instructor.

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Your title provides the first impression of your lab report – effective titles communicate the topic and/or the findings of your study in specific terms.

Create a title that directly conveys the main focus or purpose of your study. It doesn’t need to be creative or thought-provoking, but it should be informative.

• The effects of varying nitrogen levels on tomato plant height.
• Testing the universality of the McGurk effect.
• Comparing the viscosity of common liquids found in kitchens.

An abstract condenses a lab report into a brief overview of about 150–300 words. It should provide readers with a compact version of the research aims, the methods and materials used, the main results, and the final conclusion.

Think of it as a way of giving readers a preview of your full lab report. Write the abstract last, in the past tense, after you’ve drafted all the other sections of your report, so you’ll be able to succinctly summarize each section.

To write a lab report abstract, use these guiding questions:

• What is the wider context of your study?
• What research question were you trying to answer?
• How did you perform the experiment?
• What did your results show?
• How did you interpret your results?
• What is the importance of your findings?

Nitrogen is a necessary nutrient for high quality plants. Tomatoes, one of the most consumed fruits worldwide, rely on nitrogen for healthy leaves and stems to grow fruit. This experiment tested whether nitrogen levels affected tomato plant height in a controlled setting. It was expected that higher levels of nitrogen fertilizer would yield taller tomato plants.

Levels of nitrogen fertilizer were varied between three groups of tomato plants. The control group did not receive any nitrogen fertilizer, while one experimental group received low levels of nitrogen fertilizer, and a second experimental group received high levels of nitrogen fertilizer. All plants were grown from seeds, and heights were measured 50 days into the experiment.

The effects of nitrogen levels on plant height were tested between groups using an ANOVA. The plants with the highest level of nitrogen fertilizer were the tallest, while the plants with low levels of nitrogen exceeded the control group plants in height. In line with expectations and previous findings, the effects of nitrogen levels on plant height were statistically significant. This study strengthens the importance of nitrogen for tomato plants.

Your lab report introduction should set the scene for your experiment. One way to write your introduction is with a funnel (an inverted triangle) structure:

• Start with the broad, general research topic
• Narrow your topic down your specific study focus
• End with a clear research question

Begin by providing background information on your research topic and explaining why it’s important in a broad real-world or theoretical context. Describe relevant previous research on your topic and note how your study may confirm it or expand it, or fill a gap in the research field.

This lab experiment builds on previous research from Haque, Paul, and Sarker (2011), who demonstrated that tomato plant yield increased at higher levels of nitrogen. However, the present research focuses on plant height as a growth indicator and uses a lab-controlled setting instead.

Next, go into detail on the theoretical basis for your study and describe any directly relevant laws or equations that you’ll be using. State your main research aims and expectations by outlining your hypotheses .

Based on the importance of nitrogen for tomato plants, the primary hypothesis was that the plants with the high levels of nitrogen would grow the tallest. The secondary hypothesis was that plants with low levels of nitrogen would grow taller than plants with no nitrogen.

Your introduction doesn’t need to be long, but you may need to organize it into a few paragraphs or with subheadings such as “Research Context” or “Research Aims.”

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A lab report Method section details the steps you took to gather and analyze data. Give enough detail so that others can follow or evaluate your procedures. Write this section in the past tense. If you need to include any long lists of procedural steps or materials, place them in the Appendices section but refer to them in the text here.

You should describe your experimental design, your subjects, materials, and specific procedures used for data collection and analysis.

Experimental design

Briefly note whether your experiment is a within-subjects  or between-subjects design, and describe how your sample units were assigned to conditions if relevant.

A between-subjects design with three groups of tomato plants was used. The control group did not receive any nitrogen fertilizer. The first experimental group received a low level of nitrogen fertilizer, while the second experimental group received a high level of nitrogen fertilizer.

Describe human subjects in terms of demographic characteristics, and animal or plant subjects in terms of genetic background. Note the total number of subjects as well as the number of subjects per condition or per group. You should also state how you recruited subjects for your study.

List the equipment or materials you used to gather data and state the model names for any specialized equipment.

List of materials

35 Tomato seeds

15 plant pots (15 cm tall)

Light lamps (50,000 lux)

Nitrogen fertilizer

Measuring tape

Describe your experimental settings and conditions in detail. You can provide labelled diagrams or images of the exact set-up necessary for experimental equipment. State how extraneous variables were controlled through restriction or by fixing them at a certain level (e.g., keeping the lab at room temperature).

Light levels were fixed throughout the experiment, and the plants were exposed to 12 hours of light a day. Temperature was restricted to between 23 and 25℃. The pH and carbon levels of the soil were also held constant throughout the experiment as these variables could influence plant height. The plants were grown in rooms free of insects or other pests, and they were spaced out adequately.

Your experimental procedure should describe the exact steps you took to gather data in chronological order. You’ll need to provide enough information so that someone else can replicate your procedure, but you should also be concise. Place detailed information in the appendices where appropriate.

In a lab experiment, you’ll often closely follow a lab manual to gather data. Some instructors will allow you to simply reference the manual and state whether you changed any steps based on practical considerations. Other instructors may want you to rewrite the lab manual procedures as complete sentences in coherent paragraphs, while noting any changes to the steps that you applied in practice.

If you’re performing extensive data analysis, be sure to state your planned analysis methods as well. This includes the types of tests you’ll perform and any programs or software you’ll use for calculations (if relevant).

First, tomato seeds were sown in wooden flats containing soil about 2 cm below the surface. Each seed was kept 3-5 cm apart. The flats were covered to keep the soil moist until germination. The seedlings were removed and transplanted to pots 8 days later, with a maximum of 2 plants to a pot. Each pot was watered once a day to keep the soil moist.

The nitrogen fertilizer treatment was applied to the plant pots 12 days after transplantation. The control group received no treatment, while the first experimental group received a low concentration, and the second experimental group received a high concentration. There were 5 pots in each group, and each plant pot was labelled to indicate the group the plants belonged to.

50 days after the start of the experiment, plant height was measured for all plants. A measuring tape was used to record the length of the plant from ground level to the top of the tallest leaf.

In your results section, you should report the results of any statistical analysis procedures that you undertook. You should clearly state how the results of statistical tests support or refute your initial hypotheses.

The main results to report include:

• any descriptive statistics
• statistical test results
• the significance of the test results
• estimates of standard error or confidence intervals

The mean heights of the plants in the control group, low nitrogen group, and high nitrogen groups were 20.3, 25.1, and 29.6 cm respectively. A one-way ANOVA was applied to calculate the effect of nitrogen fertilizer level on plant height. The results demonstrated statistically significant ( p = .03) height differences between groups.

Next, post-hoc tests were performed to assess the primary and secondary hypotheses. In support of the primary hypothesis, the high nitrogen group plants were significantly taller than the low nitrogen group and the control group plants. Similarly, the results supported the secondary hypothesis: the low nitrogen plants were taller than the control group plants.

These results can be reported in the text or in tables and figures. Use text for highlighting a few key results, but present large sets of numbers in tables, or show relationships between variables with graphs.

You should also include sample calculations in the Results section for complex experiments. For each sample calculation, provide a brief description of what it does and use clear symbols. Present your raw data in the Appendices section and refer to it to highlight any outliers or trends.

The Discussion section will help demonstrate your understanding of the experimental process and your critical thinking skills.

In this section, you can:

• Interpret your results
• Compare your findings with your expectations
• Identify any sources of experimental error
• Explain any unexpected results
• Suggest possible improvements for further studies

Interpreting your results involves clarifying how your results help you answer your main research question. Report whether your results support your hypotheses.

• Did you measure what you sought out to measure?
• Were your analysis procedures appropriate for this type of data?

Compare your findings with other research and explain any key differences in findings.

• Are your results in line with those from previous studies or your classmates’ results? Why or why not?

An effective Discussion section will also highlight the strengths and limitations of a study.

• Did you have high internal validity or reliability?
• How did you establish these aspects of your study?

When describing limitations, use specific examples. For example, if random error contributed substantially to the measurements in your study, state the particular sources of error (e.g., imprecise apparatus) and explain ways to improve them.

The results support the hypothesis that nitrogen levels affect plant height, with increasing levels producing taller plants. These statistically significant results are taken together with previous research to support the importance of nitrogen as a nutrient for tomato plant growth.

However, unlike previous studies, this study focused on plant height as an indicator of plant growth in the present experiment. Importantly, plant height may not always reflect plant health or fruit yield, so measuring other indicators would have strengthened the study findings.

Another limitation of the study is the plant height measurement technique, as the measuring tape was not suitable for plants with extreme curvature. Future studies may focus on measuring plant height in different ways.

The main strengths of this study were the controls for extraneous variables, such as pH and carbon levels of the soil. All other factors that could affect plant height were tightly controlled to isolate the effects of nitrogen levels, resulting in high internal validity for this study.

Your conclusion should be the final section of your lab report. Here, you’ll summarize the findings of your experiment, with a brief overview of the strengths and limitations, and implications of your study for further research.

Some lab reports may omit a Conclusion section because it overlaps with the Discussion section, but you should check with your instructor before doing so.

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A lab report conveys the aim, methods, results, and conclusions of a scientific experiment . Lab reports are commonly assigned in science, technology, engineering, and mathematics (STEM) fields.

The purpose of a lab report is to demonstrate your understanding of the scientific method with a hands-on lab experiment. Course instructors will often provide you with an experimental design and procedure. Your task is to write up how you actually performed the experiment and evaluate the outcome.

In contrast, a research paper requires you to independently develop an original argument. It involves more in-depth research and interpretation of sources and data.

A lab report is usually shorter than a research paper.

The sections of a lab report can vary between scientific fields and course requirements, but it usually contains the following:

• Abstract: summarizes your research aims, methods, results, and conclusions
• References: list of all sources cited using a specific style (e.g. APA)
• Appendices: contains lengthy materials, procedures, tables or figures

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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Writing Studio

Writing a lab report: introduction and discussion section guide.

In an effort to make our handouts more accessible, we have begun converting our PDF handouts to web pages. Download this page as a PDF:   Writing a Lab Report Return to Writing Studio Handouts

Part 1 (of 2): Introducing a Lab Report

The introduction of a lab report states the objective of the experiment and provides the reader with background information. State the topic of your report clearly and concisely (in one or two sentences). Provide background theory, previous research, or formulas the reader should know. Usually, an instructor does not want you to repeat whatever the lab manual says, but to show your understanding of the problem.

Questions an Effective Lab Report Introduction Should Answer

What is the problem.

Describe the problem investigated. Summarize relevant research to provide context, key terms, and concepts so that your reader can understand the experiment.

Why is it important?

