graphical representation of educational data

Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

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• v.1(9); 2020 Dec 11

Principles of Effective Data Visualization

Stephen r. midway.

1 Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA

We live in a contemporary society surrounded by visuals, which, along with software options and electronic distribution, has created an increased importance on effective scientific visuals. Unfortunately, across scientific disciplines, many figures incorrectly present information or, when not incorrect, still use suboptimal data visualization practices. Presented here are ten principles that serve as guidance for authors who seek to improve their visual message. Some principles are less technical, such as determining the message before starting the visual, while other principles are more technical, such as how different color combinations imply different information. Because figure making is often not formally taught and figure standards are not readily enforced in science, it is incumbent upon scientists to be aware of best practices in order to most effectively tell the story of their data.

The Bigger Picture

Visuals are an increasingly important form of science communication, yet many scientists are not well trained in design principles for effective messaging. Despite challenges, many visuals can be improved by taking some simple steps before, during, and after their creation. This article presents some sequential principles that are designed to improve visual messages created by scientists.

Many scientific visuals are not as effective as they could be because scientists often lack basic design principles. This article reviews the importance of effective data visualization and presents ten principles that scientists can use as guidance in developing effective visual messages.

Introduction

Visual learning is one of the primary forms of interpreting information, which has historically combined images such as charts and graphs (see Box 1 ) with reading text. 1 However, developments on learning styles have suggested splitting up the visual learning modality in order to recognize the distinction between text and images. 2 Technology has also enhanced visual presentation, in terms of the ability to quickly create complex visual information while also cheaply distributing it via digital means (compared with paper, ink, and physical distribution). Visual information has also increased in scientific literature. In addition to the fact that figures are commonplace in scientific publications, many journals now require graphical abstracts 3 or might tweet figures to advertise an article. Dating back to the 1970s when computer-generated graphics began, 4 papers represented by an image on the journal cover have been cited more frequently than papers without a cover image. 5

Regarding terminology, the terms graph , plot , chart , image , figure , and data visual(ization) are often used interchangeably, although they may have different meanings in different instances. Graph , plot , and chart often refer to the display of data, data summaries, and models, while image suggests a picture. Figure is a general term but is commonly used to refer to visual elements, such as plots, in a scientific work. A visual , or data visualization , is a newer and ostensibly more inclusive term to describe everything from figures to infographics. Here, I adopt common terminology, such as bar plot, while also attempting to use the terms figure and data visualization for general reference.

There are numerous advantages to quickly and effectively conveying scientific information; however, scientists often lack the design principles or technical skills to generate effective visuals. Going back several decades, Cleveland 6 found that 30% of graphs in the journal Science had at least one type of error. Several other studies have documented widespread errors or inefficiencies in scientific figures. 7 , 8 , 9 In fact, the increasing menu of visualization options can sometimes lead to poor fits between information and its presentation. These poor fits can even have the unintended consequence of confusing the readers and setting them back in their understanding of the material. While objective errors in graphs are hopefully in the minority of scientific works, what might be more common is suboptimal figure design, which takes place when a design element may not be objectively wrong but is ineffective to the point of limiting information transfer.

Effective figures suggest an understanding and interpretation of data; ineffective figures suggest the opposite. Although the field of data visualization has grown in recent years, the process of displaying information cannot—and perhaps should not—be fully mechanized. Much like statistical analyses often require expert opinions on top of best practices, figures also require choice despite well-documented recommendations. In other words, there may not be a singular best version of a given figure. Rather, there may be multiple effective versions of displaying a single piece of information, and it is the figure maker's job to weigh the advantages and disadvantages of each. Fortunately, there are numerous principles from which decisions can be made, and ultimately design is choice. 7

The data visualization literature includes many great resources. While several resources are targeted at developing design proficiency, such as the series of columns run by Nature Communications , 10 Wilkinson's The Grammar of Graphics 11 presents a unique technical interpretation of the structure of graphics. Wilkinson breaks down the notion of a graphic into its constituent parts—e.g., the data, scales, coordinates, geometries, aesthetics—much like conventional grammar breaks down a sentence into nouns, verbs, punctuation, and other elements of writing. The popularity and utility of this approach has been implemented in a number of software packages, including the popular ggplot2 package 12 currently available in R. 13 (Although the grammar of graphics approach is not explicitly adopted here, the term geometry is used consistently with Wilkinson to refer to different geometrical representations, whereas the term aesthetics is not used consistently with the grammar of graphics and is used simply to describe something that is visually appealing and effective.) By understanding basic visual design principles and their implementation, many figure authors may find new ways to emphasize and convey their information.

The Ten Principles

Principle #1 diagram first.

The first principle is perhaps the least technical but very important: before you make a visual, prioritize the information you want to share, envision it, and design it. Although this seems obvious, the larger point here is to focus on the information and message first, before you engage with software that in some way starts to limit or bias your visual tools. In other words, don't necessarily think of the geometries (dots, lines) you will eventually use, but think about the core information that needs to be conveyed and what about that information is going to make your point(s). Is your visual objective to show a comparison? A ranking? A composition? This step can be done mentally, or with a pen and paper for maximum freedom of thought. In parallel to this approach, it can be a good idea to save figures you come across in scientific literature that you identify as particularly effective. These are not just inspiration and evidence of what is possible, but will help you develop an eye for detail and technical skills that can be applied to your own figures.

Principle #2 Use the Right Software

Effective visuals typically require good command of one or more software. In other words, it might be unrealistic to expect complex, technical, and effective figures if you are using a simple spreadsheet program or some other software that is not designed to make complex, technical, and effective figures. Recognize that you might need to learn a new software—or expand your knowledge of a software you already know. While highly effective and aesthetically pleasing figures can be made quickly and simply, this may still represent a challenge to some. However, figure making is a method like anything else, and in order to do it, new methodologies may need to be learned. You would not expect to improve a field or lab method without changing something or learning something new. Data visualization is the same, with the added benefit that most software is readily available, inexpensive, or free, and many come with large online help resources. This article does not promote any specific software, and readers are encouraged to reference other work 14 for an overview of software resources.

Principle #3 Use an Effective Geometry and Show Data

Geometries are the shapes and features that are often synonymous with a type of figure; for example, the bar geometry creates a bar plot. While geometries might be the defining visual element of a figure, it can be tempting to jump directly from a dataset to pairing it with one of a small number of well-known geometries. Some of this thinking is likely to naturally happen. However, geometries are representations of the data in different forms, and often there may be more than one geometry to consider. Underlying all your decisions about geometries should be the data-ink ratio, 7 which is the ratio of ink used on data compared with overall ink used in a figure. High data-ink ratios are the best, and you might be surprised to find how much non-data-ink you use and how much of that can be removed.

Most geometries fall into categories: amounts (or comparisons), compositions (or proportions), distributions , or relationships . Although seemingly straightforward, one geometry may work in more than one category, in addition to the fact that one dataset may be visualized with more than one geometry (sometimes even in the same figure). Excellent resources exist on detailed approaches to selecting your geometry, 15 and this article only highlights some of the more common geometries and their applications.

Amounts or comparisons are often displayed with a bar plot ( Figure 1 A), although numerous other options exist, including Cleveland dot plots and even heatmaps ( Figure 1 F). Bar plots are among the most common geometry, along with lines, 9 although bar plots are noted for their very low data density 16 (i.e., low data-ink ratio). Geometries for amounts should only be used when the data do not have distributional information or uncertainty associated with them. A good use of a bar plot might be to show counts of something, while poor use of a bar plot might be to show group means. Numerous studies have discussed inappropriate uses of bar plots, 9 , 17 noting that “because the bars always start at zero, they can be misleading: for example, part of the range covered by the bar might have never been observed in the sample.” 17 Despite the numerous reports on incorrect usage, bar plots remain one of the most common problems in data visualization.

Examples of Visual Designs

(A) Clustered bar plots are effective at showing units within a group (A–C) when the data are amounts.

(B) Histograms are effective at showing the distribution of data, which in this case is a random draw of values from a Poisson distribution and which use a sequential color scheme that emphasizes the mean as red and values farther from the mean as yellow.

(C) Scatterplot where the black circles represent the data.

(D) Logistic regression where the blue line represents the fitted model, the gray shaded region represents the confidence interval for the fitted model, and the dark-gray dots represent the jittered data.

(E) Box plot showing (simulated) ages of respondents grouped by their answer to a question, with gray dots representing the raw data used in the box plot. The divergent colors emphasize the differences in values. For each box plot, the box represents the interquartile range (IQR), the thick black line represents the median value, and the whiskers extend to 1.5 times the IQR. Outliers are represented by the data.

(F) Heatmap of simulated visibility readings in four lakes over 5 months. The green colors represent lower visibility and the blue colors represent greater visibility. The white numbers in the cells are the average visibility measures (in meters).

(G) Density plot of simulated temperatures by season, where each season is presented as a small multiple within the larger figure.

For all figures the data were simulated, and any examples are fictitious.

Compositions or proportions may take a wide range of geometries. Although the traditional pie chart is one option, the pie geometry has fallen out of favor among some 18 due to the inherent difficulties in making visual comparisons. Although there may be some applications for a pie chart, stacked or clustered bar plots ( Figure 1 A), stacked density plots, mosaic plots, and treemaps offer alternatives.

Geometries for distributions are an often underused class of visuals that demonstrate high data density. The most common geometry for distributional information is the box plot 19 ( Figure 1 E), which shows five types of information in one object. Although more common in exploratory analyses than in final reports, the histogram ( Figure 1 B) is another robust geometry that can reveal information about data. Violin plots and density plots ( Figure 1 G) are other common distributional geometries, although many less-common options exist.

Relationships are the final category of visuals covered here, and they are often the workhorse of geometries because they include the popular scatterplot ( Figures 1 C and 1D) and other presentations of x - and y -coordinate data. The basic scatterplot remains very effective, and layering information by modifying point symbols, size, and color are good ways to highlight additional messages without taking away from the scatterplot. It is worth mentioning here that scatterplots often develop into line geometries ( Figure 1 D), and while this can be a good thing, presenting raw data and inferential statistical models are two different messages that need to be distinguished (see Data and Models Are Different Things ).

Finally, it is almost always recommended to show the data. 7 Even if a geometry might be the focus of the figure, data can usually be added and displayed in a way that does not detract from the geometry but instead provides the context for the geometry (e.g., Figures 1 D and 1E). The data are often at the core of the message, yet in figures the data are often ignored on account of their simplicity.

Principle #4 Colors Always Mean Something

The use of color in visualization can be incredibly powerful, and there is rarely a reason not to use color. Even if authors do not wish to pay for color figures in print, most journals still permit free color figures in digital formats. In a large study 20 of what makes visualizations memorable, colorful visualizations were reported as having a higher memorability score, and that seven or more colors are best. Although some of the visuals in this study were photographs, other studies 21 also document the effectiveness of colors.

In today's digital environment, color is cheap. This is overwhelmingly a good thing, but also comes with the risk of colors being applied without intention. Black-and-white visuals were more accepted decades ago when hard copies of papers were more common and color printing represented a large cost. Now, however, the vast majority of readers view scientific papers on an electronic screen where color is free. For those who still print documents, color printing can be done relatively cheaply in comparison with some years ago.

Color represents information, whether in a direct and obvious way, or in an indirect and subtle way. A direct example of using color may be in maps where water is blue and land is green or brown. However, the vast majority of (non-mapping) visualizations use color in one of three schemes: sequential , diverging , or qualitative . Sequential color schemes are those that range from light to dark typically in one or two (related) hues and are often applied to convey increasing values for increasing darkness ( Figures 1 B and 1F). Diverging color schemes are those that have two sequential schemes that represent two extremes, often with a white or neutral color in the middle ( Figure 1 E). A classic example of a diverging color scheme is the red to blue hues applied to jurisdictions in order to show voting preference in a two-party political system. Finally, qualitative color schemes are found when the intensity of the color is not of primary importance, but rather the objective is to use different and otherwise unrelated colors to convey qualitative group differences ( Figures 1 A and 1G).

While it is recommended to use color and capture the power that colors convey, there exist some technical recommendations. First, it is always recommended to design color figures that work effectively in both color and black-and-white formats ( Figures 1 B and 1F). In other words, whenever possible, use color that can be converted to an effective grayscale such that no information is lost in the conversion. Along with this approach, colors can be combined with symbols, line types, and other design elements to share the same information that the color was sharing. It is also good practice to use color schemes that are effective for colorblind readers ( Figures 1 A and 1E). Excellent resources, such as ColorBrewer, 22 exist to help in selecting color schemes based on colorblind criteria. Finally, color transparency is another powerful tool, much like a volume knob for color ( Figures 1 D and 1E). Not all colors have to be used at full value, and when not part of a sequential or diverging color scheme—and especially when a figure has more than one colored geometry—it can be very effective to increase the transparency such that the information of the color is retained but it is not visually overwhelming or outcompeting other design elements. Color will often be the first visual information a reader gets, and with this knowledge color should be strategically used to amplify your visual message.

Principle #5 Include Uncertainty

Not only is uncertainty an inherent part of understanding most systems, failure to include uncertainty in a visual can be misleading. There exist two primary challenges with including uncertainty in visuals: failure to include uncertainty and misrepresentation (or misinterpretation) of uncertainty.

