Time-Series Matrix (TSMx): A visualization tool for plotting multiscale temporal trends TSMx is an R script that was developed to facilitate multi-temporal-scale visualizations of time-series data. The script requires only a two-column CSV of years and values to plot the slope of the linear regression line for all possible year combinations from the supplied temporal range. The outputs include a time-series matrix showing slope direction based on the linear regression, slope values plotted with colors indicating magnitude, and results of a Mann-Kendall test. The start year is indicated on the y-axis and the end year is indicated on the x-axis. In the example below, the cell in the top-right corner is the direction of the slope for the temporal range 2001–2019. The red line corresponds with the temporal range 2010–2019 and an arrow is drawn from the cell that represents that range. One cell is highlighted with a black border to demonstrate how to read the chart—that cell represents the slope for the temporal range 2004–2014. This publication entry also includes an excel template that produces the same visualizations without a need to interact with any code, though minor modifications will need to be made to accommodate year ranges other than what is provided. TSMx for R was developed by Georgios Boumis; TSMx was originally conceptualized and created by Brad G. Peter in Microsoft Excel. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 TSMx sample chart from the supplied Excel template. Data represent the productivity of rice agriculture in Vietnam as measured via EVI (enhanced vegetation index) from the NASA MODIS data product (MOD13Q1.V006). TSMx R script: # import packages library(dplyr) library(readr) library(ggplot2) library(tibble) library(tidyr) library(forcats) library(Kendall) options(warn = -1) # disable warnings # read data (.csv file with "Year" and "Value" columns) data <- read_csv("EVI.csv") # prepare row/column names for output matrices years <- data %>% pull("Year") r.names <- years[-length(years)] c.names <- years[-1] years <- years[-length(years)] # initialize output matrices sign.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) pval.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) slope.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) # function to return remaining years given a start year getRemain <- function(start.year) { years <- data %>% pull("Year") start.ind <- which(data[["Year"]] == start.year) + 1 remain <- years[start.ind:length(years)] return (remain) } # function to subset data for a start/end year combination splitData <- function(end.year, start.year) { keep <- which(data[['Year']] >= start.year & data[['Year']] <= end.year) batch <- data[keep,] return(batch) } # function to fit linear regression and return slope direction fitReg <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(sign(slope)) } # function to fit linear regression and return slope magnitude fitRegv2 <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(slope) } # function to implement Mann-Kendall (MK) trend test and return significance # the test is implemented only for n>=8 getMann <- function(batch) { if (nrow(batch) >= 8) { mk <- MannKendall(batch[['Value']]) pval <- mk[['sl']] } else { pval <- NA } return(pval) } # function to return slope direction for all combinations given a start year getSign <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) signs <- lapply(combs, fitReg) return(signs) } # function to return MK significance for all combinations given a start year getPval <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) pvals <- lapply(combs, getMann) return(pvals) } # function to return slope magnitude for all combinations given a start year getMagn <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) magns <- lapply(combs, fitRegv2) return(magns) } # retrieve slope direction, MK significance, and slope magnitude signs <- lapply(years, getSign) pvals <- lapply(years, getPval) magns <- lapply(years, getMagn) # fill-in output matrices dimension <- nrow(sign.matrix) for (i in 1:dimension) { sign.matrix[i, i:dimension] <- unlist(signs[i]) pval.matrix[i, i:dimension] <- unlist(pvals[i]) slope.matrix[i, i:dimension] <- unlist(magns[i]) } sign.matrix <-...

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

R, a programming language, is an attractive tool for data visualization because it is free and open source. However, learning R can be intimidating and cumbersome for many. In this report, we introduce an R package called “smplot” for easy and elegant data visualization. The R package “smplot” generates graphs with defaults that are visually pleasing and informative. Although it requires basic knowledge of R and ggplot2, it significantly simplifies the process of plotting a bar graph, a violin plot, a correlation plot, a slope chart, a Bland-Altman plot and a raincloud plot. The aesthetics of the plots generated from the package are elegant, highly customisable and adhere to important practices of data visualization. The functions from smplot can be used in a modular fashion, thereby allowing the user to further customise the aesthetics. The smplot package is open source under the MIT license and available on Github (https://github.com/smin95/smplot), where updates will be posted. All the example figures in this report are reproducible and the codes and data are provided for the reader in a separate online guide (https://smin95.github.io/dataviz/).

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

This folder contained a R script and data set used to generate bubble plots as shown Fig. 3.

This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air temperature data; has been used in undergraduate classrooms.

