Use the Chart Viewer template to display bar charts, line charts, pie charts, histograms, and scatterplots to complement a map. Include multiple charts to view with a map or side by side with other charts for comparison. Up to three charts can be viewed side by side or stacked, but you can access and view all the charts that are authored in the map. Examples: Present a bar chart representing average property value by county for a given area. Compare charts based on multiple population statistics in your dataset. Display an interactive scatterplot based on two values in your dataset along with an essential set of map exploration tools. Data requirements The Chart Viewer template requires a map with at least one chart configured. Key app capabilities Multiple layout options - Choose Stack to display charts stacked with the map, or choose Side by side to display charts side by side with the map. Manage chart - Reorder, rename, or turn charts on and off in the app. Multiselect chart - Compare two charts in the panel at the same time. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This study aimed to obtain the reference curves of birth weight for gestational age percentiles for the Polish population and to compare them to published charts in terms of detected proportions of small for gestational age (SGA) and large for gestational age (LGA). The reference curves of birth weight from 24 to 42 weeks of gestation were computed based on 39,092 singleton deliveries. The nomograms included the 3rd to the 97th percentiles and standard deviations. The percentiles were calculated for female and male newborns. The theoretical and true proportions of percentiles for the studied population were estimated based on six growth charts (Fenton, Intergrowth Project, global reference chart, Yudkin, Dubiel, and the World Health Organization chart). The 50th percentile male and female newborns at 40 weeks weighed 3645.8 and 3486.7 g, respectively. The difference was 159.1 g. The ranges between the 3rd and 97th percentile at 40 weeks were 1481.5 g for males and 1423.5 for females. A total of 9.8% SGA and 10.27% LGA were defined, higher than that identified using the Fenton chart and even higher than that identified using the Intergrowth Project. Population growth charts identify more newborns with abnormal growth (both LGA and SGA). The similarity between charts in LGA above the 95th percentile is observed. The discrepancies in SGA are significantly greater, specifically in preterm births than in term births. Similar coverage is found in term pregnancies, regardless of birth weight for gestational age or intrauterine charts. The feasibility of a Polish population growth chart needs to be validated for predicting adverse perinatal outcomes.

How BYD EVs compare to VinFast's cheapest models in Vietnam.

Match reported 2.5K in Employees for its fiscal year ending in December of 2024. Data for Match | MTCH - Employees Total Number including historical, tables and charts were last updated by Trading Economics this last December in 2025.

We compare the performance of various matching estimators using a novel approach that is feasible in the absence of experimental data. We estimate a structural model of hospital choices and catheterization for Medicare heart attack victims using hospital chart data on patient heterogeneity. With the estimated structural parameters, we simulate data for which the treatment effect is known. We find that as measures of individual heterogeneity are added to the controls, matching estimators perform well. However, the estimators do a poor job recovering the true treatment effect when measures of individual heterogeneity are unavailable.

Notice: Due to funding limitations, this data set was recently changed to a “Basic” Level of Service. Learn more about what this means for users and how you can share your story here: Level of Service Update for Data Products.NOTE: The data product titled U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format supersedes this product. It begins with charts from January 2003 and is updated weekly. The U.S. National Ice Center (NIC) is an inter-agency sea ice analysis and forecasting center comprised of the Department of Commerce/NOAA, the Department of Defense/U.S. Navy, and the Department of Homeland Security/U.S. Coast Guard components. Since 1972, NIC has produced Arctic and Antarctic sea ice charts. This data set is comprised of Arctic sea ice concentration climatology derived from the NIC weekly or biweekly operational ice-chart time series. The charts used in the climatology are from 1972 through 2007; and the monthly climatology products are median, maximum, minimum, first quartile, and third quartile concentrations, as well as frequency of occurrence of ice at any concentration for the entire period of record as well as for 10-year and 5-year periods. These climatologies and the charts from which they are derived are provided in the 25-km Equal-Area Scalable Earth Grid (EASE-Grid) binary (.bin) format. The climatologies are also available in ArcGIS geodatabases (.mdb), and GIF format browse files (.gif) are also provided.To view the browse files and compare climatological periods visually, choose Search Database under the Download Data tab.

