https://dataviz.shef.ac.ukhttps://github.com/researchdata-sheffield/dataviz-hub2/blob/development/src/images/author/dataviz.pnghttps://dataviz.shef.ac.ukGatsbyJSMon, 11 May 2026 06:23:28 GMT1440https://dataviz.shef.ac.uk/blog/18/02/2022/Glue-Viz-for-Heterogeneous-Datahttps://dataviz.shef.ac.uk/blog/18/02/2022/Glue-Viz-for-Heterogeneous-DataFri, 18 Feb 2022 00:00:00 GMTThis is an event blog for the GlueViz workshop hosted by the N8 Centre of Excellence in Computationally Intensive Research (N8 CIR). You can access the training materials from here . Heterogeneous data Heterogeneous data refers to data samples coming from a number of distinct sources which could well be independent or very different to each other. However, modern research often involves exploration and analysis of interrelated heterogeneous data which cannot be done simply by programming scripts. For example, how do you interpret the daily recorded rainfall in the context of hourly recorded…https://dataviz.shef.ac.uk/visualisation/31/01/2022/ORDA-datasets-FAIRness-assessmenthttps://dataviz.shef.ac.uk/visualisation/31/01/2022/ORDA-datasets-FAIRness-assessmentMon, 31 Jan 2022 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/27/01/2022/Why-Gardenhttps://dataviz.shef.ac.uk/visualisation/27/01/2022/Why-GardenThu, 27 Jan 2022 00:00:00 GMThttps://dataviz.shef.ac.uk/blog/08/11/2021/New-Visualisation-Pagehttps://dataviz.shef.ac.uk/blog/08/11/2021/New-Visualisation-PageMon, 08 Nov 2021 00:00:00 GMTData visualisation is a great way to create impact, in support of the University’s vision, and supports the promotion of research. Looking back over the past year, data visualisation has been an effective tool for understanding worldwide trending topics such as climate change, CoVid-19, and elections. Attractive and understandable data visualisations increase the visibility of the University's research and appeal to different stakeholders. In addition, by sharing any source code, data, and other resources involved in making the visualisation, others can benefit from this openness, allowing…https://dataviz.shef.ac.uk/visualisation/20/10/2021/Marine-Fishes-Near-Extinctionhttps://dataviz.shef.ac.uk/visualisation/20/10/2021/Marine-Fishes-Near-ExtinctionWed, 20 Oct 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/blog/05/10/2021/Paraviewhttps://dataviz.shef.ac.uk/blog/05/10/2021/ParaviewTue, 05 Oct 2021 00:00:00 GMTThis post is based on Suzana Silva's presentation at Research IT Forum: Image processing, techniques and technology. What is ParaView? ParaView is free software that can visualise both observational and numerical data. It has been used in many different research fields, as illustrated in Figure 1, where we see ParaView applied in cosmology and Figure 2, where we see it applied to medical research. ParaView can readily render the images based on the research data, facilitating visualisation of the data from different perspectives by rotating and post-processing the original data. Thereby, this…https://dataviz.shef.ac.uk/visualisation/22/09/2021/Improved-Rice-Cooking-Approachhttps://dataviz.shef.ac.uk/visualisation/22/09/2021/Improved-Rice-Cooking-ApproachWed, 22 Sep 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/06/09/2021/Modeling-the-Effects-of-Alcohol-Pricing-Policieshttps://dataviz.shef.ac.uk/visualisation/06/09/2021/Modeling-the-Effects-of-Alcohol-Pricing-PoliciesMon, 06 Sep 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/03/09/2021/LearningPath-Statistical-Modelling-3https://dataviz.shef.ac.uk/docs/03/09/2021/LearningPath-Statistical-Modelling-3Fri, 03 Sep 2021 00:00:00 GMTIntroduction Much of modern research is interested in the relationship between variables. A statistical model is a formal representation of this relationship that lets us test a relationship or predict the value of an unknown based on the value of a known quantity. The purpose for which the model is created and the data used will significantly influence how the model is interpreted. Experimental scientists may assume a causal relationship between the variables, for example, plant growth rate based on the application of fertilises. Others may be interested in the relationship between variables…https://dataviz.shef.ac.uk/visualisation/23/08/2021/Leading-risk-factors-for-DALYshttps://dataviz.shef.ac.uk/visualisation/23/08/2021/Leading-risk-factors-for-DALYsMon, 23 Aug 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/04/08/2021/Which-Statistical-Test-To-Use-For-Two-Variableshttps://dataviz.shef.ac.uk/visualisation/04/08/2021/Which-Statistical-Test-To-Use-For-Two-VariablesWed, 04 Aug 