Review relevant research to provide a rationale for the investigation. What conflict, unanswered question, untested population, or untried method in existing research does your experiment address? How will you challenge or extend the findings of other researchers?

What solution (or step toward a solution) do you propose?

Briefly describe your experiment : hypothesis , research question , general experimental design or method , and a justification of your method (if alternatives exist).

Tips on Composing Your Lab Report’s Introduction

• Move from the general to the specific – from a problem in research literature to the specifics of your experiment.
• Engage your reader – answer the questions: “What did I do?” “Why should my reader care?”
• Clarify the links between problem and solution, between question asked and research design, and between prior research and the specifics of your experiment.
• Be selective, not exhaustive, in choosing studies to cite and the amount of detail to include. In general, the more relevant an article is to your study, the more space it deserves and the later in the introduction it appears.
• Ask your instructor whether or not you should summarize results and/or conclusions in the Introduction.
• “The objective of the experiment was …”
• “The purpose of this report is …”
• “Bragg’s Law for diffraction is …”
• “The scanning electron microscope produces micrographs …”

Part 2 (of 2): Writing the “Discussion” Section of a Lab Report

The discussion is the most important part of your lab report, because here you show that you have not merely completed the experiment, but that you also understand its wider implications. The discussion section is reserved for putting experimental results in the context of the larger theory. Ask yourself: “What is the significance or meaning of the results?”

Elements of an Effective Discussion Section

What do the results indicate clearly? Based on your results, explain what you know with certainty and draw conclusions.

Interpretation

What is the significance of your results? What ambiguities exist? What are logical explanations for problems in the data? What questions might you raise about the methods used or the validity of the experiment? What can be logically deduced from your analysis?

Tips on the Discussion Section

1. explain your results in terms of theoretical issues..

How well has the theory been illustrated? What are the theoretical implications and practical applications of your results?

For each major result:

• Describe the patterns, principles, and relationships that your results show.
• Explain how your results relate to expectations and to literature cited in your Introduction. Explain any agreements, contradictions, or exceptions.
• Describe what additional research might resolve contradictions or explain exceptions.

2. Relate results to your experimental objective(s).

If you set out to identify an unknown metal by finding its lattice parameter and its atomic structure, be sure that you have identified the metal and its attributes.

3. Compare expected results with those obtained.

If there were differences, how can you account for them? Were the instruments able to measure precisely? Was the sample contaminated? Did calculated values take account of friction?

4. Analyze experimental error along with the strengths and limitations of the experiment’s design.

Were any errors avoidable? Were they the result of equipment?  If the flaws resulted from the experiment design, explain how the design might be improved. Consider, as well, the precision of the instruments that were used.

5. Compare your results to similar investigations.

In some cases, it is legitimate to compare outcomes with classmates, not in order to change your answer, but in order to look for and to account for or analyze any anomalies between the groups. Also, consider comparing your results to published scientific literature on the topic.

The “Introducing a Lab Report” guide was adapted from the University of Toronto Engineering Communications Centre and University of Wisconsin-Madison Writing Center.

The “Writing the Discussion Section of a Lab Report” resource was adapted from the University of Toronto Engineering Communications Centre and University of Wisconsin-Madison Writing Center.

Last revised: 07/2008 | Adapted for web delivery: 02/2021

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

How to Write the Introduction to a Scientific Paper?

• Samiran Nundy 4 ,
• Atul Kakar 5 &
• Zulfiqar A. Bhutta 6  
• Open Access
• First Online: 24 October 2021

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

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

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1 What is the Importance of an Introduction?

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

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

2 What Are the Principles of Writing a Good Introduction?

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

The known—The background scientific data

The unknown—Gaps in the current knowledge

Research hypothesis or question

Methodologies used for the study

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

3 What Are the Models of Writing an Introduction?

The Problem-solving model

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

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

The two important components of this model are:

Establishing a territory (situation)

Establishing a niche (problem)

Occupying a niche (the solution)

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

4 What Is Establishing a Territory?

This includes: [ 9 ]

Stating the general topic and providing some background about it.

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

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

5 What Is Establishing a Niche?

Establishing a niche includes:

Stating the importance of the problem.

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

Evaluating the current situation (advantages/ disadvantages).

Identifying the gaps.

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

Stating the research problem/ questions.

Stating the hypotheses briefly.

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

Flow of ideas from the general to the specific

6 What Does Occupying a Niche Mean?

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

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

7 How Long Should the Introduction Be?

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

8 How Many References Should an Introduction Have?

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

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

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

How should the abstract, introduction, and discussion look

The following points are important to consider:

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

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

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

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

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

10 Example of an Introduction

11 Conclusions

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

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

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

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

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How to write an introduction section of a scientific article?

An article primarily includes the following sections: introduction, materials and methods, results, discussion, and conclusion. Before writing the introduction, the main steps, the heading and the familiarity level of the readers should be considered. Writing should begin when the experimental system and the equipment are available. The introduction section comprises the first portion of the manuscript, and it should be written using the simple present tense. Additionally, abbreviations and explanations are included in this section. The main goal of the introduction is to convey basic information to the readers without obligating them to investigate previous publications and to provide clues as to the results of the present study. To do this, the subject of the article should be thoroughly reviewed, and the aim of the study should be clearly stated immediately after discussing the basic references. In this review, we aim to convey the principles of writing the introduction section of a manuscript to residents and young investigators who have just begun to write a manuscript.

Introduction

When entering a gate of a magnificent city we can make a prediction about the splendor, pomposity, history, and civilization we will encounter in the city. Occasionally, gates do not give even a glimpse of the city, and it can mislead the visitors about inner sections of the city. Introduction sections of the articles are like gates of a city. It is a presentation aiming at introducing itself to the readers, and attracting their attention. Attractiveness, clarity, piquancy, and analytical capacity of the presentation will urge the reader to read the subsequent sections of the article. On the other hand as is understood from the motto of antique Greek poet Euripides “a bad beginning makes a bad ending”, ‘Introduction’ section of a scientific article is important in that it can reveal the conclusion of the article. [ 1 ]

It is useful to analyze the issues to be considered in the ‘Introduction’ section under 3 headings. Firstly, information should be provided about the general topic of the article in the light of the current literature which paves the way for the disclosure of the objective of the manuscript. Then the specific subject matter, and the issue to be focused on should be dealt with, the problem should be brought forth, and fundamental references related to the topic should be discussed. Finally, our recommendations for solution should be described, in other words our aim should be communicated. When these steps are followed in that order, the reader can track the problem, and its solution from his/her own perspective under the light of current literature. Otherwise, even a perfect study presented in a non-systematized, confused design will lose the chance of reading. Indeed inadequate information, inability to clarify the problem, and sometimes concealing the solution will keep the reader who has a desire to attain new information away from reading the manuscript. [ 1 – 3 ]

First of all, explanation of the topic in the light of the current literature should be made in clear, and precise terms as if the reader is completely ignorant of the subject. In this section, establishment of a warm rapport between the reader, and the manuscript is aimed. Since frantic plunging into the problem or the solution will push the reader into the dilemma of either screening the literature about the subject matter or refraining from reading the article. Updated, and robust information should be presented in the ‘Introduction’ section.

Then main topic of our manuscript, and the encountered problem should be analyzed in the light of the current literature following a short instance of brain exercise. At this point the problems should be reduced to one issue as far as possible. Of course, there might be more than one problem, however this new issue, and its solution should be the subject matter of another article. Problems should be expressed clearly. If targets are more numerous, and complex, solutions will be more than one, and confusing.

Finally, the last paragraphs of the ‘Introduction’ section should include the solution in which we will describe the information we generated, and related data. Our sentences which arouse curiosity in the readers should not be left unanswered. The reader who thinks to obtain the most effective information in no time while reading a scientific article should not be smothered with mysterious sentences, and word plays, and the readers should not be left alone to arrive at a conclusion by themselves. If we have contrary expectations, then we might write an article which won’t have any reader. A clearly expressed or recommended solutions to an explicitly revealed problem is also very important for the integrity of the ‘Introduction’ section. [ 1 – 5 ]

We can summarize our arguments with the following example ( Figure 1 ). The introduction section of the exemplary article is written in simple present tense which includes abbreviations, acronyms, and their explanations. Based on our statements above we can divide the introduction section into 3 parts. In the first paragraph, miniaturization, and evolvement of pediatric endourological instruments, and competitions among PNL, ESWL, and URS in the treatment of urinary system stone disease are described, in other words the background is prepared. In the second paragraph, a newly defined system which facilitates intrarenal access in PNL procedure has been described. Besides basic references related to the subject matter have been given, and their outcomes have been indicated. In other words, fundamental references concerning main subject have been discussed. In the last paragraph the aim of the researchers to investigate the outcomes, and safety of the application of this new method in the light of current information has been indicated.

An exemplary introduction section of an article

Apart from the abovementioned information about the introduction section of a scientific article we will summarize a few major issues in brief headings

Important points which one should take heed of:

• Abbreviations should be given following their explanations in the ‘Introduction’ section (their explanations in the summary does not count)
• Simple present tense should be used.
• References should be selected from updated publication with a higher impact factor, and prestigous source books.
• Avoid mysterious, and confounding expressions, construct clear sentences aiming at problematic issues, and their solutions.
• The sentences should be attractive, tempting, and comjprehensible.
• Firstly general, then subject-specific information should be given. Finally our aim should be clearly explained.

How to Write a Research Paper Introduction (with Examples)

The research paper introduction section, along with the Title and Abstract, can be considered the face of any research paper. The following article is intended to guide you in organizing and writing the research paper introduction for a quality academic article or dissertation.

The research paper introduction aims to present the topic to the reader. A study will only be accepted for publishing if you can ascertain that the available literature cannot answer your research question. So it is important to ensure that you have read important studies on that particular topic, especially those within the last five to ten years, and that they are properly referenced in this section. 1 What should be included in the research paper introduction is decided by what you want to tell readers about the reason behind the research and how you plan to fill the knowledge gap. The best research paper introduction provides a systemic review of existing work and demonstrates additional work that needs to be done. It needs to be brief, captivating, and well-referenced; a well-drafted research paper introduction will help the researcher win half the battle.

The introduction for a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your research topic
• Capture reader interest
• Summarize existing research
• Position your own approach
• Define your specific research problem and problem statement
• Highlight the novelty and contributions of the study
• Give an overview of the paper’s structure

The research paper introduction can vary in size and structure depending on whether your paper presents the results of original empirical research or is a review paper. Some research paper introduction examples are only half a page while others are a few pages long. In many cases, the introduction will be shorter than all of the other sections of your paper; its length depends on the size of your paper as a whole.