(1) dataandpathway_eisner.R, dataandpathway_bordbar.R, dataandpathway_taware.R and dataandpathway_almutawa.R: functions and codes to clean the realdata sets and obtain the annotation databases, which are save as .RData files in sudfolders Eisner, Bordbar, Taware and Al-Mutawa respectively. (2) FWER_excess.R: functions to show the inflation of FWER when integrating multiple annotation databases and to generate Table 1. (3) data_info.R: code to obtain Table 2 and Table 3. (4) rejections_perdataset.R and triangulartable.R: functions to generate Table 4. The runing time of rejections_perdataset.R is 7 hours around, we thus save the corresponding results as res_eisner.RData, res_bordbar.RData, res_taware.RData and res_almutawa.RData in subfolders Eisner, Bordbar, Taware and Al-Mutawa respectively. (5) pathwaysizerank.R: code for generating Figure 4 based on res_eisner.RData from (h). (6) iterationandtime_plot.R: code for generating Figure 5 based on “Al-Mutawa” data. The code is really time-consuming, nearly 5 days, we thus save the corresponding results and plot them in the main manuscript by pgfplot.

A visual summary of the contents of the 4TU Research Data Repository, in 4 plots.

This data set includes the *.csv data and the used scripts to reproduce the plots of the three different scenarios presented in S. Kiemle, K. Heck, E. Coltman, R. Helmig (2022) Stable water isotopologue fractionation during soil-water evaporation: Analysis using a coupled soil-atmosphere model. (Under review) Water Resources Research. *.csv files The isotope distribution has been analyzed in the vertical and in horizontal direction of a soil column for all scenarios. Therefore, we provide *.csv files generated using the ParaView Tools "plot over line" or "plot over time". Each *.csv file contains information about the saturation, temperature, and component composition for each phase in mole fraction or in the isotopic-specific delta notation. Additionally, information about the evaporation rate is given in a separate file *.txt file. python scripts For each scenario, we provide scripts to reproduce the presented plots. Scenarios We used different free flow conditions to analyze the fractionation processes inside the porous medium. Scenario 1. laminar flow, Scenario 2. laminar flow, but with isolation of parameter affecting the fractionation process, Scenario 3. turbulent flow. Please find below a detailed description of the data labeling and needed scripts to reproduce a certain plot for each scenario. Scenario: The spatial distribution of stable water isotopologues in horizontal (-0.01 m depth) and vertical (at 0.05 m width) inside a soil column at selected days (DoE (Day of Experiment)): Use the python scripts plot_concentration_horizontal_all.py (horizontal direction) and plot_concentration_spatial_all.py (vertical direction) to create the specific plots. In the folder IsotopeProfile_Horizontal and IsotopeProfile_Vertical the belonging *.csv can be found. The *.csv files are named after the selected day (e.g. DoE_80 refers to day 80 of the virtual experiment). The influence of the evaporation rate on isotopic fractionation processes in various depths (-0.001, -0.005, -0.009, and -0.018 m ) during the whole virtual experiment time: Use the python script plot_evap_isotopes_v2.py to create the plots. The data for the isotopologues distribution and the saturation can be found in the folder PlotOverTime. All data is named as PlotOverTime_xxxxm with xxxx representing the respective depth (e.g. PlotOverTime_0.001m refers to -0.001 m depth). The data for the evaporation rate can be found in the folder EvaporationRate. Note, the evaporation rate data is available as a .txt because we extract the information about the evaporation directly during the simulation and do not derive it through any post-processing. Scenario: Process behavior of isolated parameters that influences the isotopic fractionation: Use plot_concentration.py to reproduce the plots either represented in the isotopic-specific delta notation or in mole fraction. The corresponding data can be found in the folder IsotopeProfile_Vertical. The data labeling refers to the single cases (1- no fractionation; 2 - only equilibrium fractionation; 3 - only kinetic fractionation; 4 - only liquid diffusion; 5 - Reference). Scenario: Evaporation rate during the virtual experiment for different flow cases: With plot_evap.py and the .txt files which can be found in the folder EvaporationRate, the evaporation progression can be plotted. The labeling of the .txt files refers to the different flow cases (1 - 0.1 m/s (laminar); 2 - 0.13 m/s (laminar); 3 - 0.5 m/s (turbulent); 4 - 1 m/s (turbulent); 5 - 3 m/s (turbulent)). The isotope profiles in the vertical and horizontal direction of the soil column (similar to Scenario 1) for selected days: With plot_cocentration_horizontal_all.py and plot_concentration_spatial_all.py the plots for the horizontal and vertical distribution of isotopologues can be generated. The corresponding data can be found in the folders IsotopeProfile_Horizontal and IsotopeProfile_Vertical. These folders are structured with subfolders containing the data of selected days of the virtual experiments (DoE - Day of Experiments), in this case, day 2, 10, and 35. The data labeling remains similar to Scenario 3a).

