Matplotlib is a tremendous visualization library in Python for 2D plots of arrays. Matplotlib may be a multi-platform data visualization library built on NumPy arrays and designed to figure with the broader SciPy stack. It had been introduced by John Hunter within the year 2002.

A bar plot or bar graph may be a graph that represents the category of knowledge with rectangular bars with lengths and heights that’s proportional to the values which they represent. The bar plots are often plotted horizontally or vertically.

A bar chart is a great way to compare categorical data across one or two dimensions. More often than not, it’s more interesting to compare values across two dimensions and for that, a grouped bar chart is needed.

Currently, in the field of chart datasets, most existing resources are mainly in English, and there are almost no open-source Chinese chart datasets, which brings certain limitations to research and applications related to Chinese charts. This dataset draws on the construction method of the DVQA dataset to create a chart dataset focused on the Chinese environment. To ensure the authenticity and practicality of the dataset, we first referred to the authoritative website of the National Bureau of Statistics and selected 24 widely used data label categories in practical applications, totaling 262 specific labels. These tag categories cover multiple important areas such as socio-economic, demographic, and industrial development. In addition, in order to further enhance the diversity and practicality of the dataset, this paper sets 10 different numerical dimensions. These numerical dimensions not only provide a rich range of values, but also include multiple types of values, which can simulate various data distributions and changes that may be encountered in real application scenarios. This dataset has carefully designed various types of Chinese bar charts to cover various situations that may be encountered in practical applications. Specifically, the dataset not only includes conventional vertical and horizontal bar charts, but also introduces more challenging stacked bar charts to test the performance of the method on charts of different complexities. In addition, to further increase the diversity and practicality of the dataset, the text sets diverse attribute labels for each chart type. These attribute labels include but are not limited to whether they have data labels, whether the text is rotated 45 °, 90 °, etc. The addition of these details makes the dataset more realistic for real-world application scenarios, while also placing higher demands on data extraction methods. In addition to the charts themselves, the dataset also provides corresponding data tables and title text for each chart, which is crucial for understanding the content of the chart and verifying the accuracy of the extracted results. This dataset selects Matplotlib, the most popular and widely used data visualization library in the Python programming language, to be responsible for generating chart images required for research. Matplotlib has become the preferred tool for data scientists and researchers in data visualization tasks due to its rich features, flexible configuration options, and excellent compatibility. By utilizing the Matplotlib library, every detail of the chart can be precisely controlled, from the drawing of data points to the annotation of coordinate axes, from the addition of legends to the setting of titles, ensuring that the generated chart images not only meet the research needs, but also have high readability and attractiveness visually. The dataset consists of 58712 pairs of Chinese bar charts and corresponding data tables, divided into training, validation, and testing sets in a 7:2:1 ratio.

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.

📹Project Video available on YouTube - https://youtu.be/b7Kd0fLwgO4

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

This Netflix Dataset has information about the TV Shows and Movies available on Netflix.

It provides various metadata such as the type of content, cast, genres, country of origin, release details, and more. This dataset can be useful for content analysis, recommendation system development, or trend studies.

This dataset is collected from Flixable which is a third-party Netflix search engine.

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

Q. 1) For 'House of Cards', what is the Show Id and Who is the Director of this show ?

Q. 2) In which year the highest number of the TV Shows & Movies were released ? Show with Bar Graph.

Q. 3) How many Movies & TV Shows are in the dataset ? Show with Bar Graph.

Q. 4) Show all the Movies that were released in year 2000.

Q. 5) Show only the Titles of all TV Shows that were released in India only.

Q. 6) Show Top 10 Directors, who gave the highest number of TV Shows & Movies to Netflix ?

Q. 7) Show all the Records, where "Category is Movie and Type is Comedies" or "Country is United Kingdom".

Q. 8) In how many movies/shows, Tom Cruise was cast ?

Q. 9) What are the different Ratings defined by Netflix ? Q. 9.1) How many Movies got the 'TV-14' rating, in Canada ? Q. 9.2) How many TV Shows got the 'R' rating, after year 2018 ?

