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.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1

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 <-...

BackgroundIn 1975, the Department of Forestry began a new management inventory designed to provide statistics of forest lands and timber volumes in a form that could be used to develop Forest Management Plans. This involved measuring over 500 Temporary Sample Plots (TSP) and measuring or remeasuring nearly 100 Stand Monitor Plots (SMP) each year. The SMPs were designed to provide information which would allow updating of inventory cover types between subsequent inventories.In 1985, nearing the completion of the second cycle of measuring TSPs, the focus began to change. It was felt that volume estimates acquired through TSPs were adequate for most strata and more emphasis be centered around collecting data on growth and yield. This led to the start of a Permanent Sample Plot (PSP) database. The program focused on establishing PSPs in regenerating and immature stand types. This focus continued between 1985 and 1991.In 1992, an evaluation of the existing PSP program and an understanding of the provinces need for growth and yield information led to the design of a 1,000 plot program focusing on growth and yield data collection. Since 1992, additional measurements have been added to the PSP program at the request of various data users. These include Damman Site Type (soils and vegetation), Hare Pellets, Woody Debris, and Song Birds.In 2007, the Newfoundland Forest Service began to use data loggers for collecting PSP data in the field. This speeds the data input process from the previous paper based system so that the data collected can be used shortly after the field season ends. The program also has controls to aid in avoiding errors during data entry; previously, errors were not detected until subsequent data analysis long after the plot measurements were completed.In 2024, the PSP database underwent a significant overhaul, involving a redesign of the 2007 Microsoft Access database and enabling data collection using more modern smartphone and tablet technology. This required the engineering of the database within the Oracle Forestry Enterprise geodatabase and management of data within the ArcGIS Enterprise environment. Data loggers have been replaced and now use iOS and Android technology to collect and measure PSPs within the ArcGIS Field Maps application.The data within this feature layer is updated daily at 24-hour intervals, beginning at 6:00PM NST. Information within this layer represents the most up-to-date and accurate information currently available.ObjectiveThe objectives of the Permanent Sample Plot Program are to provide stand growth data that can be used to calibrate and validate stand growth projection models and have a network of plots sufficient to sample the important stand conditions at an acceptable intensity. More specifically, the goal is to maintain a PSP program of at least 1,000 plots in natural and managed stands.The PSP Program incorporates measurement of other stand conditions and variables as deemed needed by the users of the data.Establishment and Allocation ProceduresThe allocation is based on proportional representation of stands by Strata (Working Group, Age Class and Site Class in a Management District). The actual plot locations are randomly located within the district and within the stand to avoid bias. As plots are lost to various disturbances, a new plot will be:Re-established at the same siteEstablished as a replacement in the same stratumEstablished in a strata type which is being under-represented, if the lost stratum is already well represented.Data CurrencyThe data within this feature layer is updated daily at 24-hour intervals, beginning at 6:00PM NST. Information within this layer represents the most up-to-date and accurate information currently available.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