Uncertainty is often not included in figures and, therefore, part of the statistical message is left out—possibly calling into question other parts of the statistical message, such as inference on the mean. Including uncertainty is typically easy in most software programs, and can take the form of common geometries such as error bars and shaded intervals (polygons), among other features. 15 Another way to approach visualizing uncertainty is whether it is included implicitly into the existing geometries, such as in a box plot ( Figure 1 E) or distribution ( Figures 1 B and 1G), or whether it is included explicitly as an additional geometry, such as an error bar or shaded region ( Figure 1 D).

Representing uncertainty is often a challenge. 23 Standard deviation, standard error, confidence intervals, and credible intervals are all common metrics of uncertainty, but each represents a different measure. Expressing uncertainty requires that readers be familiar with metrics of uncertainty and their interpretation; however, it is also the responsibility of the figure author to adopt the most appropriate measure of uncertainty. For instance, standard deviation is based on the spread of the data and therefore shares information about the entire population, including the range in which we might expect new values. On the other hand, standard error is a measure of the uncertainty in the mean (or some other estimate) and is strongly influenced by sample size—namely, standard error decreases with increasing sample size. Confidence intervals are primarily for displaying the reliability of a measurement. Credible intervals, almost exclusively associated with Bayesian methods, are typically built off distributions and have probabilistic interpretations.

Expressing uncertainty is important, but it is also important to interpret the correct message. Krzywinski and Altman 23 directly address a common misconception: “a gap between (error) bars does not ensure significance, nor does overlap rule it out—it depends on the type of bar.” This is a good reminder to be very clear not only in stating what type of uncertainty you are sharing, but what the interpretation is. Others 16 even go so far as to recommend that standard error not be used because it does not provide clear information about standard errors of differences among means. One recommendation to go along with expressing uncertainty is, if possible, to show the data (see Use an Effective Geometry and Show Data ). Particularly when the sample size is low, showing a reader where the data occur can help avoid misinterpretations of uncertainty.

Principle #6 Panel, when Possible (Small Multiples)

A particularly effective visual approach is to repeat a figure to highlight differences. This approach is often called small multiples , 7 and the technique may be referred to as paneling or faceting ( Figure 1 G). The strategy behind small multiples is that because many of the design elements are the same—for example, the axes, axes scales, and geometry are often the same—the differences in the data are easier to show. In other words, each panel represents a change in one variable, which is commonly a time step, a group, or some other factor. The objective of small multiples is to make the data inevitably comparable, 7 and effective small multiples always accomplish these comparisons.

Principle #7 Data and Models Are Different Things

Plotted information typically takes the form of raw data (e.g., scatterplot), summarized data (e.g., box plot), or an inferential statistic (e.g., fitted regression line; Figure 1 D). Raw data and summarized data are often relatively straightforward; however, a plotted model may require more explanation for a reader to be able to fully reproduce the work. Certainly any model in a study should be reported in a complete way that ensures reproducibility. However, any visual of a model should be explained in the figure caption or referenced elsewhere in the document so that a reader can find the complete details on what the model visual is representing. Although it happens, it is not acceptable practice to show a fitted model or other model results in a figure if the reader cannot backtrack the model details. Simply because a model geometry can be added to a figure does not mean that it should be.

Principle #8 Simple Visuals, Detailed Captions

As important as it is to use high data-ink ratios, it is equally important to have detailed captions that fully explain everything in the figure. A study of figures in the Journal of American Medicine 8 found that more than one-third of graphs were not self-explanatory. Captions should be standalone, which means that if the figure and caption were looked at independent from the rest of the study, the major point(s) could still be understood. Obviously not all figures can be completely standalone, as some statistical models and other procedures require more than a caption as explanation. However, the principle remains that captions should do all they can to explain the visualization and representations used. Captions should explain any geometries used; for instance, even in a simple scatterplot it should be stated that the black dots represent the data ( Figures 1 C–1E). Box plots also require descriptions of their geometry—it might be assumed what the features of a box plot are, yet not all box plot symbols are universal.

Principle #9 Consider an Infographic

It is unclear where a figure ends and an infographic begins; however, it is fair to say that figures tend to be focused on representing data and models, whereas infographics typically incorporate text, images, and other diagrammatic elements. Although it is not recommended to convert all figures to infographics, infographics were found 20 to have the highest memorability score and that diagrams outperformed points, bars, lines, and tables in terms of memorability. Scientists might improve their overall information transfer if they consider an infographic where blending different pieces of information could be effective. Also, an infographic of a study might be more effective outside of a peer-reviewed publication and in an oral or poster presentation where a visual needs to include more elements of the study but with less technical information.

Even if infographics are not adopted in most cases, technical visuals often still benefit from some text or other annotations. 16 Tufte's works 7 , 24 provide great examples of bringing together textual, visual, and quantitative information into effective visualizations. However, as figures move in the direction of infographics, it remains important to keep chart junk and other non-essential visual elements out of the design.

Principle #10 Get an Opinion

Although there may be principles and theories about effective data visualization, the reality is that the most effective visuals are the ones with which readers connect. Therefore, figure authors are encouraged to seek external reviews of their figures. So often when writing a study, the figures are quickly made, and even if thoughtfully made they are not subject to objective, outside review. Having one or more colleagues or people external to the study review figures will often provide useful feedback on what readers perceive, and therefore what is effective or ineffective in a visual. It is also recommended to have outside colleagues review only the figures. Not only might this please your colleague reviewers (because figure reviews require substantially less time than full document reviews), but it also allows them to provide feedback purely on the figures as they will not have the document text to fill in any uncertainties left by the visuals.

What About Tables?

Although often not included as data visualization, tables can be a powerful and effective way to show data. Like other visuals, tables are a type of hybrid visual—they typically only include alphanumeric information and no geometries (or other visual elements), so they are not classically a visual. However, tables are also not text in the same way a paragraph or description is text. Rather, tables are often summarized values or information, and are effective if the goal is to reference exact numbers. However, the interest in numerical results in the form of a study typically lies in comparisons and not absolute numbers. Gelman et al. 25 suggested that well-designed graphs were superior to tables. Similarly, Spence and Lewandowsky 26 compared pie charts, bar graphs, and tables and found a clear advantage for graphical displays over tabulations. Because tables are best suited for looking up specific information while graphs are better for perceiving trends and making comparisons and predictions, it is recommended that visuals are used before tables. Despite the reluctance to recommend tables, tables may benefit from digital formats. In other words, while tables may be less effective than figures in many cases, this does not mean tables are ineffective or do not share specific information that cannot always be displayed in a visual. Therefore, it is recommended to consider creating tables as supplementary or appendix information that does not go into the main document (alongside the figures), but which is still very easily accessed electronically for those interested in numerical specifics.

Conclusions

While many of the elements of peer-reviewed literature have remained constant over time, some elements are changing. For example, most articles now have more authors than in previous decades, and a much larger menu of journals creates a diversity of article lengths and other requirements. Despite these changes, the demand for visual representations of data and results remains high, as exemplified by graphical abstracts, overview figures, and infographics. Similarly, we now operate with more software than ever before, creating many choices and opportunities to customize scientific visualizations. However, as the demand for, and software to create, visualizations have both increased, there is not always adequate training among scientists and authors in terms of optimizing the visual for the message.

Figures are not just a scientific side dish but can be a critical point along the scientific process—a point at which the figure maker demonstrates their knowledge and communication of the data and results, and often one of the first stopping points for new readers of the information. The reality for the vast majority of figures is that you need to make your point in a few seconds. The longer someone looks at a figure and doesn't understand the message, the more likely they are to gain nothing from the figure and possibly even lose some understanding of your larger work. Following a set of guidelines and recommendations—summarized here and building on others—can help to build robust visuals that avoid many common pitfalls of ineffective figures ( Figure 2 ).

Overview of the Principles Presented in This Article

The two principles in yellow (bottom) are those that occur first, during the figure design phase. The six principles in green (middle) are generally considerations and decisions while making a figure. The two principles in blue (top) are final steps often considered after a figure has been drafted. While the general flow of the principles follows from bottom to top, there is no specific or required order, and the development of individual figures may require more or less consideration of different principles in a unique order.

All scientists seek to share their message as effectively as possible, and a better understanding of figure design and representation is undoubtedly a step toward better information dissemination and fewer errors in interpretation. Right now, much of the responsibility for effective figures lies with the authors, and learning best practices from literature, workshops, and other resources should be undertaken. Along with authors, journals play a gatekeeper role in figure quality. Journal editorial teams are in a position to adopt recommendations for more effective figures (and reject ineffective figures) and then translate those recommendations into submission requirements. However, due to the qualitative nature of design elements, it is difficult to imagine strict visual guidelines being enforced across scientific sectors. In the absence of such guidelines and with seemingly endless design choices available to figure authors, it remains important that a set of aesthetic criteria emerge to guide the efficient conveyance of visual information.

Acknowledgments

Thanks go to the numerous students with whom I have had fun, creative, and productive conversations about displaying information. Danielle DiIullo was extremely helpful in technical advice on software. Finally, Ron McKernan provided guidance on several principles.

Author Contributions

S.R.M. conceived the review topic, conducted the review, developed the principles, and wrote the manuscript.

Steve Midway is an assistant professor in the Department of Oceanography and Coastal Sciences at Louisiana State University. His work broadly lies in fisheries ecology and how sound science can be applied to management and conservation issues. He teaches a number of quantitative courses in ecology, all of which include data visualization.

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Data Visualization: Resources for Teaching, Learning, and Research

Common tools used for data visualization include R and Python, third-party applications like Tableau , Gephi , and Voyant Tools , JavaScript libraries like D3  and temporal, geospatial, and exhibition tools like Omeka, StoryMap, Timeline.js, WorldMap, and Carto. Resources for data visualization and data science training and assistance are available from several organizations around campus, including (but not limited to) Academic Technology for FAS , the Harvard Library, Research Computing in the Arts and Humanities , and the Institute for Quantitative Social Science .

Access to a suite of visualization applications, and to assistance with data visualization, is available on computers in the Multimedia Lab at Lamont Library, while workshops on approaches and tools used for effective visualization are frequently offered to faculty, staff, teaching fellows, and students by a combination of groups on campus. For more information on the digital toolkit or on visualization resources in general, please contact AT-FAS at [email protected] .

Workshop Example

The following is an excerpt from a  Harvard Gazette article about the two-day workshop "Thinking With Your Eyes: Visualizing the Arts, Humanities, and Sciences," held in February 2014 and sponsored by the interdisciplinary Digital Futures Consortium :

It seems like big data is everywhere you look. And in a way, it is: Maps, medical scans, and weather charts are commonplace forms of data visualization. Each was examined during “Thinking with Your Eyes,” a two-day conference that brought together experts in the arts, sciences, humanities, and technology — as well as academic and computing groups from across Harvard — to investigate how graphic representation brings knowledge to life. “In a technological age where large amounts of data can be captured like never before, how big data is used and portrayed presents significant challenges,” said keynote speaker Martin Wattenberg, who along with Fernanda Viégas leads Google’s “Big Picture” visualization research group. As presenters acknowledged the long and cross-cultural history of visual representation, it was often in the context of seeking new ways to make information more memorable. Read more...

Faculty Research Examples (provided by Research Computing in the Arts and Humanities)

Steven Clancy, Senior Lecturer on Slavic Languages and Literatures / Director of the Slavic Language Program

Russian Modules is a Russian language textbook currently under development. It makes use of the Neo4j graph database to support the visualization of Russian lemmas, both within context and in isolation. Additional functionality comes from D3js, a data visualization library that displays words within the database in the form of a series of force layouts. The goal of the project is to tie the graph database into the entirety of the book in order to create a unique interactive environment for learning. This involves the ability to highlight terms, explore meanings, noun declensions, and verb conjugations. In addition, this will allow for curriculum planning by analysing Russian language texts for their difficulty (as assigned by Steven and his colleagues). Copying and pasting text into the text analysis tool will highlight words based on word difficulty as it appears in the larger Russian language curriculum.

Malika Zeghal, Prince Alwaleed Bin Talal Professor in Contemporary Islamic Thought and Life in the Department of Near Eastern Languages and Civilization

Malika Zeghal is the principal investigator for Afkar, the Arabic word for ideas. Her project endeavors to trace Muslim intellectual networks during the interwar period of the early twentieth century. The research team began by parsing the content of various religious journals in the Middle East, beginning in Cairo, but expanding outward as far as Paris and the Philippines. This information will be used to create a dynamic world map that will display the provenance and movement of fatwa requests included in the journals. The goal is to display the various paths of knowledge contained within each of the journals, and to begin asking questions about the various imaginary networks (e.g. transcontinental intellectual communities) that existed at the time.

Research Visualization Example: Visualisation des Billets Vendus

Based on the research of Pannill Camp, Associate Professor of Drama at Washington University at St. Louis, Juliette Cherbuliez, Associate Professor of French at the University of Minnesota, and Derek Miller, Assistant Professor of English at Harvard University,  Visualisation des Billets Vendus   is a data interactive created by Christophe Schuwey, Lecturer at Université de Fribourg (Switzerland), and Christopher Morse, Senior Research Computing Specialist with Harvard's Research Computing in the Arts and Humanities group that reveals ticket sales at performances during the 1784-1785 season at the Odéon-Théâtre de l’Europe in Paris. 

Visualisation des Billets Vendus, while still in its early stages, has been an interesting thought experiment in theater representation and history, and presents a number of unique challenges. For example, how should one visualize a theater? Does it suffice to abstract a theater into shapes like a seating chart one might see on a website like Ticketmaster? What can be learned (or not) by specificity, that is to say, by attempting to recreate each individual seating area, or even each seat? Moving forward, the visualization seeks to encompass the entirety of the Comédie-Française registers collection, totaling over one hundred years of ticket sales, and various user interface improvements over time will make it easier for users to work with the heat map in more detail.

