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.

Submarine Cable Features Dataset

Submarine Cable Features: Scale, Description, and Effective Dates

By Homeland Infrastructure Foundation [source]

About this dataset

The Submarine Cables dataset provides a comprehensive collection of features related to submarine cables. It includes information such as the scale band, description, and effective dates of these cables. These data are specifically designed to support coastal planning at both regional and national scales.

The dataset is derived from 2010 NOAA Electronic Navigational Charts (ENCs), along with 2009 NOAA Raster Navigational Charts (RNCs) which were updated in 2013 using the most recent RNCs as a reference point. The source material's scale varied significantly, resulting in discontinuities between multiple sources that were resolved with minimal spatial adjustments.

Polyline features representing submarine cables were extracted from the original sources while excluding 'cable areas' noted within the data. The S-57 data model was modified for improved readability and performance purposes.

Overall, this dataset provides valuable information regarding the occurrence and characteristics of submarine cables in and around U.S. navigable waters. It serves as an essential resource for coastal planning efforts at various geographic scales

How to use the dataset

Here's a guide on how to effectively utilize this dataset:

1. Familiarize Yourself with the Columns

The dataset contains multiple columns that provide important information:

• scaleBand: This categorical column indicates the scale band of each submarine cable.
• description: The text column provides a description of each submarine cable.
• effectiveDate: Indicates the effective date of the information about each submarine cable.

Understanding these columns will help you navigate and interpret the data effectively.

2. Explore Scale Bands

Start by analyzing the distribution of different scale bands in the dataset. The scale band categorizes submarines cables based on their size or capacity. Identifying patterns or trends within specific scale bands can provide valuable insights into how submarine cables are deployed.

For example, you could analyze which scale bands are most commonly used in certain regions or countries, helping coastal planners understand infrastructure needs and potential connectivity gaps.

3. Analyze Cable Descriptions

The description column provides detailed information about each submarine cable's characteristics, purpose, or intended use. By examining these descriptions, you can uncover specific attributes related to each cable.

This information can be crucial when evaluating potential impacts on marine ecosystems, identifying areas prone to damage or interference with other maritime activities, or understanding connectivity options for coastal regions.

4. Consider Effective Dates

While excluding dates from this analysis as per your request (as we exclude them here), effective dates play an important role in keeping track of when information about a particular cable was collected or updated.

By considering effective dates over time: - You can monitor changes in infrastructure deployment strategies. - Identify areas where new cables have been installed. - Track outdated infrastructure that may need replacements or upgrades.

5. Combine with Other Datasets

To gain a comprehensive understanding and unlock deeper insights, consider integrating this dataset with other relevant datasets. For example: - Population density data can help identify areas in high need of improved connectivity. - Coastal environmental data can help assess potential ecological impacts of submarine cables.

By merging datasets, you can explore relationships, draw correlations, and make more informed decisions based on the available information.

6. Visualize the Data

Create meaningful visualizations to better understand and communicate insights from the dataset. Utilize scatter plots, bar charts, heatmaps, or GIS maps

Research Ideas

• Coastal Planning: The dataset can be used for coastal planning at both regional and national scales. By analyzing the submarine cable features, planners can assess the impact of these cables on coastal infrastructure development and design plans accordingly.
• Communication Network Analysis: The dataset can be utilized to analyze the connectivity and coverage of submarine cable networks. This information is valuable for telecommunications companies and network providers to understand gaps in communication infras...

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

"Classification and Quantification of Strawberry Fruit Shape" is a dataset that includes raw RGB images and binary images of strawberry fruit. These folders contain JPEG images taken from the same experimental units on 2 different harvest dates. Images in each folder are labeled according to the 4 digit plot ID from the field experiment (####_) and the 10 digit individual ID (_##########).

"H1" and "H2" folders contain RGB images of multiple fruits. Each fruit was extracted and binarized to become the images in "H1_indiv" and "H2_indiv".

"H1_indiv" and "H2_indiv" folders contain images of individual fruit. Each fruit is bordered by ten white pixels. There are a total of 6,874 images between these two folders. The images were used then resized and scaled to be the images in "ReSized".

