Here are the files to download the example Visualization Using the 2014 American Community Survey Data. Users will need to download Tableau Reader (http://www.tableau.com/products/reader) to view the Dashboard, but it is Free to Download and allows users the interactivity that dashboards provide.

This file is used by the SampleQC tableau workbook to provide insights on which samples passed QC. It is a subset of the file that is generated by the RNASeq pipeline where all the genes are dropped out.

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="">

This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

This data set contains a list of Olympic medals won by USA athletes between 2000 and 2012. The data elements are Athlete name, Year, Age, Country, Gold, Silver, Bronze, Total medals. Source of this data set from http://www.tableau.com/public/community/sample-data-sets.

In order to practice writing SQL queries in a semi-realistic database, I discovered and imported Microsoft's AdventureWorks sample database into Microsoft SQL Server Express. The Adventure Works [fictious] company represents a bicycle manufacturer that sells bicycles and accessories to global markets. Queries were written for developing and testing a Tableau dashboard.

The dataset presented here represents a fraction of the entire manufacturing relational database. Tables within the dataset include product, purchasing, work order, and transaction data.

The full database sample can be found on Microsoft SQL Docs website: https://learn.microsoft.com/en-us/sql/samples/ and additionally on Github: https://github.com/microsoft/sql-server-samples

Data for replication package Paper Title: Drivers of firm-government engagement for technology ventures Authors: Lauren Lanahan; Iman Hemmatian; Amol M. Joshi; Evan E. Johnson Lead Author of Data Curation, Code, & Analysis: Lauren Lanahan (llanahan@uoregon.edu) Software: Stata 18; Tableau Computational Requirements: We utilized a powerful server (32-core processors, 384 GB memory, 32 TB storage) to construct the sample and run the analyses. We provide the code and data for all empirical assessments. And for additional transparency, we provide the log file for the set of empirical assessments (i.e., descriptive statistics and regression assessments). Replication package includes: [1] do file Do File_READ FIRST.do [3] dta files Descriptive Statistics Data File.dta (Table 4, Table 5; S1 Table; S3 Table) Regression Data File.dta (Table 6; S2 Table; S4 Table; S5 Table; S6 Table; Table 7; Table 8) Data for Tableau.dta (Figure 2) [1] log file Replication Log.smcl Comment: Code is organized in manner that reflects ordering of the empirical results presented in paper. Note, we report the raw data for Table 1 in the table itself.

A high-quality, clean dataset simulating global cosmetics and skincare product sales between January and August 2022. This dataset mirrors real-world transactional data, making it perfect for data analysis, Excel training, visualization projects, and machine learning prototypes.

📁 Dataset Overview

🧾 Sample Products

• Hydrating Face Serum
• Vitamin C Cream
• Aloe Vera Gel
• Charcoal Face Wash
• SPF 50 Sunscreen
• Niacinamide Toner
• Anti-Aging Serum
• Face Sheet Masks
• Hair Repair Oil
• Lip Balm Pack
• Body Butter Cream
• Salicylic Acid Cleanser

🌏 Countries Covered

• India
• USA
• UK
• Canada
• Australia
• New Zealand

📊 Quick Stats

• Total Rows: 374
• Date Range: Jan 1, 2022 – Aug 31, 2022
• Revenue Range: Varies from ~$100 to ~$20,000 per order
• Box Quantity Range: 10 – 500 boxes

🎯 Ideal For

• Excel Practice (VLOOKUP, IF, AVERAGEIFS, INDEX-MATCH, etc.)
• Pivot tables & data cleaning tasks
• Power BI / Tableau dashboards
• Sales trend forecasting
• Exploratory Data Analysis (EDA)
• Retail analytics & product demand modeling

📌 Suggested Projects & Questions

• Which salesperson generated the highest revenue overall?
• What’s the average amount per order in each country?
• Which product was most frequently sold?
• What month had the highest total boxes shipped?
• Create a dashboard comparing revenue across countries.