Graphical Data Representation in Education: Enhancing Understanding and Analysis

Table of Contents

Ever stared at a sea of numbers on a spreadsheet and felt your eyes glaze over? You’re not alone. Numbers alone can be daunting, but when transformed into visuals, they tell a story that is both comprehensible and captivating. This is where graphical representation of data comes into play, especially within the realm of education. Let’s embark on a journey to understand the various types of graphs and their unique roles in presenting data.

Why use graphs in educational research?

Graphs are more than just pretty pictures; they are powerful tools for communication. They help researchers, educators, and students alike to visualize complex data, identify trends, and make comparisons at a glance. In educational research, graphs serve as a bridge between raw data and actionable insights, making it easier for decision-makers to understand and act upon the information.

Types of graphs and their educational applications

There’s a cornucopia of graphs out there, each with its particular use. Let’s explore some of the most common types and how they aid in data analysis within education.

Pictographs

What is a pictograph ? A pictograph uses images or symbols to represent data. Each icon in a pictograph corresponds to a certain number of items.

Where are pictographs used? Pictographs are particularly useful when teaching younger students or those new to data interpretation. They are visually engaging and can represent data in a way that is easy to understand. For instance, a pictograph could be employed to show the number of books read by students in a classroom, with each icon representing five books.

What is a bar graph ? Bar graphs consist of rectangular bars, with the length of each bar representing the magnitude of the data. They can be displayed horizontally or vertically.

When to use a bar graph? Bar graphs are versatile and can be used when comparing different groups or tracking changes over time. For example, a vertical bar graph could illustrate the test scores of students across different subjects, making it easy to see which subjects had the highest scores.

What is a pie chart ? A pie chart is a circular graph divided into slices, with each slice representing a proportion of the whole.

The use of pie charts in education Pie charts are perfect when you need to show a part-to-whole relationship. In an educational setting, a pie chart could demonstrate the percentage of students who prefer different types of learning activities, like lectures, group work, or hands-on experiments.

Line graphs

What is a line graph ? A line graph uses points connected by lines to show how data changes over time.

Line graphs in educational research Line graphs are ideal for displaying data trends over a period. For instance, a line graph could be used to depict the progression of student enrollment numbers over several years, highlighting peaks and troughs in the data.

Choosing the right graph for your data

While each graph type has its merits, the key to effective data visualization lies in choosing the graph that best suits the data and the story you want to tell. Consider the following when making your choice:

• Objective: What do you want the graph to achieve? Clarify whether you want to show a comparison, a trend, or a distribution.
• Clarity: Which graph type will present your data most clearly to your audience? Avoid graphs that are overly complex for the data presented.
• Accuracy: Ensure the graph you choose accurately represents the data without distorting the information.

Best practices for creating graphs

Creating an effective graph is not just about selecting the right type; it’s also about attention to detail. Here are some best practices to keep in mind:

• Keep it simple: Don’t overload your graph with too much information. Stick to the essentials to maintain readability.
• Label clearly: Make sure all axes, bars, lines, and segments are clearly labeled with appropriate titles and units of measurement.
• Choose color wisely: Use color to help distinguish data points, but be mindful of colorblind readers and avoid overly bright or clashing colors.
• Provide context: Always accompany your graph with an explanation of what it shows and why it’s important.

Integrating graphs into the curriculum

Graphical representation of data should not be an afterthought in education. Integrating this skill into the curriculum can enhance students’ analytical abilities and prepare them for a data\-driven world. From science experiments to social studies research, every subject can benefit from the clear communication of data through graphs.

Graphs are a testament to the adage “a picture is worth a thousand words.” By transforming numerical data into visual stories, graphs play a vital role in educational research and beyond. As educators and learners, harnessing the power of graphical data representation can lead to more informed decisions and a deeper understanding of the world around us.

Have you encountered a graph that significantly changed your understanding of a topic? Or, what challenges have you faced when interpreting graphical information? Share your experiences and thoughts on the importance of data visualization in education.

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Assessment for Learning

1 Concept and Purpose of Evaluation

• Basic Concepts
• Relationships among Measurement, Assessment, and Evaluation
• Teaching-Learning Process and Evaluation
• Assessment for Enhancing Learning
• Other Terms Related to Assessment and Evaluation

2 Perspectives of Assessment

• Behaviourist Perspective of Assessment
• Cognitive Perspective of Assessment
• Constructivist Perspective of Assessment
• Assessment of Learning and Assessment for Learning

3 Approaches to Evaluation

• Approaches to Evaluation: Placement Formative Diagnostic and Summative
• Distinction between Formative and Summative Evaluation
• External and Internal Evaluation
• Norm-referenced and Criterion-referenced Evaluation
• Construction of Criterion-referenced Tests

4 Issues, Concerns and Trends in Assessment and Evaluation

• What is to be Assessed?
• Criteria to be used to Assess the Process and Product
• Who will Apply the Assessment Criteria and Determine Marks or Grades?
• How will the Scores or Grades be Interpreted?
• Sources of Error in Examination
• Learner-centered Assessment Strategies
• Question Banks
• Semester System
• Continuous Internal Evaluation
• Choice-Based Credit System (CBCS)
• Marking versus Grading System
• Open Book Examination
• ICT Supported Assessment and Evaluation

5 Techniques of Assessment and Evaluation

• Concept Tests
• Self-report Techniques
• Assignments
• Observation Technique
• Peer Assessment
• Sociometric Technique
• Project Work
• School Club Activities

6 Criteria of a Good Tool

• Evaluation Tools: Types and Differences
• Essential Criteria of an Effective Tool of Evaluation
• Reliability
• Objectivity

7 Tools for Assessment and Evaluation

• Paper Pencil Test
• Aptitude Test
• Achievement Test
• Diagnostic–Remedial Test
• Intelligence Test
• Rating Scales
• Questionnaire
• Inventories
• Interview Schedule
• Observation Schedule
• Anecdotal Records
• Learners Portfolios and Rubrics

8 ICT Based Assessment and Evaluation

• Importance of ICT in Assessment and Evaluation
• Use of ICT in Various Types of Assessment and Evaluation
• Role of Teacher in Technology Enabled Assessment and Evaluation
• Online and E-examination
• Learners’ E-portfolio and E-rubrics
• Use of ICT Tools for Preparing Tests and Analyzing Results

9 Teacher Made Achievement Tests

• Understanding Teacher Made Achievement Test (TMAT)
• Types of Achievement Test Items/Questions
• Construction of TMAT
• Administration of TMAT
• Scoring and Recording of Test Results
• Reporting and Interpretation of Test Scores

10 Commonly Used Tests in Schools

• Achievement Test Versus Aptitude Test
• Performance Based Achievement Test
• Diagnostic Testing and Remedial Activities
• Question Bank
• General Observation Techniques
• Practical Test

11 Identification of Learning Gaps and Corrective Measures

• Educational Diagnosis
• Diagnostic Tests: Characteristics and Functions
• Diagnostic Evaluation Vs. Formative and Summative Evaluation
• Diagnostic Testing
• Achievement Test Vs. Diagnostic Test
• Diagnosing and Remedying Learning Difficulties: Steps Involved
• Areas and Content of Diagnostic Testing
• Remediation

12 Continuous and Comprehensive Evaluation

• Continuous and Comprehensive Evaluation: Concepts and Functions
• Forms of CCE
• Recording and Reporting Students Performance
• Students Profile
• Cumulative Records

13 Tabulation and Graphical Representation of Data

• Use of Educational Statistics in Assessment and Evaluation
• Meaning and Nature of Data
• Organization/Grouping of Data: Importance of Data Organization and Frequency Distribution Table
• Graphical Representation of Data: Types of Graphs and its Use
• Scales of Measurement

14 Measures of Central Tendency

• Individual and Group Data
• Measures of Central Tendency: Scales of Measurement and Measures of Central Tendency
• The Mean: Use of Mean
• The Median: Use of Median
• The Mode: Use of Mode
• Comparison of Mean, Median, and Mode

15 Measures of Dispersion

• Measures of Dispersion
• Standard Deviation

16 Correlation – Importance and Interpretation

• The Concept of Correlation
• Types of Correlation
• Methods of Computing Co-efficient of Correlation (Ungrouped Data)
• Interpretation of the Co-efficient of Correlation

17 Nature of Distribution and Its Interpretation

• Normal Distribution/Normal Probability Curve
• Divergence from Normality

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Physical Review Physics Education Research

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• Editorial Team
• Open Access

Graphical representations of data improve student understanding of measurement and uncertainty: An eye-tracking study

Ana susac, andreja bubic, petra martinjak, maja planinic, and marijan palmovic, phys. rev. phys. educ. res. 13 , 020125 – published 31 october 2017.

• Citing Articles (28)
• INTRODUCTION
• RESEARCH QUESTIONS
• STUDY 1: PAPER-AND-PENCIL ASSESSMENT OF…
• STUDY 2: EYE-TRACKING MEASUREMENT
• ACKNOWLEDGMENTS

Developing a better understanding of the measurement process and measurement uncertainty is one of the main goals of university physics laboratory courses. This study investigated the influence of graphical representation of data on student understanding and interpreting of measurement results. A sample of 101 undergraduate students (48 first year students and 53 third and fifth year students) from the Department of Physics, University of Zagreb were tested with a paper-and-pencil test consisting of eight multiple-choice test items about measurement uncertainties. One version of the test items included graphical representations of the measurement data. About half of the students solved that version of the test while the remaining students solved the same test without graphical representations. The results have shown that the students who had the graphical representation of data scored higher than their colleagues without graphical representation. In the second part of the study, measurements of eye movements were carried out on a sample of thirty undergraduate students from the Department of Physics, University of Zagreb while students were solving the same test on a computer screen. The results revealed that students who had the graphical representation of data spent considerably less time viewing the numerical data than the other group of students. These results indicate that graphical representation may be beneficial for data processing and data comparison. Graphical representation helps with visualization of data and therefore reduces the cognitive load on students while performing measurement data analysis, so students should be encouraged to use it.

• Received 10 January 2016

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.13.020125

Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License . Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

• Research Areas
• Professional Topics

Authors & Affiliations

• 1 Department of Physics, Faculty of Science, University of Zagreb, Bijenicka 32, 10000 Zagreb, Croatia
• 2 Department of Applied Physics, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
• 3 Chair for Psychology, Faculty of Humanities and Social Sciences, University of Split, Sinjska 2, 21000 Split, Croatia
• 4 Laboratory for Psycholinguistic Research, Department of Speech and Language Pathology, University of Zagreb, Borongajska cesta 83h, 10000 Zagreb, Croatia
• * Corresponding author. [email protected]

Article Text

Vol. 13, Iss. 2 — July - December 2017

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Distribution of students’ scores on paper-and-pencil test.

Scores on paper-and-pencil test for first-year students and senior-year students divided into groups with graphical representation of data and without it. Average scores for test items 1–7 are shown. The error bars represent 1 SEM (standard error of mean).

Scores of all students on seven test items in study 1 divided into groups with graphical representation of data and without it. The error bars represent 1 SEM.

Example of one participant’s scan path. Centers of circles show position of fixations and lines show saccades. Radius of circle illustrates the duration of the fixation.

Students’ scores in the eye-tracking study for groups with and without graphical representation of data. Average scores for test items 1–7 are shown. Test item 8 did not have graphical representation of data and it was used to control for the differences between groups. The error bars represent 1 SEM.

Scores of all students on seven test items in Study 2 divided into groups with graphical representation of data and without it. The error bars represent 1 SEM.

Heat maps of students who solved the same test item with and without graphical representation of data. Heat maps show how long students looked at different parts of the test item. Red indicates the area of the highest fixation time.

Heat maps of students from two groups (with and without graphical representation of data) for the control test item 8 without graphical representation. Red indicates the area of the highest fixation time.

(a) Average total viewing time at AOI All for groups with and without graphical representation of data. (b) Example of definition of AOIs for one test item. (c) Average total viewing times at defined AOIs for groups with and without graphical representation of data. Average total viewing time is calculated for test items 1–7. The error bars represent 1 SEM.

Students’ scores on paper-and-pencil test and eye-tracking test divided into groups with graphical representation of data and without it. Average scores for test items 1–8 (total test scores) are shown. The error bars represent 1 SEM.

Pie diagrams of distribution of students’ responses for all questions and all participants in study 1. The correct answers are given in bold and denoted by stripes in pie diagrams.

(a) Average total number of fixations (calculated as the sum of number of fixations for items 1a, 2a, 3a, 4a, 5a, 6, and 7) at AOI All for groups with and without graphical representation of data. (b) Example of definition of AOIs for one test item. (c) Average total number of fixations at defined AOIs for groups with and without graphical representation of data. The error bars represent 1 SEM.

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Guide On Graphical Representation of Data – Types, Importance, Rules, Principles And Advantages

What are Graphs and Graphical Representation?

Graphs, in the context of data visualization, are visual representations of data using various graphical elements such as charts, graphs, and diagrams. Graphical representation of data , often referred to as graphical presentation or simply graphs which plays a crucial role in conveying information effectively.

Principles of Graphical Representation

Effective graphical representation follows certain fundamental principles that ensure clarity, accuracy, and usability:Clarity : The primary goal of any graph is to convey information clearly and concisely. Graphs should be designed in a way that allows the audience to quickly grasp the key points without confusion.