• Break through writer’s block. Write your research paper introduction with Paperpal Copilot

Table of Contents

What is the introduction for a research paper, why is the introduction important in a research paper, craft a compelling introduction section with paperpal. try now, 1. introduce the research topic:, 2. determine a research niche:, 3. place your research within the research niche:, craft accurate research paper introductions with paperpal. start writing now, frequently asked questions on research paper introduction, key points to remember.

The introduction in a research paper is placed at the beginning to guide the reader from a broad subject area to the specific topic that your research addresses. They present the following information to the reader

• Scope: The topic covered in the research paper
• Context: Background of your topic
• Importance: Why your research matters in that particular area of research and the industry problem that can be targeted

The research paper introduction conveys a lot of information and can be considered an essential roadmap for the rest of your paper. A good introduction for a research paper is important for the following reasons:

• It stimulates your reader’s interest: A good introduction section can make your readers want to read your paper by capturing their interest. It informs the reader what they are going to learn and helps determine if the topic is of interest to them.
• It helps the reader understand the research background: Without a clear introduction, your readers may feel confused and even struggle when reading your paper. A good research paper introduction will prepare them for the in-depth research to come. It provides you the opportunity to engage with the readers and demonstrate your knowledge and authority on the specific topic.
• It explains why your research paper is worth reading: Your introduction can convey a lot of information to your readers. It introduces the topic, why the topic is important, and how you plan to proceed with your research.
• It helps guide the reader through the rest of the paper: The research paper introduction gives the reader a sense of the nature of the information that will support your arguments and the general organization of the paragraphs that will follow. It offers an overview of what to expect when reading the main body of your paper.

What are the parts of introduction in the research?

A good research paper introduction section should comprise three main elements: 2

• What is known: This sets the stage for your research. It informs the readers of what is known on the subject.
• What is lacking: This is aimed at justifying the reason for carrying out your research. This could involve investigating a new concept or method or building upon previous research.
• What you aim to do: This part briefly states the objectives of your research and its major contributions. Your detailed hypothesis will also form a part of this section.

How to write a research paper introduction?

The first step in writing the research paper introduction is to inform the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening statement. The second step involves establishing the kinds of research that have been done and ending with limitations or gaps in the research that you intend to address. Finally, the research paper introduction clarifies how your own research fits in and what problem it addresses. If your research involved testing hypotheses, these should be stated along with your research question. The hypothesis should be presented in the past tense since it will have been tested by the time you are writing the research paper introduction.

The following key points, with examples, can guide you when writing the research paper introduction section:

• Highlight the importance of the research field or topic
• Describe the background of the topic
• Present an overview of current research on the topic

Example: The inclusion of experiential and competency-based learning has benefitted electronics engineering education. Industry partnerships provide an excellent alternative for students wanting to engage in solving real-world challenges. Industry-academia participation has grown in recent years due to the need for skilled engineers with practical training and specialized expertise. However, from the educational perspective, many activities are needed to incorporate sustainable development goals into the university curricula and consolidate learning innovation in universities.

• Reveal a gap in existing research or oppose an existing assumption
• Formulate the research question

Example: There have been plausible efforts to integrate educational activities in higher education electronics engineering programs. However, very few studies have considered using educational research methods for performance evaluation of competency-based higher engineering education, with a focus on technical and or transversal skills. To remedy the current need for evaluating competencies in STEM fields and providing sustainable development goals in engineering education, in this study, a comparison was drawn between study groups without and with industry partners.

• State the purpose of your study
• Highlight the key characteristics of your study
• Describe important results
• Highlight the novelty of the study.
• Offer a brief overview of the structure of the paper.

Example: The study evaluates the main competency needed in the applied electronics course, which is a fundamental core subject for many electronics engineering undergraduate programs. We compared two groups, without and with an industrial partner, that offered real-world projects to solve during the semester. This comparison can help determine significant differences in both groups in terms of developing subject competency and achieving sustainable development goals.

Write a Research Paper Introduction in Minutes with Paperpal

Paperpal Copilot is a generative AI-powered academic writing assistant. It’s trained on millions of published scholarly articles and over 20 years of STM experience. Paperpal Copilot helps authors write better and faster with:

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With Paperpal Copilot, create a research paper introduction effortlessly. In this step-by-step guide, we’ll walk you through how Paperpal transforms your initial ideas into a polished and publication-ready introduction.

How to use Paperpal to write the Introduction section

Step 1: Sign up on Paperpal and click on the Copilot feature, under this choose Outlines > Research Article > Introduction

Step 2: Add your unstructured notes or initial draft, whether in English or another language, to Paperpal, which is to be used as the base for your content.

Step 3: Fill in the specifics, such as your field of study, brief description or details you want to include, which will help the AI generate the outline for your Introduction.

Step 4: Use this outline and sentence suggestions to develop your content, adding citations where needed and modifying it to align with your specific research focus.

Step 5: Turn to Paperpal’s granular language checks to refine your content, tailor it to reflect your personal writing style, and ensure it effectively conveys your message.

You can use the same process to develop each section of your article, and finally your research paper in half the time and without any of the stress.

The purpose of the research paper introduction is to introduce the reader to the problem definition, justify the need for the study, and describe the main theme of the study. The aim is to gain the reader’s attention by providing them with necessary background information and establishing the main purpose and direction of the research.

The length of the research paper introduction can vary across journals and disciplines. While there are no strict word limits for writing the research paper introduction, an ideal length would be one page, with a maximum of 400 words over 1-4 paragraphs. Generally, it is one of the shorter sections of the paper as the reader is assumed to have at least a reasonable knowledge about the topic. 2 For example, for a study evaluating the role of building design in ensuring fire safety, there is no need to discuss definitions and nature of fire in the introduction; you could start by commenting upon the existing practices for fire safety and how your study will add to the existing knowledge and practice.

When deciding what to include in the research paper introduction, the rest of the paper should also be considered. The aim is to introduce the reader smoothly to the topic and facilitate an easy read without much dependency on external sources. 3 Below is a list of elements you can include to prepare a research paper introduction outline and follow it when you are writing the research paper introduction. Topic introduction: This can include key definitions and a brief history of the topic. Research context and background: Offer the readers some general information and then narrow it down to specific aspects. Details of the research you conducted: A brief literature review can be included to support your arguments or line of thought. Rationale for the study: This establishes the relevance of your study and establishes its importance. Importance of your research: The main contributions are highlighted to help establish the novelty of your study Research hypothesis: Introduce your research question and propose an expected outcome. Organization of the paper: Include a short paragraph of 3-4 sentences that highlights your plan for the entire paper

Cite only works that are most relevant to your topic; as a general rule, you can include one to three. Note that readers want to see evidence of original thinking. So it is better to avoid using too many references as it does not leave much room for your personal standpoint to shine through. Citations in your research paper introduction support the key points, and the number of citations depend on the subject matter and the point discussed. If the research paper introduction is too long or overflowing with citations, it is better to cite a few review articles rather than the individual articles summarized in the review. A good point to remember when citing research papers in the introduction section is to include at least one-third of the references in the introduction.

The literature review plays a significant role in the research paper introduction section. A good literature review accomplishes the following: Introduces the topic – Establishes the study’s significance – Provides an overview of the relevant literature – Provides context for the study using literature – Identifies knowledge gaps However, remember to avoid making the following mistakes when writing a research paper introduction: Do not use studies from the literature review to aggressively support your research Avoid direct quoting Do not allow literature review to be the focus of this section. Instead, the literature review should only aid in setting a foundation for the manuscript.

Remember the following key points for writing a good research paper introduction: 4

• Avoid stuffing too much general information: Avoid including what an average reader would know and include only that information related to the problem being addressed in the research paper introduction. For example, when describing a comparative study of non-traditional methods for mechanical design optimization, information related to the traditional methods and differences between traditional and non-traditional methods would not be relevant. In this case, the introduction for the research paper should begin with the state-of-the-art non-traditional methods and methods to evaluate the efficiency of newly developed algorithms.
• Avoid packing too many references: Cite only the required works in your research paper introduction. The other works can be included in the discussion section to strengthen your findings.
• Avoid extensive criticism of previous studies: Avoid being overly critical of earlier studies while setting the rationale for your study. A better place for this would be the Discussion section, where you can highlight the advantages of your method.
• Avoid describing conclusions of the study: When writing a research paper introduction remember not to include the findings of your study. The aim is to let the readers know what question is being answered. The actual answer should only be given in the Results and Discussion section.

To summarize, the research paper introduction section should be brief yet informative. It should convince the reader the need to conduct the study and motivate him to read further. If you’re feeling stuck or unsure, choose trusted AI academic writing assistants like Paperpal to effortlessly craft your research paper introduction and other sections of your research article.

1. Jawaid, S. A., & Jawaid, M. (2019). How to write introduction and discussion. Saudi Journal of Anaesthesia, 13(Suppl 1), S18.

2. Dewan, P., & Gupta, P. (2016). Writing the title, abstract and introduction: Looks matter!. Indian pediatrics, 53, 235-241.

3. Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific Manuscript1. Journal of Surgical Research, 128(2), 165-167.

4. Bavdekar, S. B. (2015). Writing introduction: Laying the foundations of a research paper. Journal of the Association of Physicians of India, 63(7), 44-6.

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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Writing a scientific lab report is significantly different from writing for other classes like philosophy, English, and history. The most prominent form of writing in biology, chemistry, and environmental science is the lab report, which is a formally written description of results and discoveries found in an experiment. College lab reports should emulate and follow the same formats as reports found in scholarly journals, such as Nature , Cell , and The American Journal of Biochemistry .

Report Format

Title: The title says what you did. It should be brief (aim for ten words or less) and describe the main point of the experiment or investigation.

• Example:  Caffeine Increases Amylase Activity in the Mealworm ( Tenebrio molitar).
• If you can, begin your title using a keyword rather than an article like “The” or “A.”

Abstract: An abstract is a very concise summary of the purpose of the report, data presented, and major conclusions in about 100 - 200 words.  Abstracts are also commonly required for conference/presentation submissions because they summarize all of the essential materials necessary to understand the purpose of the experiment. They should consist of a background sentence , an introduction sentence , your hypothesis/purpose of the experiment, and a sentence about the results and what this means.

Introduction: The introduction of a lab report defines the subject of the report, provides background information and relevant studies, and outlines scientific purpose(s) and/or objective(s).

• The introduction is a place to provide the reader with necessary research on the topic and properly cite sources used.
• Summarizes the current literature on the topic including primary and secondary sources.
• Introduces the paper’s aims and scope.
• States the purpose of the experiment and the hypothesis.

Materials and Methods: The materials and methods section is a vital component of any formal lab report. This section of the report gives a detailed account of the procedure that was followed in completing the experiment as well as all important materials used. (This includes bacterial strains and species names in tests using living subjects.)

