ChatGPT advice on Archival Research

I imagine many of you have tried asking ChatGPT questions like these, but we thought it was worth providing this blog, for those who haven’t tried it out. For anyone who has managed to avoid the headlines around this technology, ChatGPT is an artificial intelligence chatbot developed by OpenAI and released in November 2022.

First of all, we wanted to try a question that focussed on using one archive, so we asked:

Can you give me advice on how to write a dissertation on Stanley Kubrick using primary sources or archives?

ChatGPT answered:

Certainly, here are some tips for writing a dissertation on Stanley Kubrick using primary sources or archives:

  1. Choose a research question: The first step in writing a dissertation is to identify a research question that you want to explore. This could be anything from analyzing Kubrick’s use of color in his films to examining his working methods and creative process. Make sure that your research question is focused enough to be manageable within the scope of your dissertation.
  2. Identify relevant primary sources and archives: Once you have a research question, you need to identify primary sources and archives that are relevant to your topic. This could include Kubrick’s personal papers, scripts, storyboards, correspondence, and production notes. You can also look for archives and collections related to Kubrick’s collaborators and contemporaries, such as other film directors, writers, or actors.
  3. Conduct a literature review: Before diving into primary sources and archives, conduct a thorough literature review to see what has already been published on your topic. This will help you identify any gaps in the existing research and refine your research question further. It will also give you a sense of the debates and issues within the field that your dissertation will need to address.
  4. Plan your research visits: Before visiting archives, you need to plan ahead and make sure that you have enough time to access and review the materials you need. Some archives have strict access requirements or limited opening hours, so check this in advance. Make a list of the specific documents or materials you need to examine, and plan your research visits accordingly.
  5. Analyze and evaluate primary sources: Once you have access to primary sources, you will need to analyze and evaluate the materials carefully. Make notes of important details, such as dates, locations, and people involved. Look for patterns, themes, and connections in the materials, and identify any contradictions or inconsistencies. Keep in mind the broader research question you are trying to answer, and look for evidence that can support your argument.
  6. Write your dissertation: Once you have reviewed and analyzed your primary sources, you can start writing your dissertation. Make sure to frame your analysis within the broader context of existing scholarship, and use primary sources to support your arguments and interpretations. Use quotations and examples from primary sources to illustrate your points and make your argument more convincing.

Writing a dissertation on Stanley Kubrick using primary sources and archives can be a rewarding and challenging task. Make sure to plan ahead, conduct thorough research, and use primary sources effectively to support your arguments and interpretations.

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We then wanted to see how well ChatGPT would summarise the role and value of Archives Hub, so we asked:

How do I use Archives Hub to help with my research?

ChatGPT answered:

The Archives Hub can be a valuable resource for researchers who are looking to explore archival collections held in institutions across the UK. Here are some tips on how to use Archives Hub to help with your research:

  1. Search the Archives Hub database: Start by using the search bar on the Archives Hub homepage to search for keywords, names, or locations that are relevant to your research topic. This will bring up a list of archives and collections that match your search terms. You can then click on each archive or collection to view more detailed information, including a description of the materials held, contact information for the holding institution, and links to related collections.
  2. Refine your search: You can refine your search results using the menus on the left-hand side of the page. These menus allow you to narrow your search by institution, keyword, date range, or subject. Use these filters to focus your search on the most relevant materials.
  3. Contact the holding institution: Once you have identified an archive or collection that seems relevant to your research, contact the holding institution to confirm the availability of the materials and to arrange a research visit if necessary. The contact information for each institution is listed on the Archives Hub website.
  4. Plan your visit: Before visiting an archive, make sure to plan ahead and think about what materials you want to examine. Familiarize yourself with the archive’s policies on access and handling of materials, and bring any necessary equipment, such as a camera or laptop. Make sure to take detailed notes during your visit, as well as copies or photographs of any relevant documents or materials.
  5. Follow ethical guidelines: When conducting research in archives, it is important to follow ethical guidelines and practices. This means properly citing your sources, respecting the privacy and confidentiality of individuals mentioned in archival materials, and obtaining permission to use any copyrighted materials. This will help to maintain the trust and cooperation of archival repositories and their staff.

By using the Archives Hub to identify collections and institutions that are relevant to your research, you can gain access to a vast array of archival materials held across the UK. Be sure to plan ahead, follow ethical guidelines, and take detailed notes during your research visits to make the most of these valuable resources.

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Both answers are very convincing and it is hard to argue that we would have done better if we had written them ourselves, though we might want to add more information to what is there. The guide for using the Archives Hub seems to utilise text from different parts of our website, including our Guide for Beginners, access information and search help. But the text is not lifted directly – it is far more sophisticated than that. There are many examples of text that does not echo anything we have said on our website and I can’t find the text elsewhere. For example, ChatGPT refers to possible dissertation subjects for Kubrick: ‘analyzing Kubrick’s use of color in his films to examining his working methods and creative process’. There are sentences that widen the scope, for example: ‘Make sure to frame your analysis within the broader context of existing scholarship, and use primary sources to support your arguments and interpretations.’ There are sentiments that I don’t recall seeing stated in quite the same way that ChatGPT has done, for example: ‘This means properly citing your sources, respecting the privacy and confidentiality of individuals mentioned in archival materials, and obtaining permission to use any copyrighted materials. This will help to maintain the trust and cooperation of archival repositories and their staff.’

It is easy to see why ChatGPT is seen as a means to write effectively. Maybe there are questions around what is left out of the above answers, but I would certainly be happy to use them as a basis for our own guidelines.

Machine Learning: training a model by creating a labelled dataset

In this post I will go through the steps we took to create a human labelled dataset (i.e. naming objects within images), applying the labels to bounding boxes (showing where the objects are in the image) in order to identify objects and train an ML model. Note that the other approach, and one we will talk about in another post, is to simply let a pre-trained tool do the work of labelling without any human intervention. But we thought that it would be worthwhile to try the human labelling out before seeing what the out-of-the-box results are.

I used the photographs in the Claude William Jamson archive, kindly provided by Hull University Archives. This is a collection with a variety of content that lends itself to this kind of experiment.

An image from the Jamson archive

I used Amazon SageMaker for this work. In SageMaker you can set up a labelling job using the Ground Truth service, by giving the location of the source material – in this case, the folder containing the Jamson photographs. Images have to be jpg, or png, so if you have tif images, for example, they have to be converted. You give the job a name and provide the location of the source material (in our case an S3 bucket, which is the Amazon Simple Storage Service).

Location and output information are added – I have specified that we are working with images.

I then decide on my approach. I trained the algorithm with a random sample of images from this collection. This is because I wanted this sample to be a subset of the full Jamson Archive dataset of images we are working with. We can then use the ML model created from the subset to make object detection predictions for the rest of the dataset.

Random sample is selected, and I can also specify the size of the sample, e.g. 25%.

Once I had these settings completed, I started to create the labels for the ‘Ground Truth’ job. You have to provide the list of labels first of all from which you will select individual labels for each image. You cannot create the labels as you go. This immediately seemed like a big constraint to me.