✅ Clean Data Guarantee

• ✅ No missing/null values
• ✅ No duplicates
• ✅ Realistic values
• ✅ Globally relatable product categories
• ✅ Ready for ML, BI, and teaching use cases

Cancer, the second-leading cause of mortality, kills 16% of people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack of exercise have been linked to cancer incidence and mortality. However, it is hard. Cancer and lifestyle correlation analysis and cancer incidence and mortality prediction in the next several years are used to guide people’s healthy lives and target medical financial resources. Two key research areas of this paper are Data preprocessing and sample expansion design Using experimental analysis and comparison, this study chooses the best cubic spline interpolation technology on the original data from 32 entry points to 420 entry points and converts annual data into monthly data to solve the problem of insufficient correlation analysis and prediction. Factor analysis is possible because data sources indicate changing factors. TSA-LSTM Two-stage attention design a popular tool with advanced visualization functions, Tableau, simplifies this paper’s study. Tableau’s testing findings indicate it cannot analyze and predict this paper’s time series data. LSTM is utilized by the TSA-LSTM optimization model. By commencing with input feature attention, this model attention technique guarantees that the model encoder converges to a subset of input sequence features during the prediction of output sequence features. As a result, the model’s natural learning trend and prediction quality are enhanced. The second step, time performance attention, maintains We can choose network features and improve forecasts based on real-time performance. Validating the data source with factor correlation analysis and trend prediction using the TSA-LSTM model Most cancers have overlapping risk factors, and excessive drinking, lack of exercise, and obesity can cause breast, colorectal, and colon cancer. A poor lifestyle directly promotes lung, laryngeal, and oral cancers, according to visual tests. Cancer incidence is expected to climb 18–21% between 2020 and 2025, according to 2021. Long-term projection accuracy is 98.96 percent, and smoking and obesity may be the main cancer causes.

Context

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking your knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid.

You can even take this a step further and try and build a Regression model to predict Sales or Profit.

Go crazy with the dataset, but also make sure to provide some business insights to improve.

Metadata

Row ID => Unique ID for each row. Order ID => Unique Order ID for each Customer. Order Date => Order Date of the product. Ship Date => Shipping Date of the Product. Ship Mode=> Shipping Mode specified by the Customer. Customer ID => Unique ID to identify each Customer. Customer Name => Name of the Customer. Segment => The segment where the Customer belongs. Country => Country of residence of the Customer. City => City of residence of of the Customer. State => State of residence of the Customer. Postal Code => Postal Code of every Customer. Region => Region where the Customer belong. Product ID => Unique ID of the Product. Category => Category of the product ordered. Sub-Category => Sub-Category of the product ordered. Product Name => Name of the Product Sales => Sales of the Product. Quantity => Quantity of the Product. Discount => Discount provided. Profit => Profit/Loss incurred.

Acknowledgements

I do not own this data. I merely found it from the Tableau website. All credits to the original authors/creators. For educational purposes only.

Introduction: The depth-focal points of earthquakes can determine the likelihood of how light or strong, in terms of magnitude, an earthquake is likely to be. This may be a factor in determining where or how critical-infrastructure buildings, such as hospitals or prisons, can be better built to withstand stronger earthquakes, should they lie on or near deeper-earthquake starting focal points. Objectives: The primary objective of this project is to see if there is a correlation between earthquake depth and intensity of earthquakes in both Northern and Southern California, using a sample data of earthquakes from the last 20 years. With the same data available, our secondary objective will be to compare the number and intensities of earthquakes for Northern and Southern California. Methods: Upon obtaining the dataset of earthquakes from the Incorporated Research Institutions for Seismology (IRIS), we will query earthquakes greater than 4.0, and within the last 20 years. IRIS provides a map visual of the area of interest for data to be downloaded. In this case, we will zoom into the western United States. OpenRefine (v. 3.5) will be used to filter only earthquakes in Northern California and Southern California, by applying a text facet to the data. Descriptive analysis will be displayed using Tableau to see if there are any correlations, as well as to make comparisons between Northern and Southern California. Results: As a result of plotting the data, we can see that there is a statistically slight positive relationship between earthquake depth and magnitude. The majority of the smaller earthquakes seem to suggest that they have a more-shallow focal point as compared to the larger earthquakes, which have a deeper focal point. Conclusions: Critical infrastructure, as well as general housing, should be avoided in areas where deeper earthquakes are likely to occur, as these most likely will result in stronger, more-intense earthquakes.

