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.

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.

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 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.

Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

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!

This part of the data release includes graphical representation (figures) of data of sediment cores collected in 2014 in Monterey Canyon. It is one of five files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each vibracore, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyon_Figures.pdf. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

Following the format of the Open Graph Benchmark (OGB), we design four prediction tasks of relations (mag-write, mag-cite) and higher-order patterns (tags-math, DBLP-coauthor) and construct the corresponding datasets over heterogeneous graphs and hypergraphs [1]. The original ogb-mag dataset only contains features for 'paper'-type nodes. We add the node embedding provided by [2] as raw features for other node types in MAG(P-A)/(P-P). For these four tasks, the model is evaluated by one positive query paired with a certain number of randomly sampled negative queries (1:1000 by default, except for tags-math 1:100).

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.

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

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).

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

Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

The global bar graph displays market is anticipated to experience remarkable growth in the coming years, driven by increasing demand from various end-user industries. The market size was valued at USD XXX million in 2025 and is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. This growth can be attributed to factors such as technological advancements, rising demand for visual data representation, and increasing adoption in sectors like electronics, medical, and aerospace. Among the key segments, the LED and LCD display types are expected to witness significant growth, owing to their superior brightness, clarity, and energy efficiency. The major regions driving the market include North America, Europe, and Asia Pacific. North America holds a dominant market share, with the United States being a notable contributor. The Asia Pacific region is projected to grow at a higher rate during the forecast period, driven by the rapidly expanding electronics and semiconductor industries in countries like China, India, and Japan. Key players in the bar graph displays market include akYtec, Everlight Electronics, Kingbright, Sifam Tinsley, and Texmate, among others. These companies are focusing on innovation, strategic partnerships, and geographical expansion to enhance their market presence.

Dataset, PDB Files, and Protein Graph Representation Data for MAPLE-GNN. When downloaded, extracted graphrepresentation.zip files should be put into the codebase/data/npy folder path. Extracted PDB files can be put into the codebase/data/pdb folder path.

This survey aims to investigate research data management practices at EPFL and integrate their results into specific academic services. The previous two editions, in collaboration with TU Delft, Cambridge University and Illinois University, were carried out in 2017 and 2019.

The objective of these surveys is to collect information on researchers' habits in terms of management of their research data, as well as to identify their needs for data curation services/support. For this edition of the survey, a particular focus has been given to the ways in which they disseminate data and code.

You can find here a file corresponding to the report, in PDF, highlighting the findings of the survey, plus the file of the underlying data, in CSV, and a file with the graphical representation of such data, in PDF.

For more information about this survey, a description on how the survey might be re-used by other institutions, and RDM services offered by the EPFL Library, please contact researchdata@epfl.ch.

Database provides cell markers for various cell types in tissues of human and mouse. Manually curated resource of cell markers in human and mouse. Provides user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Summarized marker prevalence in each cell type is graphically presented., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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.)

ABSTRACT Randomized controlled trials are known to be the best tool to determine the effects of an intervention; however, most healthcare professionals are not able to adequately understand the results. In this report, concepts, applications, examples, and advantages of using visual data as a complementary tool in the results section of original articles are presented. Visual simplification of data presentation will improve general understanding of clinical research.

Warning: the ground truth is missing in certain of these datasets. This was fixed in version 1.0.1, which you should use instead.

Description: this corpus was designed as an experimental benchmark for a task of signed graph classification. It is composed of three datasets derived from external sources and adapted to our needs:

• SpaceOrigin Conversations [1]: set of conversational graphs, each one associated to a situation of verbal abuse vs. normal situation. These conversations model interactions happening in chatrooms hosted by an MMORPG/ The graphs were originally unsigned: we attributed signed to the edges based on the polarity of the exchanged messages.
• Correlation Clustering Instances [2]: set of graph generated randomly as instances of the Correlation Clustering problem, which consists in partitioning signed graphs. These graphs are not associated in any class in the original paper. We proposed a class based on certain features of the space of optimal solutions explored in [2].
• European Parliament Roll-Calls [3]: vote networks extracted from the activity of French Members of the European Parliament. The original data does not have any class associated to the networks: we proposed one based on the number of political factions identified in each network in [3].

These data were used in [4] in order to train and assess various representation learning methods. The authors proposed Signed Graph2vec, a signed variant of Graph2vec; WSGCN, a whole-graph variant of Signed Graph Convolutional Networks (SGCN), and use an aggregated version of Signed Network Embeddings (SiNE) as a baseline. The article provides more information regarding the properties of the datasets, and how they were constituted.

Software: the software used to train the representation learning methods and classifiers is publicly available online: SWGE.

References:

1. Papegnies, É.; Labatut, V.; Dufour, R. & Linarès, G. Conversational Networks for Automatic Online Moderation. IEEE Transactions on Computational Social Systems, 2019, 6:38-55. DOI: 10.1109/TCSS.2018.2887240 ⟨hal-01999546⟩
2. Arınık, N.; Figueiredo, R. & Labatut, V. Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs. Journal of Complex Networks, 2020, 8(6):cnaa025. DOI: 10.1093/comnet/cnaa025 ⟨hal-02994011⟩
3. Arınık, N.; Figueiredo, R. & Labatut, V. Multiple partitioning of multiplex signed networks: Application to European parliament votes. Social Networks, 2020, 60:83-102. DOI: 10.1016/j.socnet.2019.02.001 ⟨hal-02082574⟩
4. Cécillon, N.; Labatut, V.; Dufour, R. & Arınık, N. Whole-Graph Representation Learning For the Classification of Signed Networks. IEEE Access, 2024, 12:151303-151316. DOI: 10.1109/ACCESS.2024.3472474 ⟨hal-04712854⟩

Funding: part of this work was funded by a grant from the Provence-Alpes-Côte-d'Azur region (PACA, France) and the Nectar de Code company.

Citation: If you use this data or the associated source code, please cite article [4]:

@Article{Cecillon2024,
author = {Cécillon, Noé and Labatut, Vincent and Dufour, Richard and Arınık, Nejat},
title = {Whole-Graph Representation Learning For the Classification of Signed Networks},
journal = {IEEE Access},
year = {2024},
volume = {12},
pages = {151303-151316},
doi = {10.1109/ACCESS.2024.3472474},
}