Interface for adding labels, and a description of the task

I went through the photographs and decided upon the labels – you can only add up to 50 labels. It is probably worth noting here that ‘label bias’ is a known issue within machine learning. This is where the set of labelled data is not fully representative of the entirety of potential labels, and so it can create bias. This might be something we come back to, in order to think about the implications.

Creating a list of labels that I can then apply to each individual image

I chose to add some fairly obvious labels, such as boat or church. But I also wanted to try adding labels for features that are often not described in the metadata for an image, but nonetheless might be of interest to researchers, so I added things like terraced house, telegraph pole, hat and tree, for example.

Once you have the labels, there are some other options. You can assign to a labelling team, and make the task time bound, which might be useful for thinking about the resources involved in doing a job like this. You can also ask for automated data labelling, which does add to the cost, so it is worth considering this when deciding on your settings. The automated labelling uses ML to learn from the human labelling. As the task will be assigned to a work team, you need to ensure that you have the people you want in the team already added to Ground Truth.

Confirming the team and the task timeout

Those assigned to the labelling job will receive an email confirming this and giving a link to access to the labelling job.

Workers assigned to the job can now start to work to create the bounding boxes and add the labels

You can now begin the job of identifying objects and applying labels.

The interface for adding labels to images

First up I have a photograph showing rowing boats. I didn’t add the label ‘rowing boat’ as I didn’t go through every single photograph to find all the objects that I might want to label, so not a good start! ‘Boat’ will have to do. As stated above, I had to work with the labels that I created, I can’t add more labels at this stage.

I added as many labels as I could to each photograph, which was a fairly time intensive exercise. For example, in the image below I added not only boat and person but also hat and chimney. I also added water, which could be optimistic, as it is not really an object that is bound within a box, and it is rather difficult to identify in many cases, but it’s worth a try.

Adding labels using bounding boxes

I can zoom in and out and play with exposure and contrast settings to help me identify objects.

Bounding boxes with labels

Here is another example where I experimented with some labels that seem quite ambitious – I tried shopfront and pavement, for example, though it is hard to classify a shop from another house front, and it is hard to pin-point a pavement.

The more I went through the images, drawing bounding boxes and adding labels, the more I could see the challenges and wondered how the out-of-the-box ML tools would fare identifying these things. My aim in doing the labelling work was partly to get my head into that space of identification, and what the characteristics are of various objects (especially objects in the historic images that are common in archive collections). But my aim was also to train the model to improve accuracy. For an object like a chimney, this labelling exercise looked like it might be fruitful. A chimney has certain characteristics and giving the algorithm lots of examples seems like it will improve the model and thus identify more chimneys. But giving the algorithm examples of shop fronts is harder to predict. If you try to identify the characteristics, it is often a bay window and you can see items displayed in it. It will usually have a sign above, though that is indistinct in many of these pictures. It seems very different training the model on clear, full view images of shops, as opposed to the reality of many photographs, where they are just part of the whole scene, and you get a partial view.

There were certainly some features I really wanted to label as I went along. Not being able to do this seemed to be a major shortcoming of the tool. For example, I thought flags might be good – something that has quite defined characteristics – and I might have added some more architectural features such as dome and statue, and even just building (I had house, terraced house, shop and pub). Having said that, I assume that identifying common features like buildings and people will work well out-of-the-box.

Running a labelling job is a very interesting form of classification. You have to decide how thorough you are going to be. It is more labour intensive than simply providing a description like ‘view of a street’ or ‘war memorial’. I found it elucidating as I felt that I was looking at images in a different way and thinking about how amazing the brain is to be able to pick out a rather blurred cart or a van or a bicycle with a trailer, or whatever it might be, and how we have all these classifications in our head. It took more time than it might have done because I was thinking about this project, and about writing blog posts! But, if you invest time in training a model well, then it may be able to add labels to unlabelled photographs, and thus save time down the line. So, investing time at this point could reap real rewards.

Part of a photograph with labels added

In the above example, I’ve outlined an object that i’ve identified as a telegraph pole. One question I had is whether I am are right in all of my identifications, and I’m sure there will be times when things will be wrongly identified. But this is certainly the type of feature that isn’t normally described within an image, and there must be enthusiasts for telegraph poles out there! (Well, maybe more likely historians looking at communications or the history of the telephone). It also helps to provide examples from different periods of history, so that the algorithm learns more about the object. I’ve added a label for a cart and a van in this photo. These are not all that clear within the image, but maybe by labelling less distinct features, I will help with future automated identification in archival images.

I’ve added hat as a label, but it strikes me that my boxes also highlight heads or faces in many cases, as the people in these photos are small, and it is hard to distinguish hat from head. I also suspect that the algorithm might be quite good with hats, though I don’t yet know for sure.

person and child labels

I used ‘person’ as a label, and also ‘child’, and I tended not to use ‘person’ for ‘child’, which is obviously incorrect, but I thought that it made more sense to train the algorithm to identify children, as person is probably going to work quite well. But again, I imagine that person identification is going to be quite successful without my extra work – though identifying a child is a rather more challenging task. In the end, it may be that there is no real point in doing any work identifying people as that work has probably been done with millions of images, so adding my hundred odd is hardly going to matter!

I had church as a label, and then used it for anything that looked like a church, so that included Beverly Minster, for example. I couldn’t guarantee that every building I labelled as a church is a church, and I didn’t have more nuanced labels. I didn’t have church interior as a label, so I did wonder whether labelling the interior with the same label as the exterior would not be ideal.

I was interested in whether pubs and inns can be identified. Like shops, they are easy for us to identify, but it is not easy to define them for a machine.

Green Dragon at Welton

A pub is usually a larger building (but not always) with a sign on the facade (but not always) and maybe a hanging sign. But that could be said for a shop as well. It is the details such as the shape of the sign that help a human eye distinguish it. Even a lantern hanging over the door, or several people hanging around outside! In many of the photos the pub is indistinct, and I wondered whether it is better to identify it as a pub, or whether that could be misleading.

I found that things like street lamps and telegraph poles seemed to work well, as they have clear characteristics. I wanted to try to identify more indistinct things like street and pavement, and I added these labels in order to see if they yield any useful results.

I chose to label 10% of the images. That was 109 in total, and it took a few hours. I think if I did it again I would aim to label about 50 for an experiment like this. But then the more labels you provide, the more likely you will get results.

The next step will be to compare the output using the Rekognition out of the box service with one trained using these labels. I’m very interested to see how the two compare! We are very aware that we are using a very small labelled dataset for training, but we are using the transfer learning approach that builds upon existing models, so we are hopeful we may see some improvement in label predications. We are also working on adding these labels to our front end interface and thinking about how they might enhance discoverability.

Thanks to Adrian Stevenson, one of the Hub Labs team, who took me through the technical processes outlined in this post.

Running Machine Learning in AWS

For our Machine Learning experiments we are using Amazon Web Services (AWS). We thought it would be useful to explain what we have been doing.