Visualizing institute based repository data re-usage trends

Abstract: These are results from a network of 65 tree census plots in Panama. At each, every individual stem in a rectangular area of specified size is given a unique number and identified to species, then stem diameter measured in one or more censuses. Data from these numerous plots and inventories were collected following the same methods as, and species identity harmonized with, the 50-ha long-term tree census at Barro Colorado Island. Precise location of every site, elevation, and estimated rainfall (for many sites) are also included. These data were gathered over many years, starting in 1994 and continuing to the present, by principal investigators R. Condit, R. Perez, S. Lao, and S. Aguilar. Funding has been provided by many organizations.

Description:

marenaRecent.full.Rdata5Jan2013.zip: A zip archive holding one R Analytical Table, a version of the Marena plots' census data in R format, designed for data analysis. This and all other tables labelled 'full' have one record per individual tree found in that census. Detailed documentations of the 'full' tables is given in RoutputFull.pdf (see component 10 below); an additional column 'plot' is included because the table includes records from many different locations. Plot coordinates are given in PanamaPlot.txt (component 12 below). This one file, 'marenaRecent.full1.rdata', has data from the latest census at 60 different plots. These are the best data to use if only a single plot census is needed. marena2cns.full.Rdata5Jan2013.zip: R Analytical Tables of the style 'full' for 44 plots with two censuses: 'marena2cns.full1.rdata' for the first census and 'marena2cns.full2.rdata' for the second census. These 44 plots are a subset of the 60 found in marenaRecent.full (component 1): the 44 that have been censused two or more times. These are the best data to use if two plot censuses are needed. marena3cns.full.Rdata5Jan2013.zip. R Analytical Tables of the style 'full' for nine plots with three censuses: 'marena3cns.full1.rdata' for the first census through 'marena2cns.full3.rdata' for the third census. These nine plots are a subset of the 44 found in marena2cns.full (component 2): the nine that have been censused three or more times. These are the best data to use if three plot censuses are needed. marena4cns.full.Rdata5Jan2013.zip. R Analytical Tables of the style 'full' for six plots with four censuses: 'marena4cns.full1.rdata' for the first census through 'marena4cns.full4.rdata' for the fourth census. These six plots are a subset of the nine found in marena3cns.full (component 3): the six that have been censused four or more times. These are the best data to use if four plot censuses are needed. marenaRecent.stem.Rdata5Jan2013.zip. A zip archive holding one R Analytical Table, a version of the Marena plots' census data in R format. These are designed for data analysis. This one file, 'marenaRecent.full1.rdata', has data from the latest census at 60 different plots. The table has one record per individual stem, necessary because some individual trees have more than one stem. Detailed documentations of these tables is given in RoutputFull.pdf (see component 11 below); an additional column 'plot' is included because the table includes records from many different locations. Plot coordinates are given in PanamaPlot.txt (component 12 below). These are the best data to use if only a single plot census is needed, and individual stems are desired. marena2cns.stem.Rdata5Jan2013.zip. R Analytical Tables of the style 'stem' for 44 plots with two censuses: 'marena2cns.stem1.rdata' for the first census and 'marena3cns.stem2.rdata' for the second census. These 44 plots are a subset of the 60 found in marenaRecent.stem (component 1): the 44 that have been censused two or more times. These are the best data to use if two plot censuses are needed, and individual stems are desired. marena3cns.stem.Rdata5Jan2013.zip. R Analytical Tables of the style 'stem' for nine plots with three censuses: 'marena3cns.stem1.rdata' for the first census through 'marena3cns.stem3.rdata' for the third census. These nine plots are a subset of the 44 found in marena2cns.stem (component 6): the nine that have been censused three or more times. These are the best data to use if three plot censuses are needed, and individual stems are desired. marena4cns.stem.Rdata5Jan2013.zip. R Analytical Tables of the style 'stem' for six plots with four censuses: 'marena3cns.stem1.rdata' for the first census through 'marena3cns.stem3.rdata' for the third census. These six plots are a subset of the nine found in marena3cns.stem (component 7): the six that have been censused four or more times. These are the best data to use if four plot censuses are needed, and individual stems are desired. bci.spptable.rdata. A list of the 1414 species found across all tree plots and inventories i... Visit https://dataone.org/datasets/urn%3Auuid%3A07030ed9-e51f-4ffa-a4b5-921392681123 for complete metadata about this dataset.

