The source data includes original data on all the plotted figures involved in the main content and supplementary information.

Discover the booming bar graph array market! Explore key trends, growth drivers, leading companies (Broadcom, London Electronics, etc.), and regional insights in our comprehensive market analysis. Forecast to 2033.

This horizontal bar chart displays tax revenue (% of GDP) by country using the aggregation average, weighted by gdp. The data is about countries.

(G) Expression pattern of genes associated with group I and II loci (from Fig. 2E) within differentially expressed gene sets. Significance calculated by prop.test R, (***) P<0.001. (H) Venn diagram shows overlap between SOX3 binding in NPCs and GPCs. Bar graph shows expression pattern of genes continuously bound by SOX3 NPCs and GPCs. (I) Venn diagram shows overlap between SOX3 and SOX9 binding in GPCs. Bar graph shows expression pattern of genes co-bound by SOX3 and SOX9 in GPCs. (J) ChIP-seq peak graphics around the astrocyte gene Fgfbp3. ChIP-seq peaks are derived from three different experiments; SOX3 ChIPs in NPCs, SOX3 ChIPs in GPCs, SOX9 ChIPs in GPCs. Both ChIP-seq reads and called peak regions (underlying black lines) are shown for all data sets. Bar graphs shows the distribution of differentially expressed genes that are bound by all three factors. P-values (phyper, R) were calculated from the total number of protein coding genes in mm10 assembly (23´389). List of tagged entities: multiple components, Fgfbp3 (ncbigene:72514), Sox3 (uniprot:P53784), Sox9 (uniprot:Q04887), , ChIP assay (obi:OBI_0001954),ChIP-seq assay (obi:OBI_0000716),gene expression assay (bao:BAO_0002785)

The three components of the Earnings Tax, withholding, wage earner, and profits, provide the mechanism used by the Revenue Division for the collection of the City’s single largest tax revenue stream.

The global Bar Graph Displays market is poised for robust expansion, projected to reach an estimated $XXX million by 2025, with a significant Compound Annual Growth Rate (CAGR) of XX% through 2033. This substantial growth is primarily fueled by the escalating demand for visually intuitive and compact data representation solutions across a multitude of industries. The Electronics and Semiconductors sector stands out as a major consumer, leveraging bar graph displays for real-time performance monitoring and diagnostic tools. Similarly, the Medical industry increasingly relies on these displays for patient monitoring equipment, offering clear and immediate insights into vital signs. The Aerospace sector also contributes to market growth, utilizing bar graph displays in cockpit instrumentation and control systems for efficient information delivery. Emerging applications in industrial automation and consumer electronics are further broadening the market's reach. The market's trajectory is being shaped by several key drivers and trends. Advancements in display technologies, including the increasing adoption of LED and LCD variants, are enhancing the performance, energy efficiency, and visual clarity of bar graph displays, making them more attractive for diverse applications. Miniaturization and the integration of smart functionalities are also pivotal trends, enabling the development of more sophisticated and user-friendly display solutions. However, the market is not without its restraints. The high initial cost associated with some advanced display technologies and the availability of alternative data visualization methods, such as digital readouts and advanced graphical interfaces, could pose challenges to widespread adoption in certain price-sensitive segments. Despite these restraints, the inherent simplicity, ease of understanding, and cost-effectiveness of bar graph displays, especially in straightforward data representation, ensure their continued relevance and market demand. Companies like akYtec, Everlight Electronics, and Kingbright are at the forefront of innovation, driving the market forward with their cutting-edge product offerings.

Interactive bar chart showing YoY WPI inflation across components like Primary Articles, Fuel & Power, and Food Index using Flourish.

Allows to represent charts such as bar charts, pie charts, etc along with their components (slices, labels, etc.).

This bar chart displays access to electricity (% of population) by region using the aggregation average, weighted by population. The data is about regions.

This bar chart shows the percentage of French people trusting or not meteorology in 2019. It reveals that ** percent of respondents declared that they rather did not trust meteorology.