AWS, like most Cloud providers, gives you access to a huge range of infrastructure, services and tools. Typically, instead of having your own servers physically on your premises, you instead utlitise the virtual servers provided in the Cloud. The Cloud is a cost effective solution, and in particular it allows for elasticity; dynamically allocating resources as required. It also provides a range of features, and that includes a set of Machine Learning services and tools.

The AWS console lists the services available. For Machine Learning there are a range of options.

One of the services available is Amazon Rekognition. This is what we have used when writing our previous blog posts.

Amazon Rekognition enables you to analyse images

One of the things Rekognition does is object detection. We have written about using Rekognition in a previous post.

Our initial experiments were done on the basis of uploading single images at a time and looking at the output. The next step is to work out how to submit a batch of images and get output from that. AWS doesn’t have an interface that allows you to upload a batch. We have batches of images stored in the Cloud (using the ‘S3’ service), and so we need to pass sets of images from S3 to the Rekognition service and store the resulting label predictions (outputs). We also need to figure out how to provide these predictions to our contributors in a user friendly display.

S3 provides a storage facility (‘buckets’), where we have uploaded images from our Labs participants

After substantial research into approaches that we could take, we decided to use the AWS Lambda and DynamoDB services along with Rekognition and S3. Lambda is a service that allows you to run code without having to set up the virtual machine infrastructure (it is often referred to as a serverless approach). We used some ‘blueprint’ Lambda code (written in Python) as the basis, and extended it for our purposes.

One of the blueprints is for using Rekognition to detect faces

Using something like AWS does not mean that you get this type of facility out of the box. AWS provides the infrastructure and the interfaces are reasonably user friendly, but it does not provide a full blown application for doing Machine Learning. We have to do some development work in order to use Rekognition, or other ML tools, for a set of images.

A slice of the code – the images are taken from the S3 bucket and Rekognition provides a response with levels of confidence.

Lambda is set up so the code will run every time an image is placed in the S3 bucket. It then passes the output (label prediction) to another AWS service, called DynamoDB, which is a ‘NoSQL’ database.

DynamoDB output

In the above image you can see an excerpt from the output from running the Lambda code. This is for image U DX336-1-6.jpg (see below) and it has predicted ‘tree’ with a confidence level of 94.51 percent. Ideally we wanted to add the ‘bounding box’ which provides the co-ordinates for where the object is within the image.

Image from the Royal Conservatoire of Scotland showing bounding boxes to identify person and chair

We spent quite a bit of time trying to figure out how to add bounding boxes, and eventually realised that they are only added for some objects – Amazon Rekognition Image and Amazon Rekognition Video can return the bounding box for common object labels such as cars, furniture, apparel or pets, but the information isn’t returned for less common object labels. Quite how things are classed as more or less common is not clear. At the moment we are working on passing the bounding box information (when there is any) to our database output.

Image from Hull University Archives
Label predictions for the above image

Clearly for this image, it would be useful to have ‘memorial’ and ‘cross’ as label predictions, but these terms are absent. However, sometimes ML can provide terms that might not be used by the cataloguer, such as ‘tree’ or ‘monument’.

So we now have the ability to submit a batch of images, but currently the output is in JSON (the above output table is only provided if you upload the image individually). We are hoping to read the data and place the labels into our IIIF development interface.

The next step is to create a model using a subset of the images that our participants have provided. A key thing to understand is that in order to train a model so that it makes better predictions you need to provide labelled images. Therefore, if you want to try using ML, it is likely that part of the ML journey will require you to undertake a substantial amount of labelling if you don’t already have labelled images. Providing labelled content is the way that the algorithm learns. If we provided the above image and a batch of others like it and included a label of ‘memorial’ then that would make it more likely that other non-labelled images we input would be identified correctly. We could also include the more specific label ‘war memorial’ – but it would seem like a tall order for ML to distinguish war memorials from other types. Having said that, the fascinating thing is that often machines learn to detect patterns in a way that surpasses what humans can achieve. We can only give it a go and see what we get.

Thanks to Adrian Stevenson, one of the Hub Labs team, who took me through the technical processes outlined in this post.

Assessing Machine Learning Outputs

One of the challenges that we face with our Labs project is presentation of the Machine Learning results.  We thought there would be many out of the box tools to help with this, but we have not found this to be the case.

If we use the AWS console Rekognition service interface for example, we get presented with results, but they are not provided in a way that will readily allow us and our project participants to assess them. Here is a screenshot of an image from Cardiff University – an example of out of the box use of AWS Rekognition:

Excavation at Stonehenge, Cardiff University Photographic Archive

This is just one result – but we want to present the results from a large collection of images. Ideally we would run the image recognition on all of the Cardiff images, and/or on the images from one collection, assess the results within the project team and also present them back to our colleagues at Cardiff.

The ML results are actually presented in JSON:

Excerpt from JSON showing ML output

Here you can see some of the terms identified and the confidence scores.

These particular images, from the University archive, are catalogued to item level. That means they may not benefit so much from adding tags or identifying objects. But they are unlikely to have all the terms (or ‘labels’ in ML parlance) that the Rekognition service comes up with.  Sometimes the things identified are not what a cataloguer would necessarily think to add to a description. The above image is identified as ‘outdoors’, ‘ground’ and ‘soil. These terms could be useful for a researcher. Just identifying photographs with people in them could potentially be useful.

Another example below is of a printed item – a poem.

Up in the Wind, Papers of Edward Thomas, Cardiff University

Strange formatting of the transcript aside, the JSON below shows the detected text (squirrels), confidence and area of the image where the word is located.

Detected word ‘squirrels’

If this was provided to the end user, then anyone interested in squirrels in literature (surely there must be someone…) can find this digital content.  

But we have to figure out how to present results and what functionality is required. It reminds me of using Open Refine to assess person name matches.  The interface provides for a human eye to assess and confirm or reject the results.

Screenshot of names matching using Open Refine
Screenshot of names matching using Open Refine

We want to be able to lead discussions with our contributors on the usefulness, accuracy, bias – lack of bias – and peculiarities of machine learning, and for that a usable interface is essential.

How we might knit this in with the Hub description is something to consider down the line. The first question is whether to use the results of ML at all.  However, it is hard to imagine that it won’t play a part as it gets better at recognition and classification.  Archvists often talk about how they don’t have time to catalogue. So it is arguable that machine learning, even if the results are not perfect, will be an improvement on the backlogs that we currently have.    

AWS Rekognition tools

We have thought about which tools we would like to use and we are currently creating a spreadsheet of the images we have from our participants and which tools to use with each group of images.

Some tools may seem less likely, for example, image moderation. But with the focus on ethics and sensitive data, this could be useful for identifying  potentially offensive or controversial images.

blanked out image
Blanked out image

The Image Moderation tool recognises nudity in the above image. 

confidence scores for nudity
The confidence scores are high that this image represents nudity

This could be carried through to the end user interface, and a user could click on ‘view content’ if they chose to do so.

image of nude
Art Design and Architecture Collection, Glasgow School of Art (NMC/1137)

The image moderation tool may classify images art images as sensitive when they are very unlikely to cause offence.  The tools may not be able to distinguish offensive nudity from classical art nudity. With training it is likely to improve, but when you think about it, it is not always an easy line for a human to draw.

