We evaluated several animations for transitions between scatter plots in a crowd-sourcing study. We published the results in a paper and provide additional information within this supplemental material. Contents: Tables that did not fit into the original paper, due to page limits. An anonymized print-out of the preregistration. The original preregistration is available at OSF (DOI) and on the internet archive. Videos demonstrating the tasks used in the study: used to record samples for the study, used for participant training, and used to detect distracted participants and bots. An interactive demonstration of all study tasks (including training and attention checks). The source code is contained within the directory ./interactive-demo/ of this supplemental material and also available at GitHub. The animation library that we used for the study. We also include a test page for readers to use with their own data sets. The source code is contained within the directory ./animation-library/ of this supplemental material and also avialable at GitHub. The list of nonsensical statements that we used for attention checks on Prolific. The statistical tests with the recorded study data, some of which we reported in the main paper. We also provide reports from the preliminary power analysis that we performed to determine the number of participants for the study. The recorded pseudo-anonymized study data for further analysis.

Blockchain data query: Animal scatter plot (random 2)

Context

Data Visualization

Content

a. Scatter plot

  i. The webapp should allow the user to select genes from datasets and plot 2D scatter plots between 2 variables(expression/copy_number/chronos) for 
    any pair of genes.

  ii. The user should be able to filter and color data points using metadata information available in the file “metadata.csv”.

  iii. The visualization could be interactive - It would be great if the user can hover over the data-points on the plot and get the relevant information (hint - 
    visit https://plotly.com/r/, https://plotly.com/python)

  iv. Here is a quick reference for you. The scatter plot is between chronos score for TTBK2 gene and expression for MORC2 gene with coloring defined by
    Gender/Sex column from the metadata file.

b. Boxplot/violin plot

  i. User should be able to select a gene and a variable (expression / chronos / copy_number) and generate a boxplot to display its distribution across 
   multiple categories as defined by user selected variable (a column from the metadata file)

 ii. Here is an example for your reference where violin plot for CHRONOS score for gene CCL22 is plotted and grouped by ‘Lineage’

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

New cytometric techniques continue to push the boundaries of multi-parameter quantitative data acquisition at the single-cell level particularly in immunology and medicine. Sophisticated analysis methods for such ever higher dimensional datasets are rapidly emerging, with advanced data representations and dimensional reduction approaches. However, these are not yet standardized and clinical scientists and cell biologists are not yet experienced in their interpretation. More fundamentally their range of statistical validity is not yet fully established. We therefore propose a new method for the automated and unbiased analysis of high-dimensional single cell datasets that is simple and robust, with the goal of reducing this complex information into a familiar 2D scatter plot representation that is of immediate utility to a range of biomedical and clinical settings. Using publicly available flow cytometry and mass cytometry datasets we demonstrate that this method (termed CytoBinning), recapitulates the results of traditional manual cytometric analyses and leads to new and testable hypotheses.

Blockchain data query: ETH-EIGEN Correlation (Scatter Plot)

Blockchain data query: Scatter Plots: Price vs Flows

This database studies the performance inconsistency on the biomass HHV ultimate analysis. The research null hypothesis is the consistency in the rank of a biomass HHV model. Fifteen biomass models are trained and tested in four datasets. In each dataset, the rank invariability of these 15 models indicates the performance consistency.

The database includes the datasets and source codes to analyze the performance consistency of the biomass HHV. These datasets are stored in tabular on an excel workbook. The source codes are the biomass HHV machine learning model through the MATLAB Objected Orient Program (OOP). These machine learning models consist of eight regressions, four supervised learnings, and three neural networks.

An excel workbook, "BiomassDataSetUltimate.xlsx," collects the research datasets in six worksheets. The first worksheet, "Ultimate," contains 908 HHV data from 20 pieces of literature. The names of the worksheet column indicate the elements of the ultimate analysis on a % dry basis. The HHV column refers to the higher heating value in MJ/kg. The following worksheet, "Full Residuals," backups the model testing's residuals based on the 20-fold cross-validations. The article (Kijkarncharoensin & Innet, 2021) verifies the performance consistency through these residuals. The other worksheets present the literature datasets implemented to train and test the model performance in many pieces of literature.