Q. 10) What is the maximum duration of a Movie/Show on Netflix ?

Q. 11) Which individual country has the Highest No. of TV Shows ?

Q. 12) How can we sort the dataset by Year ?

Q. 13) Find all the instances where: Category is 'Movie' and Type is 'Dramas' or Category is 'TV Show' & Type is 'Kids' TV'.

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

• Show_Id: A unique identifier assigned to each Netflix title (e.g., s1, s2...).

• Category: Indicates whether the content is a Movie or a TV Show.

• Title: The name of the movie or TV show as it appears on Netflix.

• Director: The name(s) of the director(s). This can be empty for some TV shows or content with no known director.

• Cast: List of main actors and actresses featured in the title. It may contain multiple names, separated by commas.

• Country: The country (or countries) where the content was produced or released.

• Release_Date: The date on which the content was made available on Netflix.

• Rating: The maturity rating of the content (e.g., TV-MA, PG-13, R), indicating the appropriate audience.

• Duration: For movies, this shows the length in minutes (e.g., "93 min"). For TV shows, it displays the number of seasons (e.g., "4 Seasons").

• Type: Genres or categories that describe the content (e.g., "Dramas", "Horror Movies", "International TV Shows").

• Description: A short synopsis or summary of the movie or TV show.

(G) Expression pattern of genes associated with group I and II loci (from Fig. 2E) within differentially expressed gene sets. Significance calculated by prop.test R, (***) P<0.001. (H) Venn diagram shows overlap between SOX3 binding in NPCs and GPCs. Bar graph shows expression pattern of genes continuously bound by SOX3 NPCs and GPCs. (I) Venn diagram shows overlap between SOX3 and SOX9 binding in GPCs. Bar graph shows expression pattern of genes co-bound by SOX3 and SOX9 in GPCs. (J) ChIP-seq peak graphics around the astrocyte gene Fgfbp3. ChIP-seq peaks are derived from three different experiments; SOX3 ChIPs in NPCs, SOX3 ChIPs in GPCs, SOX9 ChIPs in GPCs. Both ChIP-seq reads and called peak regions (underlying black lines) are shown for all data sets. Bar graphs shows the distribution of differentially expressed genes that are bound by all three factors. P-values (phyper, R) were calculated from the total number of protein coding genes in mm10 assembly (23´389). List of tagged entities: multiple components, Fgfbp3 (ncbigene:72514), Sox3 (uniprot:P53784), Sox9 (uniprot:Q04887), , ChIP assay (obi:OBI_0001954),ChIP-seq assay (obi:OBI_0000716),gene expression assay (bao:BAO_0002785)

This data contains Index, Text box, Button, Slicer, Image, Card, Multi row card, Table, Matrix, Conditional Formatting, Stacked Column Chart, Clustered Column Chart, Stacked Bar chart, 100% stacked column chart, background image, Line chart, Donut Chart, Gauge, Filters & Bookmarks, Maps, Scatter Chart, Anomalies, Tooltip, Animated Bar Chart Race, Enlighten Aquarium, Scroller, Measures, Dax, All Dax, Switch Dax, Waterfall Chart, Treemap.

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.

Mega60k: Chart Question Answering Dataset

  Dataset Overview

A multimodal chart question answering dataset featuring charts in multiple formats (CSV, PNG, SVG) and degraded PNG images with components omission, occlusion, blurring, and rotation to enhance robustness evaluation. Languages: English

  Chart Type Distribution

Chart Type Count Chart Type Count Chart Type Count

Area 200 Bar 200 Box 200

Bubble 200 Chord 200 Fill-bubble 200

Funnel 200… See the full description on the dataset page: https://huggingface.co/datasets/guodaosun/Mega60k.

Public RNA-Seq data was used to quantify gene expression abundance for 604 Aspergillus flavus samples from 52 experiments (bioprojects). Using abundance data, we created an Aspergillus flavus expression database (AFED) accessible through a web-based interface that allows for the expression profiles of genes to be conveniently examined across different growth conditions and life cycle stages. Expression profiles can be visualized through either an interactive bar plot for single gene queries or a heatmap for multiple gene queries. A gene co-expression network based on samples containing at least 10 million mapped reads is also available, which allows users to identify genes that are co-expressed with an individual gene or set of genes and displays the functional enrichment among the co-expressed genes.

