Data visualization is important for statistical analysis, as it helps convey information efficiently and shed lights on the hidden patterns behind data in a visual context. It is particularly helpful to display circular data in a two-dimensional space to accommodate its nonlinear support space and reveal the underlying circular structure which is otherwise not obvious in one-dimension. In this article, we first formally categorize circular plots into two types, either height- or area-proportional, and then describe a new general methodology that can be used to produce circular plots, particularly in the area-proportional manner, which in our opinion is the more appropriate choice. Formulas are given that are fairly simple yet effective to produce various circular plots, such as smooth density curves, histograms, rose diagrams, dot plots, and plots for multiclass data. Supplemental materials for this article are available online.

Hello all, this dataset involves various factors effecting cancer and based upon those factors, I have created a Histogram of various columns of the table which leads to heart disease. A histogram is a bar graph-like representation of data that buckets a range of outcomes into columns along the x-axis. The y-axis represents the number count or percentage of occurrences in the data for each column and can be used to visualize data distribution. At last I have created combined histogram of entire table which involves all the columns. Giving Titles, X-axis name, Y-axis name, Sizes and Colors is also done in this notebook.

Histogram plot of the average alignment accuracy averaged over 10 runs for each viral genome shown in Table 1 and each aligner. Reads crossing splice junction regions are shown in pink, reads not crossing splice junction regions are shown in blue).

Charts, Histograms, and Time Series • Create a histogram graph from band values of an image collection • Create a time series graph from band values of an image collection

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Network of 29 papers and 63 citation links related to "Histogram-Based Image Retrieval Keyed by Normalized HSY Histograms and Its Experiments on a Pilot Dataset".

Figures containing a histogram of frequency of effect sizes on AG and BG herbivores and a funnel plot of effect size and sample sizes indicating absence of publication bias.

THE FOLLOWING COMMENTS ARE TAKEN FROM THE PI N COMPILATION OF R.L. KELLY. THEY ARE THAT COMPILATION& apos;S COMPLETE SET OF COMMENTS FOR PAPERS RELATED TO THE SAME EXPERIMENT (DESIGNATED VANDEWAL68) AS THE CURRENT PAPER. (THE IDENTIFIER PRECEDING THE REFERENCE AND COMMENT FOR EACH PAPER IS FOR CROSS-REFERENCING WITHIN THESE COMMENTS ONLY AND DOES NOT NECESSARILY AGREE WITH THE SHORT CODE USED ELSEWHERE IN THE PRESENT COMPILATION.) /// VANDEWAL68 [R. T. VAN DE WALLE,NC 53,745(1968).] -- DATA READ FROM HISTOGRAM. PI- P DCS AT 848 MEV/C FROM .8K ELASTIC EVENTS IN SACLAY 35 CM HBC AT SATURNE. THE DATA IS PRESENTED AS LEGENDRE COEFFICIENTS WITH A FULL ERROR MATRIX AND IN A SMALL HISTOGRAM BASED ON .6K EVENTS. WE USED THE HISTOGRAM. /// COMMENTS FROM LOVELACE71 COMPILATION OF THESE DATA -- NORMALISED TO TOT=37.2(DEVLIN). READ FROM GRAPH. /// COMMENTS ON MODIFICATIONS TO LOVELACE71 COMPILATION BY KELLY -- IN REREADING THE HISTOGRAM WE FOUND ONE MAJOR MISTAKE IN THE LOVELACE71 VERSION (LOVELACE ID=WALLE68).. DATA ARE UNNORMALIZED OR NORMALIZED TO OTHER DATA.

Network of 45 papers and 109 citation links related to "Context-Based Novel Histogram Bin Stretching Algorithm for Automatic Contrast Enhancement".