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/22/07/2021/Millions-of-UK-residents-struggle-to-access-foodhttps://dataviz.shef.ac.uk/visualisation/22/07/2021/Millions-of-UK-residents-struggle-to-access-foodThu, 22 Jul 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/21/07/2021/Contribute-visualisationhttps://dataviz.shef.ac.uk/docs/21/07/2021/Contribute-visualisationWed, 21 Jul 2021 00:00:00 GMTNote Thank you for considering contributing visualisations to Dataviz.Shef! We are committed to open research so there are a couple of things to bear in mind before you proceed: Are you willing to make your code open sourced for the visualisation? Are other people able to access the data? Are you willing to include some simple guidance on how to reproduce the visualisation? Are your resources licensed? If you could answer yes to most of the questions above, please go to the next section. Setup The setup process is identical to the one in contributing blog post . You will need to install…https://dataviz.shef.ac.uk/visualisation/12/07/2021/Low-vaccination-rate-in-Londonhttps://dataviz.shef.ac.uk/visualisation/12/07/2021/Low-vaccination-rate-in-LondonMon, 12 Jul 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/24/06/2021/Increased-snack-intake-in-UK-and-Australiahttps://dataviz.shef.ac.uk/visualisation/24/06/2021/Increased-snack-intake-in-UK-and-AustraliaThu, 24 Jun 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/22/06/2021/Covid-Plothttps://dataviz.shef.ac.uk/visualisation/22/06/2021/Covid-PlotTue, 22 Jun 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/17/06/2021/Which-Politicians-Receive-Abuse-During-2019-Election-Campaignhttps://dataviz.shef.ac.uk/visualisation/17/06/2021/Which-Politicians-Receive-Abuse-During-2019-Election-CampaignThu, 17 Jun 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/blog/15/06/2021/Visualising-Cultural-Heritage-Datahttps://dataviz.shef.ac.uk/blog/15/06/2021/Visualising-Cultural-Heritage-DataTue, 15 Jun 2021 00:00:00 GMTCredit: This post is based on videos and notes made available on N8 CIR for the workshop - Data Visualisation for Cultural Heritage Collections, presented by Dr Olivia Vane , a Research Software Engineer at The British Library. I would also like to thank Rosie for reviewed this blog post. Resources for the workshop This two part workshop gave a good overview of the history and key concepts in data visualisation, as well as practical steps in making visualisations from datasets. The videos provide a unique and interesting opportunity to learn more about how data visualisations play an…https://dataviz.shef.ac.uk/visualisation/08/06/2021/Mortality-Rate-as-Maphttps://dataviz.shef.ac.uk/visualisation/08/06/2021/Mortality-Rate-as-MapTue, 08 Jun 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/04/06/2021/Workplace-Conflicthttps://dataviz.shef.ac.uk/visualisation/04/06/2021/Workplace-ConflictFri, 04 Jun 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/visualisation/02/06/2021/Green-Industrial-Revolutionhttps://dataviz.shef.ac.uk/visualisation/02/06/2021/Green-Industrial-RevolutionWed, 02 Jun 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modelinghttps://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-ModelingThu, 18 Mar 2021 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-1https://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-1Thu, 18 Mar 2021 00:00:00 GMTIntroduction A random variable is a numerical representation (can be either a set of values or a function mapped to a continuous range) of an experiment's outcomes. A probability distribution is a mathematical function that gives the probabilities for different possible outcomes of the experiment. Consider an experiment of drawing a ball three times with replacement from an urn which consists of seven red balls and three blue balls. If a random variable X represents the total number of times we draw a blue ball, then the set of possible values that X can be are: { }. Then the…https://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2https://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2Thu, 18 Mar 2021 00:00:00 GMTIntroduction Sampling is the process of selecting a number of individuals to study from the entire population. In practice, it is often impractical if not infeasible to get hold of many entire populations due to many reasons such as the scale, time, and cost. A typical situation when it is impossible is when the population is theoretically infinite such as all the 5 year old cats in the world throughout time. However, even a finite population can take too long or be too