Chart Viewer allows app viewers to explore your map beside charts related to your data. App authors can display multiple data-based graphics configured in Map Viewer to compliment information in the map. Up to ten charts can be included in the app and each can be viewed alongside your map or side by side with other charts for comparison.Examples:Present a bar chart representing average property value by county for a given areaCompare charts based on multiple population statistics in your datasetDisplay an interactive scatter plot based on two values in your dataset along with an essential set of map exploration toolsData RequirementsThis app requires a map with at least one chart configured. For more information, see the Charts help topic.Key App CapabilitiesMultiple layout options - Choose to display your charts stacked with the map or side by side with the mapManage charts - Reorder, rename, or turn off and on charts in the appMultiselect chart - Compare two charts in the panel at the same timeBookmarks - Enable bookmarks configured in the Map Viewer to include a collection of preset extentsNavigation boundary - Keep the area in the map in focus by using a navigation boundary or disabling the ability to scrollHome, Zoom Controls, Legend, Layer List, SearchSupportabilityThis 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.

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

Summary of features for proportional ink violation detection.

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

The map provides an overview of drought conditions as reported through household surveys from 6-15th November 2021. The bar charts provide the percentage reported responses on drought length. For example, in Galole 100% of respondents reported drought of over 6 months, the bar is therefore completely red. The map also shows targeted number of households, by sub-county, for the planned and ongoing multi-purpose cash program implemented by the ASAL Humanitarian Network since November 2021.

The “All Activity Trend” shows the number of positive cases that were interviewed (bar graph) and the percentage of those interviewed who reported each select high to moderate exposure activity types (i.e. personal care, dining out, social-related activities, work, travel, gym/fitness, sports, and faith-related events) during their exposure period (trend lines) on a weekly basis.Note: Data subject to change on a daily basis. Data are restricted to positive cases with a completed contact tracing interview. Possible exposure data are collected during the contact tracing interview as self-reported activities occurring within the 2-week period before the date of symptom onset for symptomatic individuals or the date of test sample collection for asymptomatic individuals. Data collection methods were altered starting the week of Dec 11 for gym/fitness and sports, so should not be compared to previous values.* High to Moderate Exposure Activity Types are not exhaustive and include travel, personal care, faith events, work, dining out, social events, gym/fitness, and sports.Data is updated on a weekly basis.

The Medical Expenditure Panel Survey (MEPS) Household Component collects data on all members of sample households from selected communities across the United States. With the MEPS-HC Data Tools, users can explore trends and cross-sectional bar charts for nationally representative estimates of household medical utilization and expenditures, demographic and socioeconomic characteristics, health insurance coverage, accessibility and quality of care, treated medical conditions, and prescribed medicine purchases.

Overview

Data points present in this dataset were obtained following the subsequent steps: To assess the secretion efficiency of the constructs, 96 colonies from the selection plates were evaluated using the workflow presented in Figure Workflow. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m²s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning Deep-well plates at 3000 × g for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.

Info

ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3

barplot_R.R -> code to generate bar plot in R statistic 3.3.3

boxplotv2.R -> code to generate boxplot in R statistic 3.3.3

pRFU_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_+_bl.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_raw.csv -> supernatant mCherry fluorescence dataset of 96 colonies for each construct.

who_+_bl2.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

who_raw.csv -> whole culture mCherry fluorescence dataset of 96 colonies for each construct.

who_+_Chlo.csv -> whole culture chlorophyll fluorescence dataset of 96 colonies for each construct.

Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content

ANOVA_pRFU_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_sup_+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_who_+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_Chlo.doc -> ANOVA of whole culture chlorophyll fluorescence of all constructs, plus average and standard deviation values.

Consider citing our work.

Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

The Gross Domestic Product (GDP) in Philippines was worth 437.15 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Philippines represents 0.41 percent of the world economy. This dataset provides - Philippines GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Additional file 1. Example of chromoMap interactive plot constructed using various features of chromoMap including polyploidy (used as multi-track), feature-associated data visualization (scatter and bar plots), chromosome heatmaps, data filters (color-coded scatter and bars). Differential gene expression in a cohort of patients positive for COVID19 and healthy individuals (NCBI Gene Expression Omnibus id: GSE162835) [12]. Each set of five tracks labeled with the same chromosome ID (e.g. 1-22, X & Y) contains the following information: From top to bottom: (1) number of differentially expressed genes (DEGs) (FDR < 0.05) (bars over the chromosome depictions) per genomic window (green boxes within the chromosome). Windows containing ≥ 5 DEGs are shown in yellow. (2) DEGs (FDR < 0.05) between healthy individuals and patients positive for COVID19 visualized as a scatterplot above the chromosome depiction (genes with logFC ≥ 2 or logFC ≤ −2 are highlighted in orange). Dots above the grey dashed line represent upregulated genes in COVID19 positive patients. Heatmap within chromosome depictions indicates the average LogFC value per window. (3–4) Normalized expression of differentially expressed genes (scatterplot) and of each genomic window containing DEG (green scale heatmap) in (3) patients with severe/critical outcomes and (4) asymptomatic/mild outcome patients. (5) logFC of DEGs between healthy individuals and patients positive for COVID19 visualized as scatter plot color-coded based on the metabolic pathway each DEG belongs to.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Unemployment Rate in India decreased to 8.20 percent in January from 8.30 percent in December of 2024. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in India was worth 3567.55 billion US dollars in 2023, according to official data from the World Bank. The GDP value of India represents 3.38 percent of the world economy. This dataset provides the latest reported value for - India GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The total population in Philippines was estimated at 112.9 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Philippines Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.