We start by cleaning our data. Tasks during this section include: - Drop NaN values from DataFrame - Removing rows based on a condition - Change the type of columns (to_numeric, to_datetime, astype)

Once we have cleaned up our data a bit, we move the data exploration section. In this section we explore 5 high level business questions related to our data: - What was the best month for sales? How much was earned that month? - What city sold the most product? - What time should we display advertisemens to maximize the likelihood of customer’s buying product? - What products are most often sold together? - What product sold the most? Why do you think it sold the most?

To answer these questions we walk through many different pandas & matplotlib methods. They include: - Concatenating multiple csvs together to create a new DataFrame (pd.concat) - Adding columns - Parsing cells as strings to make new columns (.str) - Using the .apply() method - Using groupby to perform aggregate analysis - Plotting bar charts and lines graphs to visualize our results - Labeling our graphs

Brands' Foundation Color Names

Variations in Naming Conventions Across Different Products

By Amber Thomas [source]

About this dataset

This dataset contains data on foundation products from Sephora and Ulta, including brand, product, shade name, and color information. It was used in a visual essay on The Pudding entitled The Naked Truth to better understand how beauty brands name their foundation products.

Data were collected from the US versions of Sephora and Ulta’s websites using Microsoft Playwright. Shades that were transparent or untinted were removed resulting in 6,816 swatches from 107 brands and 328 products. Shade names were determined by scraping the alt text of the swatch images using RegEx to extract the name from the description listed, with 10% of names manually extracted from the alt text. Hex values and lightness for each shade on the website for each product were determined as well using packages in R language.

Categories for shade names were assigned manually based on interpretation and may differ based on context clues provided by entire product lines. For example Estée Lauder has a shade called dawn and another called dusk, presumably referring to time of day while EXA has a similarly named shade that presumably refers to a person's name. Categories include Animal; Color; Compliment; Descriptor; Drink; Food; Gem; Location; Metal; Misc.; Name ; Plant ; Rock ; Skin ; Textile ; Wood etc..

Raw files may be different than clean files as additional manual extraction steps have been taken into account when compiling allShades.csv. Get ready to uncover The Naked Truth with this data set!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information about brands, products, URLs, descriptions, images sources, image alt texts and shade names for different foundation products. With this data you can gain insight into the variety of color-naming conventions used by beauty brands. The organization of this dataset includes raw files (Sephora & Ulta) and a clean file (AllShades).

Accessing the Dataset

This dataset is available as multiple .csv files on kaggle.com under “Brands’ Foundation Color Names”. All data were collected from the US versions of Sephora and Ulta’s websites on January 11th and January 18th, respectively using Microsoft Playwright. There may be some variation in the name column between these datasets because additional manual extraction was incorporated into the AllShades file for those names that couldn't be programmatically extracted in order to make all of them consistent across all platforms. Furthermore, there are categories associated with each label which have been manually assigned based on common words or phrases that appear in many product shade names. These include animals, colors, compliments, descriptors etc., which you can use to analyze trend patterns in how brands are naming their makeup products from natural elements to non-traditional vocabulary!

## Columns Overview Column names in this dataset include Brand Name; URL; Description; Image Source; Image Alt Text; Name (shade name); Specific Shade Information i..e colorspace/Hex Value/Hue/Saturation/Lightness as well as a Category describing each shade label based on its content(animal/color/compliment/descriptor etc). This column also includes an estimate of 10% per brand whose name had to be manually extracted from image alt text due lists such as 001. By creating visualizations such lightness distributions or bar graphs looking at different categorizes like type or country with filter functions you can closely look at trends between shades of foundations separated by gender norms over time! There are lots of possibilities here so get creative!

## Analyzing Trends
By leveraging R packages such as imager & magick alongside structure query language queries it’s possible analyze trends concerning things like lightness values & hex codes across multiple brands while using plots like histograms & bar charts one can compare items that have been categorized according to topic (plant versus animal etc.) Finally depending upon what type visual your goal is

Research Ideas

• Analyzing the prevalence of certain categories (e.g. colors, foods, animals) in foundation shade names across different brands to better understand potential target markets and marketing strategies
• Using trend analyses on the dataset to track changing trends in foundation shade names over time
• Creating visualizations to compare lightness, hue and saturation measurements for each swatc...

