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.

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.

Raw gaze, interview and other data from 50 secondary school students (grade 10 - 12) solving statical graph tasks: estimating or comparing the mean from histograms, case-value plots, (stacked) dotplots and horizontal histograms. It contains some processed data. Furthermore, it contains all relevant information needed to reproduce or replicate this data collection process, for example, the design of the data collection, html-files with the webpages that were used, letters to participants, sizes and screen shots of AOIs, heatmaps and static gazeplots. Also: transcripts, legends, overview of tasks. (Note, these data are not processed for a specific article)

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>

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

This repository entails the data and Pythoncode for the publication "Dynamics of Lagrangian Sensor Particles: The Effect of Non-Homogeneous Mass Distribution" in the journal "Processes". In the following a brief introduction and guide based on the folders in the repository is laid out. More code specific instructions can be found in the respective codes. 01 --> The tracking always begins with the same 01_milti[...] folder in which the python code with OpenCV algorithm is located. For tracking the tracking to work certain directories are required in which the raw images are to be stored (separate from anything else) as well as a directory in which the results are to be save (not the same directory as the raw data). After tracking is completed for all respective experiments and the results directories are adequately labelled and stored any of the other code files can be used for respective analyses. The order of folders beyond the first 01 directory has no relevance to the order of evaluation however can ease the understanding of evaluated data if followed. 02 --> Evaluation of amount of circulations and respective circulation time in experimental vat. (code can be extended to calculate the circulation time based on the various plains that are artificially set) 03 --> Code for the calculation of the amount of contacts with the vat floor. Code requires certain visual evaluations based on the LP trajectories, as the plain/barrier for the contact evaluation has to be manually set. 04 --> Contains two codes that can be applied to results data to combine individual results into larger more processable arrays within python 05 --> Contains the code to plot the trajectory of single experiments of Lagrangian particles based on their positional results and velocity at respective position, highlighting the trajectory over the experiment. 06 --> Condes to create 1D histograms based on the probability density distribution and velocity distributions in cumulative experiments. 07 --> Codes for plotting the 2D probability density distribution (2D Histograms) of Lagrangian Particles based on the cumulative experiments. Code provides values for the 2D grid, plotting is conducted in Origin Lab or similar graphing tools, graphing can also be conducted in python whereby the seaborn (matplotlib) library is suggested. 08 --> Contain the code for the dimensionless evaluation of the results based on the respective Stokes number approaches and weighted averages. 2D histograms are also vital to this evaluation, whereby the plotting is again conducted in Origin Lab as values are only calculated in code. 09 --> Directory does not contain any python codes but instead contains the respective Origin Lab files for the graphing, plotting and evaluation of results calculated via python is given. Respective tables, histograms and heat maps are hereby given to be used as templates if necessary. The project used the Origin 2023 (64-bit) version, if no Origin license is available then Origin Lab provides a free Origin Viewer with which the projects can be opened and viewed. (https://www.originlab.com/viewer/)

This project provides an automatic image data extractor that can extract curve graphs, line graphs, and histograms to accelerate data collection in the field of materials science.

Pass measurements.csv This dataset contains the pass-level metrics calculated from each pass. It includes the following information: subject number, field number, pass number, steering type, unique pass identification number, time (s), speed (kph), distance (m), number of stressful events, stressful event rate, magnitude all stressful events (uS), magnitude/stressful event (uS), duration all stressful events (s), duration/stressful event (s), area under curve all stressful events (uS-s), area under curve/stressful event (uS-s), XTE SD (cm), number of zero crossings, zero crossing/min. Stressful events dataset.csv This file contains data from each stressful event. It includes the following information: valley EDA (uS), peak EDA (uS), magnitude (uS), duration (s), area under curve (uS-S), field number, pass number, subject number, and steering type. Steering performance datasets.csv This file contains the 5-Hz or 2-Hz data from each pass that was used to evaluate steering performance. It includes the following information: unique pass identification number, pass time (s), x (m), y (m), speed (kph), XTE (cm), field number, pass number, subject number, steering type. This may be a useful dataset to analyze the position and speed of sprayers in fields. Histograms datasets.csv This file contains the datasets for the histogram graph (Figure 7). It includes the following information: XTE bin left edge (cm), XTE bin right edge (cm), S1, S2, S3, S4 frequencies of measurements within those bins. Literature steering performance dataset.csv This file contains the data used for the graph that compares the steering performance results from this study to those reported in the literature. It includes the following information: first author, year, steering type, steering metric type, speed (kph), SD speed (kph), steering metric value (cm), SD steering metric (cm), comment.

Numerical data used to generate graphs and histograms (Fig 2A–2C, S2B–S2G, 3C, S3A–S3B, S4A–S4D, S5, S6A–S6H, S7B–S7C, S8A–S8B, 4B–4C, S8C–S8D, 5, 7A, 7B, 7D, 8A, 8E, S11).

The normal distribution of residential land prices in 2014–2017. (ZIP)

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.