• Simplicity: Simplicity is key to effective data visualization. Extraneous details and unnecessary complexity should be avoided to prevent confusion and distraction.
• Relevance: Include only relevant information that contributes to the understanding of the data. Irrelevant or redundant elements can clutter the graph.
• Visualization: Select a graph type that is appropriate for the supplied data. Different graph formats, like bar charts, line graphs, and scatter plots, are appropriate for various sorts of data and relationships.

Rules for Graphical Representation of Data

Creating effective graphical representations of data requires adherence to certain rules:

• Select the Right Graph: Choosing the appropriate type of graph is essential. For example, bar charts are suitable for comparing categories, while line charts are better for showing trends over time.
• Label Axes Clearly: Axis labels should be descriptive and include units of measurement where applicable. Clear labeling ensures the audience understands the data’s context.
• Use Appropriate Colors: Colors can enhance understanding but should be used judiciously. Avoid overly complex color schemes and ensure that color choices are accessible to all viewers.
• Avoid Misleading Scaling: Scale axes appropriately to prevent exaggeration or distortion of data. Misleading scaling can lead to incorrect interpretations.
• Include Data Sources: Always provide the source of your data. This enhances transparency and credibility.

Importance of Graphical Representation of Data

Graphical representation of data in statistics is of paramount importance for several reasons:

• Enhances Understanding: Graphs simplify complex data, making it more accessible and understandable to a broad audience, regardless of their statistical expertise.
• Helps Decision-Making: Visual representations of data enable informed decision-making. Decision-makers can easily grasp trends and insights, leading to better choices.
• Engages the Audience: Graphs capture the audience’s attention more effectively than raw data. This engagement is particularly valuable when presenting findings or reports.
• Universal Language: Graphs serve as a universal language that transcends linguistic barriers. They can convey information to a global audience without the need for translation.

Advantages of Graphical Representation

The advantages of graphical representation of data extend to various aspects of communication and analysis:

• Clarity: Data is presented visually, improving clarity and reducing the likelihood of misinterpretation.
• Efficiency: Graphs enable the quick absorption of information. Key insights can be found in seconds, saving time and effort.
• Memorability: Visuals are more memorable than raw data. Audiences are more likely to retain information presented graphically.
• Problem-Solving: Graphs help in identifying and solving problems by revealing trends, correlations, and outliers that may require further investigation.

Use of Graphical Representations

Graphical representations find applications in a multitude of fields:

• Business: In the business world, graphs are used to illustrate financial data, track performance metrics, and present market trends. They are invaluable tools for strategic decision-making.
• Science: Scientists employ graphs to visualize experimental results, depict scientific phenomena, and communicate research findings to both colleagues and the general public.
• Education: Educators utilize graphs to teach students about data analysis, statistics, and scientific concepts. Graphs make learning more engaging and memorable.
• Journalism: Journalists rely on graphs to support their stories with data-driven evidence. Graphs make news articles more informative and impactful.

Types of Graphical Representation

There exists a diverse array of graphical representations, each suited to different data types and purposes. Common types include:

1.Bar Charts:

Used to compare categories or discrete data points, often side by side.

2. Line Charts:

Ideal for showing trends and changes over time, such as stock market performance or temperature fluctuations.

3. Pie Charts:

Display parts of a whole, useful for illustrating proportions or percentages.

4. Scatter Plots:

Reveal relationships between two variables and help identify correlations.

5. Histograms:

Depict the distribution of data, especially in the context of continuous variables.

In conclusion, the graphical representation of data is an indispensable tool for simplifying complex information, aiding in decision-making, and enhancing communication across diverse fields. By following the principles and rules of effective data visualization, individuals and organizations can harness the power of graphs to convey their messages, support their arguments, and drive informed actions.

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FAQs on Graphical Representation of Data

What is the purpose of graphical representation.

Graphical representation serves the purpose of simplifying complex data, making it more accessible and understandable through visual means.

Why are graphs and diagrams important?

Graphs and diagrams are crucial because they provide visual clarity, aiding in the comprehension and retention of information.

How do graphs help learning?

Graphs engage learners by presenting information visually, which enhances understanding and retention, particularly in educational settings.

Who uses graphs?

Professionals in various fields, including scientists, analysts, educators, and business leaders, use graphs to convey data effectively and support decision-making.

Where are graphs used in real life?

Graphs are used in real-life scenarios such as business reports, scientific research, news articles, and educational materials to make data more accessible and meaningful.

Why are graphs important in business?

In business, graphs are vital for analyzing financial data, tracking performance metrics, and making informed decisions, contributing to success.

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• Math Article

Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

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Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

Thanks very much for the information

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Graphical Representation: A Powerful Tool for Data Analysis

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Graphical representation plays a crucial role in data interpretation, transforming complex datasets into visual formats like graphs, charts, and plots. These tools help identify patterns, trends, and correlations, making data comparison and communication more effective. The text delves into the fundamentals, principles, and types of graphical representations, as well as their importance in data analysis and real-world applications.

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Importance of Graphical Representation

Transforming complex datasets into visual formats.

Graphical representation simplifies the interpretation and communication of numerical data by converting it into visual formats such as graphs, charts, and plots

Facilitating identification of patterns, trends, and correlations

The use of coordinate system

The coordinate system, with its x-axis and y-axis, helps organize data points and depict relationships between variables in a structured and comprehensible manner

Principles for constructing effective graphs

Effective graphs should have a descriptive title, labels for axes, appropriate scale, legends or keys, and a simple design to facilitate easy interpretation

Essential in various fields

Graphical representation is crucial in fields such as science, business, and education for data analysis and effective communication of insights

Types of Graphical Representations

Bar graphs use bars to compare categorical data

Histograms group data into intervals to display frequency distributions

Pie charts show the relative proportions of parts to a whole with slices of a circle

Line graphs

Line graphs are ideal for illustrating trends over time in time-series data

Scatter plots

Scatter plots are used to explore relationships between two quantitative variables by plotting data points representing paired values

Frequency tables

Frequency tables organize data into rows and columns to show the frequency of each value or category

Application of Graphical Representation

Deriving actionable insights.

Graphical representations are applied to real-world data to derive actionable insights, such as investigating correlations between temperature and humidity or summarizing quiz results

Enhancing data accessibility and comparison

Graphical representation simplifies the comparison of vast amounts of data and enables the visual detection of patterns and trends that may be hidden in tabular data

Communicating results effectively

By converting data into a graphical format, graphical representation reduces the potential for misunderstanding and improves the capacity to convey findings in a compelling and straightforward manner

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Visual tools like graphs and charts are vital in fields such as ______, ______, and ______, as they help in comparing datasets and communicating insights.

science business education

Components of the coordinate system

Horizontal x-axis, vertical y-axis, intersecting at the origin.

Function of quadrants in a coordinate system

Represent unique combinations of positive and negative values for plotted variables.

To enhance a graph's credibility, one should include ______ for multiple data sets and cite the ______ sources.

legends or keys data

Bar Graphs vs. Histograms

Bar graphs compare categorical data; histograms show frequency distributions by intervals.

Pie Chart Purpose

Pie charts depict relative proportions of parts to a whole with circle slices.

Scatter Plot Function

Scatter plots explore relationships between two quantitative variables with paired data points.

______ plots are utilized to examine the relationship between ______ and ______, with each represented on an axis.

Scatter temperature humidity

Bar graphs are useful for displaying the occurrence of events, such as a ______'s logged ______ times, organizing the times on one axis and their frequencies on the other.

nurse's waking

Impact of graphical representation on data accessibility

Graphs make complex data more accessible, simplifying understanding and comparison.

Role of scatter plots in data analysis

Scatter plots help examine correlations between variables, visually indicating relationship strength and nature.

Advantages of visual over tabular data presentation

Visuals aid in pattern detection, reduce misunderstandings, and enhance communication of findings.

The use of a ______ is vital for marking data points on graphs, which helps in their accurate creation.

coordinate system

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What is the significance of graphical representation in understanding numerical data, what is the basic structure upon which graphical representations are built, what guidelines should be followed to create a clear and effective graph, can you name some different types of graphical representations and their uses, how are graphical representations utilized in practical scenarios, why are graphical representations crucial in the analysis of data, what are the concluding thoughts on the role of graphical representation in data analysis, contenuti simili, esplora altre mappe su argomenti simili.

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Observed and Critical Values in Statistical Analysis

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Polynomial Rings and Their Applications

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The Role of Graphical Representation in Data Interpretation

Fundamentals of Graphical Representation

Principles for constructing effective graphs, practical application of graphical representation, importance of graphical representation in data analysis, concluding insights on graphical representation.

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Graphical Representation of Data

Graphical Representation of Data: Graphical Representation of Data,” where numbers and facts become lively pictures and colorful diagrams . Instead of staring at boring lists of numbers, we use fun charts, cool graphs, and interesting visuals to understand information better. In this exciting concept of data visualization, we’ll learn about different kinds of graphs, charts, and pictures that help us see patterns and stories hidden in data.

There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics .

The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, Astro statistics, and so on . In this article, we have provided everything about the graphical representation of data, including its types, rules, advantages, etc.

Table of Content

What is Graphical Representation

Types of graphical representations, line graphs, histograms , stem and leaf plot , box and whisker plot .

• Graphical Representations used in Maths

Value-Based or Time Series Graphs 

Frequency based, principles of graphical representations, advantages and disadvantages of using graphical system, general rules for graphical representation of data, frequency polygon, solved examples on graphical representation of data.

Graphics Representation is a way of representing any data in picturized form . It helps a reader to understand the large set of data very easily as it gives us various data patterns in visualized form.

There are two ways of representing data,

• Pictorial Representation through graphs.

They say, “A picture is worth a thousand words”.  It’s always better to represent data in a graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in the form of visuals as Human beings process data better in visual form than any other form.

Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time.

Check: Graph and its representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux of the data about different items. Let’s look at all the different types of graphical representations briefly: 

A line graph is used to show how the value of a particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data and predicting further trends. 

A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars. 

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range. 

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point.  

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).  

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data. 

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole. 

Graphical Representations used in Math’s

Graphs in Math are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths: 

• Value-Based or Time Series Graphs

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values. 

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study.

Also read: Types of Statistical Data

• All types of graphical representations follow algebraic principles.
• When plotting a graph, there’s an origin and two axes.
• The x-axis is horizontal, and the y-axis is vertical.
• The axes divide the plane into four quadrants.
• The origin is where the axes intersect.
• Positive x-values are to the right of the origin; negative x-values are to the left.
• Positive y-values are above the x-axis; negative y-values are below.

• It gives us a summary of the data which is easier to look at and analyze.
• It saves time.
• We can compare and study more than one variable at a time.

Disadvantages

• It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data. 
• Interpretation of graphs can vary based on individual perspectives, leading to subjective conclusions.
• Poorly constructed or misleading visuals can distort data interpretation and lead to incorrect conclusions.

Check : Diagrammatic and Graphic Presentation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs: 

• Whenever possible, the data source must be mentioned for the viewer.
• Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
• The measurement Unit should be mentioned in the top right corner of the graph.
• The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
• Last but not the least, a suitable title should be chosen.

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval. 

Question 1: What are different types of frequency-based plots? 

Types of frequency-based plots:  Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels. 

Draw a bar graph for the following data. 

• Put each of the channels on the x-axis
• The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data 

• Put each of the x-axis row value on the x-axis
• joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data: 

• Draw the class intervals on the x-axis and frequencies on the y-axis.
• Calculate the midpoint of each class interval.

Now join the mid points of the intervals and their corresponding frequencies on the graph. 

This graph shows both the histogram and frequency polygon for the given distribution.

Related Article:

Graphical Representation of Data| Practical Work in Geography Class 12 What are the different ways of Data Representation What are the different ways of Data Representation? Charts and Graphs for Data Visualization

Conclusion of Graphical Representation

Graphical representation is a powerful tool for understanding data, but it’s essential to be aware of its limitations. While graphs and charts can make information easier to grasp, they can also be subjective, complex, and potentially misleading . By using graphical representations wisely and critically, we can extract valuable insights from data, empowering us to make informed decisions with confidence.

Graphical Representation of Data – FAQs

What are the advantages of using graphs to represent data.

Graphs offer visualization, clarity, and easy comparison of data, aiding in outlier identification and predictive analysis.

What are the common types of graphs used for data representation?

Common graph types include bar, line, pie, histogram, and scatter plots , each suited for different data representations and analysis purposes.

How do you choose the most appropriate type of graph for your data?

Select a graph type based on data type, analysis objective, and audience familiarity to effectively convey information and insights.

How do you create effective labels and titles for graphs?

Use descriptive titles, clear axis labels with units, and legends to ensure the graph communicates information clearly and concisely.

How do you interpret graphs to extract meaningful insights from data?

Interpret graphs by examining trends, identifying outliers, comparing data across categories, and considering the broader context to draw meaningful insights and conclusions.

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Representation in Education: Using Data to Understand Our Students

How important is representation in education? Dr. Catherine Quinlan, associate professor of science education at Howard University and collaborator on LabXchange's Data Science–Driven Science Education Project , shares findings from her research on diversity and its effect on students' persistence in STEM education.

_________________________________________________________________________

After a brief stint at YouTube university, someone finally said that you could not easily create overlapping histograms with two data sets using Excel, so I decided to settle for as many ways of creating bar graphs from every angle and direction—upwards, sideways, one on top of the other. My goal was to reimagine some of my already published data, to tease out ideas on representation, which is the crux of my research.