Please note: this archive requires support for dangling symlinks, which excludes the Windows operating system.

To use this dataset, you will need to download the MS COCO 2017 detection images and expand them to a folder called coco17 in the train_val_combined directory. The download can be found here: https://cocodataset.org/#download You will also need to download the AI2D image description dataset and expand them to a folder called ai2d in the train_val_combined directory. The download can be found here: https://prior.allenai.org/projects/diagram-understanding

License Notes for Train and Val: Since the images in this dataset come from different sources, they are bound by different licenses.

Images for bar charts, x-y plots, maps, pie charts, tables, and technical drawings were downloaded directly from wikimedia commons. License and authorship information is stored independently for each image in these categories in the wikimedia_commons_licenses.csv file. Each row (note: some rows are multi-line) is formatted so:

Images in the slides category were taken from presentations which were downloaded from Wikimedia Commons. The names of the presentations on Wikimedia Commons omits the trailing underscore, number, and file extension, and ends with .pdf instead. The source materials' licenses are shown in source_slices_licenses.csv.

Wikimedia commons photos' information page can be found at "https://commons.wikimedia.org/wiki/File:

License Notes for Testing: The testing images have been uploaded to SlideWiki by SlideWiki users. The image authorship and copyright information is available in authors.csv.

Further information can be found for each image using the SlideWiki file service. Documentation is available at https://fileservice.slidewiki.org/documentation#/ and in particular: metadata is available at "https://fileservice.slidewiki.org/metadata/

This is the SlideImages dataset, which has been assembled for the SlideImages paper. If you find the dataset useful, please cite our paper: https://doi.org/10.1007/978-3-030-45442-5_36

A web-based tool and database for the gene ontology analysis. Its focus is on agricultural species and is user-friendly. The agriGO is designed to provide deep support to agricultural community in the realm of ontology analysis. Compared to other available GO analysis tools, unique advantages and features of agriGO are: # The agriGO especially focuses on agricultural species. It supports 45 species and 292 datatypes currently. And agriGO is designed as an user-friendly web server. # New tools including PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA) were developed. The arrival of these tools provides users with possibilities for data mining and systematic result exploration and will allow better data analysis and interpretation. # The exploratory capability and result visualization are enhanced. Results are provided in different formats: HTML tables, tabulated text files, hierarchical tree graphs, and flash bar graphs. # In agriGO, PAGE and SEACOMPARE can be used to carry out cross-comparisons of results derived from different data sets, which is very important when studying multiple groups of experiments, such as in time-course research. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Silver fell to 57.28 USD/t.oz on December 2, 2025, down 1.22% from the previous day. Over the past month, Silver's price has risen 19.11%, and is up 84.81% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on December of 2025.

Description

This dataset contains a list of 186 Digital Humanities projects leveraging information visualisation techniques. Each project has been classified according to visualisation and interaction methods, narrativity and narrative solutions, domain, methods for the representation of uncertainty and interpretation, and the employment of critical and custom approaches to visually represent humanities data.

Classification schema: categories and columns

The project_id column contains unique internal identifiers assigned to each project. Meanwhile, the last_access column records the most recent date (in DD/MM/YYYY format) on which each project was reviewed based on the web address specified in the url column.
The remaining columns can be grouped into descriptive categories aimed at characterising projects according to different aspects:

Narrativity. It reports the presence of information visualisation techniques employed within narrative structures. Here, the term narrative encompasses both author-driven linear data stories and more user-directed experiences where the narrative sequence is determined by user exploration [1]. We define 2 columns to identify projects using visualisation techniques in narrative, or non-narrative sections. Both conditions can be true for projects employing visualisations in both contexts. Columns:

• non_narrative (boolean)

• narrative (boolean)

Domain. The humanities domain to which the project is related. We rely on [2] and the chapters of the first part of [3] to abstract a set of general domains. Column:

• domain (categorical):

• History and archaeology

• Art and art history

• Language and literature

• Music and musicology

• Multimedia and performing arts

• Philosophy and religion

• Other: both extra-list domains and cases of collections without a unique or specific thematic focus.

