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.

This bar chart displays environmental score (ESG) (/ 100) by CEO using the aggregation average. The data is filtered where the industry is Multi-Utilities. The data is about companies.

This horizontal bar chart displays ESG score (/ 100) by CEO using the aggregation average. The data is filtered where the industry is Multi-Utilities. The data is about companies.

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).

This document presents the complete questionnaire and the aggregated survey results from an expert study on the design and technical requirements for data trustees. It includes Likert scale items, multiple-answer questions, and associated visualizations (bar charts) that illustrate the collective responses. This is the blinded version intended for the review process. After the blind review, an updated (authorized) version may be uploaded under the same DOI via Zenodo’s versioning system.

This repository contains the materials used in the Systematic Literature Review (SLR) on the application of Howard Gardner’s Theory of Multiple Intelligences in digital educational technologies. The structure is organized as follows:bib_database/: Contains .bib files with all bibliographic entries returned from the search string applied in selected digital libraries. Also includes a Python script used to merge and process these entries into a single database.plot_scripts/: Includes data files and Python scripts used to generate the visualizations presented in the review (e.g., bar charts, pie charts, distribution graphs).all_articles.xlsx: Master list of all studies retrieved using the search string, including metadata such as title, authors, year, and source.srl_protocol.xlsx: Final set of selected articles after applying inclusion/exclusion criteria. Also includes quality assessment scores and detailed classification for each research question (Q1–Q4), along with written justifications.

The Charts extension for CKAN enhances the platform's data visualization capabilities, allowing users to create, manage, and share charts that are linked to CKAN datasets. It allows users to create interactive and visually appealing chart representations of data directly within the CKAN environment, providing essential data analysis tools. This streamlines the process of visualizing data for a more intuitive and accessible experience. Key Features: Chart Creation: Enables users to create charts directly from CKAN datasets. Chart Editing: Allows users to modify and customize existing charts. Chart Embedding: Provides the ability to embed created charts into other web applications or platforms for wider dissemination. Chart Sharing: Supports sharing of chart visualizations with other users or groups within or outside the CKAN ecosystem. Multiple Chart Types: Supports a variety of common chart types, including bar charts, line charts, and pie charts. Further chart types are not mentioned explicitly, but it is implied the extension can be extended as well. Technical Integration: The extension integrates with CKAN primarily as a plugin. To enable the Charts extension, the chartsview and chartsbuilderview plugins must be added to the CKAN configuration file. The documentation also mentions the need to set CHARTS_FIELDS when autogenerating documentation for chart types fields, which implies a level of customization and extensibility for different chart types. It requires proper initialization of the CKAN instance and relies on validators and helpers, emphasizing the need for a correctly configured CKAN environment. Benefits & Impact: The primary benefit of the CKAN Charts extension is the enhancement of data analysis and presentation capabilities within CKAN. By providing tools to create, manage, and share charts, the extension makes it easier for users to understand and communicate insights from their data, fostering better data-driven decision-making. Also the documentation for chart types can be autogenerated.

This bar graph compares how many people were employed in the transport sector in France in 2017. Road passenger transport in France accounted for the majority of the transport workforce, as just under ******* people were estimated to be employed in this sector in 2017.

This bar chart displays revenues ($) by industry using the aggregation sum. The data is filtered where the industry is Multi-Utilities. The data is about companies.

Data DescriptionThe layers on this map contain population, employed labour force counts, private dwelling counts, and employment counts at Census Subdivision and Census Tract geographies from the 2006, 2011, and 2016 Census. The definition of each variable is described next:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.InstructionsZoom in and out of the map to update the bar charts. Use the Select Tool to select specific geographies to display on the bar chart.“Select by rectangle” allows you to draw a rectangle and select multiple geography to view in the chart.“Select by point” allows you select an area by clicking on its geography."Add Data" allows you add separate public data as need from ArcGIS Online, URL (an ArcGIS Server Web Service, a WMS OGC Web Service, a KML file, a GeoRSS file, a CSV file), and local files (shapefile, csv, kml, gpx, geojson)Project lead: A.MaruicioDevelopers: C.Riccardo, W.Huang, D.Robbin

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.