Indicators of Persistence in STEM

When I initially created and published my instrument to measure self-efficacy indicators of persistence in a Historically Black College and University (HBCU) population , I began with questions that I believed were important based on the literature, my own personal experiences and those of our students, and feedback from other professors at an HBCU. Even though I went in with an open mind, I was still somewhat surprised when some of my assumptions and perspectives were not supported by the statistical analyses. While some questions might not have been good predictors for the self-efficacy factors, taken in isolation, they can tell a different story. Some of these stand-alone ideas are worth exploring as they might help us to better understand our Black student population and possibly other students of color.

In today’s society, we are asked to think about learning holistically. My curiosity about students’ perspectives of the financial versus emotional cost of being a scientist attends to some additional factors that might impact on students’ commitment to STEM. I posed the following:

• ‍ The financial cost of being a scientist is too much compared with what I will get out of it.
• The emotional cost of being a scientist is too much compared with what I will get out of it.

Even though these questions did not make it into the finalized instrument, I wondered what we could learn by looking at the distributions of students’ responses. The approximately 163 participants were enrolled in an introductory biology course which was overrepresented by biology majors but also included other STEM majors, allied heath majors, and non-STEM majors at an HBCU. The data was also overly represented by students who identified as female and first year students.

You might have assumed correctly that more students agreed that the emotional cost of being a scientist was too much compared to what they would get out of being a scientist. Perhaps this understanding might influence the importance you place on students’ emotional investment in your class content. How do you know when your students are emotionally invested in your class? To what extent is this important to you? What do you do to attend to students’ emotional investment in your class?

So, Does Representation Matter—and to Whom?

As a society, we tackle the issue of representation from different angles, but more popular is highlighting a scientist that reflects the racial/ethnic background of our students. The research shows mixed results when it comes to representation by mentors or teachers. Here I share with you the perspectives of our HBCU students. The question— It is important to me that my mentor or teacher is from a similar ethnic and racial background as I am —did not make it into the final questionnaire or into the final paper as an important indicator of self-efficacy for this population. The distribution, however, sheds light on the importance of representation. Asking students about their preference for matched mentor was important when considering that over 80% of our teachers are White and only about 7% or so are Black .

You might think of this question differently depending on how you identify. If you are among those teachers who are well represented in science by race/ethnicity, culture, gender, and/or beliefs, you may or may not consider representation as important. Likewise, you may or may not fully understand on an emotional level why this is important to some. It is not farfetched to believe that those whose needs are well met might not consider representation important at all. It is even possible that we might only be alerted to this importance through a negative experience, or repeated negative or disconnecting experiences. Then we begin to question our belongingness or fit.

I have the luxury of having some of these conversations with our HBCU students, some of whom have interned in predominantly White institutions. One conversation is worth noting, which this student gave me permission to share. She participated in a prestigious internship and was approached by one of her peers who asked, on different occasions, whether or not she was accepted through a special HBCU program. She replied that she went through the same application process. However, when she was approached again, she said she began to doubt herself and to doubt her accomplishments.

The importance and benefits of representation for all must be underscored. A lack of representation can send the wrong message and become correlated with a lack of competence, especially to those unable to see societal barriers, and especially for those unable to see how individual barriers and individual responses become accumulative and systemic, and a societal problem. As history has shown, as Black people, we work hard and accomplish in spite of and not necessarily because of our lived realities—whatever these may be. The latter “because of” might lead to increased trust, but the former “in spite of” reminds us that we are alone. The former leaves us tired and others surprised and confused when we thrive, and the latter builds trusting, peaceful, and happy relationships within our society. I’ve come to understand that some of our students welcome our HBCU classrooms because they get reprieve from negative but sometimes subtle interactions they’ve experienced in K-12. They can accomplish alongside those who believe in them, and who expect them to achieve great things.

Impact of Others Like Yourself or Not Like Yourself

It was interesting to see how students perceived the impact of others like themselves on their feelings about their own capability. I posed two statements: 1. I know I can do well when I see people like me able to do something challenging; 2. I doubt my own ability to do well in science when others like me have difficulty. These are included in the paper but might not have made it into the final questionnaire as significant indicators for persistence. The distributions are still worth exploring.

The results show that for these first-year students enrolled in this introductory biology course with both STEM and non-STEM majors, Black students' success is more influenced by seeing others like themselves do something challenging than by the difficulty others like themselves have. Are you surprised by these results, and given what you know about your students, how do you think they might compare? Additional understandings can be captured in discussions with your students.

Engagement Discussion:

Use these questions and graphs as an opportunity to get to know and understand your students. As a matter of fact, pointing specifically to this data sample and graphs could generate interest from your students of similar background and provide them with an opportunity to understand themselves better or not feel alone in their perceptions. Ask them which questions they agree with and why, and which they are surprised by.

Learn the Fundamentals of Experimental Design in a New Pathway from DSDSE

Explore data visualization in a new learning pathway from dsdse, data science project releases first learning pathway: summary statistics and probability, science teachers needed to review data science learning resources.

August 15th, 2024

29 Best Types of Charts and Graphs for Data Visualization

By: Alysha Gullion · 8 min read

Selecting the right chart is crucial for effective data presentation. The choice depends on your data type, audience, and intended message. For example, line charts work well for time trends, while pie charts show proportions. Complex visualizations like correlation heat maps may not suit audiences unfamiliar with data science. This article will outline various graph types and their typical uses, noting that some graphs may fit multiple categories but will be mentioned only once for simplicity. By understanding these options, you can choose the most impactful way to present your data.

How to Find Data for Graphs and Charts

Trying to find high-quality, interesting data for creating charts and graphs is always difficult. We used the following open-source repo of datasets for all of the graphs and charts in this post: vincentarelbundock.github.io . Other options for finding datasets include Kaggle , which is a prominent data science community and data repository, or the UC Irvine Machine Learning Repository .

How to Create Charts and Graphs

Various tools cater to different needs in chart and graph creation. Excel is widely used in business for its simplicity. Tableau is favored by data analysts for interactive visualizations. Researchers often use SPSS for complex statistical graphs, while data scientists prefer R for its programming flexibility. For those seeking a more intuitive approach, Julius offers a unique alternative. Supporting both Python and R, Julius allows users to generate graphs using plain language descriptions, making it accessible to both beginners and experienced users. When choosing a tool, consider your technical skills and visualization requirements.

Comparison Charts

Comparison charts or graphs are used to compare quantities across different categories. Their purpose is to highlight the differences and similarities within data sets, making it easier for viewers to draw conclusions about the variations amongst various groups.

You can find the code associated with these charts by visiting our community forum . 

1. Bar/Column charts

Bar and column charts provide clear comparisons between discrete categories (i.e., car models) based on a quantitative measure (e.g., miles per gallon, MPG). They are widely used as they offer a quick and effective way to visualize differences amongst categorical variables. The difference between bar and column charts is based on their orientation: bar charts display their bars horizontally, while column charts display them vertically.

The data used in this visualization can be accessed here . This data frame consists of 32 observations on 11 numeric variables and was collected in 1974 from Motor Trend US magazine. It details fuel consumption of 10 different motor vehicles. We will create a bar chart to compare miles per gallon between each car model. 

Python Example

The images above compare the fuel efficiency of each car model. The graph shows that the Mercedes-Benz 240D outperforms its counterparts in terms of miles per gallon.

2. Grouped/Clustered Bar Chart

Grouped or clustered bar charts are used to compare frequencies, counts, or other measures across multiple categories and groups. 

For this visualization, we will be using a dataset from the College Scorecard, which contains college-by-year data on how students are doing after graduation, available here . This data frame contains 48,445 rows and 8 variables. We will create a grouped bar chart to compare the counts of working vs. not working for five institutions in the year 2007.

In the images above, we can see that graduates from ASA college tended to have a substantially higher count of ‘working’ individuals compared to the other institutions.

3. Dumbbell Plot

Often mistaken for a type of bar chart, the dumbbell plot differs by displaying two values for each category rather than one. It shows two points connected by a line, which displays the minimum and maximum values of data points for each category. Dumbbell plots are useful for displaying variability, distributions, and confidence intervals within categories. 

For this visualization, we will be using a dataset that contains daily temperatures (minimum and maximum) for Clemson, South Carolina from January 1st, 1930 to December 31st, 2020 (33,148 observations). The dataset can be accessed here .

For simplicity, we will focus on the year 1930 and 2020, which contains 365 observations each. We will plot the average minimum and maximum temperature for each month in the year 1930 and 2020.

Overall, the trend suggests that 2020 experienced higher temperatures compared to 1930. For yearly averages, 2020 had a higher average minimum temperature (52.43°F vs 48.68°F in 1930) but a slightly lower average maximum temperature (72.77°F vs 73.90°F in 1930).

4. Radar Chart

Radar charts are useful for displaying multivariate data in a way that is easy to compare across different variables. However, some users may find this chart difficult to interpret depending on the information and message presented. 

For this example, we are going to plot the fitness scores of five individuals. The assessed fitness components included: cardiovascular endurance, muscle strength, flexibility, body composition, balance and nutrition. Each component was ranked from a scale of 1 to 10, with 10 being the highest and 1 being the worst. The dataset can be accessed here .

These radar charts show how each individual's fitness varies across the six components, providing an overall comparison on a single plot.

5. Dot Plot

Dot plots show one or more qualitative values for each category, allowing for comparison across multiple values within and between categories. They provide an informative visualization, effectively condensing information in an easy to read format. 

For this visualization, we will use a dataset containing the stats of starter Pokémon and from Generations I through VI (19 entries). This dataset can be accessed here .

In the images above, we can see the different stats for the starters from generations I through VI. Who will you choose? I always choose Mudkip, he is my favourite. 

Correlation Charts

Correlation graphs are used to visualize relationships between variables, showing how one variable changes in relation to another. They show the strength and direction of these relationships, which is important in fields like statistics, economics, and data science.

6. Heatmap & Correlation Matrices

Heatmaps and correlation matrices are great visualizations that are simple for readers to understand. They use a colour gradient to represent the value of variables in a two-dimensional space. They are good tools for identifying patterns, variable-variable relationships, and anomalies in complex datasets. 

For this visualization, we will use a dataset called ‘cerebellum_gene_expression2, accessible here . We will randomly choose 20 genes and create a correlation matrix to visualize gene expression rates via a heatmap. 

The original dataset can be accessed through this file , which is an example dataset provided by the tissueGeneExpression package from the genomicsclass GitHub repository. It contains 500 genes, randomly selected from a dataset of 22,215 entries. 

The image above displays the correlation matrix for 20 randomly selected genes. In the matrix, yellow indicates a strong positive correlation (both variables increase or decrease together), while dark blue indicates a strong negative correlation (as one increases the other decreases). Green represents a weak correlation or no correlation.

7. Bubble Chart

A bubble chart is a data visualization technique that displays multiple dimensions of data within a two-dimensional plot. The ‘bubbles’ represent data points, with their positions determined by two variables, and the size representing the third variable. 

The dataset used to create this graph was from the 2000 US census, and can be accessed here . It contains 437 entries and 28 columns representing various demographic measurements. We will visualize the relationship between education level, poverty, total population and population density in the top 15 counties from Illinois.

The R and Python graphs follow the same formatting. Each bubble represents one of the top 15 counties in Illinois. The size of the bubble corresponds to the total population density of the county, the colour indicates the population density (with lighter colours representing higher density). Each bubble is labeled with the county abbreviation. 

8. Scatter Plot

A scatter plot is a type of data visualization technique that displays values for two variables for a set of data points. It shows how one variable is affected by another, which can reveal relationships between them. Each point on the plot represents an individual data point, with its position along the x-axis representing one variable and its position on the y-axis indicating another variable. 

For this visualization, we are using a dataset called ‘insurance’, which can be accessed here . This dataset includes data on monthly quotes and television advertising expenditure from a US-based insurance company, collected from January 2002 to April 2002. This dataset contains 40 entries and 3 columns. The visualization will examine the relationship between TV advertisements and quotes given. A trendline will be added to help visualize the relationship. 

Python Example 

A positive relationship was observed between increases in TV advertisement and quotes given, as displayed by the increasing trendline.

9. Hexagonal binning

Hexagonal binning is a technique used for large, complex datasets with continuous numerical data in two dimensions. It displays the distribution and density of points, which is particularly useful when over-plotting occurs.

For this visualization, we will use a dataset containing daily observations made for the S&P 500 stock market from 1950 to 2018. The dataset includes 17,346 observations and 7 variables. It can be accessed here . The visualization will be plotting the volume by closing price.  

The yellow hexagon at the lower left corner indicates a clustering of points (high density of points here) that represents low closing price and trading volume. Here, the closing price was equal to $44.64 per share, and the volume of trade is ≤ 2.5 million shares. This specific point makes up ~8.0% of the total dataset.

10. Contour plot + Surface Plot

This is another technique that is used for visualizing data distributions and densities within a two dimensional field. It is oftentimes used to create topographic maps of data. For simplicity, we are going to plot the function Z = sin(sqrt(X^2 + Y^2)).

You can manipulate the surface plot directly within Julius itself to examine different angles, allowing for an in-depth exploration of the plotted points.

Part-to-Whole & Hierarchical Charts

Part-to-Whole visualizations show how individual portions contribute to the whole. Hierarchical graphs represent data in a tree-like structure, displaying relationships between different levels of data.