• Discusses the procedure of the experiment in as much detail as possible.
• Provides information about participants, apparatus, tools, substances, location of experiment, etc.
• For field studies, be sure to clearly explain where and when the work was done.
• It must be written so that anyone can use the methods section as instructions for exact replications.
• Don’t hesitate to use subheadings to organize these categories.
• Practice proper scientific writing forms. Be sure to use the proper abbreviations for units. Example: The 50mL sample was placed in a 5ºC room for 48hrs.

Results: The results section focuses on the findings, or data, in the experiment, as well as any statistical tests used to determine their significance.

• Concentrate on general trends and differences and not on trivial details.
• Summarize the data from the experiments without discussing their implications (This is where all the statistical analyses goes.)
• Organize data into tables, figures, graphs, photographs, etc.  Data in a table should not be duplicated in a graph or figure. Be sure to refer to tables and graphs in the written portion, for example, “Figure 1 shows that the activity....”
• Number and title all figures and tables separately, for example, Figure 1 and Table 1 and include a legend explaining symbols and abbreviations. Figures and graphs are labeled below the image while tables are labeled above.

  Discussion: The discussion section interprets the results, tying them back to background information and experiments performed by others in the past.This is also the area where further research opportunities shold be explored.

• Interpret the data; do not restate the results.
• Observations should also be noted in this section, especially anything unusual which may affect your results.

For example, if your bacteria was incubated at the wrong temperature or a piece of equipment failed mid-experiment, these should be noted in the results section.

• Relate results to existing theories and knowledge.This can tie back to your introduction section because of the background you provided.
• Explain the logic that allows you to accept or reject your original hypotheses.
• Include suggestions for improving your techniques or design, or clarify areas of doubt for further research.

Acknowledgements and References: A references list should be compiled at the end of the report citing any works that were used to support the paper. Additionally, an acknowledgements section should be included to acknowledge research advisors/ partners, any group or person providing funding for the research and anyone outside the authors who contributed to the paper or research.

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• In scientific papers, passive voice is perfectly acceptable. On the other hand, using “I” or “we” is not.

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• Published: 02 April 2024

Efficient DNA-based data storage using shortmer combinatorial encoding

• Inbal Preuss 1 , 3 ,
• Michael Rosenberg 2 ,
• Zohar Yakhini 1 , 3 &
• Leon Anavy 1 , 3  

Scientific Reports volume  14 , Article number:  7731 ( 2024 ) Cite this article

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• Computational biology and bioinformatics
• Computational methods
• Computational science
• Computer science
• DNA and RNA
• DNA computing
• Information technology

Data storage in DNA has recently emerged as a promising archival solution, offering space-efficient and long-lasting digital storage solutions. Recent studies suggest leveraging the inherent redundancy of synthesis and sequencing technologies by using composite DNA alphabets. A major challenge of this approach involves the noisy inference process, obstructing large composite alphabets. This paper introduces a novel approach for DNA-based data storage, offering, in some implementations, a 6.5-fold increase in logical density over standard DNA-based storage systems, with near-zero reconstruction error. Combinatorial DNA encoding uses a set of clearly distinguishable DNA shortmers to construct large combinatorial alphabets, where each letter consists of a subset of shortmers. We formally define various combinatorial encoding schemes and investigate their theoretical properties. These include information density and reconstruction probabilities, as well as required synthesis and sequencing multiplicities. We then propose an end-to-end design for a combinatorial DNA-based data storage system, including encoding schemes, two-dimensional (2D) error correction codes, and reconstruction algorithms, under different error regimes. We performed simulations and show, for example, that the use of 2D Reed-Solomon error correction has significantly improved reconstruction rates. We validated our approach by constructing two combinatorial sequences using Gibson assembly, imitating a 4-cycle combinatorial synthesis process. We confirmed the successful reconstruction, and established the robustness of our approach for different error types. Subsampling experiments supported the important role of sampling rate and its effect on the overall performance. Our work demonstrates the potential of combinatorial shortmer encoding for DNA-based data storage and describes some theoretical research questions and technical challenges. Combining combinatorial principles with error-correcting strategies, and investing in the development of DNA synthesis technologies that efficiently support combinatorial synthesis, can pave the way to efficient, error-resilient DNA-based storage solutions.

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Data storage in DNA with fewer synthesis cycles using composite DNA letters

Leon Anavy, Inbal Vaknin, … Zohar Yakhini

A digital twin for DNA data storage based on comprehensive quantification of errors and biases

Andreas L. Gimpel, Wendelin J. Stark, … Robert N. Grass

Scaling logical density of DNA storage with enzymatically-ligated composite motifs

Yiqing Yan, Nimesh Pinnamaneni, … Raja Appuswamy

Introduction

DNA is a promising media storage candidate for long-term data archiving, due to its high information density, long-term stability, and robustness. In recent years, several studies have demonstrated the use of synthetic DNA for storing digital information on a megabyte scale, exceeding the physical density of current magnetic tape-based systems by roughly six orders of magnitude 1 , 2 . Physical density is one of several quantitative metrics for evaluating the efficiency of DNA-based storage systems, measured by the data unit per gram of DNA. Another performance metric, which was introduced in 3 , is called logical density, refering to the amount of data encoded in each synthesis cycle. Since DNA synthesis is the main cost component in DNA-based storage systems, increasing the logical density is the main focus of this work.

Research efforts in the field of DNA-based storage systems have mainly focused on the application of various encoding schemes, while relying on standard DNA synthesis and sequencing technologies. These include the development of error-correcting codes for the unique information channel of DNA-based data storage 4 , 5 , 6 , 7 , 8 . Random access capabilities for reading specific information stored in DNA also require advanced coding schemes 9 , 10 , 11 . Yet, despite the enormous benefits potentially associated with capacity, robustness, and size, existing DNA-based storage technologies are characterized by inherent information redundancy. This is due to the nature of DNA synthesis and sequencing methodologies, which process multiple molecules that represent the same information bits in parallel. Recent studies suggest exploiting this redundancy to increase the logical density of the system, by extending the standard DNA alphabet using composite letters (also referred to as degenerate bases), and thereby encoding more than 2 bits per letter 12 , 13 . In this approach, a composite DNA letter uses all four DNA bases (A, C, G, and T), combined or mixed in a specified predetermined ratio \(\sigma =({\sigma }_{A},{\sigma }_{C},{\sigma }_{G},{\sigma }_{T})\) . A resolution parameter \(k={\sigma }_{A}+{\sigma }_{C}+{\sigma }_{G}+{\sigma }_{T}\) is defined, for controlling the alphabet size. The full composite alphabet of resolution \(k\) , denoted \({\Phi }_{k}\) , is the set of all composite letters, so that \({\Sigma }_{i\in (A,C,G,T\}}{\sigma }_{i}=k\) . Writing a composite letter is done by using a mixture of the DNA bases, determined by the letter’s ratio in the DNA synthesis cycle. Current synthesis technologies produce multiple copies, and by using the predetermined base mixture each copy will contain a different base, thus preserving the ratio of the bases at the sequence-population level.

While the use of numerical ratios supports higher logical density in composite synthesis, it also introduces challenges related to the synthesis and inference of exact ratios. Combinatorial approaches, which also consist of mixtures, address these challenges in a different way. Studies by Roquet et al. (2021) and Yan et al. (2023) contribute significantly to advancing DNA-based data storage technology. To encode and store data, Roquet et al. focus on a novel combinatorial assembly method for DNA. Yan et al. extend the frontiers of this technology by enhancing the logical density of DNA storage, using enzymatically-ligated composite motifs 13 , 14 .

In this paper, we present a novel approach for encoding information in DNA, using combinatorial encoding and shortmer DNA synthesis, leading to an efficient sequence design and improved DNA synthesis and readout interpretation. The method described herein leverages the advantages of combinatorial encoding schemes while relying on existing DNA chemical synthesis methods with some modifications. Using shortmer DNA synthesis also minimizes the effect of synthesis and sequencing errors. We formally define shortmer-based combinatorial encoding schemes, explore different designs, and analyze their performance. We use computer-based simulations of an end-to-end DNA-based data storage system built on combinatorial shortmer encodings, and study its performance. To demonstrate the potential of our suggested approach and experimentally test its validity, we performed an assembly-based molecular implementation of the proposed combinatorial encoding scheme and analyzed the resulting data. Finally, we discuss the potential of combinatorial encoding schemes and the future work required to enable these schemes in large-scale DNA-based data storage systems and other DNA data applications.

Design of shortmer combinatorial encoding for DNA storage

We suggest a novel method to extend the DNA alphabet while ensuring near-zero error rates.

Let \(\Omega\) be a set of DNA k-mers that will serve as building blocks for our encoding scheme. Denote the elements in \(\Omega\) as \({X}_{1},\dots ,{X}_{N}\) . Elements in \(\Omega\) are designed to be sufficiently different from each other, to minimize mix-up error probability. Formally, the set is designed to satisfy \(d\left({X}_{i},{X}_{j}\right)\ge d;\forall i\ne j\) , with the minimal Hamming distance \(d\) serving as a tunable parameter.

Other design criteria can be applied to the shortmers in \(\Omega\) , taking into consideration the properties of DNA synthesis, manipulation, and sequencing. These may include minimal Levenshtein distance, GC context, and avoiding long homopolymers. Clearly, any such filtering process will result in reduced alphabet size and reduced logical density.

Note that \(N=\left|\Omega \right|\le {4}^{k}\) . The elements in \(\Omega\) will be used as building blocks for combinatorial DNA synthesis in a method similar to the one used for composite DNA synthesis 3 . Examples of k-mer sets \(\Omega\) are presented in Supplementary Sect.  8.3 .

We define a combinatorial alphabet \(\Sigma\) over \(\Omega\) as follows. Each letter in the alphabet represents a non-empty subset of the elements in \(\Omega\) . Formally, a letter \(\sigma \in \Sigma\) , representing a subset \(S\subseteq \Omega /\varnothing\) , can be written as an N-dimensional binary vector where the indices for which \({\sigma }_{i}=1\) represents the k-mers from \(\Omega\) included in the subset S. We denote the k-mers in \(S\) as member k-mers of the letter \(\sigma\) . For example, \(\sigma =(\mathrm{0,1},\mathrm{0,1},\mathrm{1,0})\) represents \(S=\{{X}_{2},{X}_{4},{X}_{5}\}\) and \(\left|\Omega \right|=N=6\) . Figure  1 a,b illustrate an example of a combinatorial alphabet using \(N=16\) , in which every letter represents a subset of size 5 of \(\Omega\) . In Sect. “ Binary and binomial combinatorial alphabets ” includes a description of the construction of different combinatorial alphabets.

