From 86c71768a78533990005e5e623854906be7560ed Mon Sep 17 00:00:00 2001 From: Yuliang Weng <59968766+yld-weng@users.noreply.github.com> Date: Fri, 4 Nov 2022 16:47:29 +0000 Subject: [PATCH] style: new branding --- .../2020-03-22-datavizhub-guide/index.mdx | 202 ++++++---- .../index.mdx | 15 +- .../2020-07-04-LearningPath-Lab/index.mdx | 15 +- .../index.mdx | 350 ++++++++++++------ .../workconflict.jsx | 2 +- .../FairnessVis.jsx | 2 +- gatsby-config.js | 2 +- src/components/blog/blogPostGrid.jsx | 6 +- .../__snapshots__/eventNotice.spec.js.snap | 4 +- .../__snapshots__/featuredItem.spec.js.snap | 44 +-- .../__snapshots__/homeCommunity.spec.js.snap | 48 +-- .../__snapshots__/latestPost.spec.js.snap | 48 +-- .../__snapshots__/learningPath.spec.js.snap | 52 +-- src/components/home/eventNotice.jsx | 2 +- src/components/home/featuredItem.jsx | 8 +- src/components/home/homeCommunity.jsx | 2 +- src/components/home/homeShowcase.jsx | 2 +- src/components/home/latestPost.jsx | 6 +- src/components/home/learningPath.jsx | 2 +- .../__snapshots__/header.spec.js.snap | 70 ++-- .../__snapshots__/search.spec.js.snap | 10 +- src/components/shared/footer.jsx | 2 +- src/components/shared/header.jsx | 97 ++--- src/components/shared/layout.jsx | 50 +-- src/components/shared/seo.jsx | 9 +- src/components/style/styleComponent.jsx | 23 +- src/components/style/visStyle.jsx | 2 +- .../__snapshots__/visLayout.spec.js.snap | 8 +- .../__snapshots__/wordCloud.spec.js.snap | 4 +- src/css/animation.scss | 100 ++--- src/css/index.scss | 16 +- src/css/style.scss | 10 +- src/data/nav.json | 30 ++ src/images/Favicon_128x128.png | Bin 0 -> 5716 bytes src/images/UOSLogo_Primary_Violet_RGB.svg | 1 + src/images/UOSLogo_Primary_White_RGB.svg | 1 + src/pages/404.jsx | 17 +- src/pages/about.jsx | 31 +- src/pages/index.jsx | 8 +- tailwind.config.js | 15 +- 40 files changed, 770 insertions(+), 546 deletions(-) create mode 100644 src/data/nav.json create mode 100644 src/images/Favicon_128x128.png create mode 100644 src/images/UOSLogo_Primary_Violet_RGB.svg create mode 100644 src/images/UOSLogo_Primary_White_RGB.svg diff --git a/content/docs/2020-03-22-datavizhub-guide/index.mdx b/content/docs/2020-03-22-datavizhub-guide/index.mdx index 2c4fd3318..5fd4bb448 100644 --- a/content/docs/2020-03-22-datavizhub-guide/index.mdx +++ b/content/docs/2020-03-22-datavizhub-guide/index.mdx @@ -2,98 +2,170 @@ type: docs author: ["Dataviz Team"] title: "Data Visualisation" -description: "What, Why, How, Where. Learn more by go through three different learning paths - which includes contents on explore data visualisations, tutorials and guides on creating - data visualisation, and adopt reproducible workflow." +description: + "What, Why, How, Where. Learn more by go through three different learning paths + which includes contents on explore data visualisations, tutorials and guides on creating + data visualisation, and adopt reproducible workflow." date: 2020-03-22 template: "custom" featured: false --- -import React from 'react' -import Scrollspy from 'react-scrollspy' -import Fade from 'react-reveal/Fade' -import bg from './bg1.jpg' - +import React from "react"; +import Scrollspy from "react-scrollspy"; +import Fade from "react-reveal/Fade"; +import bg from "./bg1.jpg"; -
What is data visualisation?
++ What is data visualisation? +
- Data visualisation generally considered as the graphical representation of information and data, it
- is currently an extremely active and critical aspect in research, teaching, and development, and
- is closely related to information graphics, information visualisation, scientific visualisation,
- and statistical graphics.