Visualisation of uncertainty and interpretation. Buiding upon the frameworks proposed by [4] and [5], a set of categories was identified, highlighting a distinction between precise and impressional communication of uncertainty. Precise methods explicitly represent quantifiable uncertainty such as missing, unknown, or uncertain data, precisely locating and categorising it using visual variables and positioning. Two sub-categories are interactive distinction, when uncertain data is not visually distinguishable from the rest of the data but can be dynamically isolated or included/excluded categorically through interaction techniques (usually filters); and visual distinction, when uncertainty visually “emerges” from the representation by means of dedicated glyphs and spatial or visual cues and variables. On the other hand, impressional methods communicate the constructed and situated nature of data [6], exposing the interpretative layer of the visualisation and indicating more abstract and unquantifiable uncertainty using graphical aids or interpretative metrics. Two sub-categories are: ambiguation, when the use of graphical expedients—like permeable glyph boundaries or broken lines—visually convey the ambiguity of a phenomenon; and interpretative metrics, when expressive, non-scientific, or non-punctual metrics are used to build a visualisation. Column:

• uncertainty_interpretation (categorical):

• Interactive distinction

• Visual distinction

• Ambiguation

• Interpretative metrics

Critical adaptation. We identify projects in which, with regards to at least a visualisation, the following criteria are fulfilled: 1) avoid repurposing of prepackaged, generic-use, or ready-made solutions; 2) being tailored and unique to reflect the peculiarities of the phenomena at hand; 3) avoid simplifications to embrace and depict complexity, promoting time-consuming visualisation-based inquiry. Column:

• critical_adaptation (boolean)

Non-temporal visualisation techniques. We adopt and partially adapt the terminology and definitions from [7]. A column is defined for each type of visualisation and accounts for its presence within a project, also including stacked layouts and more complex variations. Columns and inclusion criteria:

• plot (boolean): visual representations that map data points onto a two-dimensional coordinate system.

• cluster_or_set (boolean): sets or cluster-based visualisations used to unveil possible inter-object similarities.

• map (boolean): geographical maps used to show spatial insights. While we do not specify the variants of maps (e.g., pin maps, dot density maps, flow maps, etc.), we make an exception for maps where each data point is represented by another visualisation (e.g., a map where each data point is a pie chart) by accounting for the presence of both in their respective columns.

• network (boolean): visual representations highlighting relational aspects through nodes connected by links or edges.

• hierarchical_diagram (boolean): tree-like structures such as tree diagrams, radial trees, but also dendrograms. They differ from networks for their strictly hierarchical structure and absence of closed connection loops.

• treemap (boolean): still hierarchical, but highlighting quantities expressed by means of area size. It also includes circle packing variants.

• word_cloud (boolean): clouds of words, where each instance’s size is proportional to its frequency in a related context

• bars (boolean): includes bar charts, histograms, and variants. It coincides with “bar charts” in [7] but with a more generic term to refer to all bar-based visualisations.

• line_chart (boolean): the display of information as sequential data points connected by straight-line segments.

• area_chart (boolean): similar to a line chart but with a filled area below the segments. It also includes density plots.

• pie_chart (boolean): circular graphs divided into slices which can also use multi-level solutions.

• plot_3d (boolean): plots that use a third dimension to encode an additional variable.

• proportional_area (boolean): representations used to compare values through area size. Typically, using circle- or square-like shapes.

• other (boolean): it includes all other types of non-temporal visualisations that do not fall into the aforementioned categories.

Temporal visualisations and encodings. In addition to non-temporal visualisations, a group of techniques to encode temporality is considered in order to enable comparisons with [7]. Columns:

• timeline (boolean): the display of a list of data points or spans in chronological order. They include timelines working either with a scale or simply displaying events in sequence. As in [7], we also include structured solutions resembling Gantt chart layouts.

• temporal_dimension (boolean): to report when time is mapped to any dimension of a visualisation, with the exclusion of timelines. We use the term “dimension” and not “axis” as in [7] as more appropriate for radial layouts or more complex representational choices.