The Business Intelligence (BI) software market, incorporating location analytics, is experiencing robust growth, driven by the increasing need for data-driven decision-making across various sectors. The convergence of geospatial data with traditional BI tools allows businesses to gain deeper insights into customer behavior, optimize supply chains, improve operational efficiency, and gain a competitive edge. A projected Compound Annual Growth Rate (CAGR) of, for example, 15% (a reasonable estimate given the high growth potential of this niche market) from 2025 to 2033 suggests a significant expansion of this market. Key drivers include the rising adoption of cloud-based BI solutions, the proliferation of location-based data sources (e.g., GPS, mobile devices), and increasing government investments in geospatial technologies. Furthermore, advancements in machine learning and artificial intelligence are enhancing the analytical capabilities of these platforms, leading to more sophisticated predictive modeling and improved decision support. Market restraints include data security and privacy concerns, the complexity of integrating disparate data sources, and the need for specialized expertise in both BI and geospatial technologies. The competitive landscape is characterized by a mix of established players like SAP, IBM, Oracle, and Microsoft, alongside specialized providers such as Tableau and SAS. These companies are actively investing in research and development to enhance their offerings and cater to the growing demand. The market is segmented by deployment type (cloud, on-premise), industry (retail, healthcare, logistics), and geography. North America and Europe currently hold significant market shares, but rapidly growing economies in Asia-Pacific and other regions are expected to contribute significantly to future market growth. The forecast period (2025-2033) promises substantial opportunities for businesses to leverage location analytics within their BI strategies, ultimately leading to improved business outcomes and enhanced decision-making processes.

🇬🇧 United Kingdom English This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. Nationality data can be found here: https://data.london.gov.uk/dataset/nationality Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

Analysis of ‘🤰 Pregnancy, Birth & Abortion Rates (1973 - 2016)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pregnancy-birth-abortion-rates-in-the-united-stae on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Source: OSF | Downloaded on 29 October 2020

This data source is a subset of the original data source. The data has been split by State, Metric and Age Range. It has been limited to pregnancy rate, birth rate and abortion rate per 1,000 women. The original data contains many more measures.

The data was prepared with Tableau Prep.

Summary via OSF -

A data set of comprehensive historical statistics on the incidence of pregnancy, birth and abortion for people of all reproductive ages in the United States. National statistics cover the period from 1973 to 2016, the most recent year for which comparable data are available; state-level statistics are for selected years from 1988 to 2016. For a report describing key highlights from these data, as well as a methodology appendix describing our methods of estimation and data sources used, see https://guttmacher.org/report/pregnancies-births-abortions-in-united-states-1973-2016.

​

This dataset was created by Andy Kriebel and contains around 20000 samples along with Age Range, Events Per 1,000 Women, technical information and other features such as: - State - Year - and more.

How to use this dataset

• Analyze Metric in relation to Age Range
• Study the influence of Events Per 1,000 Women on State
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Snapshot img

The visual analytics market has the potential to grow by USD 4.39 billion during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 11.32%.

This visual analytics market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by end-user (BFSI, CPG and retail, healthcare, manufacturing, and others) and geography (North America, APAC, Europe, MEA, and South America). The visual analytics market report also offers information on several market vendors, including Altair Engineering Inc., Alteryx Inc., Arcadia Data Inc., Datameer Inc., International Business Machines Corp., Microsoft Corp., QlikTech international AB, SAP SE, SAS Institute Inc., and Tableau Software LLC among others.

What will the Visual Analytics Market Size be in 2021?