A file named "SourceCodeUltimate.rar" collects the MATLAB machine learning models implemented in the article. The list of the folders in this file is the class structure of the machine learning models. These classes extend the features of the original MATLAB's Statistics and Machine Learning Toolbox to support, e.g., the k-fold cross-validation. The MATLAB script, name "runStudyUltimate.m," is the article's main program to analyze the performance consistency of the biomass HHV model through the ultimate analysis. The script instantly loads the datasets from the excel workbook and automatically fits the biomass model through the OOP classes.

The first section of the MATLAB script generates the most accurate model by optimizing the model's higher parameters. It takes a few hours for the first run to train the machine learning model via the trial and error process. The trained models can be saved in MATLAB .mat file and loaded back to the MATLAB workspace. The remaining script, separated by the script section break, performs the residual analysis to inspect the performance consistency. Furthermore, the figure of the biomass data in the 3D scatter plot, and the box plots of the prediction residuals are exhibited. Finally, the interpretations of these results are examined in the author's article.

Reference : Kijkarncharoensin, A., & Innet, S. (2022). Performance inconsistency of the Biomass Higher Heating Value (HHV) Models derived from Ultimate Analysis [Manuscript in preparation]. University of the Thai Chamber of Commerce.

This scatter chart displays deaths (people) against cases (people). The data is filtered where the disease is COVID-19. The data is about diseases per day.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

Blockchain data query: Swell - rswETH through LI.FI scatter plot

The predicted compositions for each of the dataset

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network The following selected graphics are included in this data release in .pdf format. Also included in this data release are web pages accessible for people with disabilities provided in compressed .zip format. The web pages present the same information as the .pdf files: Annual and seasonal discharge trends.pdf -- Graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Annual_and_seasonal_discharge_trends_htm.zip -- Compressed web page presenting graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of simulated quarterly sampling frequency trends.pdf -- Graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_simulated_quarterly_sampling_frequency_trends_htm.zip -- Compressed web page presenting graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of median parameter values.pdf -- Graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_median_parameter_values_htm.zip -- Compressed web page presenting graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus time.pdf -- Scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_time_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus discharge.pdf -- Scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_discharge_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by season.pdf -- Seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_season_htm.zip -- Compressed web page presenting seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of sampled discharge compared with mean daily discharge.pdf -- Boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_sampled_discharge_compared_with_mean_daily_discharge_htm.zip -- Compressed web page presenting boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by month.pdf -- Monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_month_htm.zip -- Compressed web page presenting monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report.

IRIS FLOWER SCATTER PLOT IMAGES DATASET

Overview

This dataset is derived from the well-known Iris flower dataset and contains 5000 images in PNG format. These images represent scatter plots that visually capture the relationships between different pairs of features in the Iris dataset. The original Iris dataset consists of 150 samples from three species of Iris flowers (Iris setosa, Iris versicolor, and Iris virginica), with each sample having four features: sepal length, sepal width, petal length, and petal width. The scatter plot images in this dataset provide visual insights into how these features correlate and differentiate the three species.

Dataset Description

• Total Images: 5000 PNG images
• Image Format: PNG
• Resolution: High-resolution scatter plots (resolution details can be specified)
• Source: Derived from the Iris dataset available in Scikit-learn
• Feature Pairs: Scatter plots are generated for all possible pairs of features (sepal length vs. sepal width, petal length vs. petal width, etc.) ##**Features of the Dataset** Diverse Visual Representations: The dataset includes scatter plots with various feature pairings, providing comprehensive visual analysis of feature relationships. Species Differentiation: Each scatter plot clearly distinguishes between the three species of Iris flowers using different colors or markers. High Quality: The images are generated with high-quality plotting techniques to ensure clarity and precision in the representation of data points. Annotations: Scatter plots are annotated with axes labels and legends to facilitate easy interpretation. Randomized Samples: The dataset contains 5000 images, which implies multiple scatter plots for each pair of features, with randomized sample selections to cover different aspects and variations within the dataset. ##**Use Cases** Data Visualization: Ideal for educational purposes to demonstrate data visualization techniques and the importance of scatter plots in exploratory data analysis. Machine Learning: Useful for training machine learning models on image recognition tasks, particularly in distinguishing between different species based on visual patterns. Research and Analysis: Can be used in research studies that require a large number of scatter plot images for testing new algorithms in image processing or pattern recognition. ##**Conclusion** The Iris Flower Scatter Plot Images Dataset provides a rich resource for visual data analysis, machine learning training, and educational purposes. By leveraging the classic Iris dataset, it offers a unique way to explore feature relationships through high-quality scatter plot images.