expensive such as every household in Sheffield on a specific date. One of the most recent examples is the national census…https://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2-more-random-samplinghttps://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2-more-random-samplingThu, 18 Mar 2021 00:00:00 GMTNote: This page is an option chapter of Statistical Modeling Part 2 - Sampling . Random Sampling Random sampling (or probability sampling) refers to nonsubjective sampling methods that apply some mechanism to ensure randomness. Systematic The systematic sampling method starts with a random starting point and selects individuals from the population with a periodic interval. For example, we start from the 2nd individual and choose every 10th individual (12th, 22nd, 32nd, ...) that are encountered until the end of the population or up to the target sample size. The interval is usually…https://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2-non-random-samplinghttps://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2-non-random-samplingThu, 18 Mar 2021 00:00:00 GMTNote: This page is an option chapter of Statistical Modeling Part 2 - Sampling . Non-random Sampling Non-random sampling (or non-probability sampling) refers to subjective sampling methods in which researchers draw samples according to his/her own convenience or subjective judgment. It does not strictly follow the principle of random sampling to draw samples so it cannot determine the sampling error, and cannot correctly explain to what extent the statistical value of the sample is suitable for the population. Convenience The convenience sampling method refers to the way that researchers…https://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2-optionalhttps://dataviz.shef.ac.uk/docs/18/03/2021/LearningPath-Statistical-Modeling-2-optionalThu, 18 Mar 2021 00:00:00 GMTNote: This page is an option chapter of Statistical Modeling Part 2 - Sampling . Sampling from distributions If we have samples from a population then we can use these samples to estimate the distribution and parameters. Why do we need sampling from distributions if the distribution is already known or partially known? There are several applications: approximate integrals or value/parameter of interest estimate expectations simulation or demonstration As in many examples we have seen in Part 1, we usually plug-in the parameters in the distribution then find the corresponding value to…https://dataviz.shef.ac.uk/blog/16/03/2021/Good-Practice-Guide/Indexhttps://dataviz.shef.ac.uk/blog/16/03/2021/Good-Practice-Guide/IndexTue, 16 Mar 2021 00:00:00 GMTSome good practice guidelines There is a lot of readily available advice to help us improve our plots and avoid common pitfalls. Seminal work from Edward Tufte and John Tukey are excellent places to look for guidance. As well as a plethora of modern examples including; from Data to Viz , Cynthia Brewer's work on colour, Bang Wong's points of view series for Nature Methods and of course, the work of Jeffery Heer . With so much excellent advice out there, what I hope to do with this blog is synthesise some of the more common issues that may face you when creating data visualisations. I…https://dataviz.shef.ac.uk/docs/01/03/2021/Hosting-Shiny-Appshttps://dataviz.shef.ac.uk/docs/01/03/2021/Hosting-Shiny-AppsMon, 01 Mar 2021 00:00:00 GMTPublish your apps We recommend registering for a personal ShinyApps.io account for hosting your Shiny apps. This allows you to host up to five public apps and allows for a certain number of execution hours per month. If you want to create more than five apps, need your app(s) to be password-protected or think you have or will exceed your execution hours quota then please get in touch via helpdesk@sheffield.ac.uk. Data governance You should not host Shiny apps on ShinyApps.io that rely on / include sensitive data . If you want to interactively explore sensitive data then contact…https://dataviz.shef.ac.uk/blog/18/02/2021/Useful-Resources-for-Rhttps://dataviz.shef.ac.uk/blog/18/02/2021/Useful-Resources-for-RThu, 18 Feb 2021 00:00:00 GMTIntroduction Welcome to the fourth blog post of the series Exploring packages in R . In this blog post you will see a summary of resources that you might find useful when using R. (last updated on 18th Feb 2021). I'm looking for ... Tutorials R Intro Learning R (video) R for Data Science: Lunchbreak Lessons (video) Learning the R Tidyverse (ideo) Blog / Articles RStudio FAQ R bloggers RStudio Blog Cheat sheets This RStudio cheat sheets website provided by RStudio contains cheat sheets for popular