Browse TOC and LoE with selected illustrations and example pages of Visual Analytics Market

Get Your FREE Sample Now!

Visual Analytics Market: Key Drivers and Trends

The growing availability and complexity of data are notably driving the visual analytics market growth, although factors such as data privacy and security concerns may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the visual analytics industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

      The growing availability and complexity of data will fuel the growth of the visual analytics market size.
      The availability of a large volume of data and rapidly growing data complexity in organizations are the major drivers for the development of various intelligence-based data analysis techniques.
      Intelligent techniques involving technologies such as ML and AI can help companies retrieve the huge amount of complex data in a useful manner and use that data to enhance their services and business processes. This, in turn, is expected to drive the growth of the market for visual analytics.




      The increased dependency on Internet for critical operations will drive the visual analytics market growth during the forecast period.
      E-commerce vendors are posting advertisements on search engines and other websites to attract several customers. This will increase the demand for visual analytics to help e-commerce vendors track customers, analyze customer behavior, and ensure proper decision-making.
      With the rising popularity and use of e-commerce, the number of digital media advertisements by e-commerce vendors is expected to increase, which will drive the growth of the market during the forecast period.

This visual analytics market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Visual Analytics Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Altair Engineering Inc.
Alteryx Inc.
Arcadia Data Inc.
Datameer Inc.
International Business Machines Corp.
Microsoft Corp.
QlikTech international AB
SAP SE
SAS Institute Inc.
Tableau Software LLC

This statistical study of the visual analytics market encompasses successful business strategies deployed by the key vendors. The visual analytics market is fragmented and the vendors are deploying growth strategies such as providing customized solutions to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The visual analytics market forecast report offers in-depth insights into key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Visual Analytics Market?

For more insights on the market share of various regions Request for a FREE sample now!

35% of the market’s growth will originate from North America during the forecast period. The US is a key market for visual analytics in North America. Market growth in this region will be faster than the growth of the market in Europe, MEA, and South America.

This market research report entails detailed information on the competitive intelligence, marketing gaps, and regional opportunities in store for vendors, which will assist in creating efficient business plans.

What are the Revenue-generating End-user Segments

About our monitoring index solution: We have developed a cost-effective methodology to create indices of economic activity in any open facility across the US.

Our solution uses satellite imagery to monitor surface changes over time. Our advanced Machine Learning models detect and report relevant changes in the area of interest.

Our methodology can help you analyze a significant number of locations without requiring manual supervision. It employs clustering image models and can detect changes within the same cluster over time. This feature lets you monitor as many locations as you need to make informed and strategic decisions.

Main use cases: - Monitor business and economic activity of any target company worldwide. - Understand and predict seasonal patterns from your target companies. - Make better decisions faster and more precisely. - Identify inventory threats and illicit or abnormal behaviors in target locations. - Get alerts of significant changes in the monitored facilities for a fraction of the cost compared to other solutions. - Detect changes on structures and buildings (For example, you can easily detect if a manufacturing plant is expanding its infraestructure).

Industries that are using our indices: - Investment funds - Construction & Real Estate - Transport & and Logistics - Agriculture

Considerations: - This solution measures relative changes as a percentage index. For example, it can measure parking occupancy percentage but will not indicate the number of parked cars (Learn more: https://docs.predikdata.com/Parking_Occupancy_Sample.pdf) - Objects must be at least 3x3m to be visible and measured.

Deliverables: Our index reports adapt to your specific requirements. We can provide a live database, Tableau reporting, or alert notifications.