Plot data of the manuscript Safety evaluation of the venom from scorpion Rhopalurus junceus: assessment of oral short term, subchronic toxicity and teratogenic effect

Blockchain data query: KTA DAU vs. Volume Correlation (Scatter Chart Data)

The data set comprises more than 7000 time series of ocean currents from moored instruments. The records contain horizontal current speed and direction and often concurrent temperature data. They may also contain vertical velocities, pressure and conductivity data. The majority of data originate from the continental shelf seas around the British Isles (for example, the North Sea, Irish Sea, Celtic Sea) and the North Atlantic. Measurements are also available for the South Atlantic, Indian, Arctic and Southern Oceans and the Mediterranean Sea. Data collection commenced in 1967 and is currently ongoing. Sampling intervals normally vary between 5 and 60 minutes. Current meter deployments are typically 2-8 weeks duration in shelf areas but up to 6-12 months in the open ocean. About 25 per cent of the data come from water depths of greater than 200m. The data are processed and stored by the British Oceanographic Data Centre (BODC) and a computerised inventory is available online. Data are quality controlled prior to loading to the databank. Data cycles are visually inspected by means of a sophisticated screening software package. Data from current meters on the same mooring or adjacent moorings can be overplotted and the data can also be displayed as time series or scatter plots. Series header information accompanying the data is checked and documentation compiled detailing data collection and processing methods.

This data annex contains the supplementary data to the IEA PVPS Task 16 report "Worldwide benchmark of modeled solar irradiance data" from 2023. The dataset includes visualizations and tables of the results as well as information concerning the reference stations.

The dataset contains the following type of files:

• StationList.xlsx: list of all stations, including their coordinates, climate zone, station code, continent, altitude AMSL, data source, number of available test data sets, station type (Tier-1 or Tier-2), and available calibration record.
• Result tables in folder “ResultTables”: Folders “climate_zones” and “continents” contain the tables described in Section 5.3. The filenames are “Component_metric_in_subgroup.html” with “component” DNI or GHI, “metric” describing the metric (see Table 3), and “subgroup” describing the continent or climate zone.
• World maps: The folder “Resultmaps” contains world maps of the metrics described in Section 5.2. Either four or three metrics, depending on the map, are included in each pdf. A legend describing the meaning of the point size is also included.
• Scatter plots of test vs. reference irradiance: The folder “Scatterplots” contains two folders, “DNI” and “GHI”, for the two investigated components. Three subfolders are also contained in these two folders:
  • The subfolders “plotsPerSiteYear” contain plots named “scatOverviewCOMPONENT_SITEYYYY.png”, where “COMPONENT” is either DNI or GHI, SITE is the three-letter site abbreviation, and YYYY is the evaluated year. The png plots include the scatterplots for all test data sets evaluated for the case specified by the filename.
  • The subfolders “plotsPerTestdataProvider” contain plots named “scatOverviewTESTDATASET_COMPONENTYYYY.png”, where “TESTDATASET” describes the test data set, “COMPONENT” is either DNI or GHI, and YYYY is the evaluated year. The png plots include the scatterplots for all sites evaluated for the case specified by the filename.
  • The subfolders “plotsPerTestdataProviderSamePosPerStat” contain the same scatterplots as “plotsPerTestdataProvider”, but using a slightly different visualization method. Here, the position of each scatterplot for a given site within the plot is always the same. Although this yields many empty subplots and small scatterplots, it can be helpful to rapidly browse through the plots if only one or a few stations are of interest.

Summary of sample information.

The Excel spreadsheet contains ion track data collected by CR-39, including track diameter, average grayscale, x-y coordinates, etc. And preliminary exclusion of non-ionic track data was performed in the Excel spreadsheet, marked as 0. The. fig file contains scatter plots of ion track position distribution collected by CR-39, as well as parabolic fitting based on TPS parameters.

ATMOWeb ( as the part of FTPBrowser interface) provides a graphical browsing, subsetting and retrieval capability for selected ionospheric and atmospheric data. Data can be displayed as time series plots, filtering and scatter plot options are also included for a few spacecraft. The Space Physics Data Facility (SPDF) is the archive of non-solar data for the Heliospheric Science Division (HSD) at NASA's Goddard Space Flight Center.