Main publication Poll report and form on HAL Authors The raw data was generated by the poll respondents The authors of this Dataset, excluding Vlad Visan, are such respondents. There are also other respondents who chose to remain anonymous The script was written by Vlad Visan The raw format was adapted to a numerical format by Vlad Visan Overall description A poll took place in February 2024, to understand the administrative burden of using Galaxy, specifically for small-scale admins. Context Useful to anyone considering using Galaxy Done as part of the technology monitoring phase of the "Gestionnaire de workflows" (Workflow Management System) project of the OSUG LabEx File descriptions raw_data_names_removed.tsv Raw poll answers. With any personally identifiable information redacted. SSA-Poll-19-Feb-2024-Filtered-Numerical.tab This numerically filtered format is required by the script The transformation could be done automatically in the future, but there are some subtleties: "-1" denotes "ignore/invalid" Some empty answers have to manually be converted to "0" I manually changed one answer that was "0" to "-1" after reading the associated comment which made it clear that "invalid" was more appropriate numericalCsvImportAndGenerateCharts.R The script parses the data, and creates one distribution/histogram graph per column It expects a filtered version, with only the numerical fields. Form-V2.pdf Survey questions, with several errors corrected: End-user assistance questions were worded wrongly Various spelling/wording mistakes

Description. The NetVote dataset contains the outputs of the NetVote program when applied to voting data coming from VoteWatch (http://www.votewatch.eu/).

These results were used in the following conference papers:

1. I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament,” in 2nd European Network Intelligence Conference, 2015, pp. 122–129. ⟨hal-01176090⟩ DOI: 10.1109/ENIC.2015.25
2. I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Informative Value of Negative Links for Graph Partitioning, with an application to European Parliament Votes,” in 6ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques, 2015, p. 12p. ⟨hal-02055158⟩

Source code. The NetVote source code is available on GitHub: https://github.com/CompNet/NetVotes.

Citation. If you use our dataset or tool, please cite article [1] above.

@InProceedings{Mendonca2015,
author = {Mendonça, Israel and Figueiredo, Rosa and Labatut, Vincent and Michelon, Philippe},

title = {Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the {E}uropean {P}arliament},
booktitle = {2\textsuperscript{nd} European Network Intelligence Conference ({ENIC})},
year = {2015},
pages = {122-129},
address = {Karlskrona, SE},
publisher = {IEEE Publishing},
doi = {10.1109/ENIC.2015.25},
}

-------------------------

Details. This archive contains the following folders:

• `votewatch_data`: the raw data extracted from the VoteWatch website.
  • `VoteWatch Europe European Parliament, Council of the EU.csv`: list of the documents voted during the considered term, with some details such as the date and topic.
  • `votes_by_document`: this folder contains a collection of CSV files, each one describing the outcome of the vote session relatively to one specific document.
  • `intermediate_files`: this folder contains several CSV files:
    • `allvotes.csv`: concatenation of all vote outcomes for all documents and all MEPS. Can be considered as a compact representation of the data contained in the folder `votes_by_document`.
    • `loyalty.csv`: same thing than allvotes.csv, but for the loyalty (i.e. whether or not the MEP voted like the majority of the MEPs in his political group).
    • `MPs.csv`: list of the MEPs having voted at least once in the considered term, with their details.
    • `policies.csv`: list of the topics considered during the term.
    • `qtd_docs.csv`: list of the topics with the corresponding number of documents.
• `parallel_ils_results`: contains the raw results of the ILS tool. This is an external algorithm able to estimate the optimal partition of the network nodes in terms of structural balance. It was applied to all the networks extracted by our scripts (from the VoteWatch data), and the produced files were placed here for postprocessing. Each subfolder corresponds to one of the topic-year pair.
• `output_files`: contains the file produced by our scripts.
  • `agreement`: histograms representing the distributions of agreement and rebellion indices. Each subfolder corresponds to a specific topic.
  • `community_algorithms_csv`: Performances obtained by the partitioning algorithms (for both community detection and correlation clustering). Each subfolder corresponds to a specific topic.
  • `xxxx_cluster_information.csv`: table containing several variants of the imbalance measure, for the considered algorithms.
  • `community_algorithms_results`: Comparison of the partitions detected by the various algorithms considered, and distribution of the cluster/community sizes. Each subfolder corresponds to a specific topic.
  • `xxxx_cluster_comparison.csv`: table comparing the partitions detected by the community detection algorithms, in terms of Rand index and other measures.
  • `xxxx_ils_cluster_comparison.csv`: like `xxxx_cluster_comparison.csv`, except we compare the partition of community detection algorithms with that of the ILS.
  • `xxxx_yyyy_distribution.pdf`: histogram of the community (or cluster) sizes detected by algorithm `yyyy`.
  • `graphs`: the networks extracted from the vote data. Each subfolder corresponds to a specific topic.
  • `xxxx_complete_graph.graphml`: network at the Graphml format, with all the information: nodes, edges, nodal attributes (including communities), weights, etc.
  • `xxxx_edges_Gephi.csv`: only the links, with their weights (i.e. vote similarity).
  • `xxxx_graph.g`: network at the g format (for ILS).
  • `xxxx_net_measures.csv`: table containing some stats on the network (number of links, etc.).
  • `xxxx_nodes_Gephi.csv`: list of nodes (i.e. MEPs), with details.
  • `plots`: synthesis plots from the paper.