This repository includes the data of the study Rising greenhouse gas emissions embodied in the global bioeconomy supply chain published in Communications Earth & Environment. It includes two folders:

1) GHG_data_REX3: GHG data on LULUCF emissions and distinction of emission sources in REX3, which are stored in the following matrices as mat.files:

• d_CC_REX3_timeline.mat: 6 emission sources x 30807 country-sector combinations x 28 years (1995 to 2022)
• Q_LULUCF_REX3_timeline.mat: 30807 country-sector combinations x 28 years (1995 to 2022)
• Q_Y_LULUCF_REX3_timeline.mat: 189 countries x 28 years (1995 to 2022)

The labels of these matrices are found in the separate Labels folder:

• Labels_Countries_REX3.mat: 189 countries
• Labels_Emission_sources.mat: 6 emission sources
• Labels_Countrysector_combinations.mat: 30807 country-sector combinations
• Labels_years: 28 years from 1995 to 2022

These data can be combined with the REX3 database to calculate GHG emissions (including LULUCF) embodied in global supply chains for 189 countries and 163 sectors (1995 to 2022).

2) GHG_bioeconomy_code: MATLAB codes to calculate the results for the study "Rising greenhouse gas emissions embodied in the global bioeconomy supply chain" and its interactive data visualizer:

• Integrate_Blue_into_REX3.m: code to integrate the LULUCF data from the BLUE model, which are stored under the folder Files/LUC_Blue_Data_for_REX.csv and based on Hansis et al and Schwingshackl et al.
• Impact_coeff_emission_source.m: code to calculates the impact coefficients for the different emission sources, using the price vector from the folder Files/price_final_REX.mat as input.
• SCIM_calculations_6D.m: code to calculate the 6D impact array
• Compile_data_for_sankeys.m code to compile the data for the sankeys
• Compile_data_for_tableau_6D.m code to compile data for tableau to create the interactive data visualizer.

The codes rely on the REX3 database.

Download & conversion from .mat to .zarr files for efficient data handling:
A package for downloading, extracting, and converting REX3 data from MATLAB (.mat) to .zarr format has been provided by Yanfei Shan here: https://github.com/FayeShan/REX3_handler. Once the files are converted to .zarr format, the data can be explored and processed flexibly. For example, you can use pandas to convert the data into CSV, or export it as Parquet, which is more efficient for handling large datasets. Please note note that this package is still under development and that more functions for MRIO analysis will be added in the future.

Description of the REX3 database

This repository provides the Resolved EXIOBASE database version 3 (REX3) of the study "Biodiversity impacts of recent land-use change driven by increases in agri-food imports” published in Nature Sustainability. Also the REX3 database was used in Chapter 3 of the Global Resource Outlook 2024 from the UNEP International Resource Panel (IRP), including a data visualizer that allows for downscaling.

In REX3, Exiobase version 3.8 was merged with Eora26, production data from FAOSTAT, and bilateral trade data from the BACI database to create a highly-resolved MRIO database with comprehensive regionalized environmental impact assessment following the UNEP-SETAC guidelines and integrating land use data from the LUH2 database. REX3 distinguishes 189 countries, 163 sectors, time series from 1995 to 2022, and several environmental and socioeconomic extensions. The environmental impact assessment includes climate impacts, PM health impacts, water stress, and biodiversity impact from land occupation, land use change, and eutrophication.

The folders "REX3_Year" provide the database for each year. Each folder contains the following files (*.mat-files):
T_REX: the transaction matrix
Y_REX: the final demand matrix
Q_REX and Q_Y_REX: the satellite matrix of the economy and the final demand

The folder "REX3_Labels" provides the labels of the matrices, countries, sectors and extensions.

*The database is also available as textfiles --> contact livia.cabernard@tum.de

While Exiobase version 3.8.2 was used for the study "Biodiversity impacts of recent land-use change driven by increases in agri-food imports” and the Global Resource Outlook 2024, the REX3 database shared in this repository is based on Exiobase version 3.8, as this is the earliest exiobase version that can be still shared via a Creative Commons Attribution 4.0 International License. However, the matlab code attached to this repository allows to compile the REX3 database with earlier exiobase versions as well (e.g., version 3.8.2), as described in the section below.

