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.

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.

These data are examples of the ShinyLogoJS (https://siyangming.shinyapps.io/ShinyLogoJS/) web app.

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.

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.

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

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.

To compare baseball player statistics effectively using visualization, we can create some insightful plots. Below are the steps to accomplish this in Python using libraries like Pandas and Matplotlib or Seaborn.

1. Load the Data

First, we need to load the judge.csv file into a DataFrame. This will allow us to manipulate and analyze the data easily.

2. Explore the Data

Before creating visualizations, it’s good to understand the data structure and identify the columns we want to compare. The relevant columns in your data include pitch_type, release_speed, game_date, and events.

3. Visualization

We can create various visualizations, such as: - A bar chart to compare the average release speed of different pitch types. - A line plot to visualize trends over time based on game dates. - A scatter plot to analyze the relationship between release speed and the outcome of the pitches (e.g., strikeouts, home runs).

Example Code

Here is a sample code to demonstrate how to create these visualizations using Matplotlib and Seaborn:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the data
df = pd.read_csv('judge.csv')

# Display the first few rows of the dataframe
print(df.head())

# Set the style of seaborn
sns.set(style="whitegrid")

# 1. Average Release Speed by Pitch Type
plt.figure(figsize=(12, 6))
avg_speed = df.groupby('pitch_type')['release_speed'].mean().sort_values()
sns.barplot(x=avg_speed.values, y=avg_speed.index, palette="viridis")
plt.title('Average Release Speed by Pitch Type')
plt.xlabel('Average Release Speed (mph)')
plt.ylabel('Pitch Type')
plt.show()

# 2. Trends in Release Speed Over Time
# First, convert the 'game_date' to datetime
df['game_date'] = pd.to_datetime(df['game_date'])

plt.figure(figsize=(14, 7))
sns.lineplot(data=df, x='game_date', y='release_speed', estimator='mean', ci=None)
plt.title('Trends in Release Speed Over Time')
plt.xlabel('Game Date')
plt.ylabel('Average Release Speed (mph)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

# 3. Scatter Plot of Release Speed vs. Events
plt.figure(figsize=(12, 6))
sns.scatterplot(data=df, x='release_speed', y='events', hue='pitch_type', alpha=0.7)
plt.title('Release Speed vs. Events')
plt.xlabel('Release Speed (mph)')
plt.ylabel('Event Type')
plt.legend(title='Pitch Type', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

Explanation of the Code

• Data Loading: The CSV file is loaded into a Pandas DataFrame.
• Average Release Speed: A bar chart shows the average release speed for each pitch type.
• Trends Over Time: A line plot illustrates the trend in release speed over time, which can indicate changes in performance or strategy.
• Scatter Plot: A scatter plot visualizes the relationship between release speed and different events, providing insight into performance outcomes.

Conclusion

These visualizations will help you compare player statistics in a meaningful way. You can customize the plots further based on your specific needs, such as filtering data for specific players or seasons. If you have any specific comparisons in mind or additional data to visualize, let me know!

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. The permanent sample plot is a circular sampling 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 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 Division of Forestry completed a forest inventory on Alaska state owned lands in 2016. The project area encompasses forest lands in the Upper Kuskokwim River near the communities of McGrath and Nikolai. The purpose of this GIS layer, is to create a spatial coverage of vegetation on state lands to aid in forest management.

Interval data are widely used in many fields, notably in economics, industry, and health areas. Analogous to the scatterplot for single-value data, the rectangle plot and cross plot are the conventional visualization methods for the relationship between two variables in interval forms. These methods do not provide much information to assess complicated relationships, however. In this article, we propose two visualization methods: Segment and Dandelion plots. They offer much more information than the existing visualization methods and allow us to have a much better understanding of the relationship between two variables in interval forms. A general guide for reading these plots is provided. Relevant theoretical support is developed. Both empirical and real data examples are provided to demonstrate the advantages of the proposed visualization methods. Supplementary materials for this article are available online.

Abstract

The impact evaluation study of the MCA-M PRP will be the first fully randomized evaluation of a large-scale land titling program. Randomization will occur at the geographic level akin to a neighborhood. Mongolian cities are divided up into a number of administrative units - the smallest being the “kheseg”. Khesegs were chosen as the unit of randomization for the study because they are a well-defined unit that is small and numerous enough to allow for sufficient statistical power. The baseline estimation strategy will be a differences-in-differences approach, where we compare the outcomes of households in the treatment group with the control group as well as before and after the completion of the formalization activities. Exposure to treatment was 66% in Darkhan and Erdenet, and 50% in Ulaanbaatar districts. There are no results to report as of now because only the baseline has been conducted so far.