+ Data visualisation generally considered as the graphical
+ representation of information and data, it is currently an extremely
+ active and critical aspect in research, teaching, and development, and
+ is closely related to information graphics, information visualisation,
+ scientific visualisation, and statistical graphics.
- The main purpose of data visualisation is to communicate information clearly and effectively by
- means of graphical representation. However, this does not mean that data visualisation must be
- boring for its functional purpose, or extremely complicated to look gorgeous.
-
In order to effectively communicate ideas and concepts,
- aesthetic and functions need to go hand in hand, and by visually communicating key aspects and
- features, we get deep insights into fairly sparse and complex datasets.
-
+ The main purpose of data visualisation is to communicate information
+ clearly and effectively by means of graphical representation. However,
+ this does not mean that data visualisation must be boring for its
+ functional purpose, or extremely complicated to look gorgeous.
+
In order to effectively communicate ideas and concepts, aesthetic
+ and functions need to go hand in hand, and by visually communicating key
+ aspects and features, we get deep insights into fairly sparse and complex
+ datasets.
Why is it important?
-- The huge amount of data is one of the characteristics of the Internet era. Learn to quickly digest - and absorb useful information in huge data groups can greatly help to seize opportunities. Studies - have shown that the amount of information we use visually is far greater than other senses. This also - means that the possibility of finding relevant information through data visualization is far greater - than other methods, and it allow viewers to get more information. - While many organisations and companies across different sectors relying on data to gain insights and - optimising their products, data visualisation is not yet an integral part of research communication. - Today, powerful, modern, open source, interactive and web-based visualisation tools have revolutionised - the potential for research data impact, we are here to help our researchers make the most of their - data and take advantage of such tools. -
-+ Why is it important? +
++ The huge amount of data is one of the characteristics of the Internet + era. Learn to quickly digest and absorb useful information in huge + data groups can greatly help to seize opportunities. Studies have + shown that the amount of information we use visually is far greater + than other senses. This also means that the possibility of finding + relevant information through data visualization is far greater than + other methods, and it allow viewers to get more information.{" "} + + While many organisations and companies across different sectors relying + on data to gain insights and optimising their products, data visualisation + is not yet an integral part of research communication. Today, powerful, + modern, open source, interactive and web-based visualisation tools have + revolutionised the potential for research data impact, we are here to help + our researchers make the most of their data and take advantage of such + tools. +
+How this site could help?
-
- Support through:
- • Documentation on dataviz best practice and use of tools
- • Communication channels
- • Capacity building opportunities through Events
+
+ How this site could help? +
+
+ Support through:
+ • Documentation on dataviz best practice and use of tools
+ • Communication channels
+ • Capacity building opportunities through Events
• Help when you needed
• Contribution
-
Where should I start?
-
- • Learn from documentations
- • Share your data visualisations on ORDA showcase
- • Join the google group and/or slack community
- • Contribute blog post
- • Contribute to the website
- • Get in touch with ideas for training, coding clubs, hackathons
-
+ Where should I start? +
+
+ • Learn from documentations
+ • Share your data visualisations on
+ ORDA showcase
+
+ • Join the{" "}
+
+ google group
+ {" "}
+ and/or{" "}
+
+ slack community
+ {" "}
+
+ •
+ Contribute blog post
+
+ •{" "}
+
+ Contribute
+ {" "}
+ to the website
+ • Get in touch with ideas for training, coding clubs, hackathons
+
WELCOME!
++ WELCOME! +
This is the first learning path prepared from the{" "} Dataviz.Shef team that helps you to get started. This learning @@ -114,7 +113,7 @@ import { TiChevronLeft, TiChevronRight } from "react-icons/ti";
+
Data visualisation basics
+
What charts?
@@ -377,7 +376,7 @@ import { TiChevronLeft, TiChevronRight } from "react-icons/ti";
Tools!
+Tools!