11. Stacked Bar Graphs

Stacked bar graphs show the composition of different categories within a dataset. Each bar represents the total amount, with segments within the bar representing the categories and their proportion to the total. 

For this example, we will use data from a 2020 Financial Independence (FI) Survey conducted on Reddit. This dataset examined people’s finances and the changes experienced during the pandemic. The full dataset can be accessed here , which contains 1998 rows and 65 variables. We will be using a cleaned version of the full dataset, that contains the same number of rows but only 3 variables. This dataset can be accessed here . 

The visualization focuses on the columns pan_inc_chg (pandemic income change), pan_exp_chg (pandemic expense change), and pan_fi_chg (pandemic financial independence change), as they contain multiple categories relevant to the analysis.

The results show that the pandemic had varying effects on income, leading to reductions in expenses for many individuals. The combination of stable or increased income, along with decreased expenses, may have contributed to a slight improvement in the financial independence for some people.

12. Dendrogram

Dendrograms are tree-like diagrams that show the arrangement of clusters formed by a hierarchical structure. They are commonly used in fields such as biology, bioinformatics, and machine learning to visualize the relationships between data points. 

For this visualization, we will use a dataset called ‘cerebellum_gene_expression2’, which can be accessed here . We are only going to plot the first 20 genes for this visualization. 

The original dataset can be accessed through this file . This example dataset, provided by the ‘tissueGeneExpression’ package from the genomicsclass GitHub repository, includes 500 genes randomly selected from a larger dataset containing 22,215 entries.

Genes grouped together at lower heights in this dendrogram have more similar expression patterns across samples. Additionally, the higher the branching point between two pairs of genes or clusters, the more dissimilar they are. For example, x.MAML1 and x.FIBP are clustered closely together, suggesting similar expression patterns.

13. Pie Chart

A pie chart is a circular statistical graph divided into slices to show the relative proportions of different categories within a dataset. Each slice represents a category, and the size of the slice corresponds to the proportion of that category in relation to the whole. 

For this visualization, we will use a dataset from a 2010 poll on whether airports should use full-body scanners. The poll collected a total of 1137 responses and included two factors. The dataset can be accessed here .

Both visualizations show group responses regarding body scanner use in airports for security purposes, with an overall trend suggesting that people approve of their use.

14. Donut Chart

Donut charts are similar to pie charts, but they have a hole in the center of the circle, giving them their name. This inner circle’s removal allows for the additional information to be shown in the chart. The length of each arc corresponds to the proportion of the category it represents. 

For this visualization, we will use a dataset detailing the chemical composition (Aluminum, Iron, Magnesium, Calcium, and Sodium) found at four different archaeological sites in Great Britain (26 entries). We will compare the different chemical composition of pottery amongst the four sites. The dataset can be accessed here .

Across all four different sites, we can observe variations in the chemical composition of the pottery. Aluminum, the primary chemical compound, constitutes the highest percentage in composition of each pottery sample, but its percentages vary amongst sites.  

15. Population Pyramid

Also known as age-sex pyramids, population pyramids are visualizations that display the gender distribution of a population. They are typically presented as a bar chart, with age cohorts displayed horizontally to the left or right. One side represents males, while the other side shows females. 

For this visualization, we will use a dataset containing male and female birth rates in London from 1962 to 1710 (82 rows; 7 variables). For simplicity, we will only plot male and female data for the first 20 years. The dataset can be accessed here . 

The population distribution between males and females appears steady amongst the years, showing a slight decrease in births for both sexes from 1641 to 1648. 

Data Over Time (Temporal) Charts

Temporal charts are used to display data over time, revealing trends, patterns, and changes. They are essential for time series analysis and can be presented in multiple different forms depending on the type of data and the message intended to be conveyed.

You can find the code associated with these charts by visiting our community forum .

16. Area Chart

Area charts are a type of data visualizations used to represent quantitative data and show how values change over a period of time. They plot a continuous variable and are great at showing the magnitude of change over time or visualizing cumulative effects. 

We will be using the London dataset (82 rows; 7 variables) to visualize the mortality rate and plague deaths over time. The dataset can be accessed here . 

These charts visualize the impact of the plague on mortality rates. We can see a peak between 1660 and 1670, during which the majority of deaths were due to plague.

17. Line chart

Line charts are among the most commonly used types of charts worldwide. They are great at showing overall trends or progress over time. The x-axis typically represents the continuous variables (usually time), while the y-axis displays the dependent variable, showing how its value changes.

For this visualization, we will use a dataset called ‘trump_tweet’, which tracks the number of tweets by Mr. Trump from 2009 to 2017. The full dataset can be accessed here (20,761 rows; 8 variables), while the condensed dataset used for this visualization is available here (9 rows; one variable).

This line chart displays the number of tweets made by Mr. Trump over an eight year period. The lowest number of tweets was recorded in 2009 (~43 tweets/year), while his highest was in 2013 (~5,616 tweets/year). 

18. Candlestick Chart

A candlestick chart is a financial visualization used to analyze price movements of an asset, derivative, or currency. It is commonly used in technical analysis to predict market trends. The chart displays the high, low, opening, and closing prices of a product within a specific time frame. 

For this chart, we will use the S&P 500 stock market dataset. This dataset includes daily observations from 1950 to 2018, with a total of 17,346 entries and 7 variables. The original dataset can be accessed here , while the one we are using for the visualization is here . For this chart, we are only focusing on a short timeframe, specifically March 1974 high, low, opening, closing prices and volume. 

The green candlesticks indicate the days when the closing price was higher than the opening price, suggesting buyer pressure. Red candlesticks indicate days where the closing price was lower than the opening price, suggesting selling pressure. Candlesticks with small bodies, where the opening and closing prices are close together, suggest market indecision. 

Overall, this chart shows that the market started positively (as indicated by many green candlesticks), experienced a brief mid-month dip (indicated by the red candlesticks), and then recovered slightly, as shown by some green candlesticks.

19. Stream graph

A stream graph displays changes in the magnitude of categorical data over time. It is a variation of the stacked area bar graph, where the baseline is not anchored to a singular point but rather moves up or down, allowing the to display a natural flow. 

For this visualization, we will use a dataset that measures air pollutants in Leeds (UK) from 1994 to 1998 (Heffernan and Tawn, 2004). The winter dataset includes measurements between November to February of the various air pollutants (532 rows with 5 variables). The dataset can be accessed here .

The images shows how the composition of the pollutants change over time, with peaks and dips of pollutants illustrated throughout the season.

20. Gantt chart

A Gantt chart is a visual tool used in project management to plan and track the progress of tasks. It displays individual tasks or activities along a timeline, highlighting their scheduled start and end dates. Gantt charts are a great way for visualizing sequences of tasks, duration, and the dependencies between tasks. 

For this visualization, we will use a dataset showing task allocation between start and end dates of my Master’s program. The dataset can be accessed here (contains 17 rows, with 4 columns).

R Example 

Distribution Charts

Distribution charts are meant to show the spread of data across various categories or values. They help readers understand the frequency, range, and the overall shape of the data’s distribution. In addition, it can help readers understand the patterns, central tendency, and variations within their dataset.

21. Density plot

A density plot measures the probability distribution of a continuous variable. By providing a smooth curve that represents the distribution of data points over a range, it helps readers to identify patterns, trends, and the overall shape of the distribution. Density plots are useful for visualizing the distribution, identifying modes, and comparing distributions between multiple groups.

For this visualization, we will use the “iris” dataset (151 rows, 5 columns). This is a common dataset that contains information on petal width, petal length, sepal width and sepal length of three different iris species (Setosa, Versicolour, and Virginica). It is often used as an introductory model for clustering algorithms in machine learning. For this visualization, we will be using it to compare how flower features differ between species. The dataset can be accessed by simply asking Julius to retrieve it in Python or R, or it can be accessed here . 

The density plot reveals the following observations: For Setosa, the distribution of petal width and length is generally on the lower end compared to the other species of iris’s, suggesting that Setosa would be easily distinguished by its smaller petal dimensions. 

Versicolor shows some overlap with Virginica regarding sepal length and width, but exhibits less variation and tends to concentrate around 5.5cm (sepal length) and 3.0cm (sepal width).Vericolor can be identified by its intermediate petal size – larger than Setosa but smaller than Virginica. Virginica, on the other hand, displays the largest petal length and width, though it does show some high variability due to the spread of points along the x-axis.

22. Histogram

A histogram is used to display the distribution of a dataset by dividing it into intervals, or bins, and counting the data points that fall into each bin. The height of each bar represents the frequency of data points falling into that specific interval. Histograms are commonly used to display frequency distribution of a continuous variable.  

For this visualization, we will use a dataset comparing thermometer readings between Mr. Trump and Mr. Obama (3,081 rows, 3 columns). We will visualize the frequencies of scores between Mr. Trump and Mr. Obama. The dataset can be found here .

The dataset shows a non-normal distribution, as evident by the multiple peaks observed in the trendline.

23. Jitter Plot

A jitter plot is similar to scatter plot but introduces intentional random dispersions of points – referred to as ‘jittering’ – along one axis to prevent overlapping. This technique reveals the density and distribution of data points that would otherwise overlap. This is useful when your data points may have the same values or relatively close values across categories.    

For this visualization, we will use a dataset comparing dried plant weight yields (30 observations) under three different conditions (control, treatment 1, and treatment 2). The dataset can be accessed here .

Both images demonstrate how a jitter plot effectively prevents overlapping between points with identical or nearly identical values.

24. Beeswarm Chart

A beeswarm chart visualizes data points along a single axis, with dots representing each individual datapoint. This method does slightly rearrange the points to avoid overlapping.  

We will use the same plant growth dataset from the jitter plot visualization to illustrate how the data points appear in comparison to the jitter plot. The dataset can be accessed here .

The beeswarm plot is more appealing with a larger sample size, but this example provides a general idea of its format. Unlike the jitter plot, data points in a beeswarm plot are positioned in a vertical line, with slight dispersion when multiple points overlap. Although some beeswarm plots do not include boxplot and box-and-whiskers plot, adding these can help visualize interquartile ranges. 

From a general observation, treatment 2 appears to have a slightly higher overall weight compared to the control and treatment 1. However, it is important to note that outliers in treatment 1 and the control can skew this range.

25. Boxplot (Box-and-whisker plot)

A boxplot, or box-and-whiskers plot, is a standardized method for displaying the distribution of a dataset. It highlights five key aspects: the minimum value, the first quartile (Q1), median, third quartile (Q3), and the maximum value. This allows the reader to examine the spread of the data, central tendency, and identify potential outliers, making it a great tool for exploratory data analysis. 

For this visualization, we will use a dataset from Baumann & Jones, as reported by Moore & McCabe (1993). The dataset examines whether three different teaching methods – traditional (Basal), innovative 1 (DRTA), and innovative 2 (Strat) – affected reading comprehension in students. The data frame has 66 rows with 6 columns: group, pretest.1, pretest.2, post.test.1, post.test.2, post.test.3. The dataset can be accessed here .

The visualization was created by averaging the scores between the two pre-tests and three post-tests by teaching methods, and then plotting them.

From quick observation, there appears to be differences in test performance associated with teaching methods. The Basal method seems to show the lowest median test score in comparison to the DRTA and Strat. However, these initial observations should be confirmed through further statistical testing.

Geospatial & Other

Geospatial visualizations are designed to represent data with geographic information, such as coordinates, GPS, longitude, and latitude. Their purpose is to communicate spatial patterns and relationships. Also included in this section are flow charts and network diagrams, which show how ideas or concepts are related to one another.

26. Geographic Heat Map

A geographic heat map shows where points are most concentrated within a specific geographic location by using colours to represent density. This type of map is useful for highlighting patterns, trends, and hotspots in spatial data. 

For this visualization, we will use a dataset that includes the locations of 1000 seismic events near Fiji since 1964. This dataset, part of the Harvard PRIM-H project dataset, was obtained by Dr. John Woodhouse from the department of Geophysics. This dataset can be accessed here . 

27. Choropleth map

A choropleth map is a thematic map where areas are shaded (or patterned) based on the values of a variable, such as population density, income level, or election results. Colours are used to represent different densities or magnitudes, which provides a comparative visual between spatial data distributions. 

For this visualization, we will use data from the 2017 American Census Society. It has 3221 entries, with 37 columns detailing various demographic information. This dataset can be accessed here .

28. Network diagram

A network diagram is a visualization tool used to show connections between multiple different elements, illustrating how different entities (nodes) are connected to one another. 

For this visualization, we will use a document that outlines the sequence of tasks in a project. It defines the nodes (tasks), dependencies, and gives a short description of the dependencies. This document can be accessed here and the google sheet can be accessed here . 

Network diagrams are great ways to organize your thoughts and visualize how events are connected to one another.

29. Flowchart

A flowchart is a visual representation of a process, workflow, or system. It uses symbols and arrows to signify a sequence of steps, decisions, or actions. Flowcharts are similar to network diagrams, as they clearly illustrate how different activities or steps are connected, making it easy to understand the flow of activities involved in the process. 

For this example, we will create a flowchart outlining the process of online purchases. The Google document can be accessed here , which contains all the information you need to create the flowchart. You can simply copy and paste the text into the chat box. 

This article has served as a visual guide to 29 diverse chart and graph types, each designed to address specific data presentation needs. From simple bar charts to complex network diagrams, we've explored a range of visualization options to help you choose the right tool for your data story. Understanding these different graph types empowers you to communicate your insights more effectively, regardless of your audience or data complexity.