Face comparison could potentially be useful where you want to identify individuals and instances of them within a large collection of photographs for example, so we might try that out.  

However, we have decided that we won’t be using ‘celebrity recognition’, or ‘PPE detection’ for this particular project!

Text and Images

We are particularly interested in text and in text within images.  It might be a way to connect images, and we might be able to pull the text out to be used for searching.

Suffice to say that text will be very variable. We ran Transkribus Lite on some materials.

Transkribus on a handwritten letter
Letter from the Papers of Edward Thomas at Cardiff University

We compared this to use of AWS Text Rekognition.

Transkribus on a handwritten letter
Letter from the Papers of Edward Thomas at Cardiff University

These examples illustrate the problem with handwritten documents. Potentially the model could be trained to work better for handwriting, but this may require a very large amount of input data given the variability of writing styles.

Transkribus on a typescript letter
Poem from the Papers of Edward Thomas, Cardiff University

Transkribus has transcribed this short typescript text from the same archive well.  One word ‘house’ has been transcribed as ‘housd’ and ‘idea’ caused a formatting issue, but overall a good result.

Transkribus on a poster
Poster from the Design Archive, University of Brighton Design Archives

The above example is Transkribus Lite on a poster from the University of Brighton Design Archives.  In archives, many digital items are images with text – particularly collections of posters or flyers. Transkribus has not done well with this (though this is just using the Lite version out of the box).  

Rekognition on a poster

We also tried this with the AWS Rekognition Text tool, and it worked well.

Another example of images with text is maps and plans.

Lambeth Palace map of London
19th century map of Clerkenwell, Lambeth Palace Archive
JSON output showing place name
JSON output showing place name

Above are two examples of places identified from the plan output in JSON. If we can take these outputs and add them to our search interface, an end user could search for ‘clerkenwell’ or ‘northampton square’ and find this plan.  

Questions we currently have:

  • How do we present the results back to the project team?
  • How do we present the results to the participants?
  • Do we ask participants specific questions in order to get structured feedback?
  • Will we get text that is useful enough to go to the next step?
  • Which images provide good text and which don’t?
  • How might they results be used on the Archives Hub to help with discovery?

As we progress the work, we will start to think about organising a workshop for participants to get their feedback on the ML outputs.

Thanks to Adrian Stevenson, one of the Hub Labs team, who took me through the technical processes outlined in this post.

Machine Learning: Training the Model

A recent OCLC paper by Thomas Padilla highlights the need for ‘Pilot collaborations between institutions with representative collections’ and working ‘to share source data and produce “gold standard” training data.

We think that the Archives Hub Labs project exemplifes Tom’s suggested approach by working with ten of our contributing institutions from across the UK, reflecting a variety of archives.

However, it is also surely true that cultural heritage will need to engage with the broader AI and ML communities to understand and benefit fully from the range of ML services such as translation, transcription, object identification and facial recognition:

‘Advances in all of these areas are being driven and guided by the government or commercial sectors, which are infinitely better funded  than cultural memory; for example, many nation-states and major corporations are intensively interested  in facial recognition. The key strategy for the cultural memory sector will be to exploit these advantages, adapting and tuning the technologies around the margins for its own needs.’ From a short blog post by Dr Clifford Lynch from the CNI which is well worth reading.

People often criticise Machine Learning for being biased. But bias and mis-representation is essentially due to embedded bias in the input training data. The algorithm learns with what it has. So one of the key tasks for us as an archives community is to think about training data. We need algorithms that are trained to work for us to give us useful outputs.

Gathering training data in order to create useful models is going to be a challenge. Machine Learning is not like anything else that we have done before – we don’t actually know what we’ll get – we just know that we need to give the algorithm data that educates it in the way that we want. A bit like a child in school, we can teach it the curriculum, but we don’t know if it will pass the exam.

It certainly seems a given that we will need to use well labelled archival material as training data, so that the model is tailored specifically to the material we have. We will need to work together to provide this scale of training data. We have many wonderfully catalogued collections, with detail down to item level; as well as many collections that are catalogued quite basically, maybe just at collection level. If we join together as a community and utilise the well-catalogued content to train algorithms, we may be able to achieve something really useful to help make all collections more discoverable.

If an algorithm is trained on a fairly narrow set of data, then it is questionable whether it will have broad applicability. For example, if we train an algorithm on letters written in the 18th century, but just authored by two or three people, then it is unlikely to learn enough to be of real use with transcription; but if we train it on the handwriting of fifty people or more, then it could be a really useful tool for recognising and transcribing 18th century letters To do this training, we will need to bring content together. We will need to share the Machine Learning journey. The benefits could be massive in terms of discoverability of archives; effective discovery for all those materials that we currently don’t have time to catalogue. The main danger is that the resulting identification, transcription, tagging or whatever, is not to the standard that we want. We can only experiment and see what happens if we trial ML with a set of data (which is what we are doing now with our Labs project). One benefit could actually be much more consistency across collections. As someone working on aggregating data from 350 organisations, I can testify that we are not consistent! – and this lack of consistency impairs discovery.

Archival content is likely to be distinct in terms of both quality and subject. Typescripts might be old and faded, manuscripts might be hard to read, photographs might be black and white and not as high resolution as modern prints. Photographs might be of historical artefacts that are not recognised by most algorithms. We have specific challenges with our material, and we need the algorithms to learn from our material, in order to then provide something useful as we input more content.

In terms of subject, the Lotus and Delta shoe shops are a good example of a specific topic. They are represented in the Joseph Emberton papers, at the University of Brighton Design Archives, with a series of photographs. Architecture is potentially an interesting area to focus on. ML could give us some outputs that provide information on architectural features. It could be that the design of Lotus and Delta shops can be connected to other shops with similar architectures and shop fronts. ML may pick out features that a cataloguer may not include. On the other hand, we may find that it is extremely hard to train an algorithm on old black and white and potentially low resolution photographs in order for it to learn what a shop is, and maybe what a shoe shop is.

In this collection a number of the photographs are of exteriors. Some are identified by location, and some are not yet identified.

photo of Emberton shoe shop, Harrogate
Harrogate
Photo of Edinburgh shoe shop exterior
Edinburgh
Photo of unidentified shoe shop
Unidentified shop

These photographs have been catalogued to item level, and so researchers will be able to find these when searching for ‘shops’ and particularly ‘shoe shops’ on the Hub, e.g. a search for ‘harrogate shoe shop‘ finds the exterior of a shop front in Harrogate. There may not be much more that could be provided for searching this collection, unless machine learning could label the type of shop front, the type of windows and signage for example. This seems very challenging with these old photographs, but presumably not impossible. With ML it is a matter of trying things out. You might think that if artificial intelligence can master self-driving cars it can master shop exteriors….but it is not a foregone conclusion.