Geographic coverage

Regionally: Ulaanbaatar, Darkhan and Erdenet

Analysis unit

Kheseg (Neighborhood)

Universe

Households living in hashaa plots in the ger districts of Mongolia's three largest cities: Ulaanbaatar, Darkhan, and Erdenet.

Kind of data

Sample survey data [ssd]

Sampling procedure

8,552 plots were identified for surveying for the sample. Of these, 6,344 were occupied households and 5,816 were successfully interviewed for a response rate of 68%. 528 households refused to participate in the survey and 2,068 plots were unoccupied, had no one present at the time of any of the survey attempts, or were invalid plots. Plots found to be unoccupied or to be owned or occupied by a business or state entities were deemed unsuitable for the survey and were dropped from the sample. Geographic Information System data on all hashaa plots in the ger areas of the relevant districts of the capital and in Darkhan and Erdenet, were obtained from the PRP PIU. The ownership status of many of these plots was recorded in this GIS data set, though the ownership status information was known to be out of date and inaccurate. The boundaries of administrative units such as city, district, khoroo, and kheseg were also included. IPA processed the GIS data using ArcGIS and Stata computer software.

Once the GIS and administrative cadastral data sets were integrated, sample selection was stratified by kheseg, a geographical unit roughly equivalent to a neighborhood in the United States. First, the number of program-eligible plots per kheseg was calculated. Plots listed as “fully registered” in the GIS data were not included in this calculation since they would not be eligible for project assistance. Weights were then calculated for each kheseg unit that measured the proportion of the total number of eligible plots located in this unit. These weights were then multiplied by 8,000, the total number of plots it was deemed desirable and feasible to include in survey activities, to determine the number of plots to be sampled from each kheseg. After the sample size for each kheseg was determined, plots were randomly selected for inclusion in the survey.

Sampling deviation

In November of 2010, the survey contractor selected by MCA-M began administering the questionnaire to the households residing on and/or owning the plots selected during the sampling process. Due to the anticipated errors in the Geographic Information System data, not all of the hashaa plots selected for the SHPS sample were occupied. In addition, Mongolian households are extremely mobile. To minimize these challenges, the survey teams were required to make four attempts to locate the hashaa plot to determine the registration status and an additional four attempts to complete the survey questionnaire. Unfortunately, the SHPS had to be suspended after several weeks of data collection due to unforeseen delays in project implementation. The scope of the project was subsequently adjusted and the project implementation areas shifted due to the inflexibility of the data collection contract. The scope of the project was reduced from covering all districts in Ulaanbaatar to covering only the three largest districts, Bayanzurkh, Chingeltei, and Songinokhairkhan.

Research instrument

Household questionnaire prepared in both Mongolian and English. The team organized 4 pilot testings involving 109 respondents. Modules: - Log of attempts made to take survey, - 1. Registration section 2. Control section (filled by enumerator) 3. Introduction to survey 4. Basic Information 5. Demographic, education level and residential information of household members 6. Economic activities and incomes of household members 7. Household assets and properties 8. Planned future investments 9. Registration status of plot being surveyed 10. Implementation level of the 2003 amendment to the Land Law. 11. Accessability of land registration information and service quality at General Authority of State Registration 12. Land conflicts 13. Hashaa plot sales and its market value 14. Infrastructure of hashaa plots 15. Household spendings 16. Household business activities 17. Insurance 18. Household loans 19. Government policy and thoughts on its implementation 20. Citizens' involvement and labor in common 21. Risk evaluation

Cleaning operations

IDs in the dataset were checked against the original sample frame to make sure that they were correctly entered and complete. In addition,team leaders manually inspected each survey to ensure accuarcy of data collected and for logicallly consistancy. Back checks were also performed.

Response rate

The response rate was 68%.

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.

IRIS FLOWER SCATTER PLOT IMAGES DATASET

Overview

This dataset is derived from the well-known Iris flower dataset and contains 5000 images in PNG format. These images represent scatter plots that visually capture the relationships between different pairs of features in the Iris dataset. The original Iris dataset consists of 150 samples from three species of Iris flowers (Iris setosa, Iris versicolor, and Iris virginica), with each sample having four features: sepal length, sepal width, petal length, and petal width. The scatter plot images in this dataset provide visual insights into how these features correlate and differentiate the three species.