Our combinatorial encoding and synthesis approach. ( a ) Schematic view of a combinatorial alphabet (Encode legend). A set of 16 trimers, \({{\varvec{X}}}_{1},\dots ,{{\varvec{X}}}_{16}\) , is used to construct 4096 combinatorial letters, each representing a subset of 5 trimers as indicated on the right and depicted in the grayed-out cells of the table. ( b ) A suggested approach for combinatorial shortmer synthesis. A modified synthesizer would include designated containers for the 16-trimer building blocks and a mixing chamber. Standard DNA synthesis is used for the barcode sequence (1), while the combinatorial synthesis proceeds as follows: The trimers included in the synthesized combinatorial letter are injected into the mixing chamber and introduced into the elongating molecules (2). The process repeats for the next combinatorial letter (3), and finally, the resulting molecules are cleaved and collected (4).

To write a combinatorial letter \(\sigma\) in a specific position, a mixture of the member k-mers of \(\sigma\) is synthesized. To infer a combinatorial letter \(\sigma\) , a set of reads needs to be analyzed to determine which k-mers are observed in the analyzed position (See Sects. “ Binary and binomial combinatorial alphabets ” and “ Reconstruction probabilities for binomial encoding ” for more details). This set of k-mers observed in the sequencing readout and used for inferring \(\sigma\) is referred to as inferred member k-mers. While the synthesis output and the sequencing readout will include different counts for the member k-mers, the determination of the set of inferred k-mers will force binary assignment for each k-mer to fit into the design scheme of combinatorial encoding.

From a hardware/chemistry perspective, the combinatorial shortmer encoding scheme can potentially be based on using the standard phosphoramidite chemistry synthesis technology, with some alterations (See Fig.  1 b and Supplementary Sect.  8.1 ) 15 , 16 . First, DNA k-mers should be used as building blocks for the synthesis 17 . Such reagents are commercially available for DNA trimers and were used, for example, for the synthesis of codon optimization DNA libraries 18 , 19 . In addition, a mixing step should be added to each cycle of the DNA synthesis to allow mixing of the member k-mers prior to their introduction to the elongating molecules. Such systems are yet to be developed and current attempts for combinatorial DNA synthesis are based on enzymatic assembly of longer DNA fragments 13 , 14 .

Similar to composite DNA encoding, combinatorial encoding requires the barcoding of the sequences using unique barcodes composed of standard DNA barcodes. This design enables direct grouping of reads pertaining to the same combinatorial sequence. These groups of reads are the input for the process of reconstructing the combinatorial letters.

The extended combinatorial alphabets allow for a higher logical density of the DNA-based storage system, while the binary nature of the alphabet minimizes error rates.

Binary and binomial combinatorial alphabets

The main parameter that defines a combinatorial encoding scheme is the alphabet \(\Sigma\) . More specifically, it is the set of valid subsets of \(\Omega\) that can be used as letters. We define two general approaches for the construction of \(\Sigma\) . Namely, the binomial encoding and the full binary encoding .

In the binomial encoding scheme, only subsets of \(\Omega\) of size exactly \(K\) represent valid letters in \(\Sigma\) , so that every letter \(\sigma \in \Sigma\) consists of exactly \(K\) member k-mers. Therefore, all the letters in the alphabet have the same Hamming weight \(K\) . \(w\left(\sigma \right)=K, \forall \sigma \in \Sigma\) . This yields an effective alphabet of size \(\left|\Sigma \right|=\left(\begin{array}{c}N\\ K\end{array}\right)\) letters, where each combinatorial letter encodes \({{\text{log}}}_{2}\left(\left|\Sigma \right|\right)={{\text{log}}}_{2}\left(\begin{array}{c}N\\ K\end{array}\right)\) bits. An r-bit binary message requires \(\frac{r}{{{\text{log}}}_{2}\left(\begin{array}{c}N\\ K\end{array}\right)}\) synthesis cycles (and a DNA molecular segment with length \(\frac{kr}{{{\text{log}}}_{2}\left(\begin{array}{c}N\\ K\end{array}\right)}\) ). In practice, we would prefer working with alphabet sizes that are powers of two, where each letter will encode for \(\left\lfloor {\log_{2} \left( {\begin{array}{*{20}c} N \\ K \\ \end{array} } \right)} \right\rfloor\) bits. Note that this calculation ignores error correction redundancy, random access primers, and barcodes, which are all required for message reconstruction. See Supplementary Sect.  8.2 and Fig.  1 a, which illustrate a trimer-based binomial alphabet with \(N=16\) and \(K=5\) , resulting in an alphabet of size \(\left|\Sigma \right|=\left(\begin{array}{c}16 \\ 5 \end{array}\right)=\mathrm{4,368}\) that allows to encode \(\lfloor {log}_{2}(4368)\rfloor =12\) bits per letter or synthesis position.

In the full binary encoding scheme, all possible nonempty subsets of \(\Omega\) represent valid letters in the alphabet. This yields an effective alphabet of size \(\left|\Sigma \right|={2}^{N}-1\) letters, each encoding for \(\left\lfloor {\log_{2} \left( {\left| \Sigma \right|} \right)} \right\rfloor = N - 1\) bits.

From this point on, we focus on the binomial encoding.

Reconstruction probabilities for binomial encoding

In this section, the performance characteristics of binomial encoding are investigated. Specifically, we present a mathematical analysis of the probability of successfully reconstructing the intended message. In Sects. “ An end-to-end combinatorial shortmer storage system ” and " Experimental proof of concept ", results are presented from our simulations and a small-scale molecular implementation of the binomial encoding, respectively.

Reconstruction of a single combinatorial letter

Since every letter \(\sigma \in \Sigma\) consists exactly of the \(K\) member k-mers, the required number of reads for observing at least one read of each member k-mer in a single letter follows the coupon collector distribution 20 . The number of reads required to achieve this goal can be described as a random variable \(R={\sum }_{i=1}^{K}{R}_{i}\) , where \({R}_{1}=1\) and \({R}_{i}\sim Geom\left(\frac{K-i+1}{K}\right), i=2,\dots ,K\) . Hence, the expected number of required reads, is:

where \(Har(\cdot )\) is the harmonic number.

The expected number of reads required for reconstructing a single combinatorial letter thus remains reasonable for the relevant values of \(K\) . For example, when using a binomial encoding with \(K=5\) the expected number of reads required for reconstructing a single combinatorial letter is roughly \(11.5\) , which is very close to the experimental results presented in Sect. " Experimental proof of concept ".

By Chebyshev’s inequality (See Sect. " Reconstruction probability of a binomial encoding letter "), we can derive a (loose) upper bound on the probability of requiring more than \(E\left[R\right]+cK\) reads to observe at least one read of each member k-mer, where \(c>1\) is a parameter:

For example, when using a binomial encoding with \(K=5\) , the probability of requiring more than \(26.5\) reads (corresponding to \(c=3\) ) is bounded by \(0.18\) , which is consistent with the experimental result shown in Fig. 5 d.

Reconstruction of a combinatorial sequence

When we examine an entire \(K\) -subset binomial encoded combinatorial sequence of length \(l\) , we denote by \(R(l)\) the required number of reads to observe \(K\) distinct k-mers in every position. Assuming independence between different positions and not taking errors into account, we get the following relationship between \(c\) and any desired confidence level \(1-\delta\) (See Sect. " Reconstruction probability of a binomial encoding letter " for details):

And therefore:

The number of reads required to guarantee reconstruction of a binomial encoded message, at a \(1-\delta\) probability, with \(K=5,\) and \(l\) synthesized positions, is thus \(KHar\left(K\right)+cK\) when

Supplementary Table S2 shows several examples of this upper bound. As demonstrated in the simulations and the experimental results, this bound is not tight (See Sects. “ An end-to-end combinatorial shortmer storage system ” and " Experimental proof of concept ").

Note that with an online sequencing technology (such as nanopore sequencing) the sequencing reaction can be stopped after \(K\) distinct k-mers are confidently observed.

To take into account the probability of observing a k-mer that is not included in \(\Omega\) (e.g., due to synthesis or sequencing error), we can require that at least \(t>1\) reads of each of the \(K\) distinct k-mers will be observed. This is experimentally examined in Sect. " Experimental proof of concept ", while the formal derivation of the number of required reads is not as trivial, and will be addressed in future work.

The above analysis is based only on oligo recovery, which depends solely on the sampling rate, ignoring possible mix-up errors (i.e., incorrect k-mer readings). This assumption is based on the near-zero mix-up probability attained by the construction of \(\Omega\) , which maximizes the minimal Hamming distance between elements in \(\Omega\) . In Sect. " Experimental proof of concept ", this analysis is compared to experimental results obtained from using synthetic combinatorial DNA.

An end-to-end combinatorial shortmer storage system

We suggest a complete end-to-end workflow for DNA-based data storage with the combinatorial shortmer encoding presented in Fig.  2 . The workflow begins with encoding, followed by DNA synthesis, storage, and sequencing, and culminates in a final decoding step. A 2D Reed-Solomon (RS) error correction scheme, which corrects errors in the letter reconstruction (for example, due to synthesis, sequencing, and sampling errors) and any missing sequences (such as dropout errors), ensures the integrity of the system. Table 1 shows the encoding capacities of the proposed system, calculated on a 1 GB input file with standard encoding and three different binomial alphabets (See Supplementary Sect.  8.5 ). All calculations are based on error correction parameters similar to those previously described (See Sect. " Information capacities for selected encodings ") 3 , 4 , 21 , 22 . With these different alphabets, up to a 6.5-fold increase in information capacity is achieved per synthesis cycle, compared to standard DNA-based data storage. While different error correction codes can be used in this system, for our work we chose to implement a 2D RS.

End-to-end workflow of a combinatorial DNA storage system. A binary message is broken into chunks, barcoded, and encoded into a combinatorial alphabet (i). RS encoding is added to each chunk and each column (ii). The combinatorial message is synthesized using combinatorial shormer synthesis (iii), and the DNA is sequenced (iv). Next, the combinatorial letters are reconstructed (v). Finally the message goes through 2D RS decoding (vi), followed by its translation back into the binary message (vii).

An example of the proposed approach, using a binomial alphabet with \(N=16\) and \(K=5\) and 2D RS, is detailed below. A binary message is encoded into a combinatorial message using the 4096-letter alphabet. Next, the message is broken into 120-letter chunks, and each chunk is barcoded. The 12nt barcodes are encoded using RS(6,8) over \(GF({2}^{4})\) , resulting in 16nt barcodes. Each chunk of 120 combinatorial letters is encoded using RS(120,134) over \(GF({2}^{12})\) . Every block of 42 sequences is then encoded using RS(42,48) over \(GF\left({2}^{12}\right)\) (See Sect. " An end-to-end combinatorial storage system " for details).