If the model was trained with this set of photographs, then other shop fronts could potentially be identified in photographs that aren’t catalogued individually. We could potentially end up with collections from many different archives tagged with ‘shop front’ and potentially with ‘shoes’. Whether an unidentified shop front could be be identified is less certain, unless there are definite contextual features to work with.

interior of ladies department shoe shop
Interior of ladies’ dept.
photograph of shoe shop interior
Interior of men’s dept.

Shop interiors are likely to be even more of a challenge. But it will be exciting to try things like this out and see what we get.

Commercial providers offer black box solutions, and we can be sure they were not trained to work well with archives. They may be adapted to new situations, but it is unlikely they can ever work effectively for archival content. I explored this to an extent in my last blog post. However, it is worth considering that a model not trained on archival material may highlight objects or topics that we would not think of including in a catalogue entry.

The Archives Hub and Jisc could play a pivotal role in co-ordinating work to create better models for archival material. Aggregation allows for providing more training material, and thus creating more effective models.

To date, most ML projects in libraries have required bespoke data annotation to create sufficient training data. Reproducing this work for every ML project, however, risks wasting both time and labor, and there are ample opportunities for scholars to share and build upon each other’s work.’ (R. Cordell, LC Labs report)

We can have a role to play in ‘data gathering, sharing, annotation, ethics monitoring, and record-keeping processes‘ (Eun Seo Jo, Timnit Gebru, https://arxiv.org/abs/1912.10389). We will need to think about how to bring our contributors into the loop in order to check and feedback on the ML outputs. This is a non-trivial part of the process that we are considering at the moment. We need an interface that displays the results of our ML trials.

One of the interesting aspects of this is that collections that have been catalogued in detail will provide the training data for collections that are not. Will this prove to be a barrier, or will it bring us together as a community? In theory the resources that some archives have, which have enabled them to catalogue to item level, can benefit those with minimal resources. Would this be a free and open exchange, or would we start to see a commercial framework developing?

It is also important that we don’t ignore the catalogue entries from our 350 contributors. Catalogues could provide great fodder for ML – we could start to establish connections and commonalities and increase the utility of the catalogues considerably.

The issue of how to incorporate the results of ML into the end user discovery interface is yet another challenge. Is it fundamentally important that end users know what has been done through ML and what has been done by a human? I can’t help thinking that over time the lines will blur, as we become more comfortable with AI….or as AI simply becomes more integrated into our world. It is clear that many people don’t realise how much Artificial Intelligence sits behind so many systems and processes that we use on an everyday basis. But I think that for the time being, it would be useful to make that distinction within our end user interfaces, so that people know why something has been catalogued or described in a certain way and so that we can assess the effectiveness of the ML contribution.

In subsequent posts we aim to share some initial findings from doing work at scale. We will only be able to undertake some modest experiments, but we hope that we are contributing to the start of what will be a very big adventure for archives.

Machine Learning with Archive Collections

Machine Learning is a sub-set of Artificial Intelligence (AI). You might like to look at devopedia.org for a short introduction to Machine Learning (ML).

Machine Learning is a data-oriented technique that enables computers to learn from experience. Human experience comes from our interaction with the environment. For computers, experience is indirect. It’s based on data collected from the world, data about the world.

Definition of Machine Learning from devopedia.org

The idea of this and subsequent blog posts is to look at machine learning from a specifically archival point of view as well as update you on our Labs project, Images and Machine Learning. We hope that our blog posts help archivists and other information professionals within the archival or cultural heritage domain to better understand ML and how it might be used.

AI can be used for many areas of learning and research. Chatbots have been trialled at some institutions, for example, ‘Ada’ at Bolton College has generally been well received. AI can be useful for aspects of website usability and accessibility, or helping students to choose the right university degree. The Jisc National Centre for AI site has more information on how AI can add value for education and learning.

At the Archives Hub we are particularly focussed on looking at Machine Learning from the point of view of archival catalogues and digital content, to aid discoverability, and potentially to identify patterns and bias in cataloguing.

Machine Learning to aid discoverability can be carried out as supervised or unsupervised learning. Supervised learning may be the most reliable, producing the best results. It requires a set of data that contains both the inputs and the desired outputs. By ‘outputs’ we mean that the objective is provided by labelling some of the input data. This is often called training data. In a ‘traditional’ scenario, code is written to take input and create output; in machine learning, input and output is provided, and the part done by human code is instead done by machine algorithms to create a model. This model is then used to derive outputs from further inputs.

The machine learning model, or program, is the outcome of learning from data (source: Advani 2020)

So, for example, taking the Vickers instruments collection from the Borthwick: https://dlib.york.ac.uk/yodl/app/collection/detail?id=york%3a796319&ref=browse. You may want to recognise optical instruments, for example, telescopes and microscopes. You could provide training data with a set of labelled images (output data) to create a model. You could then input additional images and see if the optical instruments are identified by the model.

Of course, the Borthwick may have catalogued these photographs already (in fact, they have been catalogued), so we know which are telescopes and which are micrometers or lenses or eye pieces. If you have a specialist collection, essentially focused on a subject, and the photographs are already labelled, then there may be less scope for improving discoverability for that collection by using machine learning. If the Borthwick had only catalogued a few boxes of photographs, they might consider using machine learning to label the remaining photographs. However, a big advantage is that the enhanced telescope recognising model can now be used on all the images from the Archives Hub to discover and label images containing telescopes from other collections. This is one of the great advantages of applying ML across the aggregated data of the Archives Hub. The results of machine learning are always going to be better with more training data, so ideally you would provide a large collection of labelled photographs in order to teach the algorithm. Archive collections may not always be at the kind of scale where this process is optimised. Providing good training data is potentially a very substantial task, and does require that the content is labelled. It is possible to use models that are already available without doing this training step, but the results are likely to be far less useful.

Another scenario that could lend itself to ML is a more varied collection, such as Borthwick’s University photograph collection. These have been catalogued, but there is potential to recognise various additional elements within the photographs.

construction site with people
Construction of the J.B. Morrell Library, University of York

The above photograph has been labelled as a construction site. ML could recognise that there are people in the photograph, and this information could be added, so a researcher could then look for construction site with people. Recognising people in a photograph is something that many ML tools are able to do, having already been trained on this. However, archive collections are often composed of historic documents and old photographs that may not be as clear as modern documents. In addition, the models will probably have been trained with more current content. This is likely to be an issue for archives generally. For models to be effective, they need to have been trained with content that is similar to the content we want to catalogue.

The Amazon Web Services (AWS) Rekognition facial recognition tool finds three faces…
…the Microsoft Azure facial recognition tool doesn’t do so well.

The benefits of adding labels to photographs via ML to potentially enhance the catalogue and help with discoverability is going to depend upon a number of factors: how well the image is already catalogued, whether training data can be provided to improve the algorithm, how well ML can then pick out features that might be of use.