packages such as ggplot2 , readr , and Shiny , as well as cheat sheets (e.g…https://dataviz.shef.ac.uk/blog/05/02/2021/Shiny-Templatehttps://dataviz.shef.ac.uk/blog/05/02/2021/Shiny-TemplateFri, 05 Feb 2021 00:00:00 GMTWith the Shiny package you can build interactive web applications using R script. This tutorial will give you quick-start guide to help you begin to explore the versatile package and create your own applications as well as present some ready to use templates to help you to build your first Shiny app. With the Shiny package installed you can start to develop your app. The basic components of a shiny app are ui commands and server commands. The ui commands make up the user facing part of the app, this is where users can declare inputs; common inputs are listed in this helpful widget gallery…https://dataviz.shef.ac.uk/blog/27/01/2021/Interactive-Visualisations-In-Rhttps://dataviz.shef.ac.uk/blog/27/01/2021/Interactive-Visualisations-In-RWed, 27 Jan 2021 00:00:00 GMTIntroduction Welcome to the third blog post of the series Exploring packages in R . In this blog post we will explore some of the most popular packages in R for Interactive Visualisations and some of which you might have already noticed. Same as in the previous blog post , I will be using the Hadfield Green Roof 5-year dataset and assume you already have some experience with R. You can find all the source code in this Github repository . If you have any suggestions or want me to include any particular package feel free to send me an email ! In the first two sections we will explore…https://dataviz.shef.ac.uk/blog/25/01/2021/Morphologicahttps://dataviz.shef.ac.uk/blog/25/01/2021/MorphologicaMon, 25 Jan 2021 00:00:00 GMTIn this blog post I'm going to talk about data visualisation - making graphs - within c++ programs. I'll describe why you might want to do this and I'll try to justify why I've spent a sizeable part of my time over the last couple of years developing graphing code in c++, rather than using Python or R like everyone else! The code I'll discuss is available at https://github.com/ABRG-Models/morphologica . Most of the academic blog posts and videos about data visualisation that I see refer to scripted languages like Python, R or MATLAB. These languages have the ability to render high quality…https://dataviz.shef.ac.uk/blog/20/01/2021/Static-Visualisations-In-Rhttps://dataviz.shef.ac.uk/blog/20/01/2021/Static-Visualisations-In-RWed, 20 Jan 2021 00:00:00 GMTIntroduction This is the second blog post of the series Exploring packages in R . In this blog post we will explore some of the most popular packages in R for Static Visualisations . Same as in the previous blog post , I will be using the Hadfield Green Roof 5-year dataset and assume you already have some experience with R. You can find all the source code in this Github repository . If you have any suggestions or want me to include any particular package feel free to send me an email ! In the next section we will explore ggplot2 - most popular package (according to download…https://dataviz.shef.ac.uk/blog/15/01/2021/Data-Processing-In-Rhttps://dataviz.shef.ac.uk/blog/15/01/2021/Data-Processing-In-RFri, 15 Jan 2021 00:00:00 GMTSome Introduction A major advantage of using R is that it is highly extensible, with a broad variety of packages designed for specific purposes available on the internet. An R package is a collection that puts together reusable functions, documentation describing these functions, and sometimes examples of datasets you can use out of the box. R itself includes a set of packages by default (typically called base packages), but there are many more fantastic packages online for you to investigate. In this blog post we will explore some of the most popular packages in R for data processing when…https://dataviz.shef.ac.uk/blog/08/10/2020/moving-from-excel-to-rhttps://dataviz.shef.ac.uk/blog/08/10/2020/moving-from-excel-to-rThu, 08 Oct 2020 00:00:00 GMTThere are a lot of things to learn when you make a transition from Excel to R, especially if you haven't touched programming languages before, everything can be overwhelming and frustrating. Hopefully this article will help you make a smooth transition by helping you install and set up relevant software, introduce some fundamental concepts of R, and give some examples R codes for most commonly used functions in Excel. If you want a more structured and detailed introduction to R, see following resources: If you have any suggestions, please leave a comment below. Installation R is a