accessibility.Rmd

---
title: "Accesssibility"
description: |
  The CDU Data Science Team plan to discuss data accessibility in more depth in the Understanding Your Data meetings in the 29 September 2021 meeting. This page will detail those reflections and links.
author:
  - name: Zoë Turner
    affiliation: CDU Data Science Team
    affiliation_url: https://cdu-data-science-team.github.io/team-blog/
date: 06-24-2021
output: distill::distill_article
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

A great deal of our data and analysis is produced in various formats that may not be accessible to everyone. Whilst the majority are happy with the formats, it may be prohibitive to those with specific needs to the extent that they may not even know that data can be provided in formats that are accessible to them.

The Government Statistical Society (GSS) are doing a lot of [work around accessibility](https://gss.civilservice.gov.uk/news/accessibility-and-statistical-publications-what-do-you-want-to-know/) and the CDU Data Science Team wants to use these guidelines and promote them throughout the Trust and in our ICS. Consequently, we dedicated the [September meeting](https://cdu-data-science-team.github.io/presentations/2021-09-29%20UYD%20spreadsheet%20accessibility/UYD-Spreadsheet-Accessibility.html#1) of Understanding Your Data to starting the conversations around accessibility. 

A couple of example of accessibility that we've considered are:

* having a standard chart to accommodate accessibility for colour and contrast or at least having these charts available (without specific requests)
* machine readable documentation which is useful for screen readers, but also for the programming tools we use as analyst and data scientists
* accessible formats for documentation and reports
* Alt text (alternative text) for charts and images (alternative text which is the written explanation of what a picture is showing)
* using our data science tools to extract scanned pictures from pdfs which occur when documents are scanned. The tools we have use OCR (Optical Character Recognition) but still rely upon a person to see the text to extract

Many of these have been inspired from GSS and from following individuals on Social Media, like Twitter, to learn about best practice and the difficulties different people face with data, analysis and visualisations. We are not experts in this area and are learning, and as we learn we are sharing.

If you are interested in this, please do get in touch with the team at [CDU Data Science Team](mailto:cdudatascience@nottshc.nhs.uk). 

[Slides from September 2021's meeting](https://cdu-data-science-team.github.io/presentations/2021-09-29%20UYD%20spreadsheet%20accessibility/UYD-Spreadsheet-Accessibility.html#1)