The drawings of fossil fish at the Geological Society are another example of a very subject specific collection. We put a few of these through some out-of-the-box ML tools. These tools have been pre-trained on large diverse datasets, but we have not done any additional training ourselves yet, so you could see them as generalists in recognising entities rather than specialists with any particular material or topic.

drawing of a fossil tortoise
Fossil tortoise from Oeningen

In this case the drawing has been tagged with ‘fossil’, which could be useful if you wanted to identify fossil drawings from a varied collection of drawings. It has also tagged this with archaeology and art, both of which could potentially be useful, again depending upon the context. The label of soil is a bit more problematic, and yet it is the one that has been added with 99.5% certainty. However, a bit of training to tell the algorithm that ‘soil’ is not correct may remove this tag from subsequent drawings.

This example illustrates the above point that a subject specific collection may be tagged with labels that are already provided in the catalogue description. It also shows that machine learning is unlikely to ever be perfectly accurate (although there are many claims it outperforms humans in a number of areas). It is very likely to add labels that are not correct. Ideally we would train the model to make less mistakes – though it is unlikely that all mistakes will be eliminated – so that does mean some level of manual review.

Tagging an image using ML may draw out features that would not necessarily be added to the catalogue – maybe they are not relevant to the repository’s main theme, and in the end, it is too time-consuming for cataloguers themselves to describe each photo in great detail as part of the cataloguing process.

Queen’s University Belfast: Hart Collection – China Photographs

The above image is a simple one with not too much going on. It will be discoverable on the Queen’s website through a search for ‘china’ or ‘robert hart’ for example, but tagging could make it discoverable for those interested in plants or architectural features. Again, false positives could be a problem, so a key here is to think about levels of certainty and how to manage expectations.

As mentioned above, archival images are often difficult to interpret. They may be old and faded, and they may also represent features or items that an algorithm will not recognise.

Design Council Archive: Things in their home setting – detail of a living room

In the above example from Brighton Design Archives, the photograph is from a set made of an exhibition of 1947, Things In Their Home Setting. The AWS image Rekognition service has no problem with the chair, but it has confidently identified the oven as a refrigerator. This could probably be corrected by providing more training data, or giving feedback to improve the understanding of the algorithm and its knowledge of 1940’s kitchen furniture. But by the time you have given enough training data for the model to recognise a cooker from a fridge from a washing machine, it might have been easier simply to do the cataloguing manually.

Another option for machine learning is optical character recognition. This has been around for a while, but it has improved substantially as a result of the machine learning approach. Again, one of the challenges for archives is that many items within the collections are handwritten, faded, and generally not easily readable. So, can ML prove to be better with these items than previous OCR approaches?

A tool like Transkribus can potentially offer great benefits to archives, and is seen as a community-driven effort to create, gather and share training data. We hope to try out some experiments with it in the course of our project.

Clerkenwell St James Parish, General Plan

The above plan is from Lambeth Palace Library’s 19th century ecclesiastical maps. It can already be found searching for ‘clerkenwell’ or ‘st james parish’. But ML could potentially provide more searchable information.

OCR using Azure

The words here are fairly clear, so the character recognition using the Microsoft Azure ML service is quite good. Obviously the formatting is an issue in terms of word order. ‘James’ is recognised as ‘Iames’ due to the style of writing. ‘Church’ is recognised despite the style looking like ‘Chvrch’ – this will be something the algorithm has learnt. This analysis could potentially be useful to add to the catalogue because an end user could then search for ‘pentonville chapel’ or ‘northampton square’ and find this plan.

As well as looking at digital archives, we will be trying out examples with catalogue text. A great deal of archival cataloguing is legacy data, and archivists do not always have the time to catalogue to item level or to add index terms, which can substantially aid discoverability. So, it is tempting to look at ML as a means to substantially improve our catalogues. For example, to add to our index terms, which provide structured access points for end users searching for people, organisations, places and subjects.

In a traditional approach to adding subject terms to a catalogue, you might write rules. We have done this in our Names Project – we have written a whole load of rules in order to identify name, life dates, and additional data within index terms. We could have written even more rules – for example, to try to identify forename and surname. But it would be very difficult because the data does not present the elements of names consistently. We could potentially train an ML model with a load of names, tagging the parts of the name as forename, surname, dates, titles, epithets. But could an algorithm then successfully work out the parts of any subsequent names that we feed into it? It seems unlikely because there is no real consistency in how cataloguers input names. The algorithm might learn, for example, that a word, then a comma, then another word is surname, forename (Roberts, Elizabeth). But two words followed by a comma and another word could be surname + forename or forename + surname, (Vaughan Williams, Ralph; Gerald Finzi, composer). In this scenario, the best option may be to aim to use source data (e.g. the Virtual International Authority File) to compare our data to, rather than try to train a machine to learn patterns, when there really isn’t a model to provide the input.

We may find that analysing text within a catalogue offers more promise.

Part of the admin history for the British Linen Company archive at Lloyds

Here is an example from an administrative history of the British Linen Group, a collection held by Lloyds Banking Group. The entity recognition is pretty good – people’s names, organisations, dates, places, occupations and other entities can be picked out fairly successfully from catalogues. Of course that is only the first step; it is how to then use that information that is the main issue. You would not necessarily want to apply the terms as index terms for example, as they may not be what the collection is substantially about. But from the above example you could easily imagine tagging all the place names with a ‘place’ tag, so that a place search could find them. So, a general search for Stranraer would obviously find this catalogue entry, but if you could identify it as a place name it could be included in the more specific place name search.

With machine learning it is very difficult and sometimes impossible to understand exactly what is happening and why. By definition, the machine learns and modifies its output. Whilst you can provide training data to give inputs and desired outputs, machine learning will always be just that….a machine learning as it goes along, and not simply working through a programme that a human has written. Supervised learning provides for the most control over the outputs. Unsupervised learning, and deep learning, are where you have much less control (we’ll come onto those in later posts).

It is only by understanding the algorithms and what they are doing that you can set up your environment for the best results. But that is where things can get very complicated. We are going to try to run some experiments where we do prepare the data, but learning how to do this is a non-trivial task. Hence one of the questions we are asking is ‘is Machine Learning worth the effort required in order to improve archival discoverability?’ We hope to get at least some way along the road to answering that question.

There are, of course, other pressing questions, not least the issue of bias, and concerns about energy use with machine learning as well as how to preserve the processes and outputs of ML and document the decision making. But there could be big wins in terms of saving time that can then be dedicated to other tasks. The increasing volumes of data that we have to process may make this a necessity. We hope to touch upon some of these areas, but this is a fairly small scale project and Machine Learning it is one huge topic.

Images and Machine Learning Project

Under our new Labs umbrella, we have started a new project, ‘Images and Machine Learning’ it has three distinct and related strands.

screenshot with bullet points to describe the DAO store, IIIF and Machine Learning
The three themes of the project

We will be working on these themes with ten participants, who already contribute to the Archives Hub, and who have expressed an interest in one or more of these strands: Cardiff University, Bangor University, Brighton Design Archives at the University of Brighton, Queens University Belfast, the University of Hull, the Borthwick Institute for Archives at the University of York, the Geological Society, the Paul Mellon Centre, Lambeth Palace (Church of England) and Lloyds Bank.

