Public
Edited
Sep 23, 2024
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activeStatusDescription = md`_Active_ and _not active_ are Zooniverse settings that toggle whether a workflow is _live_ and visible or not.`
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participantsDescription = md`Volunteers can contribute to Zooniverse projects with an account, or without logging into their account if they have one.`
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agreementDescription = md`**Agreement rate** for a subject is calculated by determining the annotation that was most frequently used for that subject. The count of the most frequent annotation is then divided by the total number of annotations used for the same subject. This provides a measure of agreement, indicating the proportion of annotations that align with the most common annotation for the subject.`
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toc()
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aboutTheDataAccidents = (title, numberOfSubjects) => md`
- **Digitised newspaper pages**: automatically-transcribed text and images of historical newspaper articles come from newspapers digitised for the Living with Machines project by FindMyPast. These articles were selected by searching for keywords related to accidents and machinery in close proximity in an article. As such, they're a small sample of articles from public domain newspapers; many, many more relevant articles are available on the British Newspaper Archive, and many are still yet to be digitised.
- **Information about the newspapers** themselves - their publishers, locations, interests, circulation and cost - come from Mitchell's Press Directories, digitised and processed by the Living with Machines project.
- **Crowdsourced classifications**: volunteers on the Zooniverse platform were shown an article from a 19th century newspaper alongside options to label it - most importantly, did it mention an accident involving machinery? If an article did mention a specific accident involving machinery, then we asked additional questions it, such as the age and gender of any victims mentioned, the reported location of the accident, and the type of site where the accident occurred. Annotations were collected from 2020 to 2023. We exported the volunteers' annotations, comments and tags, and related information from the Zooniverse platform, and are working with that and data extracted from natural language processing/machine learning, for analysis and presentation.
- **Zooniverse tasks**: We went through several iterations of the 'workflows' about accidents, experimenting with the best combation of questions, task types and backend logic, alongside testing different ways of using the results.
- The primary source for the *${title}* data are from 23 newspapers titles over 74 years from 1846 to 1920, resulting in ${numberOfSubjects.toLocaleString()} images (**subjects**).
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aboutTheDataMechanisation = (title, numberOfSubjects) => md`
- **Digitised newspaper pages**: automatically-transcribed text and images of historical newspaper articles come from newspapers digitised for the British Newspaper Archive and provided to Living with Machines project by FindMyPast and the British Library. They're a small sample of articles from the newspaper titles available to use - many, many more relevant articles are available on the British Newspaper Archive, and many are still yet to be digitised.
- **Information about the newspapers** themselves - their publishers, locations, interests, circulation and cost - come from Mitchell's Press Directories, digitised and processed by the Living with Machines project.
- **Crowdsourced classifications**: volunteers on the Zooniverse platform were shown an article from a 19th century newspaper alongside options to label it with selected historical definitions for that word, along with options for images that were too hard to read, irrelevant, etc. We exported the volunteers' annotations, comments and tags, and related information from the Zooniverse platform, and are working with that and data extracted from natural language processing/machine learning, for analysis and presentation.
- **Zooniverse tasks**: building on earlier work classifying machines by type, the ‘Language of Mechanisation’ project asked members of the public to look at articles from 19th century newspapers that mentioned specific types of machine that we thought would yield interesting results. We went through several iterations of the 'workflows' to label words with their meaning in context, experimenting with the best combation of questions, task types and backend logic, alongside testing different ways of using the results.
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workflowSlug("How did?")
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workflowSlug = (title) => `-${title.toLowerCase().replace(/\W+/g, "-")}`
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accessibility()
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accessibility = (
contact = "[digitalresearch@bl.uk](mailto:digitalresearch@bl.uk)"
) => md`
## How we approached Accessibility
[King's Digital Lab](https://kdl.kcl.ac.uk) and [Living with Machines](https://livingwithmachines.ac.uk) project aim to continually improve the user experience for everyone by applying the relevant accessibility standards; however there are some limitations as outlined below:

Visual: We have tried to label all sections but some charts have dimensions/elements that are defined only by colour. This might be difficult for users with visual colour impairment to view the differences.

Device: The notebook is responsive but better viewed on larger screens as some of the charts might not display or be hard to view due to the lack of space.

Browser issues:

- We have had some feedback that some of the charts do not load on certain browsers.
- The _Table of contents_ links don't work in Safari.

### Compliance status
A full accessibility assessment will not be performed on this page as the choice of the Observable platform imposed some limitations in addition to limited project budget.

### Feedback
We might not have detected all issues so please let us know if you encounter accessibility barriers on this page by emailing ${contact}. We aim to reply within 10 working days, and might not be able to fix the issue, but your feedback will be useful to help address accessibility in our future work.
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Purpose-built for displays of data

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