• animation (boolean): temporality is perceived through an animation changing the visualisation according to time flow.

• visual_variable (boolean): another visual encoding strategy is used to represent any temporality-related variable (e.g., colour).

Interactions. A set of categories to assess affordable interactions based on the concept of user intent [8] and user-allowed perceptualisation data actions [9]. The following categories roughly match the manipulative subset of methods of the “how” an interaction is performed in the conception of [10]. Only interactions that affect the aspect of the visualisation or the visual representation of its data points, symbols, and glyphs are taken into consideration. Columns:

• basic_selection (boolean): the demarcation of an element either for the duration of the interaction or more permanently until the occurrence of another selection.

• advanced_selection (boolean): the demarcation involves both the selected element and connected elements within the visualisation or leads to brush and link effects across views. Basic selection is tacitly implied.

• navigation (boolean): interactions that allow moving, zooming, panning, rotating, and scrolling the view but only when applied to the visualisation and not to the web page. It also includes “drill” interactions (to navigate through different levels or portions of data detail, often generating a new view that replaces or accompanies the original) and “expand” interactions generating new perspectives on data by expanding and collapsing nodes.

• arrangement (boolean): the organisation of visualisation elements (symbols, glyphs, etc.) or multi-visualisation layouts spatially through drag and drop or

Dataset: The dataset used for this project is a Coffee Shop Sales Dataset sourced from Kaggle. It contains detailed sales transaction data from a coffee shop, including product categories (e.g., coffee, tea, bakery items), store locations, transaction dates, and revenue generated. This dataset is ideal for analyzing various business metrics, such as product performance, sales patterns, and store efficiency.

The dataset has the following key attributes:

Product Categories: Coffee, tea, bakery, branded items, etc. Store Locations: Hell’s Kitchen, Astoria, Lower Manhattan. Sales Transactions: Includes revenue, product type, and date/time details. Link to dataset: https://www.kaggle.com/datasets/ahmedmohamedibrahim1/coffee-shop-sales-dataset

Methodology Wrap-Up: In this project, the goal was to create an Interactive Sales Dashboard using Excel to derive actionable insights from the coffee shop's sales data. The process began with data collection and preparation, ensuring the dataset was ready for analysis. Various data analysis techniques were applied using Excel Pivot Tables, and multiple charts were created to visualize the data clearly and effectively.

The visualizations, including bar charts, pie charts, line charts, and treemaps, were enhanced by interactive slicers, enabling users to explore specific data segments, such as product categories, store locations, and time patterns. The analysis focused on identifying the best-performing products and stores, revenue trends, and sales distribution, providing business insights that could help the coffee shop optimize its operations.

This dashboard demonstrates the power of data visualization and business intelligence in understanding customer behavior and improving decision-making processes within a retail context.

Before	After
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As you can see in the above bar chart that the dataset is highly imbalanced as the No Finding class contains 60361 images which is about 53% of the whole dataset whereas Hernia contains only 227 images. And this imbalanced dataset further result in a high biased model predictions and poor performance on minority classes. So it is very important to first balance the dataset with the help of techniques like Undersampling & Oversampling to make it balance then proceed for training.

Data Preprocessing ( Code )

The following steps were taken to balance the dataset: - As dataset was highly imbalanced, so an undersampling technique was applied by randomly removing examples from over-represented classes to balance the dataset. This was done for each class individually, ensuring fairness in a multi-label setting. - After undersampling , oversampling of dataset was done by using data augmention with the help of Albumentations library to increase diversity. The following transformations were applied to the images: - HorizontalFlip - RandomRotate90 - VerticalFlip - Sharpen (with different probability of transform after each iteration)

These steps helped balance the dataset for further training of our model.

File contents

new_images: Zip file containing over 51k Balanced 1024x1024 CLAHE Enhanced Chest X-ray images.

new_labels.csv: Contains one-hot encoded format for labels.

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.

US Recorded Music Revenues by Format

US Recorded Music Revenues by Format in 2019 (Inflation Adjusted)

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information about the US recorded music industry in 2019 Week 10. It includes details on the various formats of recorded music, such as CDs, vinyl records, digital downloads, and more. The dataset also provides data on the respective years in which these records were made, allowing for accurate historical comparison and analysis.