This project is not about pre-selecting participants or content that meet any kind of criteria. The point is to work with a whole variety of descriptions and images, and not in any sense to ‘cherry pick’ descriptions or images in order to make our lives easier. We want a realistic sense of what is required to implement digital storage and IIIF display, and we want to see how machine learning tools work with a range of content. Some of the participants will be able to dedicate more time to the project, others will have very little time, some will have technical experience, others won’t. A successful implementation that runs beyond our project and into service will need to fit in with our contributors needs and limitations. It is problematic to run a project that asks for unrealistic amounts of time from people that will not be achievable long-term, as trying to turn a project into a service is not likely to work.

DAO Store

Over the years we have been asked a number of times about hosting content for our contributors. Whilst there are already options available for hosting, there are issues of cost, technical support, fit for purpose-ness, trust and security for archives that are not necessarily easily met.

Jisc can potentially provide a digital object store that is relatively inexpensive, integrated with the current Archives Hub tools and interfaces, and designed specifically to meet our own contributors’ requirements. In order to explore this proposal, we are going to invest some resource into modifying our current administrative interface, the CIIM, to enable the ingest of digital content.

We spent some time looking at the feasibility of integrating an archival digital object store with the current Jisc Preservation Service. However, for various reasons this did not prove to be a practical solution. One of the main issues is the particular nature of archives as hierarchical multi-level collections. Archival metadata has its own particular requirements. The CIIM is already set up to work with EAD descriptions and by using the CIIM we have full control over the metadata so that we can design it to meet the needs of archives. It also allows us to more easily think about enabling IIIF (see below).

The idea is that contributors use the CIIM to upload content and attach metadata. They can then organise and search their content, and publish it, in order to give it web address URIs that can be added to their archival descriptions – both in the Archives Hub and elsewhere.

It should be noted that this store is not designed to be a preservation solution. As said, Jisc already provides this service, and there are many other services available. This is a store for access and use, and for providing IIIF enabled content.

The metadata fields have not yet been finalised, but we have a working proposal and some thoughts about each field.

Titlemandatory? individual vs batch?
Datespreferably structured, options for approx. and not dated.
Licencepossibly a URI. option to add institution’s rights statement.
Resource typecontrolled list. values to be determined with participants. could upload a thesaurus. could try ML to identify type.
Keywordsfree text
Taggingenable digital objects to be grouped e.g by topic or e.g. ‘to do’ to indicate work is required
Statusunpublished/published. May refer to IIIF enabled.
URLunique URI of image (at individual level)
Proposed fields for the Digital Object Store

We need to think about the workflow and user interface. The images would be uploaded and not published by default, so that they would only be available to the DAO Store user at that point. On publication, they would be available at a designated URL. Would we then give the option to re-size? Would we set a maximum size? How would this fit in with IIIF and the preference for images of a higher resolution? We will certainly need to think about how to handle low resolution images.

International Image Interoperability Framework

IIIF is a framework that enables images to be viewed in any IIIF viewer. Typically, they can be sequenced, such as for a book, and they are zoomable to a very high resolution. At the heart of IIIF is the principle that organisations expose images over the web in a way that allows researchers to use images from anywhere, using any platform that speaks IIIF. This means a researcher can group images for their own research purposes, and very easily compare them. IIIF promotes the idea of fully open digital content, and works best with high resolution images.

There are a number of demos here: https://matienzo.org/iiif-archives-demo/

And here is a demo provided by Project Mirador: http://projectmirador.org/demo/

An example from the University of Cambridge: https://cudl.lib.cam.ac.uk/view/MS-RGO-00014-00051/358

And one from the University of Manchester: https://www.digitalcollections.manchester.ac.uk/collections/ruskin/1

There are very good reasons for the Archives Hub to get involved in IIIF, but there are challenges being an aggregator that individual institutions don’t face, or at least not to the same degree. We won’t know what digital content we will receive, so we have to think about how to work with images of varying resolutions. Our contributors will have different preferences for the interface and functionality. On the plus side, we are a large and established service, with technical expertise and good relationships with our contributors. We can potentially help smaller and less well-resourced institutions into this world. In addition, we are well positioned to establish a community of use, to share experiences and challenges.

One thing that we are very convinced by: IIIF is a really effective way to surface digital content and it is an enormous boon to researchers. So, it makes total sense for us to move into this area. With this in mind, Jisc has become a member of the IIIF Consortium, and we aim to take advantage of the knowledge and experience within the community – and to contribute to it.

Machine Learning

This is a huge area, and it can feel rather daunting. It is also very complicated, and we are under no illusions that it will be a long road, probably with plenty of blind alleys. It is very exciting, but not without big challenges.

It seems as if ML is getting a bad reputation lately, with the idea that algorithms make decisions that are often unfair or unjust, or that are clearly biased. But the main issue lies with the data. ML is about machines learning from data, and if the data is inadequate, biased, or suspect in some way, then the outcomes are not likely to be good. ML offers us a big opportunity to analyse our data. It can help us surface bias and problematic cataloguing.

We want to take the descriptions and images that our participants provide and see what we can do with ML tools. Obviously we won’t do anything that affects the data without consulting with our contributors. But it is best with ML to have a large amount of data, and so this is an area where an aggregator has an advantage.

This area is truly exploratory. We are not aiming for anything other than the broad idea of improved discoverability. We will see if ML can help identify entities, such as people, places and concepts. But we are also open to looking at the results of ML and thinking about how we might benefit from them. We may conclude that ML only has limited use for us – at least, as it stands now. But it is changing all the time, and becoming more sophisticated. It is something that will only grow and become more embedded within cultural heritage.

Over the next several months we will be blogging about the project, and we would be very pleased to receive feedback and thoughts. We will also be holding some webinar sessions. These will be advertised to contributors via our contributors list, and advertised on the JiscMail archives-nra list.

Thoughts on the Heritage PIDs Project

I attended the final Zoom session for the Heritage Persistent Identifiers Project this week.

PID or Persistent Identifiers can be incredibly useful within the heritage sector. The PID project was looking at the use of PIDs across collections. They were aiming to increase uptake of PIDs, so that they service as a foundation infrastructure for drawing collections together.

The project ran two surveys with responses mainly from the UK but a number from other countries. 66 and 47 responses were received for the 1st and 2nd surveys respectively. Both surveys showed that most institutions have pockets of awareness of PIDs, although the number of people with no awareness decreased slightly over time.

The main barriers according to the surveys are lack of resources and technical issues. It is also clear that decision makers need to be more appreciative the benefits of PIDs.

The project case studies were found to be particularly useful by survey respondents, and also the PID demonstrator that showed how collections can be linked through PIDs. The case studies included the National Gallery – interestingly they are using the CIIM, as we are, so their PIDs were created as a component of the CIIM.

One thing that struck me as I was listening is that PIDs apply to all sorts of things – documents, objects, collections, publications, people, organisations, places. I think that this can make it difficult to grasp the context when people are talking about PIDs in general. I found myself getting a bit lost in the conversation because it is such a large landscape, and I am someone who has a reasonable knowledge of this area.