popular…https://dataviz.shef.ac.uk/blog/30/09/2020/making-the-best-data-visualisations-in-excelhttps://dataviz.shef.ac.uk/blog/30/09/2020/making-the-best-data-visualisations-in-excelWed, 30 Sep 2020 00:00:00 GMTAbout Excel Microsoft Excel is a well-known spreadsheet developed by Microsoft for platforms like Windows, macOS, and various mobile operating systems. Excel has been the industry standards for spreadsheet since 1993 and commonly used for calculations, creating graphs and charts, and pivoting tables. Microsoft built Visual Basic for Application (also known as VBA, derived from the programming language Visual Basic) into Excel that allows you to write scripts that automates repeated tasks, this script often called a macro - a series of instructions. Excel is easy to use with graphics…https://dataviz.shef.ac.uk/blog/09/09/2020/data-visualizations-social-rolehttps://dataviz.shef.ac.uk/blog/09/09/2020/data-visualizations-social-roleWed, 09 Sep 2020 00:00:00 GMTI have been researching the social role of data visualizations since 2013. I was working on a research project exploring how resource-poor public sector organisations were engaging with social media data, when I was struck by the visceral ways in which collaborators responded to visual representations of said data. Without taking the time to make sense of the data visualizations included in a report that the research team produced, one collaborator told us that he loved them, and he wanted more. This set me off on a path researching how people engage with data visualizations, the role that…https://dataviz.shef.ac.uk/blog/05/09/2020/challenges-in-visualising-datahttps://dataviz.shef.ac.uk/blog/05/09/2020/challenges-in-visualising-dataSat, 05 Sep 2020 00:00:00 GMTAt dataviz.shef.ac.uk we generally focus on practical matters such as how to create plots using Python and best practice for choosing accessible colours for visualisation . But as researchers and practitioners we also need to be conscious of the wider social impact which can arise from visualising data and consider how to do so responsibly. This post was inspired by two recent Open Access publications; firstly, Data Visualisation in Society (2020, Amsterdam University Press) edited by Sheffield’s Professor Helen Kennedy and Professor Martin Engebretsen from the University of…https://dataviz.shef.ac.uk/blog/26/08/2020/D3js-for-data-visualisationhttps://dataviz.shef.ac.uk/blog/26/08/2020/D3js-for-data-visualisationWed, 26 Aug 2020 00:00:00 GMTWhat is D3.js D3 stands for Data-Driven Document and D3.js is known as an open-source Javascript library that is capable of producing web-based interactive data visualisations in web browsers using SVG , HTML and CSS . It is powerful and customisable yet has a steep learning curve . If you are interested in online data visualisation or you have used many data visulisation libraries and want to DIY your own chart then give it a go! Mike Bostock - the main author of D3.js also created an online platform called ObservableHQ where you can host your javascript notebooks (similar to…https://dataviz.shef.ac.uk/blog/18/08/2020/GMhttps://dataviz.shef.ac.uk/blog/18/08/2020/GMTue, 18 Aug 2020 00:00:00 GMTIntroduction The genetic modification of crops could alleviate some of the demands placed on modern agriculture by a burgeoning world population. However, public acceptance of genetically modified organisms is low. A team of researchers from the University of Sheffield and Sheffield Hallam University (Mallinson et al. , 2018) use a nationally representative sample of British adults to examine the public attitude towards GM crops. They go beyond traditional risk communication to investigate the socio-economic and demographic antecedents of opinion. You can read the published paper here…https://dataviz.shef.ac.uk/blog/16/07/2020/python-visualisation-templateshttps://dataviz.shef.ac.uk/blog/16/07/2020/python-visualisation-templatesThu, 16 Jul 2020 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/05/07/2020/LearningPath-Workflowhttps://dataviz.shef.ac.uk/docs/05/07/2020/LearningPath-WorkflowSun, 05 Jul 2020 00:00:00 GMT{" "} {" "} {" "}https://dataviz.shef.ac.uk/docs/04/07/2020/LearningPath-Labhttps://dataviz.shef.ac.uk/docs/04/07/2020/LearningPath-LabSat, 04 Jul 2020 00:00:00 GMThttps://dataviz.shef.ac.uk/blog/03/07/2020/Deploy-Your-Dash-Apphttps://dataviz.shef.ac.uk/blog/03/07/2020/Deploy-Your-Dash-AppFri, 03 Jul 2020 00:00:00 GMTIt is exciting to see your beautiful dash app running locally, but it would be even more interesting if others can interact with it over the web too! 1. Introduction This documentation assumes some prior knowledge of Python