R Analytical Tables: Tree and Stem These are 16 tables in R format, 8 tables in the tree format, one table per census, and 8 more in the stem format. They are named bci.tree1 through bci.tree8 and bci.stem1 through bci.stem8, with the number corresponding to a census. Every tree table includes a record for every tree appearing in the 50-ha plot across all censuses, with one row per tree and one table per census. Trees not yet censused and dead trees are included, allowing the fate of every tree to be tallied in every census. The rows in every tree table are in exactly the same order, so the 8 tree tables are effectively a single giant table, with columns for every one of the 8 censuses. They are divided into separate tables for easier access. Moreover, each individual census table is self-contained, and if a user needs only one census, then only that one census table is necessary. For most purposes the most recent, bci.tree8, should serve for a single census. The 8 stem tables form a parallel set having one row for every stem ever censused. In the tree tables, if stemID matches for a given tree in 2 censuses, then it is certain that the same stem was measured in both censuses. If the stemID differs between censuses, it often means that the first stem broke and a new stem was measured. However, in trees with 2 or more stems, it was not always possible to identify stems with certainty across censuses prior to 2010, because stem tags were not used. This means that there are cases where the stemID changes even though the stem(s) really did persist. The cleanest way to assess growth is to use only those cases where the stemID matches between censuses. It is also necessary to check the HOM (height-of-measurement) in case it changes between censuses. In the stem tables, records on matching rows are guaranteed to be the same stem, but the HOM might change. The 8 tree tables are zipped into the single file bci.tree.zip, and the 8 stem tables into bci.stem.zip. Columns in R Analytical Tables: Tree and Stem treeID: The unique tree identifier in the database. Guarantees a tree match. stemID: The unique stem identifier in the database. Guarantees a stem match. tag: Tag number on the tree (occasionally negative where a tag was duplicated by mistake). StemTag: Tag number on the individual stem, if present. sp: The species mnemonic. See the R Species Table for full Latin names. quadrat: Quadrat designation, as a 2-digit row number then 2-digit column number on a 20x20 m grid. gx: The x coordinate within the plot, meters from the west border of the plot, always in [0,1000). gy: The y coordinate, meters from the south border, always in [0,500). dbh: Diameter (mm) of one stem on the tree, the stem whose stemID is given. hom: The height-of-measure, meters above the ground, where the dbh was measured. ExactDate: The date on which a tree was measured. date: Integer date for easy calculation of time interval between censuses (the number of days since 1 Jan 1960). codes: The codes describing the measurement as recorded in the field. See Condit (1998) for a description of field codes. For analyses, status codes should be used, not field codes. status: A status code for the tree or stem. See the section below, Status Codes for Analyses. DFstatus: Alternate stem status, redundant relative to status. nostems: The number of living stems on the date of measurement. agb: Above-ground-biomass of all stems on the tree, in Mg (= metric tons), or for the individual stem. Note that agb=0 for dead trees. ba: Basal area of all stems on the tree, in square meters. Note that ba=0 for dead trees. Status Codes for Analyses These indicate whether a tree (stem) is alive (A) or dead (D), with some further complications. Status A=alive is assigned to a tree if it has any living stem or sprout. Status D=dead is only used when an entire tree is dead. A third status is P=prior, necessary because each of the analytical tables has a record for every tree (or stem). Thus, there must be records in early censuses for trees that recruited in later censuses, and these trees have status P. In a complete life cycle, a tree begins with status P, becomes A, then D; for many trees, the cycle includes only P to A, or A to D, or just A over all 8 censues. Additional status codes are necessary because a stem can be dead while the tree is still alive, and for rare cases where trees were missed during a census or recorded as dead once then later alive. Status Codes for Analyses (the status column in R Analytical Tables) A: Alive, applied to either the tree or stem. D: Dead, meaning the entire tree is dead, ie with no living stems or sprouts. P: Prior, applied to a tree (or stem) in censuses prior to its appearance in the plot. M: Missing, indicating that the tree was skipped by mistake during the census, so it could have been alive or dead. AD: A seldom-used code, applied when a tree was noted as d...

Raw data for the manuscript.