Key metrics provided include the number of units sold for each format, as well as corresponding revenue generated from their sales. In addition to the raw revenue figures, this dataset offers an extra column that presents inflation-adjusted revenue values. These adjusted figures take into account changes in purchasing power over time and enable a fair comparison of different years' revenues.

Overall, this dataset offers valuable insights into the US recorded music industry's performance in terms of format popularity and economic gains throughout a specific week in 2019. Researchers, analysts, and music professionals can utilize this comprehensive dataset to explore trends within specific formats while considering both absolute revenue and inflation-adjusted figures

How to use the dataset

Introduction:

• Understanding the Columns: a) Format: This column categorizes the format of the recorded music, such as CD, vinyl, digital download, etc. b) Year: This column represents the year in which the data was recorded. c) Units: The number of units sold for a particular format of recorded music. d) Revenue: The revenue generated from sales for a specific format. e) Revenue (Inflation Adjusted): The column that shows revenue adjusted for inflation.

• Analyzing Formats: By exploring and analyzing the Format column in this dataset, you can gain insights into changing consumer preferences over time. You can identify which formats have gained popularity or declined over different years or periods.

• Understanding Revenue Generation: To understand revenue patterns in relation to various formats and years, analyze both Revenue and Revenue (Inflation Adjusted) columns separately. Comparing these two columns will help you assess changes due to inflation accurately.

• Exploring Units Sold: The column Units provides insight into how many units were sold for each format within a specific year or period. Analyzing this data helps understand consumer demand across various formats.

• Calculating Inflation-Adjusted Revenue: Utilize the Revenue (Inflation Adjusted) column when analyzing long-term trends or comparisons across different periods without worrying about how inflation affects purchasing power over time.

• Comparing Multiple Years or Periods: This dataset includes information specifically for 2019 Week 10. However, you can use this dataset in conjunction with other datasets covering different years to compare revenue, units sold, and format performance across multiple years.

• Creating Visualizations: Visualizations such as line charts or bar graphs can help represent patterns and trends more comprehensively. Consider creating visualizations based on formats over multiple years or comparing revenue generated by different formats.

• Deriving Insights: Make use of the information provided to identify trends, understand customer preferences, and make informed decisions related to marketing strategies or product offerings in the music industry.

Conclusion:

Research Ideas

• Analyzing the impact of different music formats on revenue: This dataset provides information on the revenue and units sold for different recorded music formats such as CDs, vinyl, and digital downloads. By analyzing this data, one can identify which format generates the highest revenue and understand how consumer preferences have shifted over time.
• Tracking changes in purchasing power over time: The dataset includes both revenue and inflation-adjusted revenue figures, allowing for a comparison of how purchasing power has changed over the years. This can be useful in understanding trends in consumer spending habits or evaluating the success of marketing campaigns.
• Assessing market performance by year: With data on both units sold and revenue by year, this dataset can be used to assess the overall performance of the US recorded music industry over time. By comparing different years, one can identify periods of growth or decline and gain insights into factors driving these changes, such as technological advancements or shifts in consumer behavior

Acknowledgements

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In our work, we investigate the impact of contextual determinants (such as majority group size, the number of opposing minorities and their sizes, and the nature of the task) and personal determinants (such as self-confidence, personality and gender) on online social conformity. In order to achieve this, we deployed an online quiz with subjective and objective multiple-choice questions. For each question, participants provided their answer and self-reported confidence. Following this, they were shown a fabricated bar chart that positioned the participant either in the majority or minority, presenting the distribution of group answers across different answer options. Each question tested a unique group distribution in terms of the number of minorities against the majority and their corresponding group sizes. Subsequently, participants were given the opportunity to change their answer and reported confidence. Upon completing the quiz, participants undertook a personality test and participated in a semi-structured interview.

This data set includes the two attachments as described below:

Data.csv : This is a .csv (comma separated values) file which includes the preprocessed data we collected through the online quiz and the Big-5 personality test. script.r : This R file includes the final model after incremental removal of variables based on their predictive power.