Codes to compile REX3 and reproduce the results of the study “Biodiversity impacts of recent land-use change driven by increases in agri-food imports”

The folder "matlab code to compile REX3" provides the code to compile the REX3 database. This can also be done by using an earlier exiobase version (e.g., version 3.8.2). For this purpose, the data from EXIOBASE3 need to be saved into the subfolder Files/Exiobase/…, while the data from Eora26 need to be saved into the subfolder Files/Eora26/bp/…

The folder "R code for regionalized BD impact assessment based on LUH2 data and maps (Figure 1)" contains the R code to weight the land use data from the LUH2 dataset with the species loss factors from UNEP-SETAC and to create the maps shown in Figure 1 of the paper. For this purpose, the data from the LUH2 dataset (transitions.nc) need to be stored in the subfolder "LUH2 data".

The folder "matlab code to calculate MRIO results (Figure 2-5)" contains the matlab code to calculate the MRIO Results for Figure 2-5 of the study.

The folder "R code to illustrate sankeys – Figure 3–5, S10" contains the R code to visualize the sankeys.

Data visualizer to downscale the results of the IRP Global Resource Outlook 2024 based on REX3:

A data visualizer that is based on REX3 and allows to downscale the results of the IRP Global Resource Outlook 2024 on a country level can be found here.

Earlier versions of REX:

An earlier version of this database (REX1) with time series from 1995–2015 is described in Cabernard & Pfister 2021.

An earlier version including GTAP and mining-related biodiversity impacts for the year 2014 (REX2) is described in Cabernard & Pfister 2022.

Download & conversion from .mat to .zarr files for efficient data handling:
A package for downloading, extracting, and converting REX3 data from MATLAB (.mat) to .zarr format has been provided by Yanfei Shan here: https://github.com/FayeShan/REX3_handler. Once the files are converted to .zarr format, the data can be explored and processed flexibly. For example, you can use pandas to convert the data into CSV, or export it as Parquet, which is more efficient for handling large datasets. Please note note that this package is still under development and that more functions for MRIO analysis will be added in the future.

Health and Wellbeing of 15-year-olds in England - results from What About Youth Survey. Data has been collected on general health, diet, use of free time, physical activity, smoking, drinking, emotional wellbeing, drugs and bullying. What About YOUth? 2014 (WAY 2014) is a newly-established survey designed to collect robust local authority (LA) level data on a range of health behaviours amongst 15 year-olds.WAY 2014 is the first survey to be conducted of its kind and it is hoped that the survey will be repeated in order to form a time series of comparable data on a range of indicators for 15 year-olds across England. Questionnaire packs were sent to 295,245 young people in England and 120,115 of these responded with usable data, giving an unadjusted response rate of 40 per cent (based on the issued sample) and an adjusted response rate of 41 per cent.Participants for WAY 2014 were sampled from the Department for Education’s National Pupil Database (NPD). The NPD is a near full population database (with the exception that independent schools are not included). See this data visualised in this Tableau report. More Information from The Health and Social Care Information Centre (HSCIC) website and data downloads available from PHE Fingertips.

This dataset is associated with the study of the robust drivers of urban land surface temperature (ULST) dynamics across diverse landscape characters based on an augmented systematic literature review of 101 peer-reviewed articles. Some generic landscape drivers such as climate, parent material, topography, presence/proximity to urban thermal sinks were analysed as well as their impact on ULST. The study offers a novel perspective in understanding ULST dynamics, promoting interdisciplinary, participatory and democratic research approaches necessary for citizen and open science applications. The dataset includes: 1) Atlas.ti Files - A collection of 1,508 codes generated using Atlas.ti, relevant extracts in .csv and .xlsx formats, and coded articles; 2) Python Scripts - for deduplication of literature data, and dummy regression analysis of the relationship between ULST and landscape character elements; 3) Tableau Resources - Dataset used to develop the interactive dashboards developed to summarize and communicate the findings of our research; and 4) Supplementary Files - Comprising of a composite of all initial articles considered in the study, the deduplicated article list, results of the final screened/reviewed articles and other data that were utilized in the study, e.g. coded data from Atlas.ti, data used in the dummy regression analysis, amongst others.