The temporary sample plot is a circular sample unit that covers an area of 400 m2 (the main unit). For each tree, we observe and measure the species, diameter, and stage in relation to other trees, as well as the defoliation and the quality of the stems of deciduous trees and certain pines. In addition, the age and height of three trees are determined. Finally, a survey of the station is carried out. Plot data also includes information about the location of the plots and the sampling plan. These data are acquired as part of the second forest inventory in southern Quebec. They were used in particular to produce forest compilation results used to feed the calculation of forest opportunities in public forests in Quebec. The establishment of these plots took place between 1980 and 1993. This database covers almost all of the territory south of the 52nd parallel of Quebec's public and private forest.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The temporary sample plot is a circular sample unit that covers an area of 400 m2. For each tree, we observe and measure the species, diameter, sunlight, and floor in relation to other trees, as well as the defoliation and the quality of the stems of deciduous trees and certain pines. In addition, the age and height of three trees are determined. Finally, a survey of the station and the vegetation of the undergrowth is carried out, and the characteristics of the soil are noted. Plot data also includes information about the location of the plots and the sampling plan. These data are acquired as part of the fifth ecoforest inventory of southern Quebec. They are used in particular to produce forest compilation results used to feed the calculation of forest opportunities in public forests in Quebec. They can also be useful in the development of private forests. The establishment of these plots began in 2017. This database covers part of the territory south of the 52nd parallel of Quebec's public and private forest.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 1990 the US Geological Survey Fort Collins Science Center and National Park Service Water Resources Division established 133 permanent 1meter (m) X 2 m plots along the Gunnison River near Warner Point in Black Canyon of the Gunnison National Park. A one-dimensional hydraulic model was calibrated to determine the inundation necessary to inundate each plot, and this can be combined with daily flow data to determine the inundation duration or percentage of time a plot is under water. Occurrence of all plant species in these plots was determined in late July of 1990, 1994, 2001, 2006 and 2013. This data set includes 3 csv files of plot data. One file gives characteristics of plots, including plot name, inundating discharge, mean number of species occurring in the plot, mean vegetative cover, Mean Sorensen Index and mean inundation duration with flows exponentially weighted for recency using a half-life of 1.5 years. A second file gives characteristics of all the plant species that have occurred in the plots including scientific and common names, plant family, life span, occurrence of rhizomes, height, total occurrences across years, median inundating discharge of plots containing the plant, half-life for exponential weighting of flows selected by regressions relating species occurrence to flow in each sample year, slope, probability and variance of half-life regressions, and the optimum inundation duration, probability and variance of logistic regressions relating occurrence of species in all sample years to flow. A third file documents occurrence of species in plots in all five sample years as well as the inundation duration, number of species and total vegetative cover in each plot in each sample year. The 1990 data were interpreted by Auble et al. (1991 and 1994), and all five years of data were interpreted by Friedman et al. (2022). This data set also includes a library of repeat views of the river bars containing the plots photographed from the same set of 21 camera locations in 1990, 1994, 2001, 2006, 2013 and 2017, a total of 117 images. Finally, this data set also includes a csv file listing the years each view was photographed and the approximate latitude and longitude of each camera location.

Sample Graph Datasets in CSV Format

Note: none of the data sets published here contain actual data, they are for testing purposes only.

Description

This data repository contains graph datasets, where each graph is represented by two CSV files: one for node information and another for edge details. To link the files to the same graph, their names include a common identifier based on the number of nodes. For example:

• dataset_30_nodes_interactions.csv:contains 30 rows (nodes).
• dataset_30_edges_interactions.csv: contains 47 rows (edges).
• the common identifier dataset_30 refers to the same graph.

CSV nodes

Each dataset contains the following columns:

CSV edges

Each dataset contains the following columns:

Metadata

This repository include two (2) additional tiny graph datasets to experiment before dealing with larger datasets.

CSV nodes (tiny graphs)

Each dataset contains the following columns:

CSV edges (tiny graphs)

Each dataset contains the following columns:

Metadata (tiny graphs)

GIFplots Files containing GIF images of spectral plots: - GIFplots_splib07a.zip contains plots of measured spectra, including * plots showing the full wavelength range of the measured spectra, organized in chapter sub-folders as described previously for the ASCII data. * plots showing specific portions of the electromagnetic spectrum are organized folders within the “plots_by_wavelength_region” folder, including: - range1_uv_to_visible (0.2 - 1.0 microns) - range2_visible_to_swir (0.2 - 2.5 microns) - range3_swir (1.5 - 5.5 microns) - range4_swir_to_mir (2.5 - 25 microns) - range5_swir_to_fir_wavenumber (4,000 - 50 cm-1 which spans 2.5 - 200 microns) - plots of spectra interpolated to a higher number of more finely-spaced channels showing the full wavelength range , organized in chapter sub-folders (GIFplots_splib07b.zip) - plots of spectra convolved to other spectrometers showing the full wavelength range of the spectrometer, organized in chapter sub-folders, for example * Analytical Spectral Devices (GIFplots_splib07b_cvASD.zip) * AVIRIS-Classic 2014 characteristics (GIFplots_splib07b_cvAVIRISc2014.zip) * Hyperspectral Mapper 2014 characteristics (GIFplots_splib07b_cvHYMAP2014.zip) * and others - plots of spectra resampled to multispectral sensors showing the full wavelength range of the sensor, organized in chapter sub-folders, for example: * Advanced Spaceborne Thermal Emission and Reflection Radiometer (GIFplots_splib07b_rsASTER.zip) * and others GENERAL LIBRARY DESCRIPTION This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer.

The Hillslope Study sites represent a gradient of landscapes, including forested, valley agriculture, and mountain housing developments. These locations and plots were used to collect samples of various matrices for numerous analyses at differing intervals. The data set consists of Open Office spreadsheet and other files that document all the Hillslope Study locations.