Throughout this journey, we've used Julius to generate our examples, showcasing how it seamlessly supports both R and Python users. Julius's ability to create these visualizations through simple, natural language commands demonstrates how data visualization tools are evolving to become more accessible. As you continue to explore and apply these chart types in your own work, consider how platforms like Julius can streamline your process, allowing you to focus on the story your data tells rather than the technicalities of graph creation.

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Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective

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Bibliometrics & citations, view options, recommendations, disentangled text representation learning with information-theoretic perspective for adversarial robustness.

Adversarial vulnerability remains a major obstacle to the construction of reliable NLP systems. When imperceptible perturbations are added to raw input text, the performance of a deep learning model may drop dramatically under attacks. Recent work has ...

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Learning Robust Representation Through Graph Adversarial Contrastive Learning

Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features. Thus, it is requisite to learn robust ...

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Gender-based differences in the representation and experiences of academic leaders in medicine and dentistry: a mixed method study from Pakistan

• Muhammad Shahzad 1 , 2 ,
• Brekhna Jamil 3   na1 ,
• Mehboob Bushra 4   na1 ,
• Usman Mahboob 3   na1 &
• Fayig Elmigdadi 1   na1  

BMC Medical Education volume  24 , Article number:  885 ( 2024 ) Cite this article

187 Accesses

Metrics details

Research evidence suggests gender-based differences in the extent and experiences of academic leaders across the globe even in developed countries like USA, UK, and Canada. The under-representation is particularly common in higher education organizations, including medical and dental schools. The current study aimed to investigate gender-based distribution and explore leaders’ experiences in the medical and dental institutes in a developing country, Pakistan.

A mixed-method approach was used. Gender-based distribution data of academic leaders in 28 colleges including 18 medical and 10 dental colleges of Khyber Pakhtunkhwa, Pakistan were collected. Qualitative data regarding the experiences of academic leaders ( n  = 10) was collected through semi-structured interviews followed by transcription and thematic analysis using standard procedures.

Gender-based disparities exist across all institutes with the greatest differences among the top-rank leadership level (principals/deans) where 84.5% of the positions were occupied by males. The gender gap was relatively narrow at mid-level leadership positions reaching up to as high as > 40% of female leaders in medical/dental education. The qualitative analysis found gender-based differences in the experiences under four themes: leadership attributes, leadership journey, challenges, and support.

Conclusions

The study showed that women are not only significantly under-represented in leadership positions in medical and dental colleges in Pakistan, they also face gender-based discrimination and struggling to maintain a decent work life balance. These findings are critical and can have important implications for government, organizations, human resource managers, and policymakers in terms of enacting laws, proposing regulations, and establishing support mechanisms to improve gender-based balance and help current and aspiring leaders in their leadership journey.

Peer Review reports

An inclusive and diverse workforce is crucial for organizational success in today’s modern world. However, despite a gradual increase in the representation of women in the workforce globally, gender discrimination and unequal gender ratios still exist in workplaces both horizontally (i.e., across different sectors and industries) and vertically (women are usually excluded from the top positions at the organizational level [ 1 , 2 , 3 ]. Workplace gender descrimination, in any farm, is not only illegal and against the basic human rights [ 4 ], it also contributes towards the lower socioeconomic status of women globally [ 5 ]. The under-representation of women, especially at leadership position is particularly common in higher education organizations, including medical and dental schools, as reported previously [ 6 , 7 ]. Even those who reach a position in academic healthcare institutes publish research less frequently [ 8 ], receive fewer research grants [ 9 ], and have 23% fewer chances of promotion [ 10 ] than their male counterparts.

Recently, researchers tried to explore the extent of gender-based parity in leadership positions in academic medicine and sciences in many different countries of the world. Surprisingly, gender imbalance exists at all levels and nations, including countries ranking high on gender equality indices, such as Nordic countries [ 11 ]. According to a report by the European Commission, only 25% of the professors in European member states are women [ 12 ]. Similarly, in medical schools in the United States of America (USA), only 18% of the department heads are females [ 13 ]. Research studies also revealed that gender-based inequalities are multifaceted, and many extrinsic and intrinsic factors contributing to the issue have been identified [ 10 , 14 ]. However, most research studies exploring women and leadership in academic medicine have been conducted in developed Western countries, including Europe, the USA, and Canada [ 15 ]. There is a dire need for research to document the extent and how male and female professionals develop as leaders during their careers in academic medicine and dentistry, especially from a non-western perspective. In this context, a country like Pakistan offers an excellent opportunity to study and assess the unique barriers these women must overcome to attain leadership positions in academic medicine and dentistry.

Pakistan is a former British colony and a Muslim-majority country in South Asia with over 241 million population, of whom 48.54% are women [ 16 ]. The country’s strictly conservative and patriarchal society strongly influences culture, traditions, and gender dynamics [ 17 ] thus significantly reducing women’s opportunities for career choice and progression [ 18 ]. The overall job market in Pakistan is male-dominated, and only certain professions, such as teaching and medical practice, are deemed more suitable for women in Pakistan. Women constitute nearly 70% of the students in Pakistani medical schools, but only 50% register with PMDC, and even fewer continue medical practice as a career [ 19 ].

There exists a critical gap in our understanding of the gender-based differences in the extent and experience of leaders from a non-Western perspective. In the current study, we investigated the gender-based distribution of academic leaders in medical and dental colleges in Khyber Pakhtunkhwa (KP), Pakistan and explore the experiences of men and women occupying leadership positions in these colleges. The study results would help design gender-based leadership development strategies and promote gender equity in academic institutes across Pakistan.

Reflexivity

The individuals who conceptualized and designed this study (MS & BJ) hold leadership positions themselves. We are cognizant of both the author’s positions and to avoid our personal biases reflect in the study findings, we were constantly engaged in reflexive practices [ 20 ]. For example, the author (MS) wrote personal memos during the data collection, coding, and interpretations. Each transcript was thoroughly discussed, and codes were shared between the researchers. Moreover, before the data analysis started, the coding team set aside and bracketed their personal assumptions to further improve trustworthiness of the study findings.

Based on the study objectives, a mixed-method sequential study design [ 21 ] including (a) survey of the gender-based distribution of academic leadership positions in medical and dental colleges (b) qualitative interviews with selected individuals occupying leadership positions to explore gender-based differences in their experiences were employed. Integration occurred during development of the interview protocol and results description of both phases to gain an in-depth insight into the extent and experiences of gender based distribution in academic medicine and dentistry. The ethics approval of the study was granted by the institutional research board of Khyber Medical University (Ref No: 1–11/IHPER/MHPE/KMU/23–62) and the study design followed GRAMMS (Good Reporting of a Mixed Methods Study) guidelines.

Study setting and sampling

The sampling frame for the quantitative survey was all the medical and dental colleges of KP, both public sector and private that have been recognized by the Pakistan Medical & Dental Council. At the time of data collection (June – October 2023), there were 30 colleges in KP offering undergraduate programs in medicine (MBBS) and dentistry (BDS). Information about the gender and rank of those working in academic leadership positions were collected from the official websites of the institutes, followed by confirmation of the organogram with the HR administration of the respective college. Only two private institutes, including a dental college declined to provide the required details and were excluded from the study. Academic leaders were the individuals occupying the position of dean, director or principal, head of the department or chairs. Academic leaders in medical and dental colleges in Pakistan may be divided into three leadership tiers based on roles and responsibilities. The top level includes the dean, directors, or principals responsible for looking after the overall organizational management, including teaching, research, and administration. The mid-level academic leaders take responsibility for a specific educational/training program and include the head of the departments or chairman/women. The line-level leadership is responsible for teaching within the program, including teachers, course coordinators, etc.

For the qualitative arm of the study, we could only get email IDs of 78 academic leaders connected to the undergraduate medical and dental programs at the top and mid-level. The inclusion criteria were persons of any age and gender who were serving in a leadership position for at least one year in a medical and dental college in KP. Those serving in a leadership position on additional/ad-hoc/acting charge who did not meet the required qualifications and experience were excluded from the study. Potential participants sent an email describing the study and an invitation to participate. Only 21 responded positively to our email and they were all included in our final sample. Written informed consent was obtained from all those participants who agreed to participate in the study. Data saturation was observed after 7 interviews [ 22 ], but we continue our data collection to ensure equal representation of both genders (5 male, 5 female). A thank you email was sent to the remaining prospective participants informing them that they would not be interviewed as the data saturation has been reached.

Data collection

For quantitative data collection, college name, status (public or private), and Rank and gender of those working in leadership positions were taken from website and entered Microsoft Excel version 2013. Qualitative data was collected through semi-structured interview sessions conducted by a single researcher (MS) face-to-face or online using the Microsoft ® Team platform depending on the participant choice. Based on the available published literature, a 10-item interview guide was developed and piloted on two participants who were not part of the original study (Supplementary file 1 ). Information on age, gender, qualification, experience, and current role were also collected. Face-to-face interviews were audio recorded using the record function of a mobile phone device (iPhone 13, Apple inc USA). In the case of the online interview, the recording was conducted using the record option available in the meeting link. To capture the real essence of the experiences, the participants were free to respond in English, Urdu (the national language of Pakistan), and Pashto, most of the interviewees’ mother tongue or first language. Data was securely recorded, translated, and transcribed verbatim.

Data analysis

The survey data was analyzed using Microsoft Excel 2013 and presented as frequency and percentages. The female-to-male ratio was calculated across different institutes, programs, academic ranks, and departments. Qualitative data was transcribed, and thematic analysis was done using ATLAS Ti Version 8.4.5. The Braun and Clarke framework [ 21 ] was used for thematic analysis (Fig.  1 ). The analysis process involved a systematic approach to coding and interpretation by two researchers (MS & BM). The coding list was further discussed with supervisors (BJ & UM) for improved quality.

Braun and Clarke thematic analysis steps

Quantitative analysis

In total, 18 medical and 10 dental colleges including a total of 497 (312 in medicine and 185 in dentistry) academic leaders were surveyed. The overall gender-based distribution of academic leaders is presented in supplementary Tables 1 & 2 and summarized in Fig.  2 . Overall, women remain under-represented at all leadership tiers. Gender-based differences were highest at top leadership positions, including principal/deans/directors, where women occupy only 15.5% ( n  = 9) of the total leadership positions. However, women are relatively well represented in mid-level leadership positions. The mid-level leadership positions are further categorized into three groups (basic sciences, clinical sciences, and medical/dental education departments). Across the three categories at the mid-level, the leadership gap tends to be narrower in the medical/dental education department, where more than 40% ( n  = 12) of the heads of the departments were females.

Bar graph showing the gender-based distribution of academic leaders in KP, Pakistan’s medical and dental college

We next assessed gender-based differences in academic leadership positions in medical and dental colleges (Fig.  3 A & B). At top-level management in dental colleges (Fig.  3 A), the percentage of women leaders is almost twice (26.7%; n  = 4) that of women leaders in medical colleges (11.6%; n  = 5) (Fig.  3 B).

Bar graph representing gender-based distribution at different leadership tiers in ( A ) Dental colleges & ( B ) Medical colleges

Gender-based disparities were also observed at the level of the department as well as whether the institute is private or public sector (Fig.  4 & supplementary Tables 1 & 2 ). In general, except for medical education, the female-to-male ratio at leadership position was higher in public sector medical (average 0.44; range 0.06–1.71) and dental colleges (average 0.67; range 0.1–1.6) than private sector medical (average 0.36; range 0.14–0.67) and dental (average 0.32; range 0.1–0.5) colleges in the KP province (Fig.  4 ). Similarly, in certain clinical fields such as obstetrics gynaecology, and prosthodontics, leadership positions were entirely occupied by only one gender.

Bar graph representing female-to-male ratio in public and private sector medical and dental college

Qualitative analysis

Demographic characteristics of the participants are presented in Table  1 . Wide variations exist among the participants in terms of age, experience, academic credentials and number of years in a leadership position. The mean age of the participants was 45.4 ± 5.2 years and average experience was 16.4 ± 5.3 years.

Qualitative findings

The experiences of the participants can be described under four main themes and eleven subthemes as presented in Table  2 .

Theme 1: Leadership attributes

The participants identified various attributes that are important for a leader. Both male and female participants expressed that a leader is internally motivated to substantially impact the lives of those around them. A vast majority of the participants agreed that they possessed a clear and strong desire to bring about positive transformations in the lives of others through their leadership but also to witness and experience the visible results of their efforts personally.

If you are a leader , you are required to influence people and guide them properly (ID-1 Male)

Another leadership attribute identified by participants was good teamwork.

…One of the leadership qualities is working with your colleagues and subordinates as a team. And when you take them into confidence , they support and encourage you and facilitate you in every way… (ID7- Male)

Positive mindset, flexibility, diplomacy, and self-awareness were also cited as essential traits for leaders.

As a leader , you are required to know about yourself.… (ID-1- Female) When you are in a team. Anyone can come up with a good idea , an even better idea than yours. You must have the capacity to Embrace that (ID-10- Male)

Theme 2: Leadership journey

The participants in the study shared their views on leadership, stating that it involves multiple responsibilities and requires both education and practical experience. The path to leadership was seen as challenging, but male and female participants had different perspectives on how they achieved it. Female leaders were found to actively seek opportunities, while leadership roles were more readily offered to male participants.

The transition into leadership roles as a male has been easy (ID9-Male)

Female leaders also struggle to balance their domestic responsibilities with their leadership roles, which was not the case for male leaders.

