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.

Use the Chart Viewer template to display bar charts, line charts, pie charts, histograms, and scatterplots to complement a map. Include multiple charts to view with a map or side by side with other charts for comparison. Up to three charts can be viewed side by side or stacked, but you can access and view all the charts that are authored in the map. Examples: Present a bar chart representing average property value by county for a given area. Compare charts based on multiple population statistics in your dataset. Display an interactive scatterplot based on two values in your dataset along with an essential set of map exploration tools. Data requirements The Chart Viewer template requires a map with at least one chart configured. Key app capabilities Multiple layout options - Choose Stack to display charts stacked with the map, or choose Side by side to display charts side by side with the map. Manage chart - Reorder, rename, or turn charts on and off in the app. Multiselect chart - Compare two charts in the panel at the same time. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Additional file 6: Fig.S1 Gossypetin does not affect expression of β-, and γ-secretases and activity of β-secretase. (A to G) Time dependent β-secretase activity of mouse hippocampal lysate was measured with Relative Fluorescence Unit (RFU). Fluorescence excitation and emission wavelength was 335 nm and 495 nm respectively (A). Bar graph of RFU at each time point of 10 min (B), 20 min (C), 30 min (D), 40 min (E), 50 min (F), 60 min (G). (n = 10~12 mice per group) (H to L) Representative images of Western blot analysis for β-, γ-secretase subunits, and GAPDH (H). Bar graphs represent relative protein expression levels of BACE1 (I), Nicastrin (J), APH-1 (K), and PEN2 (L). (n = 12~15 mice per group) (M to P) Bar graphs represent relative mRNA expression level of β-, and γ-secretase subunits bace1 (M), ncstn (N), aph1 (O), pen2 (P). (n = 9~10 mice per group) Error bars represent the mean ± SD, p < 0.05, ns = not significant, two-way ANOVA followed by Tukey’s multiple comparisons test. Fig. S2 Cell type classification of brain samples. (A) UMAP plot showing all cells from the brain samples, colored by their cell types. (B) Heatmap illustrating the Z-scores of average normalized expressions of cell type markers. (C) Violin plots displaying the log-scaled number of detected genes (top), Unique Molecular Identifiers (UMIs) (middle), and the percentage of mitochondrial gene expressions (bottom) per cell for each cell type. (D) UMAP plots showing all cells from the brain samples, colored by their sampled region (left), mouse strain (middle), or drug administration (right) condition. Fig. S3 Detailed subtyping of the microglial population. (A) UMAP plots showing all microglial cells from cortex region. The cells are colored by their celltypes (left). Heatmap showing the Z-scores of average normalized expressions of representative DEGs for each cell type from cortex region (right). (B) UMAP plots showing microglial cells from cortex (left) or hippocampus (right), colored by combination of mouse strain and drug administration condition. (C) UMAP plots illustrating microglial cells from cortex (left) or hippocampus (right), colored by their inferred cell cycle. (D) Bar plots for the fraction of cortex (left) or hippocampus (right) microglial cells by sample conditions, which are the combination of mouse strain and drug administration, for each microglial subtype. Fig. S4 Differential gene expressions between vehicle- and gossypetin-treated microglia. (A) Scatter plot showing GOBP terms that are upregulated or downregulated by5xFAD construction or gossypetin administration for each microglial subtype from cortex. Significant (Fisher’s exact test, P < 0.01) terms associated with antigen presentation are colored by their biological keywords. (B) GSEA plots showing significant (P< 0.05) GOBP terms for gossypetin administration condition against vehicle treatment within 5xFAD homeostatic microglia from hippocampus region. Related to Fig. 3D. (C) Volcano plot illustrating the DEGs selected by the comparison between wild type and 5xFAD(left), or vehicle and gossypetin treated 5xFAD (right) from homeostatic microglial population of cortex region. Fig. S5 Transcriptomic transition in cortex microglia and measurement of DAM signature score. (A) Volcano plot showing significant (p < 0.05) DEGs selected by the comparison between cortex homeostatic microglia in vehicle treated wild type and 5xFAD (top left), or vehicle and gossypetin treated 5xFAD (top right). Volcano plots illustrating comparison between gossypetin administration condition against vehicle treatment within 5xFAD stage 1 DAM (bottom left) or stage 2 DAM (bottom right) from cortex are also presented. (B) Violin plot illustrating module scores for the DAM-related genes from previous studies. Cells are grouped by the combination of their mouse strain and treatment condition. (P < 0.001) Fig. S6 Gossypetin ameliorates gliosis in microglia and astrocytes. (A to D) Representative images of hippocampus (A) and cortex (C) stained with Hoechst and Iba-1. Scale bar corresponds to 200μm. Bar graph represents quantification of Iba-1 positive area in dentate gyrus of hippocampus (n = 9~12 mice per group, 3~6 slices per brain) (B) and cortex (n = 9~12 mice per group, 3~6 slices per brain) (D). (E to H) Representative images of hippocampus (E) and cortex (G) stained with Hoechst and GFAP. Scale bar corresponds to 200μm. Bar graph represents quantification of GFAP positive area in dentate gyrus of hippocampus (n = 9~12 mice per group, 3~6 slices per brain) (F) and cortex (n = 9~12 mice per group, 3~5 slices per brain) (H). The error bars represent the mean ± SEM.**p <0.0001, ***p < 0.001, **p < 0.01, ns = not significant, two-way ANOVA followed by Tukey’s multiple comparisons test (B, D, F and H). Fig. S7 Gossypetin increases Aβ phagocytic capacity and dynamics of BV2 microglial cell line. (A) Representative images of BV2 cells treated with 488-Aβ and stained with Hoechst and Iba-1. Gossypetin (25μM) was pretreated for 24 h before 488-Aβ treatment. Scale bar corresponds to 100μm. (B). Bar graph represents quantification of area of internalized 488-Aβ in BV2 (n= 3 per group, 253~656 cells per sample). (C) Line graph represents quantification of fluorescent area generated by internalized 488-Aβ in BV2 in a time dependent manner (n = 3 per group, 107~347 cells per sample). The error bars represent the mean ± SEM. ****p <0.0001, *p < 0.05, two-way ANOVA followed by Tukey’s multiple comparisons test (C), Student’s t test (B).

