This bar chart displays sites by category using the aggregation count in Yemen. The data is about sites.

The data originates from Promusicae’s official website, which provides weekly and yearly charts for albums, singles, and additional music-related content categories. The paper associated to this sample is available here:

Sánchez-Olmos, C. (2025). Gender Inequality in Spain’s Official Music Charts: Neither Representation nor Success for Female Artists (2008–2020). Journalism and Media, 6(1), 10. https://doi.org/10.3390/journalmedia6010010

This dataset features the top 50 from 2008 to 2020, comprising 1300 recording units with an equal split between albums (650) and singles (650) (Figure 1). Promusicae represents Spanish record labels affiliated with the International Federation of the Phonographic Industry (IFPI) and is responsible for publishing these official charts. The analysis period started in 2008 when Promusicae published its first top 50 singles chart, which was later expanded to a top 100 format in 2015. Since Promusicae has published the singles chart since 2008, this year marks the beginning of the analysis period, ending in 2020.

Both charts were downloaded in Excel format from the Promusicae website. All albums and singles are coded to feature the following variables: artist, title, year of chart appearance, gender (soloist or band), position on the chart, and success achieved. The gender of the featured position is also coded in the single chart.

This code has its limitations. First, the use of binary gender coding fails to capture the diversity of sexual identities (de Boise, 2019). Furthermore, several methods for categorising mixed bands were identified based on the roles of men and women (including composers, singers, or instrumentalists). However, to facilitate discussion, we chose the categories proposed by Lafrance et al. (2011). Consequently, the final coding includes five distinct categories: male artists, male bands (entirely composed of men), female artists, female bands (consisting solely of women), and male–female groups (mixed duos, trios, or bands featuring both women and men).

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/

This bar chart displays sites by category using the aggregation count in Korea. The data is about sites.

Welcome to the Sales Insights Dashboard, a comprehensive analysis of sales data with interactive visualizations and key insights. Follow the steps below to explore the dashboard:

Overview:

The dashboard consist of charts representing Monthly Sales Trends, Regional Profitability, Top 5 Products, Sales by Category and Product Wise Sales and Quantity. Line chart, Area chart, Clustered Bar chart and Doughnut chart can be found in the dashboard.

Monthly Sales Trend

Explore the "Monthly Sales Trend" to understand how sales have evolved over time. Dynamic line charts showcase monthly trends, helping you spot patterns and seasonality.

Product-wise Sales and Quantity:

Delve into the "Product-wise Sales and Quantity" section for a granular view. Clustered bar charts display sales and quantity metrics for each product.

Top 5 Products by Sales:

Identify the "Top 5 Products by Sales" to focus on high-performing items. Doughnut chart offer insights into the top-selling products.

Regional Profitability:

Evaluate "Regional Profitability" to understand which regions contribute the most to profits. Area charts visually represent regional performance.

Sales by Category:

Dive into the "Sales by Category" to identify the most lucrative product categories. Interactive Doughnut charts reveal sales performance, aiding in strategic decision-making.

How to Use:

Interact with dropdowns, sliders, and buttons to customize your view. Hover over charts for detailed tooltips and information. Click on specific elements to filter data and uncover specific insights.

Feedback:

We welcome your feedback to enhance the dashboard further. Share your thoughts in the comments section. Explore the Sales Insights Dashboard now and transform your sales data into actionable insights!

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.

United States - Average Price: Utility (Piped) Gas per Therm in the West Census Region - Size Class A was 1.78300 Index in December of 2024, according to the United States Federal Reserve. Historically, United States - Average Price: Utility (Piped) Gas per Therm in the West Census Region - Size Class A reached a record high of 3.82100 in January of 2023 and a record low of 0.22500 in December of 1978. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Average Price: Utility (Piped) Gas per Therm in the West Census Region - Size Class A - last updated from the United States Federal Reserve on November of 2025.

This pie chart displays sites per category using the aggregation count in the United States. The data is about sites.

Context

I'm just getting into data science and wanted to see if there's any kind of pattern in preference for male or female singers in the Billboard charts over time (I don't believe there is, it's just a toy problem to work on).

Some finding: https://github.com/rkibria/rkibria.github.com/blob/master/singers_gender_1.md

Content

The CSV has 3 columns: artist, gender, category. The data comes from Wikipedia where I scraped categories using the code here. I then concatenated the data with pandas in Python and wrote it to a CSV file.

NOTE: to load this CSV file into pandas you will need to use the latin1 encoding, or there will be an exception:

df = pd.read_csv('singers_gender.csv', encoding='latin1')

Inspiration

I want to apply this data to the huge Billboard Hot 100 database generated by Dayne Batten.

United States - Average Price: Electricity per Kilowatt-Hour in the West Census Region - Size Class A was 0.25200 Index in December of 2024, according to the United States Federal Reserve. Historically, United States - Average Price: Electricity per Kilowatt-Hour in the West Census Region - Size Class A reached a record high of 0.25200 in November of 2024 and a record low of 0.03800 in November of 1978. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Average Price: Electricity per Kilowatt-Hour in the West Census Region - Size Class A - last updated from the United States Federal Reserve on November of 2025.

This bar chart displays sites by category using the aggregation count in Uzbekistan. The data is about sites.

According to a study on representation and equality in the music industry, only *** percent of producers were female while approximately **** percent were male. The share of female music producers has been increasing since 2017, despite the setback in 2020 and still leaving a significant gap in terms of proportionate representation. Gender inequality in the music industry Even though music audiences are as diverse as ever, and recent data has also indicated that male and female listeners account for similar shares of digital music users in the United States, there are still significant gaps when it comes to the representation of different groups. The share of female songwriters across the top 100 songs in 2020 stood at below ** percent - a figure that has pretty much remained unchanged in the past decade. But this disparity not only unfolds behind the scenes: In 2020, just over ** percent of artists on Billboard’s top 100 charts were female, and in genres like hip-hop or alternative, this share was even lower. Grammy Awards The fact that the music industry remains a male-dominated landscape is also reflected in the Grammy Awards. While the show made headlines by merging male and female categories back in 2012, the imbalances have remained. Data on the gender distribution of Grammy nominees collected between 2013 and 2021 shows that less than ** percent of nominees for awards like Record of the Year, Album of the Year, and Producer of the Year were female. And even though the playing field was much more balanced in the Best New Artist category, many artists still fail to get the spotlight they deserve.

This bar chart displays area (m²) by category using the aggregation sum in China. The data is about sites.

This bar chart displays area (m²) by category using the aggregation sum in Guatemala. The data is about sites.

This bar chart displays area (m²) by category using the aggregation sum in Papua New Guinea. The data is about sites.

United States - Average Price: Electricity per Kilowatt-Hour in the South Census Region - Size Class A was 0.16700 Index in December of 2024, according to the United States Federal Reserve. Historically, United States - Average Price: Electricity per Kilowatt-Hour in the South Census Region - Size Class A reached a record high of 0.17400 in July of 2022 and a record low of 0.03800 in December of 1978. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Average Price: Electricity per Kilowatt-Hour in the South Census Region - Size Class A - last updated from the United States Federal Reserve on October of 2025.

This bar chart displays sites by category using the aggregation count in Seychelles. The data is about sites.

This pie chart displays sites per category using the aggregation count in India. The data is about sites.

This pie chart displays sites per category using the aggregation count in Chile. The data is about sites.

This pie chart displays sites per category using the aggregation count in China. The data is about sites.