…. I had to do all the house chores myself. The cleaning , the cooking , the washing , the daily activities , and then return home to do more work with the kids. (ID5-Female)

Both male and female participants acknowledged the existence of gender discrimination in leadership roles.

When we apply for projects to get funds , we are unable to get a percentage like the men. (ID2-Female) The gender disparity does come into play , especially when you’re working as a head of an institute where all the men are working with you , even if no one says anything , the default thinking is maybe she’s a woman and that’s why she’s saying this (ID5-Female)

Participants also highlighted various advantages of leadership in their career growth, including training, financial stability, and autonomy.

…but then they got some advantages to their financially stable, they are socially responsible, you have a strong social background… (ID4- Male)

Theme 3: challenges

Both male and female participants highlighted the underrepresentation of women in leadership positions.

Yes , because our society is male-dominated. In my own opinion if there was a female in my place , she would have surrendered and said that she would have forgiven that…. (ID6- Male)

Moreover, female participants faced discrimination while pursuing leadership positions. They were able to overcome these challenges by relinquishing some degree of control and seeking assistance from other individuals.

When I was going for my PhD , in my interview , they said , oh , you’re a woman. OK , you’re married. Ohh. You have kids. What are you going to do with your kids… (ID8-Female) ….However , within the limited end available resources , I made efforts with the support from the institute and high-ups (ID7- Female)

The challenge of work-family balance was identified by both male and female participants. However the male participants reported that their families were taken care of by their spouses, which allowed them to focus on their work but the female participants had to balance multiple responsibilities.

My wife is a housewife , so that she can give the family time. And my parents are also looked after by my wife. So , if financially I’m supporting them , the emotional support is always there , and it doesn’t make any difference if I am busy (ID1-Male) …. Yes , a terrible effect. It has badly affected my health. My family has excluded me from their social activities and their social circle… (ID2-Female)

Theme 4: support

Mentoring was found to be vital to leaders for their personal growth and advancement as they provided focused guidance and support to aspiring leaders, helping them reach their fullest potential. It was interesting to note that most female participants acknowledged the mentoring role of their father during their leadership journey.

Mentor for me as a person is my father. He retired as the Chief Secretary of the province. He was a very senior bureaucrat , a very well-read person , and a scholar… (ID5- Female)

Both male and female participants stressed the importance of developing support systems to build resilience. These include the role of institution, family, administration, and collaborative work environment. Interestingly most female participants highlighted that support from female colleagues is necessary to sustain leadership roles.

A leader cannot become a leader in a vacuum. Your bosses , your followers , your teammates , and your colleagues all have to support a leader to become the leader and there has to be a network of support available in terms of learning opportunities in terms of funding opportunities in terms of Carrying out the vision of the leader (ID-8 Male) My husband has supported me a lot… (ID-3 Female) Of Course , we don’t get to wherever we are until we have somebody as support , especially in academics. (ID2- Female) Yes , and not only that , they (female colleagues) give me support at my workplace. We give support to each other… (ID-5- Female)

Gender equity and diversity in leadership are crucially important to design and implement policies that ensure a safe learning environment for the students and produce a critical mass of healthcare professionals trained in catering to the needs of a diverse patient population. The integration of results from the quantitative and qualitative arms of the present study suggests that gender- based discrepancies in the extent and experience of academic leader exists in Pakistan. The quantitative analysis showed that women are significantly under-represented especially in the senior level leadership positions where women occupy only 15.5% of the positions. These results follow the same trends as observed in neighbouring South Asian countries such as Bangladesh and India, where women occupy 16% and 18% of the top leadership positions in the healthcare [ 23 , 24 ]. These findings were further confirmed during the qualitative study reporting gender-based inequalities at both faculty and administration, thus confirming the alarming gender gaps in leadership positions in higher education that not only exist in Pakistan [ 25 ] but also in developed countries like the USA, Canada, and UK [ 15 ]. The qualitative study also revealed the possible reasons behind the observed gender-based inequalities. For example, the subtheme “Gender discrimination” was acknowledge by both male and female participants. The findings that opportunities and sponsorship are more readily available to males than females have also been reflected in the literature previously [ 26 ]. Compared to males, it is less likely that a female at a junior rank is recognized for her leadership potential and actively helped (sponsored) in the promotion to a higher rank by a senior who has the power and influence to do so [ 27 ]. Another emerging theme was the role of “mentors” in the leadership journey. The male participants reported their colleagues, teachers, and clinical supervisors as their mentors, a finding consistent with the previously published literature [ 28 , 29 ]. However, the female participants frequently reported the role of their father in their academic success and leadership journey. Since Pakistan is a patriarchal society with traditional gender roles in place, this finding is not unsurprising. In the context of gender, the father always plays a major role in helping, guiding, and supporting daughters in pursuit of education, professional excellence or leadership journey [ 17 ] especially when they are not married.

Most of the study participant started their leadership journey at the entry-level positions. These findings are in concordance with the study by Tagoe and colleagues [ 30 ] who indicate that most of the leadership experiences especially in the case of women leaders, can be gained while working in the middle management positions. The presence of women in mid-level leadership positions and their preference for academic medicine [ 10 ] means that gender disparities in high-level positions will gradually be reduced in future.

Our study report comparatively more women in mid-level (Fig.  1 ) than in the senior level leadership positions. During the qualitative interviews, the participants especially the women leaders revealed that they are struggling to maintain a work-life balance. Long working hours (sometimes exceeding 80 h per week) along with domestic responsibilities such as taking care of the children and family [ 31 ] force women to remain in middle rather than senior leadership positions [ 32 , 33 ]. These issues also force a lot of lady doctors in Pakistan to quit their job [ 18 ].

The uneven gender-based distribution of academic leaders was also observed between private and public sector institutes as well as individual departments within the same institute. In general, except for medical education, the female-to-male ratio was higher in public sector medical and dental colleges of the KP province. These findings are similar to recently published reports from Sweden and the USA, indicating a narrower gender gap in the public than private sector [ 34 , 35 ].

Similarly, in certain clinical fields such as obstetrics & gynecology, and prosthodontics, leadership positions were entirely occupied by only one gender. This gender polarization is not surprising. Due to cultural norms and traditions, female patients mostly prefer women doctors for obstetrics and gynae services [ 36 ], thereby making it the field of choice for women doctors in Pakistan [ 37 , 38 ]. On the contrary, the number of women leaders in surgery and allied were disproportionally low in the current study. Historically, surgery is considered a masculine field, and women are scarce in leadership positions across the globe, even in developed countries like the USA [ 39 ].

This study found that gender disparities exist at leadership positions as well as experiences in medical and dental colleges of KP Pakistan. The women leaders frequently reported gender-based inequality experiences and challenges in maintaining a decent work-life balance due to multiple domestic responsibilities. Although the majority of the top-level leadership positions are occupied by men, the relatively high representation of women at the mid-level indicates that the gender-based disparities in leadership could be more balanced in the future. These finding are critical and can have important implications for government, organizations and policy makers in term of enacting laws, propose regulations and establish support mechanism that help current and aspiring leaders in their leadership journey.

Strengths and limitations

Our study provides a multi-faceted perspective on the extent and experiences of gender-based disparities in medical and dental institutes in Pakistan. The inclusion of all medical and dental institutes increased the generalizability of the study findings while the more than 93% response rate of the survey reduced the selection bias thus increasing validity. Moreover, the qualitative arm of the study provided deep insights into the experiences of academic leaders in a developing and strictly patriarchal society. However, our study has some limitations. The study findings cannot be generalized across Pakistan due to widely prevalent differences across different ethnicities, cultures, and socioeconomic background. Another limitation of the study that should be considered is that the survey information was collected based on department-wise availability of leadership roles. However, the subdivisions in some academic departments for example Medicine & Allied were not included in our study and therefore, variations are expected in the exact female-to-male ratio in the medical and dental colleges.

Data availability

This published article and its supplementary information files include all data generated or analyzed during this study.

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Brekhna Jami, Bushra Mehboob, Usman Mahboob and Fayig Elmigdadi contributed equally to this work.

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Faculty of Dentistry, Zarqa University, Zarqa, Jordan

Muhammad Shahzad & Fayig Elmigdadi

Institute of Basic Medical Sciences, Khyber Medical University, Peshawar, Pakistan

Muhammad Shahzad

Institute of Health Professions Education and Research, Khyber Medical University, Peshawar, Pakistan

Brekhna Jamil & Usman Mahboob

Department of Oral and Maxillofacial Surgery, Peshawar Dental College, Peshawar, Pakistan

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M.S: Study design, Data collection, Analysis and writing the manuscript. B.J and U.M supervision, data analysis and finalizing the manuscirpt. B.M & F.A helped in qualitative data analysis, writing, reviewing and finalizing the manuscript. All authors read and approved the final manuscript.

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Shahzad, M., Jamil, B., Bushra, M. et al. Gender-based differences in the representation and experiences of academic leaders in medicine and dentistry: a mixed method study from Pakistan. BMC Med Educ 24 , 885 (2024). https://doi.org/10.1186/s12909-024-05811-6

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Genotype Representation Graphs: Enabling Efficient Analysis of Biobank-Scale Data

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Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. Current practice is to store large-scale genetic polymorphism data using tabular data structures and file formats, where rows and columns represent samples and genetic variants. However, encoding genetic data in such formats has become unsustainable. For example, the UK Biobank polymorphism data of 200,000 phased whole genomes has exceeded 350 terabytes (TB) in Variant Call Format (VCF), cumbersome and inefficient to work with. To mitigate the computational burden, we introduce the Genotype Representation Graph (GRG), an extremely compact data structure to losslessly present phased whole-genome polymorphisms. A GRG is a fully connected hierarchical graph that exploits variant-sharing across samples, leveraging ideas inspired by Ancestral Recombination Graphs. Capturing variant-sharing in a multitree structure compresses biobank-scale human data to the point where it can fit in a typical server'ss RAM (5-26 gigabytes (GB) per chromosome), and enables graph-traversal algorithms to trivially reuse computed values, both of which can significantly reduce computation time. We have developed a command-line tool and a library usable via both C++ and Python for constructing and processing GRG files which scales to a million whole genomes. It takes 160GB disk space to encode the information in 200,000 UK Biobank phased whole genomes as a GRG, more than 13 times smaller than the size of compressed VCF. We show that summaries of genetic variants such as allele frequency and association effect can be computed on GRG via graph traversal that runs significantly faster than all tested alternatives, including vcf.gz, PLINK BED, tree sequence, XSI, and Savvy. Furthermore, GRG is particularly suitable for doing repeated calculations and interactive data analysis. We anticipate that GRG-based algorithms will improve the scalability of various types of computation and generally lower the cost of analyzing large genomic datasets.

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we have made the following four substantial changes: Benchmarking against XSI and Savvy formats. Implementing GWAS and dot product calculations. Providing biological explanations of the GRG data structure. Restructuring the manuscript to focus on compression and computation.

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EGCT: enhanced graph convolutional transformer for 3D point cloud representation learning

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• Gang Chen 1 ,
• Wenju Wang 1 ,
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It is an urgent problem of high-precision 3D environment perception to carry out representation learning on point cloud data, which complete the synchronous acquisition of local and global feature information. However, current representation learning methods either only focus on how to efficiently learn local features, or capture long-distance dependencies but lose the fine-grained features. Therefore, we explore transformer on topological structures of point cloud graphs, proposing an enhanced graph convolutional transformer (EGCT) method. EGCT construct graph topology for disordered and unstructured point cloud. Then it uses the enhanced point feature representation method to further aggregate the feature information of all neighborhood points, which can compactly represent the features of this local neighborhood graph. Subsequent process, the graph convolutional transformer simultaneously performs self-attention calculations and convolution operations on the point coordinates and features of the neighborhood graph. It efficiently utilizes the spatial geometric information of point cloud objects. Therefore, EGCT learns comprehensive geometric information of point cloud objects, which can help to improve segmentation and classification accuracy. On the ShapeNetPart and ModelNet40 datasets, our EGCT method achieves a mIoU of 86.8%, OA and AA of 93.5% and 91.2%, respectively. On the large-scale indoor scene point cloud dataset (S3DIS), the OA of EGCT method is 90.1%, and the mIoU is 67.8%. Experimental results demonstrate that our EGCT method can achieve comparable point cloud segmentation and classification performance to state-of-the-art methods while maintaining low model complexity. Our source code is available at https://github.com/shepherds001/EGCT .

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The ShapeNetPart, ModelNet40 and S3DIS datasets used in this study were obtained from public domains and are available online at https://shapenet.org/ , https://modelnet.cs.princeton.edu and http://buildingparser.stanford.edu/dataset.html .

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This work was sponsored by Natural Science Foundation of Shanghai under Grant No. 19ZR1435900.

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Gang Chen, Wenju Wang, Haoran Zhou & Xiaolin Wang

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GC contributed to conceptualization, methodology, resources and software; GC, HZ and XW performed data curation; WW carried out formal analysis and funding acquisition; WW, HZ and XW performed supervision and validation; HZ and XW contributed to visualization; GC performed writing-original draft; GC and WW performed writing-review and editing. All authors have read and agreed to the published version of the manuscript.

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Chen, G., Wang, W., Zhou, H. et al. EGCT: enhanced graph convolutional transformer for 3D point cloud representation learning. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03600-2

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Accepted : 01 August 2024

Published : 23 August 2024

DOI : https://doi.org/10.1007/s00371-024-03600-2

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