To better characterize the potential of this proposed system, we implemented an end-to-end simulation using the parameters mentioned above. We simulated the encoding and decoding of 10 KB messages with different binomial alphabets and error probabilities, and then measured the resulting reconstruction and decoding rates throughout the process. Figure  3 a depicts a schematic representation of our simulation workflow and indicates how the error rates are calculated (See Sect. " Reconstruction ").

Simulation of an end-to-end combinatorial shortmer encoding. ( a ) A schematic view of the simulation workflow. A text message is translated into a combinatorial message (1), and encoded using RS error correction on the barcode and payload (2). Each block is encoded using outer RS error correction (3). DNA synthesis and sequencing are simulated under various error schemes, and the combinatorial letters are reconstructed (4–5). RS decoding is performed on each block (6) and on each sequence (7) before translation back to text (8). The Roman numerals (i-iv) represent the different error calculations. ( b ) Error rates in different stages of the decoding process. Boxplot of the normalized Levenshtein distance (See Sect. " Reconstruction ") for the different stages in a simulation (30 runs) of sampling 100 reads, with an insertion error rate of 0.01. The X-axis represents the stages of error correction (before 2D RS decoding (iv), after RS payload decoding (iii), and after 2D RS decoding (ii)). ( c , d ) Sampling rate effect on overall performance. Normalized Levenshtein distance as a function of sampling rate before RS decoding ( c ) and after 2d RS decoding (ii). Different lines represent different error types (substitution, deletion, and insertion) introduced at a rate of 0.01.

The results of the simulation runs are summarized in Fig.  3 b–d. Each run included 30 repeats with random input texts of 10 KB encoded using 98 combinatorial sequences, each composed of 134 combinatorial letters and 16nt barcode, as described above. Each run simulated the synthesis of 1000 molecules on average per combinatorial sequence and sampling of a subset of these molecules to be sequenced. The subset size was drawn randomly from \(N\left(\mu ,\sigma =100\right),\) where \(\mu\) is a parameter. Errors in predetermined rates were introduced during the simulation of both DNA synthesis and sequencing, as expected in actual usage 23 (See Sect. " Synthesis and sequencing simulation with errors " for details on the simulation runs). Reconstruction rates and Levenshtein distances are calculated throughout the simulation process, as described in Fig.  3 a.

Notably, the sampling rate is the dominant factor where even with zero synthesis and sequencing errors, low sampling rates yield such poor results (Fig.  3 c) that the RS error correction is unable to overcome (Fig.  3 d). The effect of substitution errors on the overall performance is smaller and they are also easier to detect and correct. This is because substitution errors occur at the nucleotide level rather than at the trimer level. The minimal Hamming distance \(d=2\) of the trimer set \(\Omega\) allows for the correction of single-base substitutions. The use of 2D RS error correction significantly improved reconstruction rates, as can be observed in Fig.  3 b.

To assess the effect of using the suggested approach on the cost of DNA-based data storage systems, we performed an analysis of the different cost components. In brief, we analyzed the effect on the number of synthesis cycles and the number of bases to sequence, taking into account the required sequencing depth to achieve a desired reconstruction probability (See Sect. " Cost analysis "). Figure  4 depicts the costs of storing 1 GB of information using different combinatorial alphabets. Clearly, combinatorial DNA encoding can potentially reduce DNA-based data storage costs as the alphabet size grows and each letter encodes more bits. This is especially relevant in comparison with the composite encoding scheme presented in 3 . While both methods increase the logical density by extending the alphabet using mixtures of DNA letters/k-mers and thus reducing the synthesis cost (See Fig.  4 a), a crucial difference lies in the effect on sequencing costs. Composite DNA uses mixed letters with varying proportions of the different letters, which makes reconstruction very challenging in larger alphabets and results in very high sequencing costs that undermine the reduced synthesis costs. On the other hand, combinatorial DNA encoding uses binary mixtures, which are much simpler to reconstruct, therefore maintaining the sequencing costs relatively constant as the alphabet grows (See Fig.  4 b). For assessing the sequencing costs, we used a coupon collector model presented in 24 to calculate the required sequencing depth that ensures a decoding probability with an error rate of less than \({10}^{-4}\) (See Supplementary Sect.  8.5 ). In comparison with the composite encoding scheme, our analysis demonstrates a required sequencing depth that grows moderately. Figure  4 c analyzes the normalized overall cost, based on different assumptions regarding the ratio between synthesis costs and sequencing costs, \({C}_{syn}:{C}_{seq}\) . With a cost ratio of 500:1, 1000:1, 2000:1, it is evident that synthesis costs outweigh the fluctuations in sequencing costs, indicating a monotonic reduction in overall costs. This is an improvement compared to the composite DNA approach presented in 3 , where costs are reduced only up to a certain alphabet size, and then increase again due to the increased sequencing cost. In combinatorial DNA encoding, costs continue to drop, while alphabet size increases.

Cost analysis for a combinatorial DNA-based data storage system using different alphabets. ( a ) synthesis cost as a function of the alphabet size (presented as bit per letter, for simplicity). The cost is calculated as the number of synthesis cycles required for storing 1 GB of information. ( b ) Sequencing cost as a function of the alphabet size. Similarly to ( a ). ( c ) Normalized total cost as a function of the alphabet size for different synthesis-to-sequencing cost ratios. Costs are normalized by the total cost of a standard DNA-based system.

Experimental proof of concept

To assess and establish the potential of large combinatorial alphabets, we performed a small-scale experimental proof of concept study demonstrating the encoding and decoding of a 96-bit input message, which is equivalent to the text “DNA Storage!”. Since combinatorial DNA synthesis technology is not yet available, we demonstrated the combinatorial approach using Gibson assembly as an ad-hoc imitation for combinatorial synthesis. We constructed two combinatorial sequences, each containing a barcode and four payload cycles over a binomial alphabet with \(N=16\) and \(K=5\) . The assembly was performed using DNA fragments composed of a 20-mer information sequence and an overlap of 20 bp between adjacent fragments, as shown in Fig.  5 a. The assembled DNA was then stored and sequenced for analysis using Illumina Miseq (See Table 3 and Sect. " Cost analysis " for details about the sequencing procedures).

Experiment analysis. ( a ) A schematic view of the Gibson assembly. Each combinatorial sequence consists of a barcode segment and four payload segments (denoted as cycles 1–4). ( b ) Reconstruction results of the two combinatorial sequences. The color indicates read frequency, and the member k-mers are marked with orange boxes. ( c ) The distribution of reads over the 16 k-mers in an example combinatorial letter. Overlaid histograms represent the percentage of reads for each of the 16 k-mers for the same position in our two combinatorial sequences. This, in fact, is an enlarged view of the two c4 columns of panel b. ( d ) Required number of reads for reconstructing a single combinatorial letter. A histogram of the number of reads required to observe at least \(t=\mathrm{1,2}\) reads from \(K=5\) inferred k-mers. The results are based on resampling the reads 500 times, the data represents cycle 4. ( e ) Required number of reads for reconstructing a four-letter combinatorial sequence. Similar to d. ( f ), Reconstruction failure rate as a function of the required multiplicity \(t\) . Erroenous reconstruction rate shown for different values of required copies to observe each inferred k-mer ( \(t=\mathrm{1,2},\mathrm{3,4}\) ). The mean required number of reads for reconstruction is displayed using a secondary Y-axis in the dashed lines.

The sequencing output was then analyzed using the procedure described in Sect. “ Decoding and analysis ”. Both combinatorial sequences were successfully reconstructed from the sequencing reads, as presented in Fig.  5 b, and Supplementary Figs. S1, S2, and S3. The experiment also demonstrated the robustness of the binomial DNA encoding for synthesis and sequencing errors, as described in Fig.  5 c. We observed a minor leakage between the two synthesized sequences, which was overcome by the reconstruction pipeline (See Fig.  5 c, and Supplementary Figs. S1 , S2 , and S3 ). Note that there is a partial overlap between the member k-mers of the two sequences.

For comparison, a recent study by 14 encoded the 84-bit phrase “HelloWord” using a different encoding and synthesis approach. A comparison between the two experiments is shown in Table 2 . For example, while we used Gibson assembly as our synthesis method, they introduced a new method called Bridge Oligonucleotide Assembly. We encoded 12 bits per synthesis cycle and assembled four combinatorial fragments in each sequence, while 14 encoded 84 bits in a single combinatorial cycle. Our 96-bit were split and encoded using two combinatorial sequences, while they encoded the same 84-bits message, in its full format, on eight different sequences repeatedly. Our \({\text{N}}=16\) and 5 combinatorial factor, while their \(N=96\) and a higher 32 combinatorial factor.

To test the effect of random sampling on the reconstruction of combinatorial sequences, we performed a subsampling experiment with \(N=500\) repeats, presented in Fig.  5 d–f. We subsampled varying numbers of reads from the overall read pool and ran the reconstruction pipeline. Note that, as explained, the reconstruction of a single binomial position requires finding \(K=5\) inferred k-mers. That is, observing five unique k-mers at least \(t\) times. We tested the reconstruction performance using \(t=\mathrm{1,2},\mathrm{3,4}\) and recorded the effect on the successful reconstruction rate and required number of reads.

For \(t=1\) , reconstruction required analyzing 12.26 reads on average. These included 0.45 reads that contained an erroneous sequence that could not be mapped to a valid k-mer, and thus ignored. Note that the design of the set \(\Omega\) of valid k-mers allows us to ignore only the reads for which the Hamming distance for a valid k-mer exceeded a predefined threshold ( \(d=3\) ). If we ignored all the reads containing a sequence with non-zero Hamming distance to all k-mers, we would have skipped 2.26 extra reads, on average.

As expected, requiring \(t=2\) copies of each inferred k-mer resulted in an increase in the overall number of analyzed reads. Reconstruction of a single combinatorial letter required analyzing an average of 21.6 reads with 0.83 skipped and 3.99 non-zero Hamming distance reads. The complete distribution of the number of reads required for the reconstruction of a single position using \(t=\mathrm{1,2}\) is presented as a histogram in Fig.  5 d.

To reconstruct a complete combinatorial sequence of 4 positions, we required the condition to hold for all positions. For \(t=1\) , this entailed the analysis of 55.60 reads on average, out of which 1.04 reads were identified as erroneous and thus ignored, and with 7.36 non-zero Hamming distance reads. For \(t=2\) , an average of 102.66 reads were analyzed with 1.97 skipped and 13.24 non-zero Hamming distance reads. The complete distribution of the number of reads required for reconstructing a complete combinatorial sequence using \(t=\mathrm{1,2}\) is presented as a histogram in Fig.  5 e.