The permanent sample plot is a circular sample unit that covers an area of 400 m2. For each tree, the species, diameter, defoliation of softwoods and the quality of hardwoods are observed and measured. Some of these stems are the subject of further studies in order to know their height and age. Finally, other surveys make it possible to identify the ecological characteristics of the station where the plot is located, whether at ground level or undergrowth plants. Since 1970, more than 12,000 permanent sample plots have been established and are monitored decennial. This data is an invaluable source of information on the growth and evolution of forests in Quebec, a source that is enriched each time additional measures are added. In particular, they are used to establish forest growth rates, describe past changes, and model forest evolution. This database covers almost all of the territory south of the 52nd parallel of Quebec's public and private forest. Note: Notice to people who want to use the permanent sample plots for training needs or for sampling of any kind. Please contact the Forest Inventory Directorate at Inventaires.Forestiers@mrnf.gouv.qc.ca**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Division of Forestry completed a forest inventory on Native corporation owned lands in 2018. The project area encompasses forest lands in the Lower Kuskokwim River near the communities of Lower Kalskag, Upper Kalskag and Aniak.

Vegetation cover types used to develop a forest inventory conducted by the State of Alaska Division of Forestry. Inventory with supporting ground plots on State, Federal and Native Corporation land in the Cordova Area.

The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22. This dataset contains results from this sample plot inventory. It consists of the following files: - results_trees.csv: Results for or living and dead trees. - results_regeneration.csv: Results for trees with DBH - 7.0 cm, assessed in three height classes. - results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects - results_trems.csv: Results for occurence of tree related microhabitats (TreMs) - results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH -= 80 for living trees or -= 36 cm for dead trees respectively. - lookup.csv: Contains lookup tables which describe the respective results in-depth. - data_description.pdf: Briefly describes the datasets mentioned above.

This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0. Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time-series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time-series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis).

The data set include a list of sample trees ≥10 cm DBH from the Swedish National Forest Inventory. Individual sample trees measured and cored within a ten metre wide transect during the 1923-1929 survey or circular sample plots during the 1953-1962, 1983-1992, and 2013-2022 surveys are included. The 10 cm threshold was used to exclude smaller diameter trees measured on small parts of sections or plots. Variables include cluster and plot ID, tree species, diameter, tree age at breast height and total tree age, land-cover class and expansion factors for estimation of number of trees and volumes. The current NFI is based on an annual sample of about 20,000 circular plots, grouped into clusters, of which about 12,000 are surveyed in the field each year. Data for additional sample trees and more variables for individual sample trees can be obtained from the Swedish National Forest Inventory. More details are presented in the article, see

Jacobsson, Jonas, Fridman, Jonas, Axelsson, Anna-Lena, Milberg, Per (2025). An aging population? A century of change among Swedish forest trees. Forest Ecology and Management. 580:122509. https://doi.org/10.1016/j.foreco.2025.122509

The data file contains 17 columns and 384790 rows.

Long-term monitoring of a forest tree community is a basis for understanding forest structure and dynamics, and for evaluating ecosystem functions such as primary production. Because global climate change has impacted and will change the forest ecosystems from local to global scales, it is essential to document long-term monitoring data of the forests to examine the temporal and geographical trends of forest changes. We here report the monitoring data of 45 forest plots (average area: 0.69 ha) from 27 sites in Japan. The plots are located with latitude ranges from N 32.38 to N 43.36, and with elevation ranges from 8 m to 2453 m above sea level. These plots include both old-growth and secondary forests, and cover various forest biomes, such as warm-temperate evergreen forests, temperate deciduous broadleaved forests, and boreal or sub-alpine coniferous forests. In each plot, all living trees and liana larger than a certain minimum size (basically 15 cm stem girth at breast height) were repeatedly measured, and survival and recruitment of stems were recorded. Monitoring period varies among plots from 5 to 40 years with the average of 17.3 years. The tree measurement data are presented as a format common to that of the preceding Monitoring Sites 1000 Project in Japan, and as a sample-based Darwin Core format. This dataset expands existing open monitoring data of Japanese forests and thereby facilitates further meta-analysis on forest community structures and their changes in relation to climate change and other drivers. This dataset is published as a data paper in Ecological Research (see https://doi.org/10.1111/1440-1703.12457).

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).