-------------------------

License. These data are shared under a Creative Commons 0 license.

Contact. Vincent Labatut <vincent.labatut@univ-avignon.fr> & Rosa Figueiredo <rosa.figueiredo@univ-avignon.fr>

THE FOLLOWING COMMENTS ARE TAKEN FROM THE PI N COMPILATION OF R.L. KELLY. THEY ARE THAT COMPILATION& apos;S COMPLETE SET OF COMMENTS FOR PAPERS RELATED TO THE SAME EXPERIMENT (DESIGNATED VANDEWAL68) AS THE CURRENT PAPER. (THE IDENTIFIER PRECEDING THE REFERENCE AND COMMENT FOR EACH PAPER IS FOR CROSS-REFERENCING WITHIN THESE COMMENTS ONLY AND DOES NOT NECESSARILY AGREE WITH THE SHORT CODE USED ELSEWHERE IN THE PRESENT COMPILATION.) /// VANDEWAL68 [R. T. VAN DE WALLE,NC 53,745(1968).] -- DATA READ FROM HISTOGRAM. PI- P DCS AT 848 MEV/C FROM .8K ELASTIC EVENTS IN SACLAY 35 CM HBC AT SATURNE. THE DATA IS PRESENTED AS LEGENDRE COEFFICIENTS WITH A FULL ERROR MATRIX AND IN A SMALL HISTOGRAM BASED ON .6K EVENTS. WE USED THE HISTOGRAM. /// COMMENTS FROM LOVELACE71 COMPILATION OF THESE DATA -- NORMALISED TO TOT=37.2(DEVLIN). READ FROM GRAPH. /// COMMENTS ON MODIFICATIONS TO LOVELACE71 COMPILATION BY KELLY -- IN REREADING THE HISTOGRAM WE FOUND ONE MAJOR MISTAKE IN THE LOVELACE71 VERSION (LOVELACE ID=WALLE68).. DATA ARE UNNORMALIZED OR NORMALIZED TO OTHER DATA.

Histogram plot of the average accuracy as a function of the mutation rate for each aligner averaged over the four viral genomes (see Table 1).

Network of 33 papers and 55 citation links related to "Baseline drift correction and heart rate estimation using Histogram technique".