Match reported $4.54B in Assets for its fiscal quarter ending in September of 2025. Data for Match | MTCH - Assets including historical, tables and charts were last updated by Trading Economics this last December in 2025.

I never look at a group’s chart until after I’ve fallen for their music. But once that happens, my astrologer brain kicks in. Was there something in the stars that day? This project is my way of testing that idea, using data from 120 K-pop groups.

What’s in the dataset?

• Astrological data: Sun signs, moon signs, rising signs (when available), planetary retrogrades, and moon phases at debut

• Career metrics: PAKs, music show wins, physical album sales, YouTube views

• Time reliability: "Reliable" (verified debut time) or "Unreliable" (date only)

For years, I’ve casually tracked K-pop debuts (read: my YouTube history is 60% comeback stages, 30% astrology videos). When I started learning data analysis, I realized I could finally ask properly: do certain planetary alignments show up more often in "successful" groups? No mysticism. Just dates, numbers, and a lot of spreadsheet tabs.

How the data was collected

• Group info and career stats come from Kpopping and SoriData

• Debut times were taken from YouTube when available (for newer groups)

• For older groups, exact debut times are often unavailable because many didn’t debut with YouTube videos in the early years

• All astrological calculations were done using Astro-Seek’s calculator with Seoul as the default location

Some interesting notes

Leo sun signs appear frequently among award-winning boy groups

Want to explore?

Compare different generations: Are 4th-gen groups more likely to have certain signs?

Check if Mercury retrograde at debut had any impact on a group’s early success

This isn’t about proving astrology works. It’s about exploring whether patterns exist between the stars and K-pop success. The data is here for you to analyze and draw your own conclusions.

P.S. If your bias’s Moon sign matches yours… welcome to the "wait, why do I feel so seen?" club.

Explore global COVID-19 data with interactive charts and visualisations. Compare countries, analyse daily trends, and understand mortality and recovery rates. Beginner-friendly, visually appealing, and perfect for learning data analysis with Python and Plot.

Comparing the functional performance of biological systems often requires comparing multiple mechanical properties. Such analyses, however, are commonly presented using orthogonal plots that compare N ≤ 3 properties. Here, we develop a multidimensional visualization strategy using permutated radar charts (radial, multi-axis plots) to compare the relative performance distributions of mechanical systems on a single graphic across N ≥ 3 properties. Leveraging the fact that radar charts plot data in the form of closed polygonal profiles, we use shape descriptors for quantitative comparisons. We identify mechanical property-function correlations distinctive to rigid, flexible, and damage-tolerant biological materials in the form of structural ties, beams, shells, and foams. We also show that the microstructures of dentin, bone, tendon, skin, and cartilage dictate their tensile performance, exhibiting a trade-off between stiffness and extensibility. Lastly, we compare the feeding versus singing performance of Darwin’s finches to demonstrate the potential of radar charts for multidimensional comparisons beyond mechanics of materials.

Comparison of Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better by Model

Notice: Due to funding limitations, this data set was recently changed to a “Basic” Level of Service. Learn more about what this means for users and how you can share your story here: Level of Service Update for Data Products.NOTE: The data product titled U.S. National Ice Center Arctic and Antarctic Sea Ice Concentration and Climatologies in Gridded Format supersedes this product. It begins with charts from January 2003 and is updated weekly. The U.S. National Ice Center (NIC) is an inter-agency sea ice analysis and forecasting center comprised of the Department of Commerce/NOAA, the Department of Defense/U.S. Navy, and the Department of Homeland Security/U.S. Coast Guard components. Since 1972, NIC has produced Arctic and Antarctic sea ice charts. This data set is comprised of Arctic sea ice concentration climatology derived from the NIC weekly or biweekly operational ice-chart time series. The charts used in the climatology are from 1972 through 2007; and the monthly climatology products are median, maximum, minimum, first quartile, and third quartile concentrations, as well as frequency of occurrence of ice at any concentration for the entire period of record as well as for 10-year and 5-year periods. These climatologies and the charts from which they are derived are provided in the 25-km Equal-Area Scalable Earth Grid (EASE-Grid) binary (.bin) format. The climatologies are also available in ArcGIS geodatabases (.mdb), and GIF format browse files (.gif) are also provided.To view the browse files and compare climatological periods visually, choose Search Database under the Download Data tab.

