Annotation Guidelines for the Public

Objective Our goal is to annotate all the labels that appear on this historical map of Edinburgh, including both words (like place names) and numbers (like distances or heights). This is how an annotation should look: a box is drawn around each word of a label, then tags are added, and, when possible, a link to an external resource is found.

We will explain in more detail how to use the platform for this transcription event in the following steps.

Important: Remember to refresh the page often, to see the work of the other users appearing in real time on the map, so we avoid duplication of effort.

BEFORE WE START

Please, create a free account on Recogito at: https://recogito.uksouth.cloudapp.azure.com then access the map of Edinburgh clicking here

To facilitate the transcription process, the map has been divided into segments. Please, choose an area to start the annotation.

STEP ONE: CREATING BOUNDING BOXES

Select the most appropriate drawing tool from the menu on the right. To annotate text, we would recommend using the straight box or the tilted box.

For more complex shapes, you can use the polygon drawing tool. In general, the simplest box is always preferred. You can find more information on the various drawing tools here.

If a name is made of two words, like "Edinburgh Castle", please create one box for each word.

TIP: be sure to enclose all the relevant letters in the box, and, if possible, try not to include anything else (like tree icons or other symbols). It’s ok if these are in the background ‘behind’ the text, but eliminating extra features beyond the text characters is best.

STEP TWO: TRANSCRIPTION

The annotation pop-up has a “transcription” field. Here is where you are supposed to write the word that you are annotating from the map. Please, transcribe the word exactly as you see it, even if it is an abbreviation, or if the current name may have changed. Use upper or lowercase as they are printed on the map. This information about former names is actually very interesting to us.

STEP THREE: GROUPING

If the label is made of two or more words (for example “Royal Hospital”), you should have created a box for each word (one for “Royal” and one for “Hospital”). Now you can join them using the grouping feature. When you group the annotations, please always start from the first word (in our example: "Royal") and then tick the "ordered" option.

You can read more about how to group annotations at the bottom of this page, or look at the brief animation on this page.

STEP FOUR: TAGS

The annotation pop-up has a field for tags. Creating tags is very easy, just type in the “Add tag” area and then press the “enter” key to confirm. If you see the tag encased in a rectangle, then you have successfully added a tag to your annotation. In this event we ask you to add only one tag to each annotation, related to the kind of place or object the text is describing or naming. Please, select one option from this list of five:

• area: for all county names, district names, cities, towns and hamlets. If it is an administrative division or a populated settlement, “area” is the tag for you!
• street: use this tag for all street and road names, but also for squares, courtyards, crescents or, basically, anything that could be used as an address.
• building: use this tag for any built structure, rural or urban: palaces, hospitals, theatres, but also windmills or warehouses.
• natural: this tag can be applied to all natural features, such as rivers, hills, mountains, creeks, bays and so on.
• other: we know that there are many, many things that do not fall in any of these categories. What about farmland? Mines? Cemeteries? If you feel like what you are annotating does not fit neatly in any of the four categories above, just tag it as “other”. It may sound too generic, but it is still very useful to us to know that it does not belong to any of the other categories. So, don’t be shy with the “other” tag!

TIP: If there is anything you are not sure about, and you want to flag the annotation to the attention of the organisers, you can add the special tag "check".

TIP: Recogito will autocomplete the tag when you start typing one of the words listed above.

FAQ

What if I made a mistake? You can delete each component of the annotation (tags, transcription and geotagging) separately, clicking on their bin icons. You can also delete the entire annotation and start from scratch, if you prefer.

What if I see that someone else has made a mistake? If you believe that someone else has made a mistake in their annotation, you can leave a comment. Please, remember to be kind, and leave a comment only if it is actually useful to improve the outcome of the project. You can also add the tag “check” in the tags field, and one of the administrators will examine the annotation.

Are annotations anonymous? Each annotation has an author and a time stamp, and it shows the username of the person who created it.

What if I am not sure about one or more steps of the annotation? Feel free to create incomplete annotations. They are still much more useful than having no annotations at all! If you are in doubt about something, you can also add the tag “check” in the tags field.

Is a farm a building or an area? The OS maps, like all maps, are full of features that are difficult to define, and should be analysed on a case by case basis. This would go way beyond the scope of our project so, as a rule of thumb, we think that farms should be annotated with the tag “area”.

If there is some additional text, like a building’s capacity, should I group it with the building’s name? Yes, please, if some additional text refers specifically to an entity like a building or an area we would be grateful if you could associate this information with the name through the grouping feature (see STEP FIVE).

What are we going to do with the data after the event? The data will be made freely available for download from the NLS Data Foundry. We also plan to make the data searchable through an interactive web-mapping viewer on the NLS website. The data will also be used by the Machines Reading Maps project to refine and improve on their tools for text recognition from OS historical maps, hopefully allowing the wider automatic recognition of text from OS 25 inch maps.