Geovisualizing the modifiable areal unit problem Interactive Google Earth Engine Application https://cartoscience.users.earthengine.app/view/maup Google Earth Engine Code var img = ee.Image('MODIS/055/MOD17A3/2014_01_01').select('Npp').divide(10000).rename('NPP'); Map.setCenter(30, 20, 2).setOptions('HYBRID'); var bounds = ee.Geometry(Map.getBounds(true)); var res = Map.getScale()*10; var colors = ['e0470e','e6ad0c','ffffc3','13ff92','079b5d']; var vis = {min: 1, max: 5, palette: colors}; var design = { fontSize: 10, legend: {position: 'none'}, backgroundColor: '080c16', series: {0: {color: 'e8e8e8', textStyle: {color: 'e8e8e8'}}}, hAxis: {format: 'short', textStyle: {color: 'e8e8e8'}}, vAxis: {format: 'short', textStyle: {color: 'e8e8e8'}} }; var histogram = ui.Chart.image.histogram(img, bounds, res).setOptions(design); var legend = ui.Panel( [ ui.Label('Low', {fontSize: '10px', fontWeight: 'bold', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', backgroundColor: '080c16'}), ui.Label({style: {backgroundColor: 'e0470e', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: 'e6ad0c', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: 'ffffc3', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: '13ff92', padding: '8px', margin: '6px 0 0 0'}}), ui.Label({style: {backgroundColor: '079b5d', padding: '8px', margin: '6px 0 0 0'}}), ui.Label('High', {fontSize: '10px', fontWeight: 'bold', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', backgroundColor: '080c16'}), ], ui.Panel.Layout.flow('horizontal'), {width: '200px', position: 'bottom-left', backgroundColor: '080c16', margin: '-5px 0 10px 24px'} ); var panel = ui.Panel( [ ui.Label('Geovisualizing the Modifiable Areal Unit Problem', {fontSize: '20px', fontWeight: 'bold', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '20px 0 10px 0', backgroundColor: '080c16'}), ui.Label('Pan, zoom, and adjust the browser window to reclassify.', {fontSize: '13px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '4px 25px 4px 25px', backgroundColor: '080c16'}), ui.Label('NASA MODIS Net Primary Productivity', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '4px 20px 1px 20px', backgroundColor: '080c16'}), ui.Label('MOD17A3/055 • 2014', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '1px 20px 1px 20px', backgroundColor: '080c16'}), ui.Label('kg C / m^2', {fontSize: '9px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '10px 20px 0 20px', backgroundColor: '080c16'}), histogram, legend, ui.Label('Recategorizes using percentile thresholds (0-20-40-60-80-100) from the data distribution captured in the frame. Zoom in to reveal heterogeneity at local scales.', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '1px 20px 1px 20px', backgroundColor: '080c16'}), ui.Label('© 2019 Cartoscience', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '20px 20px 0 20px', backgroundColor: '080c16'}), ui.Label('cartoscience.com', {fontSize: '10px', color: 'e8e8e8', stretch: 'horizontal', textAlign: 'center', margin: '1px 85px 20px 85px', backgroundColor: '080c16'}, 'http://cartoscience.com') ], ui.Panel.Layout.flow('vertical'), {width: '250px', position: 'bottom-left', backgroundColor: '080c16'} ); ui.root.insert(0, panel); Map.layers().set(0, ui.Map.Layer(ee.Image(0), {palette:'000000', opacity: 0.8}, 'Dark')); Map.onChangeBounds(function() { var bounds = ee.Geometry(Map.getBounds(true)); var res = Map.getScale()*10; var params = img.reduceRegion({ reducer: ee.Reducer.percentile([0,20,40,60,80,100]), geometry: bounds, scale: res }); var p0 = ee.Number(params.get('NPP_p0')); var p20 = ee.Number(params.get('NPP_p20')); var p40 = ee.Number(params.get('NPP_p40')); var p60 = ee.Number(params.get('NPP_p60')); var p80 = ee.Number(params.get('NPP_p80')); var p100 = ee.Number(params.get('NPP_p100')); var quintiles = img.gt(p0).add(img.gt(p20)).add(img.gt(p40)) .add(img.gt(p60)).add(img.gt(p80)).add(img.gt(p100)); var histogram = ui.Chart.image.histogram(img, bounds, res).setOptions(design); panel.widgets().set(5,histogram); Map.layers().set(1, ui.Map.Layer(quintiles, vis, 'Quintiles')); Map.layers().set(2, ui.Map.Layer(img, {min: 0.13, max: 0.79}, 'MODIS/055/MOD17A3', false)); }); This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006.

The contains flight statistics for all airports in the United States from January 2011 to December 2020. Each observation is reported by month, year, airport, and airline. Flights can be categorized as on time, delayed, canceled, or diverted. Flight delays are attributed to five causes: carrier, weather, NAS, security, and late aircraft. The data was downloaded from the Bureau of Transportation Statistics website https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp.