Match reported $3.43B in Debt for its fiscal quarter ending in June of 2025. Data for Match | MTCH - Debt including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Value of $ARG during the Argentina vs Croatia World Cup match at five minute intervals.

• The HR dataset is a collection of employee data that includes information on various factors that may impact employee performance. To explore the employee performance factors using Python, we begin by importing the necessary libraries such as Pandas, NumPy, and Matplotlib, then load the HR dataset into a Pandas DataFrame and perform basic data cleaning and preprocessing steps such as handling missing values and checking for duplicates.

• The dataset also use various data visualization to explore the relationships between different variables and employee performance. For example, scatterplots to examine the relationship between job satisfaction and performance ratings, or bar charts to compare the average performance ratings across different gender or positions.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

PurposeThis study aimed to determine the prevalence of underweight, overweight, and obesity among Jordanian infants aged 0–2 years, establish national growth reference charts, and compare the growth of Jordanian infants with the WHO growth standards.MethodsThe present study analyzed 260,027 anthropometric measurements derived from 82,874 healthy Jordanian children (51% boys) aged 0–24 months. These measurements included both cross-sectional and repeated entries, with each child contributing between one visit and nine follow-up visits (10 measurements). Weight and height measurements were analyzed using the Generalized Additive Models for Location Scale and Shape (GAMLSS) statistical method to develop the growth charts.ResultsSeparate models for height-for-age, weight-for-age, and weight-for-height were constructed for each gender. Significant discrepancies were found between WHO growth references and the Jordanian references. Children in Jordan were shorter, particularly among girls, and had slightly higher weight-for-age from the age of 7 months onward.ConclusionThe availability of Jordanian-specific growth references will improve the accuracy of assessing children's growth and enhance the monitoring and evaluation of their health and development.

Comprehensive comparison of Latency (Time to First Token) vs. Output Speed (Output Tokens per Second) by Model

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

To compare and cluster the health status and disability restrictions associated with eight major physiological functions of body systems, using functioning domains of WHO Disability Assessment Schedule 2.0. Retrospective analyses of a nation-wide disability database. Population-based study. Records from patients >18 years of age with disability were obtained from the Taiwan Data Bank of Persons with Disability (July 2012–November 2017). Disability functioning profile of the following diagnosis were analyzed: stroke, schizophrenia, hearing loss, liver cirrhosis, chronic kidney disease, congestive heart failure, burn, head and neck cancer. Not applicable. Demographic data, severity of impairment, and Disability Assessment Scale scores were obtained and analyzed. Radar charts were constructed using the WHO Disability Assessment Schedule 2.0. functioning domain score. Degree of similarity between any two given diagnosis was assessed by cluster analysis, comparing the Euclidean distances between radar chart data points among the six domains. Based on cluster analysis of similarities between functioning domain profiles, the eight diagnoses were grouped into different disability clusters. Four clusters of disability were named according to the type restriction patterns: global-impact cluster (stroke); interaction-restriction cluster (schizophrenia, hearing loss); physical-limitation cluster, (liver cirrhosis, CKD, and congestive heart failure); and specific-impact cluster (burn, head and neck cancer). The rates of institutionalization and unemployment differed between the four clusters. We converted WHO Disability Assessment Schedule 2.0. functioning domain scores into six-dimensioned radar chart, and demonstrate disability restrictions can be further categorized into clusters according to similarity of functioning impairment. Understanding of disease-related disabilities provides an important basis for designing rehabilitation programs and policies on social welfare and health that reflect the daily-living needs of people according to diagnosis.Implication for RehabilitationThe use of radar charts provided a direct visualization of the scope and severity of disabilities associated with specific diagnoses.Diagnosis-related disabilities can be organized into clusters based on similarities in WHODAS 2.0 disability domain profiles.Knowledge of the characteristics of disability clusters is important to understand disease-related disabilities and provide a basis for designing rehabilitation. The use of radar charts provided a direct visualization of the scope and severity of disabilities associated with specific diagnoses. Diagnosis-related disabilities can be organized into clusters based on similarities in WHODAS 2.0 disability domain profiles. Knowledge of the characteristics of disability clusters is important to understand disease-related disabilities and provide a basis for designing rehabilitation.