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.

Displays a bar chart showing either the number or rate of reported cases for a single disease or multiple diseases (up to 6) for any selected year from 1991 up to 2016 and is displayed by age group. The source data table, limitations of the data and descriptions of the selected notifiable disease(s) are also provided.

Displays a bar chart showing either the number or rate of reported cases for a single disease or multiple diseases (up to 6) for any selected year from 1991 up to 2016 and is displayed by age group. The source data table, limitations of the data and descriptions of the selected notifiable disease(s) are also provided.

🔍 The Case of the Disguised Villains: Predicting Team Rocket with Data

In the bustling world of Kanto, where Pokémon battles shape destinies, crime lurks in the shadows. Detective Kotso, the sharpest mind in Pokémon crime investigations, has been tasked with an urgent mission. The mayor suspects that Team Rocket has infiltrated the city, disguising themselves as ordinary citizens.

But Kotso doesn’t work alone—he relies on you, a brilliant data scientist, to uncover the truth. Your job? Analyze the data of 5,000 residents to predict which of the 1,000 unclassified individuals are secretly part of Team Rocket.

Can you spot the hidden patterns? Can Machine Learning crack the case where traditional detective work fails? The fate of Kanto depends on your skills.

📊 Dataset Structure & Features

This dataset holds the key to exposing Team Rocket’s operatives. Below is a breakdown of the features at your disposal:

🕵️ Can You Crack the Case?

This dataset is not just about numbers—it’s a criminal investigation. Hidden patterns lurk beneath the surface, waiting to be uncovered.

• Are certain Pokémon types more common among Team Rocket members?
• Do suspicious financial transactions hint at illegal activities?
• Does their battle strategy betray their allegiance?

This isn’t just another classification task—it’s a race against time to stop Team Rocket before they take control of Kanto!

Detective Kotso is counting on you. Will you rise to the challenge? 🕵️‍♂️🔎

🔎 10 Key Questions & Suggested Analysis Techniques

1️⃣ Do certain Pokémon types indicate suspicious behavior?
- 📈 Graph: Stacked bar chart comparing Pokémon type distribution between Rocket & non-Rocket members.
- 🎯 Test: Chi-square test for correlation.

2️⃣ Is economic status a reliable predictor of criminal affiliation?
- 📊 Graph: Box plot of debt and economic status per Team Rocket status.
- 🏦 Test: ANOVA test for group differences.

3️⃣ Do Team Rocket members have a preference for specific PokéBalls?
- 🎨 Graph: Heatmap of PokéBall usage vs. Team Rocket status.
- ⚡ Test: Chi-square test for independence.

4️⃣ Does a high battle win ratio correlate with Team Rocket membership?
- 📉 Graph: KDE plot of win ratio distribution for both classes.
- 🏆 Test: T-test for mean differences.

5️⃣ Are migration patterns different for Team Rocket members?
- 📈 Graph: Violin plot of migration counts per group.
- 🌍 Test: Mann-Whitney U test.

6️⃣ Do Rocket members tend to avoid charity participation?
- 📊 Graph: Grouped bar chart of charity participation rates.
- 🕵️‍♂️ Test: Fisher’s Exact Test for small sample sizes.

7️⃣ Do Rocket members disguise themselves in certain professions?
- 📊 Graph: Horizontal bar chart of profession frequency per group.
- 🕵️‍♂️ Test: Chi-square test for profession-Team Rocket relationship.

8️⃣ Is there an unusual cluster of Rocket members in specific cities?
- 🗺 Graph: Geographic heatmap of city distributions.
- 📌 Test: Spatial autocorrelation test.

9️⃣ How does badge count affect the likelihood of being a Rocket member?
- 📉 Graph: Histogram of gym badge distributions.
- 🏅 Test: Kruskal-Wallis test.

🔟 **Are there any multi-feature interactions that reve...