and HTML/CSS. A minimum of being able to install Python, packages, and be able to execute code should get you a example visualisation. If you need assistance with Python, python.org provides documentation and tutorials. In this documentation you will see the app that was built in Dash tutorial deployed onto an online cloud platform - Heroku , step by step…https://dataviz.shef.ac.uk/docs/03/07/2020/LearningPath-Introductionhttps://dataviz.shef.ac.uk/docs/03/07/2020/LearningPath-IntroductionFri, 03 Jul 2020 00:00:00 GMThttps://dataviz.shef.ac.uk/blog/24/06/2020/host-jupyter-notebookhttps://dataviz.shef.ac.uk/blog/24/06/2020/host-jupyter-notebookWed, 24 Jun 2020 00:00:00 GMT"As a researcher I want to host Jupyter notebooks on the web for free so that people can access them over the internet." Introduction The Jupyter Notebook has become a very popluar web application for sharing and creating documents that contain live code, equations, visualisations and narrative text. This article put together a number of user-friendly pathways for running live, interactive Jupyter notebooks on the web, and documentation on how to use it. Binder Binder ( mybinder.org ) is a free and transparent public service offers an easy place to share computing environments to everyone…https://dataviz.shef.ac.uk/blog/16/06/2020/Jupyter-Widgetshttps://dataviz.shef.ac.uk/blog/16/06/2020/Jupyter-WidgetsTue, 16 Jun 2020 00:00:00 GMT"Widgets are eventful python objects that have a representation in the browser, often as a control like a slider, textbox, etc. You can use widgets to build interactive GUIs for your notebooks. You can also use widgets to synchronize stateful and stateless information between Python and JavaScript." - Jupyter widgets 1. Prerequsites This documentation follows from 'Plots with Python' and assumes some prior knowledge of Python . If you need assistance with Python, python.org provides documentation and tutorials. While this post introduces some basic elements of ipywidgets , you…https://dataviz.shef.ac.uk/blog/12/06/2020/dash-tutorial-2https://dataviz.shef.ac.uk/blog/12/06/2020/dash-tutorial-2Fri, 12 Jun 2020 00:00:00 GMT'Dash is a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.' 1. Prerequsites This documentation assumes some prior knowledge of Python and HTML/CSS. A minimum of being able to install Python, packages, and be able to execute code should get you a example visualisation. If you need assistance with Python, python.org provides documentation and tutorials. To follow this tutorial, it is best to use JupyterLab (2.0 or above). If you have time, read Dash tutorial and Plot with Python (you'll find some codes are coming from this post…https://dataviz.shef.ac.uk/blog/11/06/2020/simple-data-visualisations-have-become-key-to-communicating-about-the-COVID-19-pandemichttps://dataviz.shef.ac.uk/blog/11/06/2020/simple-data-visualisations-have-become-key-to-communicating-about-the-COVID-19-pandemicThu, 11 Jun 2020 00:00:00 GMTIf you had mentioned 'flattening the curve' in 2019, chances are you would have been met with a blank stare. However, almost halfway through 2020, the language of data visualisation has become commonplace, and data visualisations are widely used to communicate about the pandemic to the public. However, as Helen Kennedy observes, their power to influence the public is still little understood. Credit: This post by Helen Kennedy was originally published on the LSE Impact Blog and is reproduced with their kind permission. There have never been so many line charts, bar charts and choropleth…https://dataviz.shef.ac.uk/blog/04/06/2020/dash-tutorialhttps://dataviz.shef.ac.uk/blog/04/06/2020/dash-tutorialThu, 04 Jun 2020 00:00:00 GMTDash is a user interface library for creating analytical web applications. Those who use Python for data analysis, data exploration, visualization, modelling, instrument control, and reporting will find immediate use for Dash . 