Note that these results correspond to the analysis presented in Sect. “ Reconstruction probabilities for binomial encoding ”, for the reconstruction of a single binomial position and a complete binomial sequence. Calculating the bound presented in Supplementary Table S2 , with \(K=5\) and \(l=4\) , yields a requirement of approximately 140 reads to obtain \(1-\delta =0.99\) probability of reconstruction. Clearly, this is well above the observed number of 55.60 reads. Note, as explained, the calculated bound is a loose bound.

The reconstruction procedure ends with a set of inferred k-mers that represent the inferred combinatorial letter. This set is not guaranteed to be correct, especially when using \(t=1\) , which means that noisy reads may result in an incorrect k-mer included in the inferred letter. Figure  5 f depicts the rate of incorrect reconstructions as a function of the number of required copies for each inferred k-mer ( \(t=\mathrm{1,2},\mathrm{3,4}\) ). Note that with \(t\ge 3\) results in 100% successful reconstruction. This, however, comes with a price, where more reads must be analyzed.

In this study, we introduced combinatorial shortmer encoding for DNA-based data storage, which extends the approach of composite DNA by resolving some of its sensitivity related issues. Combinatorial shortmer encoding allows for increased logical density while maintaining low error rates and high reconstruction rates. We explored two encoding schemes, binary and binomial, and evaluated some of their theoretical and practical characteristics. The inherent consistency of the binomial encoding scheme, where every letter in the sequence consists of exactly \(K\) distinct member k-mers, ensures uniformity in the encoded DNA sequences. This approach not only simplifies the reading process, but also allows for a more streamlined decoding. For instance, technologies like nanopore sequencing enable continuous sequencing until all k-mers at a given position are confidently observed.

Our suggested approach is designed to inherently overcome base substitution errors, which are the most common errors expected in every DNA-based data storage system that includes DNA sequencing. This is achieved by the selection of a set of \(N\) k-mer building blocks to be resilient to single-base substitutions. Other considerations may also be incorporated in the selection of the set of valid k-mers, taking into account any biological, chemical, or technological constraints. This represents an inherent tradeoff in DNA-based data storage between sequence constraints and information density. Insertion and deletion errors, which usually originate in the synthesis process, are more challenging to overcome. We introduced a 2D RS error correction scheme on the shortmer level, allowing for a successful message reconstruction even with error levels exceeding those expected in reality.

Our study highlights the significant effect of sampling rates on the overall performance of the system. The accuracy and completeness of sequence reconstruction require each of the sequences to be observed with a sufficiently high coverage. Our subsampling experiments underpin this observation, demonstrating the need for calibration of sampling rates to ensure the desired fidelity in DNA-based data storage. The crucial role of the sampling rate was also highlighted in 3 . However, while composite DNA uses mixed letter with varying proportions of the different letters, the combinatorial encoding, studied in this current work, uses binary mixtures and does not rely on proportions. This potentially allows scaling up the combinatorial encoding without a significant effects on the required sampling rates.

Combinatorial DNA coding can potentially reduce the overall costs of DNA-based data storage. Considering both sequencing costs, which fluctuate, and synthesis costs, which consistently drop, the increase in the alphabet size is accompanied by a decrease in overall cost. However, combinatorial DNA synthesis or assembly is still unavailable for large-scale commercial use. Thus, further development of combinatorial DNA synthesis technologies will continue to impose limitations and constraints on combinatorial encoding, and determine the overall costs.

While our proof-of-concept experiment showed success on a small scale, there are complexities to be addressed in considering large-scale applications. These include synthesis efficiency, error correction, and decoding efficiency. Nonetheless, the resilience of our binomial DNA encoding for both synthesis and sequencing errors highlights its practical potential and scalability. One specific aspect is the effect of combinatorial encoding on possible sequence-related constraints. While sequences with unwanted compositions (e.g., containing homopolymers) will unavoidably be part of the synthesized mixtures, the uniform sampling of the combinatorial shortmers in each position, together with the independence of the different positions, guarantees that only very few such sequences will be aythesized. In particular—these will not interfere with successful reconstruction. Another challenging aspect of scaling up combinatorial DNA systems is the need to use longer DNA k-mers to construct larger sets with the desired constraints. This may make the combinatorial synthesis impractical and will require balancing the increase in logical density with the technological complexity.

Several future research directions emerge from our study. First, it is important to develop error correction methods for better handling insertion and deletion errors. One approach for achieving this goal, is to adjust sampling rates: optimizing the sampling rate, especially in large-scale experiments, can lead to data retrieval at high accuracy. While our study highlighted the role of sampling rates in achieving desired outcomes, delving deeper into the underlying theory will lead to more improvements. For example, based on theoretical bounds of sampling rates, more concrete recommendations can be provided for real-world applications. The development of error correction codes, designed specifically to overcome the error types that characterize combinatorial encoding, is another important direction for future research. Most notably, transitioning from small-scale proof-of-concept experiments to larger-scale implementations is an important next step. Evaluating the scalability of our method across various scales and complexities will be enlightening, especially when considering synthesis efficiency and error rates. Finally, the consideration of advanced sequencing technologies could redefine the potential and efficacy of our proposed method, including its future practical implementation.

To sum up, combinatorial DNA synthesis and sequence design are important beyond the scope of DNA-based data storage. Generating combinatorial DNA libraries is an efficient tool in synthetic biology, better supporting large-scale experiments. DNA synthesis technologies that can incorporate a combinatorial synthesis of longer DNA fragments will enable the design and generation of more DNA libraries with applications in data storage and beyond.

Reconstruction probability of a binomial encoding letter

Let the number of reads required for reconstruction be a random variable \(R={\sum }_{i=1}^{K}{R}_{i}\) where \({R}_{1}=1\) and \({R}_{i}\sim Geom\left(\frac{K-i+1}{K}\right), i=2,\dots ,K\) . Hence, the expected number of required reads, is:

Using the independence of \({R}_{i}\) , the variance of \(R\) can be bound by (See 25 ):

By Chebyshev’s inequality, we get an upper bound (a loose bound) on the probability of requiring more than \(E\left[R\right]+cK\) reads to observe at least one read of each member k-mer:

Let \(c=b\frac{\pi }{\sqrt{6}}\) , or \(b=\frac{c\sqrt{6}}{\pi }\) , and we obtain:

Or specifically:

We now turn to address the reconstruction of an entire oligo of length \(l\) . Let \(R(l)\) be the random variable representing the number of reads required to have observed all the \(K\) member k-mers in every position. Setting any \(\delta >0\) , if we show that \(P\left(R\left(l\right)>m\right)\ge 1-\delta\) , then we know that by accumulating \(m\) reads the probability of correct full reconstruction is more than \(1-\delta\) . From Eq. ( 11 ), and assuming independence of the positions (in terms of observing all \(K\) member k-mers), we get Eq. ( 12 ):

From which we can extract \(c\) , so that:

Which yields:

This process allows us to evaluate the sequencing depth complexity. For example, consider \(l=100\) and \(\delta =0.01\) . We want to find \(c\) , so that using \(KHar\left(K\right)+cK\) reads will reconstruct the entire sequence with 0.99 probability. We therefore set:

And therefore, using 128 reads guarantees reconstruction with 0.99 probability.

An end-to-end combinatorial storage system

In Sect. “ An end-to-end combinatorial shortmer storage system ” we propose an end-to-end combinatorial storage system, as follows.

Combinatorial encoding and padding

A binary message is encoded using a large k-mer combinatorial alphabet (e.g., trimer-based alphabet of size \(\left|\Sigma \right|=4096\) letters, with \(N=\left|\Omega \right|=16\) ), resulting in \(r=12\) bits per combinatorial letter. The binary message is zero-padded to ensure its length is divisible by \(r\) prior to the combinatorial encoding. The complete message is broken into sequences of set length \(l=120\) , each sequence is then marked with a standard DNA barcode and translated using the table presented in the Encode legend (See Supplementary Sect.  8.2 ).

The length of the complete combinatorial sequence must be divisible by the payload size \(l\) and by the block size \(B\) . As described in Fig.  6 , this is ensured using another padding step, and the padding information is included in the final combinatorial sequence.

Example of message coding, including padding and RS error correction. Encoding of a ~ 0.1 KB message into a 512 letter binomial alphabet ( \(N=16, K=3)\) . First, bit padding is added, included here in the letter \(_{{\sigma_{257} }}^{1}\) . Next, block padding is added, included here in \(_{{\sigma_{1} }}^{2}\) and \(_{{\sigma_{1} }}^{3}\) . Padding information is included in the last sequence of all blocks. The last sequence holds the number of padding binary bits. In this example, \(_{{\sigma_{149} }}^{4}\) represents 148 bits of padding, composed of \(4+\left(4*9\right)+\left(12*9\right) bits\) , 4 bits from \(_{{\sigma_{257} }}^{1}\) , 4 letters from \(_{{\sigma_{1} }}^{2}\) and 12 letters from \(_{{\sigma_{1} }}^{3}\) .

Error correction codes

The 2D error correction scheme includes the use of three RS 26 encodings: on each barcode, on the payload part of each sequence, and an outer error correction code on each block of sequences.

Each barcode is encoded using a systematic RS(6,8) code over \(GF({2}^{4})\) , transforming the unique 12nt barcode into a 16nt sequence.

Each 120 combinatorial letter payload sequence is encoded using an RS(120,134) code over \(GF({2}^{12})\) , resulting in a sequence of length 134 combinatorial letters.

To protect against sequence dropouts, an outer error correction code is used on the columns of the matrix (See Fig.  6 ). Each block of \(B=42\) sequences, is encoded using a RS(42,48) RS code \(GF\left({2}^{12}\right)\) . This is applied in each column separately.

For simplicity, Fig.  6 demonstrates the encoding of ~ 0.1 KB using shorter messages with simpler error correction codes. The following parameters are used:

A barcode length of 6nt encoded using RS(3,5) code over \(GF\left({2}^{4}\right)\) to get 10nt.

A payload length of \(l=12\) encoded using RS(12,18) over \(GF\left({2}^{9}\right)\) for the \(\left(\begin{array}{c}16\\ 3\end{array}\right)\) binomial alphabet.

A 10-sequence block encoded, column-wise, using a (10,15) RS code over \(GF({2}^{9})\) .

The 824 bits are first padded to be \(828=92*9\) . The 92 combinatorial letter message is split into \(7\) sequences of 12 letters and an additional sequence of 8 letters. Finally, a complete block of 12 sequences (total of \(10*12=120\) letters) is created by padding with one additional sequence of 12 letters and including the padding information as the last sequence.