There are many tools available out there to build visualisations and each of them has their own focus, strength and weaknesses. These tools diff --git a/content/docs/2020-07-04-LearningPath-Lab/index.mdx b/content/docs/2020-07-04-LearningPath-Lab/index.mdx index fc690509b..3e0d0320c 100644 --- a/content/docs/2020-07-04-LearningPath-Lab/index.mdx +++ b/content/docs/2020-07-04-LearningPath-Lab/index.mdx @@ -20,12 +20,7 @@ import { TiChevronLeft, TiChevronRight } from "react-icons/ti";
WELCOME!
+WELCOME!
This is the second learning path prepared from the Dataviz.Shef team that is specifically designed for those who have completed{" "} @@ -139,7 +134,7 @@ import { TiChevronLeft, TiChevronRight } from "react-icons/ti";
R
+R
Python
+Python
Matlab
+Matlab
-WELCOME!
++ WELCOME! +
- This is the third learning path prepared from the Dataviz.Shef team that is specifically designed
- for those who have completed the Learning path - Lab or those experienced in creating data
- visualisations using tools such as Python, R, Matlab, or Javascript etc.. If not, it is
- recommended that you go through
- the Learning path - Lab before you
- read on.
- We will be referring to external resources as there are already resources available on the
- internet and we have organised them in relevant sections. In addition, the university has a
- partnership with Linkedin Learning providing thousands of online training courses to staff and
- students through MUSE, we have also
- included some useful courses to help you get started.
- The previous learning path focused on exploring what we can do with each programming language
- to produce suitable data visualisations through three stages: data processing,
- Data visualisation, and Share. This learning path will guide you through a reproducible workflow
- and how it might help you to increase your research impact.
+ This is the third learning path prepared from the Dataviz.Shef
+ team that is specifically designed for those who have completed the{" "}
+ Learning path - Lab or those experienced in creating data
+ visualisations using tools such as Python, R, Matlab, or Javascript
+ etc.. If not, it is recommended that you go through the{" "}
+
+
+ Learning path - Lab
+
+ {" "}
+ before you read on.
+ We will be referring to external resources as there are already resources
+ available on the internet and we have organised them in relevant sections.
+ In addition, the university has a partnership with
+ Linkedin Learning
+ providing thousands of online training courses to staff and students
+ through
+ MUSE
+ , we have also included some useful courses to help you get started.{" "}
+
+
+ The previous learning path focused on exploring what we can do with each
+ programming language to produce suitable data visualisations through three
+ stages: data processing,
+ Data visualisation, and Share. This learning path will
+ guide you through a reproducible workflow and how it might help you to
+ increase your research impact.
Reproducibility
-
- In computer science, reproducibility means that as long as the environment and initial
- conditions are the same, when the program is executed or the program is executed repeatedly, no
- matter if it is executed from beginning to end without stopping, or "stop and restart" execution,
- we will get the same result. Therefore, reproducibility of data visualisation should follow the
- same principle.
- Given the data and code provided (by an author) we should be able to get the
- same output by following the same steps as the author does. But how do we ensure that data and
- code are preserved and is accessible even after a long period of time for further research or
- reference is important for data visulisations as well as for publications. We hope you will find
- this information useful and if you have any questions or have found any errors in this page please
- contact us on our communication
- channels (email, slack, google group).
-
+ Reproducibility +
+
+ In computer science, reproducibility means that as long as the
+ environment and initial conditions are the same, when the program is
+ executed or the program is executed repeatedly, no matter if it is
+ executed from beginning to end without stopping, or "stop and restart"
+ execution, we will get the same result. Therefore,
+ reproducibility of data visualisation should follow the same
+ principle.
+
+ Given the data and code provided (by an author) we should be able to
+ get the same output by following the same steps as the author does.
+ But how do we ensure that data and code are preserved and is
+ accessible even after a long period of time for further research or
+ reference is important for data visulisations as well as for
+ publications. We hope you will find this information useful and if you
+ have any questions or have found any errors in this page please
+ contact us on our communication channels (
+ email,{" "}
+ slack,{" "}
+
+ google group
+
+ ).
