Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Abstract This paper presents the results of the statistical graphs’ analysis according to the curricular guidelines and its implementation in eighteen primary education mathematical textbooks in Perú, which correspond to three complete series and are from different editorials. In them, through a content analysis, we analyzed sections where graphs appeared, identifying the type of activity that arises from the graphs involved, the demanded reading level and the semiotic complexity task involved. The textbooks are partially suited to the curricular guidelines regarding the graphs presentation by educational level and the number of activities proposed by the three editorials are similar. The main activity that is required in textbooks is calculating and building. The predominance of bar graphs, a basic reading level and the representation of an univariate data distribution in the graph are observed in this study.

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

Question Paper Solutions of chapter Diagrammatic and Graphical representation of Numerical Data of Numerical and statistical Methods, 5th Semester , Bachelor of Computer Application 2020-2021

Abstract Technical graphic representation presents problems concerning the reduction of dimensionality from 3D to 2D. AEC (architecture, engineering and construction) projects usually adopt the top view paradigm with two-dimensional orthogonal projection. Recently, three major changes in technical representation were the alteration of orthogonal projection into a three-dimensional perspective view, inclusion of oriented object programing as in BIM (Building Information Model) and the interactions with AR (augmented reality). In this context, the present research evaluates the proposal of symbology based on color Hue as done in Cartography and the impact of three-dimensionality of the symbol in the identification of incompatibilities in a project of a residential building. An application of the visual variable color hue was proposed improve readability to representations and evaluations were performed with expert users, using representations in CAD 2D, BIM and AR in top and perspective views. Results indicate the color hue improve the cognitive process of read, interpret and find incompatibilities in civil projects, while the change of point of view contribute to interaction and manipulation in virtual environments. Both shows significance higher than 6% in ANOVA tests.

In complex graphical representations, the relevant information for a specific task is often distributed across multiple spatial locations. In such situations, understanding the representation requires internal transformation processes in order to extract the relevant information. However, digital technology enables observers to alter the spatial arrangement of depicted information and therefore to offload the transformation processes. The objective of this study was to investigate the use of such a representation control (i.e. the users' option to decide how information should be displayed) in order to accomplish an information extraction task in terms of solution time and accuracy. In the representation control condition the participants were allowed to reorganize the graphical representation and reduce information density. In the control condition, no interactive features were offered. We observed that participants in the representation control condition solved tasks that required reorganization of the maps faster and more accurate than participants without representation control. The present findings demonstrate how processes of cognitive offloading, spatial contiguity, and information coherence interact in knowledge media intended for broad and diverse groups of recipients.

The data set consisting of four csv files is deposited here along with the corresponding code books, metadata, a R script for data preparation and analysis and an Adoby Air installer for running the experiment.

The 12th OpenAIRE Graph Community Call took place on Wednesday 19 February 2025, where OpenAIRE Graph Data Scientist, Andrea Mannocci (CNR-ISTI), presented the different avenues for accessing the Graph's data with a brief recap of the Big Query training held in October 2024. This presentation is part of the Community Call series where the OpenAIRE Graph team dives into the makings and workings of the OpenAIRE Graph, one of the world’s largest Scholarly Knowledge Graphs, and give you the floor for questions, feedback, & suggestions. Recording: https://youtu.be/6xeWTRHm3qg

The data set has been used to generate the visual presentation using graphs and charts of the techniques for the current research trends within 6 years (from years 2013 to 2018).

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Nodes represent personality facets (a description of each facet is provided in Table 3)

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

Legacy product - no abstract available

The 16th OpenAIRE Graph Community Call took place on Wednesday 18 June 2025, where Andrea Mannocci, OpenAIRE Graph Data Scientist, presented the Scholarly/Scientific Knowledge Graph Interoperability Framework (SKG-IF). This presentation is part of the Community Call series where the OpenAIRE Graph team dives into the makings and workings of the OpenAIRE Graph, one of the world’s largest Scholarly Knowledge Graphs, and give you the floor for questions, feedback, & suggestions. You can view and register for upcoming calls and consult all past call materials on the OpenAIRE Graph website. Recording: https://youtu.be/Fai8BwEZC6w

Abstract: This study analyzes the responses given by 332 8th grade Portuguese students to two questions, one with data set defined by a frequency table and the other defined by a bar graph. The goal is the evaluation of the effect of the data representation on the calculation of the median. Using a combined methodology, a frequency analysis of students' response types and a semiotic analysis of responses, based on onto-semiotic approach of knowledge and mathematics education, were applied. The semiotic analysis' main goal was the identification of objects and mathematical processes, which can characterize semiotic conflicts implied from the student responses. Generally, students revealed greater tendency to not respond to the graphic determination of the median, however, those who do answer, tend to obtain the median with less difficulty in a graphic context. Semiotic conflicts seem not to depend on the presentation of the data set, although there are more answers that are incorrect in the tabular context than in the graphic context.