Dataset Description

• Total Images: 5000 PNG images
• Image Format: PNG
• Resolution: High-resolution scatter plots (resolution details can be specified)
• Source: Derived from the Iris dataset available in Scikit-learn
• Feature Pairs: Scatter plots are generated for all possible pairs of features (sepal length vs. sepal width, petal length vs. petal width, etc.) ##**Features of the Dataset** Diverse Visual Representations: The dataset includes scatter plots with various feature pairings, providing comprehensive visual analysis of feature relationships. Species Differentiation: Each scatter plot clearly distinguishes between the three species of Iris flowers using different colors or markers. High Quality: The images are generated with high-quality plotting techniques to ensure clarity and precision in the representation of data points. Annotations: Scatter plots are annotated with axes labels and legends to facilitate easy interpretation. Randomized Samples: The dataset contains 5000 images, which implies multiple scatter plots for each pair of features, with randomized sample selections to cover different aspects and variations within the dataset. ##**Use Cases** Data Visualization: Ideal for educational purposes to demonstrate data visualization techniques and the importance of scatter plots in exploratory data analysis. Machine Learning: Useful for training machine learning models on image recognition tasks, particularly in distinguishing between different species based on visual patterns. Research and Analysis: Can be used in research studies that require a large number of scatter plot images for testing new algorithms in image processing or pattern recognition. ##**Conclusion** The Iris Flower Scatter Plot Images Dataset provides a rich resource for visual data analysis, machine learning training, and educational purposes. By leveraging the classic Iris dataset, it offers a unique way to explore feature relationships through high-quality scatter plot images.

Hi Folks,

Let's understand the importance of Data Visualization.

Here below, we have four different data sets and they are paired in the sense of x and y.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F4f6c696e3ad5e2c887b01a0bdd14b355%2Fdata_set.png?generation=1685190700223447&alt=media" alt="">

Next let's calculate some descriptive statistics such as mean, standard deviation and correlation of each variables.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F14765ba12bdc18b8ff67cb6a9f2d7c7a%2Fstatistics.png?generation=1685192394142325&alt=media" alt="">

After examining the descriptive statistics the above four data sets have nearly identical or similar simple descriptive statistics.

However, when we graphically plot the datasets on scatter plot, we can see the difference that these 4 datasets looks very different.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2Fdbccf9dc638d3de28930b9f660e5f5a4%2Fgarph.png?generation=1685191588780934&alt=media" alt="">

Data 1 has a clear linear relationship, Data 2 has a curved relationship that is not linear, Data 3 has a tight linear relationship with one outlier and Data 4 has a linear relationship with one large outlier.

Such datasets are known as Anscombe's Quartet

Anscombe's quartet is a classic example of the importance of data visualization.

Anscombe's quartet is a set of four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphically represented. Each dataset consists of eleven (x,y) points.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F2b964d437afe17db949c57988b5fba05%2Fanscombes_quartet.png?generation=1685192626504792&alt=media" alt="">

Anscombe's quartet illustrates the importance of plotting data before we analyze it. Descriptive statistics can be misleading, and they can't tell us everything we need to know about a dataset. Plotting the data on charts can help us to understand the shape of the distribution and to identify any outliers.

The link: Access the data directory is available in the sectionDataset Description Sheets; Additional Information. 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 Québec. 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). Datasets available for download

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 fourth ecoforest inventory of southern Québec. 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 took place from 2004 to 2018. 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). Datasets available for download

Permanent forest mensuration sample plots that are re-measured every 5 or 10 years as part of a rolling annual programme. Data is collected from a sample of public and private woodland sites across Great Britain.

This dataset contains tree species and associated measurements from an initial survey of 8 x 1 ha Permanent Sample Plots (PSP) and 2 x 1 ha biomass measurement plots established on the Eastern Plateau of Gorongosa National Park, Sofala, Mozambique, with intention of assessing the long-term impacts of tropical cyclone damage on woodland structure. Methodology used was in accordance with specifications of the Socio Ecological Observatory for Southern African Woodlands (SEOSAW) plot protocol. PSPs were established between 1st August 2019 to 1st October 2019. PSP establishment was undertaken by University of Edinburgh in collaboration with Eduardo Mondlane University and the Mozambique Institute of Agricultural Research (IIAM) with assistance of Gorongosa National Park Scientific Services Department.