"011221_Run15_533.tif" - an uncropped gel scan (Fig. 1A)

"Fig. 1B.pdf" - a raw plot for Fig. 1B exported from the shiny app

"Fig. 1C.pdf" - a raw plot for Fig. 1C exported from the shiny app

"Fig. 2A.pdf" - a raw plot for Fig. 2A exported from the shiny app

"Fig. 2B.png" - a raw screenshot from the shiny app for Fig. 2B

"Fig. 3.pdf" - a raw plot for Fig. 3 exported from the shiny app

These are data from 65 tree plots in Panama established over 1994-2014; 43 of the plots have been recensused, while 22 plots have just a single census. Details of census methods are described in Condit (1998) and Condit et al. (2013). The 65 plots here are mostly 1 ha in area, though several are 0.32 ha, one is 4 ha, and one is 6 ha. Those two larger censuses are the Sherman and Cocoli plots described in Condit et al. (2004). A companion data archive includes all data from the Barro Colorado 50-ha plot (Condit et al. 2019). The PIs would like to be informed of papers resulting from the marena plot data. Depending on our level of interest and how much a paper depends on the plots, co-authorship might be requested. References

Condit, R., Engelbrecht, B. M. J., Pino, D., Pérez, R., and Turner, B. L. 2013. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110:5064–5068. p...

Context

Data Visualization

Content

a. Scatter plot

  i. The webapp should allow the user to select genes from datasets and plot 2D scatter plots between 2 variables(expression/copy_number/chronos) for 
    any pair of genes.

  ii. The user should be able to filter and color data points using metadata information available in the file “metadata.csv”.

  iii. The visualization could be interactive - It would be great if the user can hover over the data-points on the plot and get the relevant information (hint - 
    visit https://plotly.com/r/, https://plotly.com/python)

  iv. Here is a quick reference for you. The scatter plot is between chronos score for TTBK2 gene and expression for MORC2 gene with coloring defined by
    Gender/Sex column from the metadata file.

b. Boxplot/violin plot

  i. User should be able to select a gene and a variable (expression / chronos / copy_number) and generate a boxplot to display its distribution across 
   multiple categories as defined by user selected variable (a column from the metadata file)

 ii. Here is an example for your reference where violin plot for CHRONOS score for gene CCL22 is plotted and grouped by ‘Lineage’

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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>

#R code.txt is the R code for plotting figures and constructing Tables

#bciabun1010.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 10m*10m

#bciabun2020.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 20m*20m

#bciabun5050.txt is the species_by_plot matrix that BCI forest Plot is divided by a plot with size 50m*50m

#fus10.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 10m*10m

#fus20.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 20m*20m

#fus50.txt is the species_by_plot matrix that Fushan forest Plot is divided by a plot with size 50m*50m

#lhc10.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 10m*10m

#lhc20.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 20m*20m

#lhc50.txt is the species_by_plot matrix that Lianhuachi forest Plot is divided by a plot with size 50m*50m

strap (Stratigraphic Tree Analysis for Palaeontology) is a new package for the freely available statistical programming language R designed to perform three main tasks: (1) to time-scale phylogenies of fossil taxa; (2) to plot those time-scaled trees against stratigraphy; and (3) to assess congruence between phylogenies and stratigraphy. Time-scaling is performed with the DatePhylo function, with three approaches offered. Plotting trees against a choice of five different geological time scaless is possible using the geoscalePhylo function. Finally, the function StratPhyloCongruence calculates stratigraphic congruence measures for one or more input phylogenies, with no taxon limit. All three major congruence measures are offered: Stratigraphic Consistency Index (SCI), Manhattan Stratigraphic Measure (MSM*) and the gap excess ratio (GER; including GERt and GER*), as well as the pseudocongruence measure, the Relative Completeness Index (RCI). Each measure has an accompanying significance t...

These are plot data from fifteen 40 m by 40 m sample plots established in Oban Division of Cross River National Park, Nigeria, between 23rd August 2019 and 9th September 2019. We have also included data summaries and RStudio codes used for analysis and generating results for the manuscript entitled: "Impact of human foraging on tree diversity, composition and abundance in a tropical rainforest", submitted for publication as an original research article in Biotropica. All data and R code required to generate the results as shown in the manuscript have been included.

Implementation of visibility graphs algorithm in R language. These scripts generate visibility graphs from series built in RStudio or imported into RStudio; Plot the series, the series histogram, the degree distribution of the graphs generated from these series; They determine the fit of the distribution curve in a log-log graph; Calculates the fundamental metrics of complex networks for the visibility graphs associated with each series.