The accompanying notebook explores commercial airplane flight delays in the United States using Python's visualization capabilities in Matplotlib and Seaborn, through the lenses of seasonality, airport traffic, and airline performance.

The clean data set (delays_clean.csv) is analyzed using the following visualizations:

Bar chart Bar chart subplots Lollipop chart Tree maps Line plot Histogram Histogram subplots Horizontal stacked bar chart Ranked horizontal bar chart Box plot Pareto chart - double axis Marginal histogram Pie charts Scatter plot Violin plot Map chart Linear regression

Histogram plot of the number of aligned reads as a function of the mutation rate for each aligner averaged over the four viral genomes (see Table 1).

ALL DATA IN THIS RECORD ARE REDUNDANT. I.E., THEY WERE OBTAINED DIRECTLY FROM OTHER DATA IN THIS FILE, USUALLY BY EXTRAPOLATION OR INTEGRATION.. THE FOLLOWING COMMENTS ARE TAKEN FROM THE PI N COMPILATION OF R.L. KELLY. THEY ARE THAT COMPILATION'S COMPLETE SET OF COMMENTS FOR PAPERS RELATED TO THE SAME EXPERIMENT (DESIGNATED BARMIN63) AS THE CURRENT PAPER. (THE IDENTIFIER PRECEDING THE REFERENCE AND COMMENT FOR EACH PAPER IS FOR CROSS-REFERENCING WITHIN THESE COMMENTS ONLY AND DOES NOT NECESSARILY AGREE WITH THE SHORT CODE USED ELSEWHERE IN THE PRESENT COMPILATION.) /// BARMIN63 [V.V.BARMIN,PROC. SIENNA ITL. CNF. ON ELM. PART.,P 213,1963] -- DATA READ FROM HISTOGRAM. CEX DCS AT 1.25,1.55,2.8,AND 4.5 GEV/C FROM 11K,20K,60K, AND 20K PHOTOGRAPHS,RESPECTIVELY. DONE AT ITEP(MOSCOW) 17-LITER PROPANE-XENON BUBBLE CHAMBER. EVENT HISTOGRAMS OF BISECTOR DISTRIBUTIONS ARE GIVEN FOR 1.25,1.55,AND 2.8 GEV/C. /// BARMIN64 [V. V. BARMIN,SOVIET PHYSICS JETP 19,102(1964)] -- SAME EXPERIMENT AS BARMIN63,EXCEPT THAT THE 1.25 GEV/C RESULTS ARE OMITTED. THE 1.55 AND 2.8 GEV/C HISTOGRAMS FROM BARMIN63 ARE REPRODUCED. /// BARMIN67 [V. V. BARMIN,SJNP 4,592(1967)] -- DATA READ FROM GRAPH. CEX DCS AT 2.8 GEV/C FROM THE BARMIN63 EXPOSURE PLUS AN ADDITIONAL 170K PICTURES IN THE SAME BUBBLE CHAMBER. /// COMMENTS ON MODIFICATIONS TO LOVELACE71 COMPILATION BY KELLY -- LOVELACE71 HAS THE 2.8 AND 1.55 GEV/C DATA,SOME LARGE ANGLE DATA AT 2.8 GEV/C IS REBINNED AND SOME POINTS ARE DROPPED AT 1.55 GEV/C. WE COPIED THE 2.8 GEV/C DATA FROM BARMIN67 LEAVING OUT POINTS WITH NO ERROR BARS (ABOUT 25 PCT). WE READ THE 1.25 AND 1.55 GEV/C DATA FROM BARMIN63, ESTIMATING THE NORMALIZATIONS AND NORMALIZATION ERRORS FROM THE INTEGRATED CEX CROSS SECTIONS OF BARMIN63 AND BARMIN64,RESPECTIVELY.

Histogram plot of the percentage of reads aligned (averaged over 10 runs) for each viral genome shown in Table 2 and each aligner. Reads crossing splice junction regions are shown in pink, reads not crossing splice junction regions are shown in blue.

Network of 44 papers and 83 citation links related to "Two-dimensional histogram equalization and contrast enhancement".