1. Prerequisites This documentation assumes some prior knowledge of Python . A minimum of being able to install Python, relevent packages, and execute code should get you an example visualisation. If you need assistance with Python, python.org provides documentation and tutorials. The code in the snippets below is able to be copied verbatim into a file to create…https://dataviz.shef.ac.uk/blog/01/06/2020/visualising-high-risk-areas-for-covid-19-mortalityhttps://dataviz.shef.ac.uk/blog/01/06/2020/visualising-high-risk-areas-for-covid-19-mortalityMon, 01 Jun 2020 00:00:00 GMTCredit: This post was originally published by Neil Dymond-Green on the UK Data Service Impact and Innovation Lab . By mid-March, emerging data from Northern Italy clearly showed that COVID-19 fatality rates were substantially higher in older age groups, particularly for men. Demographers Ilya Kashnitsky and José Aburto combined this data with data from EUROSTAT on the age-sex distribution of the population across European regions and published a fascinating pre-print . This displayed the potential risk that each area faced from a large-scale COVID-19 outbreak. Areas with large…https://dataviz.shef.ac.uk/blog/20/05/2020/Non-Numerichttps://dataviz.shef.ac.uk/blog/20/05/2020/Non-NumericWed, 20 May 2020 00:00:00 GMTOften when we think of data visualisations, we think of familiar plots like scatter plots, line graphs and bar charts; we think of numbers. This can lead to the underutilisation of visualisation techniques in non-numerical research. This is unfortunate as the benefits of visualisation are numerous and by no means exclusive to numerical data. When we visualise any set of data, many of their characteristics, such as patterns, clusters and anomalies, can all become far more prominent when compared with looking at the raw data. In this respect, it is often more effective to tell a story with a…https://dataviz.shef.ac.uk/blog/07/05/2020/dataviz-stats-2https://dataviz.shef.ac.uk/blog/07/05/2020/dataviz-stats-2Thu, 07 May 2020 00:00:00 GMTObjectives The last article introduced some basic descriptive statistics such as measures of central tendency and measures of variation. In this article, we will apply some measures to get a better understanding of selected datasets. Datasets we're using are Road Safety Data for accidents between 2016-2018, it can be download on data.gov.uk . You'll probably want to look at Variable lookup data guide on the website as well since some of the variables are coded and represented by numbers. Setup One of quickest way to use python and packages is to install Anaconda . Then go to…https://dataviz.shef.ac.uk/blog/06/05/2020/Colour-Schemeshttps://dataviz.shef.ac.uk/blog/06/05/2020/Colour-SchemesWed, 06 May 2020 00:00:00 GMThttps://dataviz.shef.ac.uk/blog/02/05/2020/dataviz-stats-1https://dataviz.shef.ac.uk/blog/02/05/2020/dataviz-stats-1Sat, 02 May 2020 00:00:00 GMTDescriptive statistics Statistics is the science of the collection, analysis, interpretation and presentation of data. Statistics can be applied to various areas such as education, biology, engineering, chemistry, psychology, sports etc. Statistics is mainly divided into descriptive statistics and inferential statistics (statistical inference). Given a set of data, the usage of descriptive statistics is to summarise and describe the data. For example, Use specific numbers or charts to reflect the concentration and dispersion of data. The average score, the highest score, the…https://dataviz.shef.ac.uk/blog/06/04/2020/chart-choicehttps://dataviz.shef.ac.uk/blog/06/04/2020/chart-choiceMon, 06 Apr 2020 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/22/03/2020/datavizhub-guidehttps://dataviz.shef.ac.uk/docs/22/03/2020/datavizhub-guideSun, 22 Mar 2020 00:00:00 GMThttps://dataviz.shef.ac.uk/docs/22/03/2020/contribute-blog-posthttps://dataviz.shef.ac.uk/docs/22/03/2020/contribute-blog-postSun, 22 Mar 2020 00:00:00 GMTIntroduction (UPDATED on 21 Apr 2021) This is a template for blog posts. This website is built from Gatsby framework and the way of styling is slightly different from HTML/CSS. You can write posts use markdown or/and HTML/CSS. A copy of this blog file is available here . This article will guide you to setup a development environment, relevant steps for produce a blog post, and introduce you to useful syntax for styling the blog post and adding resources such as images, videos, and codes. You can also use Google Docs to write a post then share the link with us, we will take care of…https://dataviz.shef.ac.uk/blog/28/02/2020/Urban-Observatories-hackathonhttps://dataviz.shef.ac.uk/blog/28/02/2020/Urban-Observatories-hackathonFri, 28 Feb 2020 00:00:00 GMTThis post was originally posted on Research & Innovation blog . In February, The University of Sheffield hosted a three-day hackathon, an intensive collaborative software development event organised by the Data & Analytics Facility for National Infrastructure (DAFNI). The event aimed to begin tackling the complex challenges involved in coordinating analytics using data from urban observatories across the UK. DAFNI provides an analytics platform to enable innovative data analysis solutions for infrastructure research. An urban observatory is a network of sensors capturing atmospheric and…