A collection of files used for a data visualization project for the Digital Humanities Praxis course at the Graduate Center, CUNY. The files represent raw data (csv), data used for the visualization(s) (gephi), and the visualizations themselves (pdf). A write-up on the project can be located at the GC Academic Commons site: http://dhpraxis14.commons.gc.cuny.edu/2014/11/12/its-big-data-to-me-data-viz-part-2

This data set contains mapping and table of the location of all Pay and Display Meters in Fingal County Councils pay and display system around the county of Fingal. We Currently have 146 Meters in use .e have Pay and Display in the following areas: Malahide, Skerries, Balbriggan, Swords, Rush and Clonsilla.Pay & Display operates from 8.00 am to 6.00 pm Monday to Saturday inclusive. However, this can vary for particular areas, check the signage at your location.Charges can vary for different areas for both on-street and car parks. However, the charge is usually €1.20 per hour or €3 per day in the long-term parking areas.Traffic Wardens ticket illegally parked vehicles in a Pay and Display area. It is your responsibility to ensure that a valid parking ticket/permit/disc is displayed and clearly visible on your vehicle.

Global trade data of Display under 95030040, 95030040 global trade data, trade data of Display from 80+ Countries.

Research dissemination and knowledge translation are imperative in social work. Methodological developments in data visualization techniques have improved the ability to convey meaning and reduce erroneous conclusions. The purpose of this project is to examine: (1) How are empirical results presented visually in social work research?; (2) To what extent do top social work journals vary in the publication of data visualization techniques?; (3) What is the predominant type of analysis presented in tables and graphs?; (4) How can current data visualization methods be improved to increase understanding of social work research? Method: A database was built from a systematic literature review of the four most recent issues of Social Work Research and 6 other highly ranked journals in social work based on the 2009 5-year impact factor (Thomson Reuters ISI Web of Knowledge). Overall, 294 articles were reviewed. Articles without any form of data visualization were not included in the final database. The number of articles reviewed by journal includes : Child Abuse & Neglect (38), Child Maltreatment (30), American Journal of Community Psychology (31), Family Relations (36), Social Work (29), Children and Youth Services Review (112), and Social Work Research (18). Articles with any type of data visualization (table, graph, other) were included in the database and coded sequentially by two reviewers based on the type of visualization method and type of analyses presented (descriptive, bivariate, measurement, estimate, predicted value, other). Additional revi ew was required from the entire research team for 68 articles. Codes were discussed until 100% agreement was reached. The final database includes 824 data visualization entries.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Statistics illustrates the import volume of Tubes; data/graphic display, colour, with a phosphor dot screen pitch smaller than 0.4mm in Chad from Jan 2019 to Oct 2025 by trade partner.

**Trending on YouTube ** Trending helps viewers see what’s happening on YouTube and in the world. Trending aims to surface videos and shorts that a wide range of viewers would find interesting. Some trends are predictable, like a new song from a popular artist or a new movie trailer. Others are surprising, like a viral video.

Trending isn't personalized and displays the same list of trending videos to all viewers in the same country, which is why you may see videos in Trending that aren’t in the same language as your browser. However, in India, Trending displays a list of results for each of the 9 most common Indic languages.

SOURCE The data has been scrapped from "Mendeley.com". The source of this file ishttps://data.mendeley.com/datasets/7pkbvjtnxm/1/files/e7763107-45e9-4613-8c81-146e6a272266 Converted the data to csv file to use it in kaggle ../input/youtube-vdos/youtube trending videos dataset.csv

The data contains following columns .