Synthesis and sequencing simulation with errors

Simulating the synthesis process. DNA molecules pertaining to the designed sequences are synthesized using combinatorial k-mer DNA synthesis (See Fig.  1 b). For each combinatorial sequence, we first determine the number of synthesized copies by sampling from \(X\sim N(\mu =1000, {\sigma }^{2}=100)\) . Let \(x\) be the number of copies for a specific sequence. Next, for every position in the sequence, we uniformly sample \(x\) independent k-mers from the set of member k-mers of the combinatorial letter in the specific position. We concatenate the sampled k-mers to the already existing \(x\) synthesized molecules.

Error simulation. Synthesis and sequencing errors are simulated as follows. Error probabilities for deletion, insertion, and substitution are given as parameters denoted as \({P}_{d}, {P}_{I},\) and \({P}_{s}\) respectively. Deletion and insertion errors are assumed to occur during k-mer synthesis and thus implemented on the k-mer level (i.e., an entire k-mer is deleted or inserted in a specific position during the synthesis simulation). Substitution errors are assumed to be sequencing errors and hence implemented on a single base level (i.e., a single letter is substituted, disregarding the position within the k-mer).

Mixing. Post synthesis, molecules undergo mixing to mirror genuine molecular combinations. This is achieved through a randomized data line shuffle using an SQLite database, enabling shuffle processes even for sizable input files 27 .

Reading and sampling. From the simulated synthesized molecule set, a subsample of predefined size \(S*number of synthesized seqeunces\) is drawn, simulating the sampling effect of the sequencing process.

Reconstruction

Barcode decoding The barcode sequence of each read is decoded using the RS(6,8) code.

Grouping by barcode The reads are then grouped by their barcode sequence to allow the reconstruction of the combinatorial sequences.

Filtering of read groups Barcodes (set of reads) with less than 10% of the sampling rate \(S\) reads are discarded.

Combinatorial reconstruction For each set of reads, every position is analyzed separately. The \(K\) most common k-mers are identified and used to determine the combinatorial letter \(\sigma\) in this position. Let \(\Delta\) be the difference between the length of the analyzed reads and the length of the designed sequence. \(\Delta =l-len(read)\) . Reads with \(\left|\Delta \right|>k-1\) are discarded from the analysis. Invalid k-mers (not in \(\Omega )\) are replaced by a dummy k-mer \({X}_{dummy}\) .

Missing barcodes Missing barcodes are replaced with dummy sequences to enable correct outer RS decoding.

Normalized Levenshtein distance Levenshtein distance between the observed sequence \(O\) and the expected sequence \(E\) is calculated 28 , 29 . Normalized Levenshtein distance is calculated by dividing the distance by the length of the expected sequence:

Cost analysis

Synthesis cost estimation was performed using the logical density calculation presented in Supplementary Sect.  8.5 and Supplementary Table S1 . To calculate the sequencing costs, we used the coupon collector model presented in 24 to assess the required sequencing depth given the combinatorial alphabet. Figure  4 b indicates the total number of reads required for reconstructing the sequences, calculated as the required sequencing depth multiplied by the number of sequences from Supplementary Sect.  8.5 and Supplementary Table S1 . The analysis was performed on the following set of combinatorial alphabets: Standard DNA, \(\left(\begin{array}{c}8\\ 4\end{array}\right), \left(\begin{array}{c}16\\ 3\end{array}\right), \left(\begin{array}{c}16\\ 5\end{array}\right), \left(\begin{array}{c}16\\ 7\end{array}\right), \left(\begin{array}{c}32\\ 10\end{array}\right), \left(\begin{array}{c}32\\ 16\end{array}\right), \left(\begin{array}{c}64\\ 32\end{array}\right), \left(\begin{array}{c}96\\ 32\end{array}\right)\) .

Proof of concept experiment

The proof-of-concept experiment was performed by imitating combinatorial synthesis using Gibson assembly of larger DNA fragments. Each DNA fragment was composed of a 20-mer information sequence and an overlap of 20 bp between adjacent fragments, as depicted in Fig.  5 a. Two combinatorial sequences were designed, each composed of a barcode fragment, 4 payload fragments, and Illumina Miseq P5 and P7 anchors at the ends. The information fragments included in each combinatorial position were chosen from a set of 16 sequences with sufficient pair-wise distance. The full list of DNA sequences and the design of combinatorial sequences is listed in Supplementary Sect.  8.6 .

DNA assembly and sequencing

Payload, barcode, and P7 anchor fragments with 20 bp overlaps for the purpose of Gibson assembly were produced by annealing complementary oligonucleotides manufactured by Integrated DNA Technologies (IDT). Oligos were dissolved in Duplex Buffer (100 mM Potassium Acetate; 30 mM HEPES, pH 7.5; available from IDT) to the final concentration of 100 micromolar. For annealing, 25 µl of each oligo in a pair were combined to the final concentration of 50 micromolars. The oligo mixes were incubated for 2 min at 94 0 C, and gradually cooled down to room temperature. The annealed payload oligos that belonged to the same cycle (5 oligos total) were mixed to the final concentration of 1 micromolar per oligo—a total of 5 micromolar, by adding 2 µl of each annealed oligo into the 90 µl of nuclease-free water—a final volume of 100 µl. Annealed barcode and P7 anchor oligos were also diluted to the final concentration of 5 micromolar in nuclease-free water, after thorough mixing by vortexing. The diluted oligos were stored at −20 °C.

Immediately prior to the Gibson assembly, payload oligo mixes, barcode, and P7 anchor oligos were further diluted 100-fold to the final working dilution of 0.05 pmol/microliter in nuclease-free water. Gibson reaction was assembled by adding 1 µl (0.05 pmol) of barcode, 4 × cycle mixes, and P7 anchor to the 4 µl of nuclease-free water and supplemented with 10 µl of NEBuilder HiFi DNA assembly master mix (New England Biolabs (NEB)) to the final volume of 20 µl, according to the manufacturer instructions. The reactions were incubated for 1 h at 50 °C, and purified with AmpPure Beads (Thermo Scientific) at 0.8X ratio (16 µl of beads per 20 µl Gibson reaction) to remove free oligos / incomplete assembly products. After adding beads and thorough mixing, the reactions were incubated for 10 min at room temperature and then placed on a magnet for 5 min at room temperature. After removing the sup, the beads were washed twice with 100 µl of 80% ethanol. The remaining washing solution was further removed by a 20 µl tip, and the beads dried for 3 min on the magnet with an open lid. After removing from the magnet, the beads were resuspended in 22 µl of IDTE buffer (IDT), incubated for 5 min at room temperature, and then placed back on the magnet.

20 µl of eluate were transferred into the separate 1.7 ml tube. 5 µl of the eluted DNA were used as a template for PCR amplification combined with 23 µl of nuclease-free water, 1 µl of 20 micromolar indexing primer 5, 1 µl of 20 micromolar indexing primer 7, and 10 µl of rhAMPseq master mix v8.1—a total of 40 µl. After initial denaturation of 3 min at 95 °C, the PCR reaction proceeded with 50 cycles of 15 s at 95 °C, 30 s at 60 °C, and 30 s at 72 °C, followed by final elongation of 1 min at 72 °C and hold at 4 °C. The PCR reactions were purified with Ampure beads at 0.8X ratio (32 µl beads per 40 µl of PCR reaction) as outlined above, and eluted in 22 µl IDTE buffer. The concentration and the average size of the eluted product were determined by Qubit High Sensitivity DNA kit and Agilent 2200 TapeStation system with D1000 high-sensitivity screen tape respectively. The eluted product was diluted to 4 nM concentration, and used as an input for denatured sequencing library preparation, per manufacturer instructions. The sequencing was performed on Illumina Miseq apparatus (V2 chemistry, 2 × 150 bp reads) using 6 picomolar denatured library supplemented with 40% PhiX sequencing control.

Decoding and analysis

This section outlines the key steps involved in our sequencing analysis pipeline, aimed at effectively processing and interpreting sequenced reads. The analysis pipeline gets the sequencing output file containing raw reads in “.fastq” format and a design file containing the combinatorial sequences.

Analysis steps:

Length filtering. We saved reads that were 220 bp in length, retaining only those corresponding to our designed read length.

Read retrieval. We carefully checked each read for the presence of BCs, universals, and payloads. To keep our data accurate, we discarded reads where the BCs, universals, or payloads had a Hamming distance of more than 3 errors.

Identifying inferred k-mers. For every BC and each cycle, we counted the \(K\) most common k-mers. We then compared these with the design file to quantify those matching (Fig.  5 b) (See Table 3 ).

Information capacities for selected encodings

Table 1 illustrates the logical densities derived from encoding a 1 GB binary message using oligonucleotides with a 12nt barcode and an additional 4nt for standard DNA RS error correction, and a 120 letters payload with 14 extra RS for the payload in combinatorial encoding schemes with parameters \(N\) and \(K\) .

The densities were calculated as follows:

Ethics declaration

No animal or human subjects were involved in the study.

Data availability

The raw data is available in ENA (European Nucleotide Archive). The datasets generated and/or analyzed during the current study are available in the ENA (European Nucleotide Archive) repository, Accession Number—ERR12364864.

Code Availability

Implementation of the algorithms and instructions on how to use them can be found in the GitHub repository in the following links: https://github.com/InbalPreuss/dna_storage_shortmer_simulation ,  https://github.com/InbalPreuss/dna_storage_experiment .

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Acknowledgements

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101115134 (DiDAX project). The authors of this paper thank the Yakhini research group for the fruitful discussions. The authors also thank Eland Nagar for his support and problem-solving approach regarding the experimental proof of concept.

European Union's Horizon Europe Research and Innovation Programme, 101115134.

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Inbal Preuss, Zohar Yakhini & Leon Anavy

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Contributions

I.P., Z.Y. and L.A conceived the idea, designed the experiments, interpreted and analyzed the data. I.P performed all computational work, including the simulations and the data analysis. M.R. was solely responsible for conducting the experimental work. All authors wrote the manuscript.

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Correspondence to Inbal Preuss .

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Zohar Yakhini, Leon Anavy have competing interests as defined by Nature Research. Z. Yakhini and L. Anavy are named as inventors on a patent related to the content of this paper: L. Anavy, Z. Yakhini, and R. Amit, "Molecular data storage systems and methods". United States of America Patent US20210141568A1, 2021.

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Preuss, I., Rosenberg, M., Yakhini, Z. et al. Efficient DNA-based data storage using shortmer combinatorial encoding. Sci Rep 14 , 7731 (2024). https://doi.org/10.1038/s41598-024-58386-z

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DOI : https://doi.org/10.1038/s41598-024-58386-z

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