This bar chart displays self-employed workers (% of total employment) by region using the aggregation average. The data is about regions.

This bar chart shows the percentage of French people trusting or not statistics in 2019. It reveals that more than half of respondents declared that they rather trusted statistics.

Source: Behavioral Risk Factor Surveillance System (BRFSS), 2012 & 2014.

Source for 2012 national data: National Occupant Protection Use Survey (NOPUS), 2012. Source for 2012 state data: State Observational Survey of Seat Belt Use, 2012. Source for 2014 national data: National Highway Traffic Safety Administration's (NHTSA) National Occupant Protection Use Survey (NOPUS), 2014. Source for 2014 state data: National Highway Traffic Safety Administration's (NHTSA) State Observation of Seat Belt Use, 2014

Data for Figure SPM.4 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure SPM.4 panel a shows global emissions projections for CO2 and a set of key non-CO2 climate drivers, for the core set of five IPCC AR6 scenarios. Figure SPM.4 panel b shows attributed warming in 2081-2100 relative to 1850-1900 for total anthropogenic, CO2, other greenhouse gases, and other anthropogenic forcings for five Shared Socio-economic Pathway (SSP) scenarios. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has two panels, with data provided for all panels in subdirectories named panel_a and panel_b. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains: - Projected emissions from 2015 to 2100 for the five scenarios of the AR6 WGI core scenario set (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) - Projected warming for all anthropogenic forcers, CO2 only, non-CO2 greenhouse gases (GHGs) only, and other anthropogenic components for 2081-2100 relative to 1850-1900, for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: The first column includes the years, while the next columns include the data per scenario and per climate forcer for the line graphs. - Data file: Carbon_dioxide_Gt_CO2_yr.csv. relates to Carbon dioxide emissions panel - Data file: Methane_Mt_CO2_yr.csv. relates to Methane emissions panel - Data file: Nitrous_oxide_Mt N2O_yr.csv. relates to Nitrous oxide emissions panel - Data file: Sulfur_dioxide_Mt SO2_yr.csv. relates to Sulfur dioxide emissions panel Panel b: - Data file: ts_warming_ranges_1850-1900_base_panel_b.csv. [Rows 2 to 5 relate to the first bar chart (cyan). Rows 6 to 9 relate to the second bar chart (blue). Rows 10 to 13 relate to the third bar chart (orange). Rows 14 to 17 relate to the fourth bar chart (red). Rows 18 to 21 relate to the fifth bar chart (brown).]. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblink are provided in the Related Documents section of this catalogue record: - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapter 1, which contains details on the input data used in Table 1.SM.1..(Cross-Chapter Box 1.4, Figure 2). - Link to related publication for input data used in panel a.

This bar chart shows the percentage of French people trusting or not artificial intelligence in 2019. It reveals that nearly ** percent of respondents declared that they did not trust artificial intelligence.

This bar chart presents the percentage of French people thinking that science can improve health in 2019. It shows that more than ** percent of respondents stated thinking that science could allow to improve health.

Updated monthly, this page presents India's Wholesale Price Index (WPI) including YoY inflation data for All Commodities, Primary Articles, Fuel & Power, Manufactured Products, and the Food Index. The data is visualized using interactive bar charts and a tabular format.

Last days Argentinian Congress was the stage for a controversial law project called "frontal labelled". According to this, foods and drinks exceeded of sugar, fats, calories and sodium must be labelled with a black octogonal warning stamp. The project had no quórum for his treatment; however, all across society we assisted to a warm debate about healthcare, particularly on bodycare and one of its main diseases: obesity.

We collected data from FAOSTAT, using filters by region (Latinoamerican and Caribbean countries), year (available series from 2000 to 2016) and indicator (percentage of obesity on adult population). We transformed it with Power Query Editor, transposing year field as headers, getting data ready for our bar chart race, powered by Flourish Studio. You can watch chart on following link: https://public.flourish.studio/visualisation/7452108/ .