) Position (int type) - An index column which gives the position of the channel in youtube channel 1) Channel Id ( Stirng ) - ID of the youtube channel 2) Channel Title ( String ) - Youtube channel title 3) Video Id (String) - ID of video in the youtube channel 4) Published At (String) - date of the video published at 5) Video Title (String ) - Title of the video 6) Video Description (String) - Description of the video(what the video is about) 6 Video Category Id ( int type) - Category of the video in youtube channel 7 Video Category Label (String) - type of category the video belongs 8 Duration (String ) - duration of the video 9 Duration Sec ( int type) - Duration of video in seconds 10 Dimension (String) - Dimension of the video (2D , Hd) 11 Definition (String) - Defining the video 12 Caption (bool ) - Boolean type caption (True or False) 13 Licensed Content (float Type) 14 View Count ( int type) - number of people viewed the video 15 Like Count (float) - Number of likes the channel got 16 Dislike Count (float) - Number of dislikes the channel got 17 Favorite Count ( int type) - Number of people marked as favourite 18 Comment Count (float) - Number of people commented on the video

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, colour, with a phosphor dot screen pitch smaller than 0.4mm in the Czech Republic from 2007 to 2024.

The size of the Business Data Visualization Tools market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

1. Overview This dataset is a collection of 10,000+ high quality images of supermarket & store display shelves that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use case The dataset could be used for various AI & Computer Vision models: Store Management, Stock Monitoring, Customer Experience, Sales Analysis, Cashierless Checkout,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email admin.bi@pixta.co.jp.

Global trade data of Display under 85176249, 85176249 global trade data, trade data of Display from 80+ Countries.

Statistics illustrates consumption, production, prices, and trade of Tubes; data/graphic display, colour, with a phosphor dot screen pitch smaller than 0.4mm in Micronesia from 2007 to 2024.

Presentation slides from the Interagency Data Team meeting. The Interagency Data Team is a community of data analysts, or agency liaisons, who convene regularly with representation from DC agencies of all persuasions. Participants engage in discussions regarding the team’s core mission and priorities for a better kind of data culture – collection, application, sharing, classification and governance to name a few. The team is coordinated by the Office of the Chief Technology Officer (OCTO), lead by the Chief Data Officer (CDO), and directly supports the District of Columbia's Data Policy.

18 Global import shipment records of Monitor For Display with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Scenario 🕶️ In 2019, the marketing team launched a campaign to attract millennial customers (born 1980-1996) with the goal of increasing revenue and enhancing the brand's appeal to a younger audience.
As the BI team, your task is to create a dashboard for users. 1. The Vice President of Sales wants to view the performance of the credit business. 2. The marketing team is interested in understanding customer segments and customer spending to measure Customer Lifetime Value (CLV) and Marketing Cost per Acquired Customer (MCAC).

⚠️Note: This is just a suggestion to guide the creation of the dashboard

Example in Tableau

Executive summary https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F508a2d2d89dabdfd368743f86c2a71e1%2Fexecutive%20overview.JPG?generation=1696110593484137&alt=media" alt=""> Customer behavior https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F1e4a1f62a25eab3c6707d002243894c7%2Fcustomer_behaviour.JPG?generation=1696110689732332&alt=media" alt="">

About this dataset

Context

The dataset tabulates the Lake View population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Lake View. The dataset can be utilized to understand the population distribution of Lake View by age. For example, using this dataset, we can identify the largest age group in Lake View.

Key observations

The largest age group in Lake View, IA was for the group of age 65-69 years with a population of 108 (11.45%), according to the 2021 American Community Survey. At the same time, the smallest age group in Lake View, IA was the 0-4 years with a population of 12 (1.27%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Lake View is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Lake View total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lake View Population by Age. You can refer the same here

Provides agency-wide totals for data pertaining to mechanical failures of transit vehicles based off of data reported to the National Transit Database for the 2022 and 2023 report years. This view is based off of the "2022 - 2023 NTD Annual Data - Breakdowns" dataset, which displays the same data at a lower level of aggregation (by mode). This view displays the data at a higher level (by agency).

Only Full Reporters report breakdowns.

NTD Data Tables organize and summarize data from the 2022 and 2023 National Transit Database in a manner that is more useful for quick reference and summary analysis. The parent dataset is based on the 2022 and 2023 Vehicle Maintenance database files.

In years 2015-2021, you can find this data in the "Breakdowns" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.

2044 Global import shipment records of Display Self with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.