The Data Visualization Corpus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1430847%2F29f7950c3b7daf11175aab404725542c%2FGettyImages-1187621904-600x360.jpg?generation=1601115151722854&alt=media" alt="">

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions

The Data Visualizaion Copus

The Data Visualization corpus consists:

• 32 cheat sheets: This includes A-Z about the techniques and tricks that can be used for visualization, Python and R visualization cheat sheets, Types of charts, and their significance, Storytelling with data, etc..

• 32 Charts: The corpus also consists of a significant amount of data visualization charts information along with their python code, d3.js codes, and presentations relation to the respective charts explaining in a clear manner!

• Some recommended books for data visualization every data scientist's should read:

  1. Beautiful Visualization by Julie Steele and Noah Iliinsky
  2. Information Dashboard Design by Stephen Few
  3. Knowledge is beautiful by David McCandless (Short abstract)
  4. The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo
  5. The Visual Display of Quantitative Information by Edward R. Tufte
  6. storytelling with data: a data visualization guide for business professionals by cole Nussbaumer knaflic
  7. Research paper - Cheat Sheets for Data Visualization Techniques by Zezhong Wang, Lovisa Sundin, Dave Murray-Rust, Benjamin Bach

Suggestions:

In case, if you find any books, cheat sheets, or charts missing and if you would like to suggest some new documents please let me know in the discussion sections!

Resources:

• Charts: I personally recommend data viz catalogue, it's easy to understand with their explanation!
• Python codes: Plotly for python and Python graph gallery
• R codes for charts:Plotly for R
• d3 codes: Visualization codes using d3

Request to kaggle users:

• A kind request to kaggle users to create notebooks on different visualization charts as per their interest by choosing a dataset of their own as many beginners and other experts could find it useful!

• To create interactive EDA using animation with a combination of data visualization charts to give an idea about how to tackle data and extract the insights from the data

Suggestion and queries:

Feel free to use the discussion platform of this data set to ask questions or any queries related to the data visualization corpus and data visualization techniques

Kindly upvote the dataset if you find it useful or if you wish to appreciate the effort taken to gather this corpus! Thank you and have a great day!

The interaction between bacteria and the intestinal mucus is crucial during the early pathogenesis of many enteric diseases in mammals. A critical step in this process employed by both commensal and pathogenic bacteria focuses on the breakdown of the protective layer presented by the intestinal mucus by mucolytic enzymes. C. perfringens type G, the causative agent of necrotic enteritis in broilers, produces two glycosyl hydrolase family 18 chitinases, ChiA and ChiB, which display distinct substrate preferences. Whereas ChiB preferentially processes linear substrates such as chitin, ChiA prefers larger and more branched substrates, such as carbohydrates presented by the chicken intestinal mucus. Here, we show via crystal structures of ChiA and ChiB in the apo and ligand-bound forms that the two enzymes display structural features that explain their substrate preferences providing a structural blueprint for further interrogation of their function and inhibition. This research focusses on the roles of ChiA and ChiB in bacterial proliferation and mucosal attachment, two processes leading to colonization and invasion of the gut. ChiA and ChiB, either supplemented or produced by the bacteria, led to a significant increase in C. perfringens growth. In addition to nutrient acquisition, the importance of chitinases in bacterial attachment to the mucus layer was shown using an in vitro binding assay of C. perfringens to chicken intestinal mucus. Both an in vivo colonization trial and a necrotic enteritis trial were conducted, demonstrating that a ChiA chitinase mutant strain was less capable to colonize the intestine and was hampered in its disease-causing ability as compared to the wild-type strain. Our findings reveal that the pathogen-specific chitinases produced by C. perfringens type G strains play a fundamental role during colonization, suggesting their potential as vaccine targets.

China Import: Colour Data/Graphic Display Tube data was reported at 0.000 USD mn in Dec 2019. This stayed constant from the previous number of 0.000 USD mn for Nov 2019. China Import: Colour Data/Graphic Display Tube data is updated monthly, averaging 0.018 USD mn from Jan 2001 (Median) to Dec 2019, with 228 observations. The data reached an all-time high of 165.303 USD mn in Feb 2001 and a record low of 0.000 USD mn in Dec 2019. China Import: Colour Data/Graphic Display Tube data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JA: USD: Import by Major Commodity: Value.

This dataset contains graphs and a movie. Both show visualizations of rotational curves in the transversal plane within a Standardized Gait Cycle from Vertebra prominens downwards, ending at the pelvis. They display 201 anonymous healthy people aged 18-70 years walking at 2,3,4, and 5 km/h on a treadmill. They are based on a SPSS (v23) syntax file and a relating graph template that can be found at our datasets as well. Files are numbered subsequently across all speeds and can be linked by number to its non-standardized counterpart in a further dataset. Positive values show vertebral body rotation to the left, negative values show rotation to the right. Percent of the Standardized Gait Cycle (0-100%) is displayed on the abscissa, always starting with Initial Contact of the right foot. Within a Standardized Gait Cycle the duration of the stance phase right is expected to be 60% (Perry, 1992). As can be seen in the graphs, interpolating spline functions work for average walking speed measurements leading to a more precise determination of relevant and characteristic points (e.g. maxima, phase shifts, lumbar and thoracic movement behavior), thereby aiding in in the clarification of individual features.