Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

Different graph types might differ in group comparison due to differences in underlying graph schemas. Thus, this study examined whether graph schemas are based on perceptual features (i.e., each graph has a specific schema) or common invariant structures (i.e., graphs share several common schemas), and which graphic type (bar vs. dot vs. tally) is the best to compare discrete groups. Three experiments were conducted using the mixing-costs paradigm. Participants received graphs with quantities for three groups in randomized positions and were given the task of comparing two groups. The results suggested that graph schemas are based on a common invariant structure. Tally charts mixed either with bar graphs or with dot graphs showed mixing costs. Yet, bar and dot graphs showed no mixing costs when paired together. Tally charts were the more efficient format for group comparison compared to bar graphs. Moreover, processing time increased when the position difference of compared groups was increased.

El Salvador's Trademark applications, by number is 8,121[Number] which is the 60th highest in the world ranking. Transition graphs on Trademark applications, by number in El Salvador and comparison bar charts (USA vs. Japan vs. El Salvador), (Kyrgyzstan vs. Denmark vs. El Salvador) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

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 extentsHome, 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.

The dataset includes 15 visual diagrams (pie and bar charts) comparing the distribution of agricultural residues, OFMSW, and used cooking oil across each state in Nigeria, province in South Africa, and county in Kenya. These summaries provide a comparative overview of regional feedstock strengths. The charts complement quantitative analyses by providing visual summaries of feedstock availability.

United States of America's Number of fixed broadband subscriptions per 100 people is 34.72[Number/100] which is the 26th highest in the world ranking. Transition graphs on Number of fixed broadband subscriptions per 100 people in United States of America and comparison bar charts (China vs. Japan vs. United States of America), (India vs. Indonesia vs. United States of America) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

The shift from one-way to two-way communication in healthcare decision-making has heightened the need to understand the role of display formats including tables and graphs as decision aids. Accordingly, we investigate the cognitive mediators explaining how risk display formats influence the decision process, in addition to the impact of analytic thinking style and positive affect on decision quality. Specifically, we find that display format’s impact on the choice of treatment is mediated by verbatim and gist knowledge respectively, and that tables compared to bar graphs improve decision quality. We also find evidence that analytic thinking, and relatively more positive feelings towards decision - making experience can improve decision quality . Implications for better engaging patients and encouraging improved decision making are discussed.

United States of America's Number of procedures to start up/operate a business is 6[Number] which is the 94th highest in the world ranking. Transition graphs on Number of procedures to start up/operate a business in United States of America and comparison bar charts (China vs. Japan vs. United States of America), (India vs. Indonesia vs. United States of America) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

BackgroundSignificant milestones have been made in the development of COVID19 diagnostics Technologies. Government of the republic of Uganda and the line Ministry of Health mandated Uganda Virus Research Institute to ensure quality of COVID19 diagnostics. Re-testing was one of the methods initiated by the UVRI to implement External Quality assessment of COVID19 molecular diagnostics.Methodparticipating laboratories were required by UVRI to submit their already tested and archived nasopharyngeal samples and corresponding meta data. These were then re-tested at UVRI using the WHO Berlin protocol, the UVRI results were compared to those of the primary testing laboratories in order to ascertain performance agreement for the qualitative & quantitative results obtained. Ms Excel window 12 and GraphPad prism ver 15 was used in the analysis. Bar graphs, pie charts and line graphs were used to compare performance agreement between the reference Laboratory and primary testing Laboratories.ResultsEleven (11) Ministry of Health/Uganda Virus Research Institute COVID19 accredited laboratories participated in the re-testing of quality control samples. 5/11 (45%) of the primary testing laboratories had 100% performance agreement with that of the National Reference Laboratory for the final test result. Even where there was concordance in the final test outcome (negative or positive) between UVRI and primary testing laboratories, there were still differences in CT values. The differences in the Cycle Threshold (CT) values were insignificant except for Tenna & Pharma Laboratory and the UVRI(p = 0.0296). The difference in the CT values were not skewed to either the National reference Laboratory(UVRI) or the primary testing laboratory but varied from one laboratory to another. In the remaining 6/11 (55%) laboratories where there were discrepancies in the aggregate test results, only samples initially tested and reported as positive by the primary laboratories were tested and found to be false positives by the UVRI COVID19 National Reference Laboratory.ConclusionFalse positives were detected from public, private not for profit and private testing laboratories in almost equal proportion. There is need for standardization of molecular testing platforms in Uganda. There is also urgent need to improve on the Laboratory quality management systems of the molecular testing laboratories in order to minimize such discrepancies.

Building construction projects have very complex activities, so they require precise and accurate methods of scheduling and control. Using the right method, the project executor can carry out the project according to plan and can be controlled if there is schedule deviations. This study aims to compare the effectiveness of using the Bar Chart-curve-S and Ms. Project-PDM methods on scheduling and controlling building construction projects. The method used by contractors in scheduling and controlling a project is the Bar Chart-S curve and Ms. Project-PDM method.

United States of America's Imports of goods and services (real) is US$2,839,772,436,000 which is the 1st highest in the world ranking. Transition graphs on Imports of goods and services (real) in United States of America and comparison bar charts (India vs. Indonesia vs. United States of America) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Italy's Ease of doing business score is 72.85[0 to 100] which is the 56th highest in the world ranking. Transition graphs on Ease of doing business score in Italy and comparison bar charts (USA vs. China vs. Japan vs. Italy), (United Kingdom vs. South Africa vs. Italy) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

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.

Iran, Islamic Republic of's Trademark applications, by number is 129,785[Number] which is the 8th highest in the world ranking. Transition graphs on Trademark applications, by number in Iran, Islamic Republic of and comparison bar charts (USA vs. Japan vs. Iran, Islamic Republic of), (Germany vs. Thailand vs. Iran, Islamic Republic of) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Argentina's Duration of elementary school (primary education) is 6[Year] which is the 28th highest in the world ranking. Transition graphs on Duration of elementary school (primary education) in Argentina and comparison bar charts (USA vs. China vs. Japan vs. Argentina), (Uganda vs. Ukraine vs. Argentina) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Viet Nam's Technicians, R&D (% of R&D) is 71.49[Per 1 million people] which is the 42nd highest in the world ranking. Transition graphs on Technicians, R&D (% of R&D) in Viet Nam and comparison bar charts (Japan vs. Viet Nam), (Egypt vs. Germany vs. Viet Nam) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Papua New Guinea's Percentage of employers is 1.56% which is the 136th highest in the world ranking. Transition graphs on Percentage of employers in Papua New Guinea and comparison bar charts (USA vs. China vs. Japan vs. Papua New Guinea), (Israel vs. Austria vs. Papua New Guinea) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Ecuador's Number of fixed broadband subscriptions per 100 people is 12.04[Number/100] which is the 85th highest in the world ranking. Transition graphs on Number of fixed broadband subscriptions per 100 people in Ecuador and comparison bar charts (USA vs. China vs. Japan vs. Ecuador), (Zambia vs. Netherlands vs. Ecuador) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Papua New Guinea's Trademark applications, by number is 861[Number] which is the 124th highest in the world ranking. Transition graphs on Trademark applications, by number in Papua New Guinea and comparison bar charts (USA vs. Japan vs. Papua New Guinea), (Austria vs. Switzerland vs. Papua New Guinea) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Saudi Arabia's Trademark applications, by number is 37,669[Number] which is the 25th highest in the world ranking. Transition graphs on Trademark applications, by number in Saudi Arabia and comparison bar charts (USA vs. Japan vs. Saudi Arabia), (Morocco vs. Uzbekistan vs. Saudi Arabia) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.