+
Make yourself identifiable
-- Sometimes two researchers could have the same name or initials due to culture differences and - writing systems etc, it is important that there is a mechanism for identifying who you are - regardless where and when you published articles, datasets, and make other contributions. To - resolve this problem, ORCID (Open Researcher and Contributor ID) was introduced in 2012 that - can provide a persistent digital identifier (an ORCID iD) that distinguishes you from other - researchers and a record that supports automatic links among all your professional activities. - Your ORCID iD and connections are stored in the ORCID Registry, in an account you - own and manage. To register, visit orcid.org. -
++ Make yourself identifiable +
++ Sometimes two researchers could have the same name or initials due to + culture differences and writing systems etc, it is important that + there is a mechanism for identifying who you are regardless where and + when you published articles, datasets, and make other contributions. + To resolve this problem, ORCID (Open Researcher and Contributor + ID) was introduced in 2012 that can{" "} + + provide a persistent digital identifier (an ORCID iD) that + distinguishes you from other researchers and a record that supports + automatic links among all your professional activities. Your{" "} + ORCID iD and connections are stored in the ORCID Registry, in + an account you own and manage + + . To register, visit{" "} + orcid.org. +
Make your data identifiable
-- Identifying datasets and codes is equally important to identify - yourself. DOI (digital object identifier) was introduced in 2000 to identify many resources across - academic, professional, and government information, this will help you to include your datasets - and code within your publication through DOI. If other researchers can easily locate your data - and make use of it to reproduce your work to make sense of your data and communicate findings - more widely. Read the next section to learn more about options of data repositories you can use. -
-+ Make your data identifiable +
++ Identifying datasets and codes is equally important to identify + yourself. DOI (digital object identifier) was introduced in + 2000 to identify many resources across academic, professional, and + government information, this will help you to include your datasets + and code within your publication through DOI. If other + researchers can easily locate your data and make use of it to + reproduce your work to make sense of your data and communicate + findings more widely. Read the{" "} + + next section + {" "} + to learn more about options of data repositories you can use. +
+Deposit your data
-- For reproducibility purposes we advise you to deposit data and codes in data repositories that - can be accessed and cited by anyone around the world through a - unique DOI (digital object identifier). For some repositories it is possible to use programming - languages like Python and R to fetch data directly from the source using URL provided. If your - data is not classified as sensitive data and no discipline-specific repository is available, we - encourage you to deposit data and code in University's data - repository (powered by figshare) - Online Research Data (ORDA). For other - recommendations including discipline-specific repositories, visit - The University Library’s Research Data Repositories Page. -
-- If you have a GitHub account, check out the following articles: -
-- There might be cases where your data comes from external sources like data.gov.uk, don't forget - acknowledge the source of the information in your data and state the licence applied to. -
-- We also encourage you not to limit to data visulisation and to visit The University - Library RDM page to give you a broad - overview of Research Data Management at the university and many specific guidances. -
++ Deposit your data +
++ For reproducibility purposes we advise you to deposit data and codes + in data repositories that can be accessed and cited by anyone{" "} + around the world through a unique DOI (digital object + identifier). For some repositories it is possible to use programming + languages like Python and R to fetch data directly from the source + using URL provided. If your data is not classified as sensitive data + and no discipline-specific repository is available, we encourage you + to deposit data and code in University's data repository (powered by{" "} + figshare) -{" "} + + Online Research Data (ORDA) + + . For other recommendations including discipline-specific + repositories, visit The University Library’s{" "} + + Research Data Repositories Page + + . +
+If you have a GitHub account, check out the following articles:
++ There might be cases where your data comes from external sources like{" "} + data.gov.uk, don't forget acknowledge the source of the + information in your data and state the licence applied to. +
++ We also encourage you not to limit to data visulisation and to visit + The University Library{" "} + + RDM page + {" "} + to give you a broad overview of Research Data Management at the + university and many specific guidances. +
Featured
@@ -43,10 +43,10 @@ const blogPostGrid = ({ allMdx }) => {+
{description}
Featured
diff --git a/src/components/home/homeCommunity.jsx b/src/components/home/homeCommunity.jsx index c4b685915..adaa4b119 100644 --- a/src/components/home/homeCommunity.jsx +++ b/src/components/home/homeCommunity.jsx @@ -42,7 +42,7 @@ const HomeCommunity = () => {(
The
@@ -165,7 +168,7 @@ const About = () => {