(C) MEF cell lines lacking the indicated genes were treated for 12h with 50 nM BafA1 fixed and immunolabeled for PC1 (568, red) and LAMP1 (488, green). CST was added where indicated. Scale bar = 10 µm. Inset panels show magnification of the boxed area. Bar graph on the right shows quantification of LAMP1 vesicles positive for PC1, expressed as % of total lysosomes (mean +/- SEM), n=8, 8, 12, 8, 8, 8 cells respectively; 3 independent experiments. One-way ANOVA with Dunnett's multiple comparisons test performed and P value adjusted for multiple comparisons. *** P<0.0001.. List of tagged entities: Lamp1 (uniprot:P11438), Plod3 (uniprot:Q9R0E1), lysosome (go:GO:0005764), vesicle (go:GO:0031982), castanospermine (CHEBI:27860), Calr (ncbigene:12317), Canx (ncbigene:12330), Pdia3 (ncbigene:14827), Uggt1 (ncbigene:320011), immunolabeling method (bao:BAO_0002425),localization assay (bao:BAO_0002196)

📹Project Video available on YouTube - https://youtu.be/gu3Ot78j_Gc

Airlines Flights Dataset for Different Cities

The Flights Booking Dataset of various Airlines is a scraped datewise from a famous website in a structured format. The dataset contains the records of flight travel details between the cities in India. Here, multiple features are present like Source & Destination City, Arrival & Departure Time, Duration & Price of the flight etc.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Airlines, Travel domain.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1. What are the airlines in the dataset, accompanied by their frequencies?

Q.2. Show Bar Graphs representing the Departure Time & Arrival Time.

Q.3. Show Bar Graphs representing the Source City & Destination City.

Q.4. Does price varies with airlines ?

Q.5. Does ticket price change based on the departure time and arrival time?

Q.6. How the price changes with change in Source and Destination?

Q.7. How is the price affected when tickets are bought in just 1 or 2 days before departure?

Q.8. How does the ticket price vary between Economy and Business class?

Q.9. What will be the Average Price of Vistara airline for a flight from Delhi to Hyderabad in Business Class ?

These are the main Features/Columns available in the dataset :

1) Airline: The name of the airline company is stored in the airline column. It is a categorical feature having 6 different airlines.

2) Flight: Flight stores information regarding the plane's flight code. It is a categorical feature.

3) Source City: City from which the flight takes off. It is a categorical feature having 6 unique cities.

4) Departure Time: This is a derived categorical feature obtained created by grouping time periods into bins. It stores information about the departure time and have 6 unique time labels.

5) Stops: A categorical feature with 3 distinct values that stores the number of stops between the source and destination cities.

6) Arrival Time: This is a derived categorical feature created by grouping time intervals into bins. It has six distinct time labels and keeps information about the arrival time.

7) Destination City: City where the flight will land. It is a categorical feature having 6 unique cities.

8) Class: A categorical feature that contains information on seat class; it has two distinct values: Business and Economy.

9) Duration: A continuous feature that displays the overall amount of time it takes to travel between cities in hours.

10) Days Left: This is a derived characteristic that is calculated by subtracting the trip date by the booking date.

11) Price: Target variable stores information of the ticket price.

This bar chart displays books by publication date using the aggregation count. The data is filtered where the book is Too many cooks. The data is about books.

Snickers is among the most well-known chocolate & candy bar brand in the United States. Around 93 percent of internet respondents are aware of Snickers. Hershey's, KitKat, Twix, and Reese's, were similarly known among the U.S. online population.For this study, brand awareness was surveyed employing the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name. Interested in more detailed results covering all brands of this ranking and many more? Explore Brand Profiles. These statistics show the results of the Statista Consumer Insights Brand KPIs.

This bar chart displays X followers (followers) by website using the aggregation sum. The data is filtered where the industry is Multi-Utilities. The data is about companies.

The graph illustrates the number of people who used illicit drugs in the United States across different time periods for the years 2021 and 2022. The x-axis represents the time periods—Lifetime, Past Year, and Past Month—while the y-axis indicates the number of individuals. In 2021, 139,677 people reported lifetime drug use, 61,995 reported past-year use, and 40,564 reported past-month use. In 2022, these numbers increased to 143,116 for lifetime use, 70,338 for past-year use, and 46,603 for past-month use. The data shows an upward trend in illicit drug use across all time periods from 2021 to 2022, with the most significant increases observed in past-year and past-month usage. This information is presented in a bar graph format, effectively highlighting the rise in illicit drug use across different timeframes in the United States between 2021 and 2022.

Background: Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient’s physical activity patterns. Objective: To explore clinicians’ opinions on different visualisations of actigraphy data. Methods: Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis. Results: Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret. Conclusion: The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.

This bar chart displays companies by talking points using the aggregation count. The data is filtered where the industry is Multi-Utilities. The data is about companies.