From bc5d236087ecf08c7ef681e92b8ea4eed7e0bc48 Mon Sep 17 00:00:00 2001 From: Lorenz Uhlmann Date: Fri, 20 Dec 2024 17:14:33 +0100 Subject: [PATCH] Add December entry --- README.md | 12 +++++++----- data/2024/2024-12-11/readme.md | 18 ++++++++++++++++++ data/Readme.md | 2 ++ 3 files changed, 27 insertions(+), 5 deletions(-) create mode 100644 data/2024/2024-12-11/readme.md diff --git a/README.md b/README.md index 1c34d91..3948292 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # Wonderful Wednesdays -**November 13th, 2024**. +**December 11th, 2024**. -The challenge for the next webinar is about inter-rater and intra-rater reliability. Details can be found [here](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-11-13). +For the next webinar, you are asked to pick the best visualisation of the year. Details can be found [here](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-12-11). For more on PSI and wonderful wednesdays see https://www.psiweb.org/sigs-special-interest-groups/visualisation/welcome-to-wonderful-wednesdays @@ -20,15 +20,15 @@ You will be able to submit your improvements for feedback via a google form on t We will make the submissions available to the community together with highlights of the strength and limitations through our [blog](https://vis-sig.github.io/blog/). Over time, this will lead to a gallery of visualizations for others to learn from. -## October data set +## December data set -**Upcoming December 11th, 2024 webinar**. The current data example focuses on [Inter-rater and intra-rater reliability](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-11-13). +**Upcoming January 8th, 2024 webinar**. The upcoming webinar is about [the best of the year](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-12-11). Submit your visualisations [here](https://docs.google.com/forms/d/e/1FAIpQLSdNAaiuUAD89LAdQm5KNnLWs-MjqA4pzX2VHAwN7iqwoKpi-Q/viewform) or send them to ! ## Next Webinar -**December 11th, 2024**. +**January 8th, 2024**. ## Previous data sets @@ -138,6 +138,8 @@ Submit your visualisations [here](https://docs.google.com/forms/d/e/1FAIpQLSdNAa * [Inter-rater and intra-rater reliability](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-11-13). Presented on **November 13th, 2024**. +* [Best of the year](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-12-11). Presented on **December 11th, 2024**. + ## Downloading single files **NOTE** to download a single data set as a csv file, click on the raw button and save the file. The [following link describes the process in further detail](https://stackoverflow.com/questions/4604663/download-single-files-from-github). diff --git a/data/2024/2024-12-11/readme.md b/data/2024/2024-12-11/readme.md new file mode 100644 index 0000000..ee1cd6e --- /dev/null +++ b/data/2024/2024-12-11/readme.md @@ -0,0 +1,18 @@ +Best of the year +================ + + +## Data set + +There will be no data set for this challenge. + +## The Challenge + +What is the best healthcare related visualization you have seen this year? + +- Most useful +- Most beautiful +- Most insightful +- Most impressing … + +Please submit the visualization (at least a screenshot) and some information, where you found it (might be a link) and -if possible- the program code. \ No newline at end of file diff --git a/data/Readme.md b/data/Readme.md index cf9078d..8a3e6c6 100644 --- a/data/Readme.md +++ b/data/Readme.md @@ -127,6 +127,8 @@ Table of contents for the 2024 webinar series data sets: - [Improving a bad chart](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-10-09). Presented on **October 9th, 2024**. - [Inter-rater and intra-rater reliability](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-10-09). Presented on **November 13th, 2024**. + + - [Best of the year](https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2024/2024-12-11). Presented on **December 11th, 2024**. **NOTE** to download a single data set as a csv file, click on the raw button and save the file. The [following link describes the process in