"ReSized" contains 6,874 binary images of individual berries. These images are all square images (1000x1000px) with the object represented by black pixels (0) and background represented with white pixels (1). Each image was scaled so that it would take up the maximum number of pixels in a 1000 x 1000px image and would maintain the aspect ratio.

"Fruit_image_data.csv" contains all of the morphometric features extracted from individual images including intermediate values.

All images title with the form "B##_NA" were discarded prior to any analyses. These images come from the buffer plots, not the experimental units of the study.

"PPKC_Figures.zip" contains all figures (F1-F7) and supplemental figures (S1-S7_ from the manuscript. Captions for the main figures are found in the manuscript. Captions for Supplemental figures are below.

Fig. S1 Results of PPKC against original cluster assignments. Ordered centroids from k = 2 to k = 8. On the left are the unordered assignments from k-means, and the on the right are the order assignments following PPKC. Cluster position indicated on the right [1, 8].

Fig. S2 Optimal Value of k. (A) Total within clusters sum of squares. (B) The inverse of the Adjusted R . (C) Akaike information criterion (AIC). (D) Bayesian information criterion (AIC). All metrics were calculated on a random sample of 3, 437 images (50%). 10 samples were randomly drawn. The vertical dashed line in each plot represents the optimal value of k. Reported metrics are standardized to be between [0, 1].

Fig. S3 Hierarchical clustering and distance between classes on PC1. The relationship between clusters at each value of k is represented as both a dendrogram and as bar plot. The labels on the dendrogram (i.e., V1, V2, V3,..., V10) represent the original cluster assignment from k-means. The barplot to the right of each dendrogram depicts the elements of the eigenvector associated with the largest eigenvalue form PPKC. The labels above each line represent the original cluster assignment.

Fig. S4 BLUPs for 13 selected features. For each plot, the X-axis is the index and the Y-axis is the BLUP value estimated from a linear mixed model. Grey points represent the mean feature value for each individual. Each point is the BLUP for a single genotype.

Fig. S5 Effects of Eigenfruit, Vertical Biomass, and Horizontal Biomass Analyses. (A) Effects of PC [1, 7] from the Eigenfruit analysis on the mean shape (center column). The left column is the mean shape minus 1.5× the standard deviation. Right is the mean shape plus 1.5× the standard deviation. The horizontal axis is the horizontal pixel position. The vertical axis is the vertical pixel position. (B) Effects of PC [1, 3] from the Horizontal Biomass analysis on the mean shape (center column). The left column is the mean shape minus 1.5× the standard deviation. Right is the mean shape plus 1.5× the standard deviation. The horizontal axis is the vertical position from the image (height). The vertical axis is the number of activated pixels (RowSum) at the given vertical position. (C) Effects of PC [1, 3] from the Vertical Biomass analysis on the mean shape (center column). The left column is the mean shape minus 1.5× the standard deviation. Right is the mean shape plus 1.5× the standard deviation. The horizontal axis is the horizontal position from the image (width). The vertical axis is the number of activated pixels (ColSum) at the given horizontal position.

Fig. S6 PPKC with variable sample size. Ordered centroids from k = 2 to k = 5 using different image sets for clustering. For all k = [2, 5], k-means clustering was performed using either 100, 80, 50%, or 20% of the total number of images; 6,874, 5, 500, 3, 437, and 1, 374 respectively. Cluster position indicated on the right [1, 5].

Fig. S7 Comparison of scale and continuous features. (A.) PPKC 4-unit ordinal scale. (B.) Distributions of the selected features with each level of k = 4 from the PPKC 4-unit ordinal scale. The light gray line is cluster 1, the medium gray line is cluster 2, the dark gray line is cluster 3, and the black line is cluster 4.

Gross National Product in Philippines increased to 6552211.68 PHP Million in the first quarter of 2025 from 6461635.30 PHP Million in the fourth quarter of 2024. This dataset provides - Philippines Gross National Product - actual values, historical data, forecast, chart, statistics, economic calendar and news.