Within the Archives Hub we have persistent identification of descriptions, at all levels – so each unit of description has a PID. e.g. https://archiveshub.jisc.ac.uk/data/gb275-davies uses the country code GB, the repository code 275 and the reference ‘davies’. These are URIs, which gives more utility, as they can be referenced on the Web as well as in publications. We had very very long discussions about the make-up of these identifiers. We did consider having completely opaque identifiers, but we felt there was some advantage of having user-friendly URIs, especially for things like analytics – if you see that ‘gb275-davies’ has had 53 views then you may know what that means, whereas if ‘27530981’ has had 53 views, you have to go and dereference it to find out what that actually is. However, references can change over time, so if you use them in persistent identifiers you have a problem when the reference changes.

Granularity is a question that needs to be addressed when thinking about PIDs for archives. Should every item have a DOI for example (digital object identifier)?. Should the DOI be assigned to the collection? Not all collections are described to item level, so in many cases this might be a moot point. So far I don’t think we’ve received archive descriptions that include DOIs so I don’t think it is going to be top of the agenda for archives any time soon. It may not be something that we, as an aggregator, necessarily get involved with anyway. If a contributor to the Hub includes a DOI, then we can display that, and maybe that is our work done. I’m not sure that it has a role in linking aggregated data to other datasets.

ARKs were mentioned in the session. We haven’t yet considered using these within our system. We’ve only had 2 contributors out of 350 who have included them, so we are not sure that it is worth us working with them at this stage. This is one of the problems with adopting PIDs – uptake and scale. ORCIDs were also referenced. An ORCID is for researchers – eventually their papers may come to the archive, so ORCID IDs may become more relevant in time. It is important for ORCID to work with Wikidata and other PIDs to enable linking. Bionomia was mentioned as a project that already works with ORCID and Wikidata.

Overall my impression listening to the presentations was of a very mixed landscape, and that is something that makes it harder to figure out how to start working with PIDs – there is no one clear way forward. In the case studies presented there was quite a bit of emphasis on internal use cases, and that can limit the external benefits, but there was also a range of approaches. This doesn’t help anyone starting out and hoping for a clear way forward.

The Archives Hub has done work on identifying personal and organisational names and we are going to be blogging more about the outcome of that when work we implement changes to our user interface over the next few months. But it is worth saying that if you want to implement PIDs for names, you have to look at the names you have and how identifiable they really are. It has been extremely difficult for us to do this work, and we cannot possibly achieve 100% identification because of the very variable state of the names that we have in the data.

PIDs need to know what they are identifying, and being clear about what that is may in itself be a big challenge. If you assign a PID to a person, an organisation, or any entity, you want to be confident that it is right. ORCIDs are for current researchers, and if you set yourself up with an ORCID, you are going to know that it identifies you (one would hope). But if we have seven ‘Elizabeth Roberts‘ referred to on the Archives Hub, referenced in a range of archives, we may find it very difficult to know if they are the same person. Assigning identification to historical records is a massive detective challenge.

We have been looking to match our names to VIAF or Wikidata, so that we can benefit from these widely used PIDs. But to do that we need to find a way to create matches and set levels of confidence for matches. Increasingly, I am wondering if Wikidata is more promising than VIAF due to the ability to add to the database. For archives, where many names are not published individuals, this might prove to be a good way forward.

The PID project came up with a number of recommendations. Many of these were about generally promoting PIDs and integrating them into workflows. Quite a few of the recommendations look like they need significant funding. One that I think is very pertinent is working with system suppliers. It needs to be straightforward to integrate PIDs when a collection is being catalogued.

The recommendations tended to just refer to PIDs and not specific PIDs and I’m not sure whether this is helpful as it is such a broad context. Maybe it is more useful to be more specific about whether you are looking at PIDs for collections/artefacts or for researchers, for all names or for topics. For example, if you recommend looking at cost analysis, is this for any and all PIDs that might be implemented across all of the cultural heritage sector? The project has found that it is not possible to be prescriptive and narrow things down, but I still feel that talking about certain kinds of identifiers rather than PIDs in general might help to give more context to the conversation.

There are many persistent identifier systems. If we all use different identifiers then we aren’t really getting towards the kind of interconnectivity that we are after. We could do with adopting a common approach – even just a common approach within the archives domain would be useful – but that requires resource and that requires funding. Having said that, it is not essential to use exactly the same PIDs. For example, if one organisation adopts VIAF IDs for their names and another adopts Wikidata Q codes, then that is not really a problem in that VIAF and Wikidata link to each other. But adopting a system that is not widely used (and not linked up to other systems) is not really going to be very helpful.

In the end, we need a very clear sense of the benefits that PIDs will bring us. As an aggregator it is very difficult to add PIDs to data that we receive. Archives should ideally add PIDs as they create descriptions. If VIAF IDs or Wikidata Q codes, or Geonames identifiers for place names, were added during cataloguing, that could potentially be of great benefit. But this raises a big issue – we need archival management systems to make it really easy to add PIDs, and at present many of them don’t do this. Our own cataloguing tool does provide a look-up and this has proved to be really successful. It makes adding identifiers easier than not adding them – and that is what you want to achieve.

The Archives Hub and IIIF: supporting the true potential of images on the Web

IIIF is a model for presenting and annotating digital content on the Web, including images and audio/visual files. There is a very active global community that develops IIIF and promotes the principles of open, shareable content. One of the strengths of IIIF is the community, which is a diverse mix of people, including developers and information professionals.

IIIF map showing where there are known IIIF projects and implementations

Images are fundamental carriers of information. They provide a huge amount of value for researchers, helping us understand history and culture. We interact with huge amounts of images, and yet we do not always get as much value out of them as we might. Content may be digitised, but it is often within silos, where the end user has to go to a specific website to discover content and to view a specific image, it is not always easy or possible to discover, gather together, compare, analyse and manipulate images.

IIIF is a particularly useful solution for cultural heritage, where analysis of images is so important. A current ‘Towards a National Collection’ project has been looking at practical applications of IIIF.

The IIIF Solution

Exactly what IIIF enables depends upon a number of factors, but in general it enables:

Deep zoom: view and zoom in closely to see all the detail of an image

Sequencing: navigate through a book or sequence of archival materials

Comparisons: bring images together and put them side-by-side. This can enable researchers to bring together images from different collections, maybe material with the same provenance that has been separated over time.

Search within text: work with transcriptions and translations

Connections: connect to resources such as Wikidata

Use of different IIIF viewers: different viewers have their own features and facilities.

How It Works

The IIIF community tends to talk in terms of APIs. These can be thought of as agreed and structured ways to connect systems. If you have this kind of agreement then you can implement different systems, or parts of systems, to work with the same content, because you are sticking to an agreed structure. The basic principle is to store an image once (on a IIIF server) and be able to use it many times in many contexts.

IIIF is like a a layer above the data stores that host content. The images are accessed through that IIIF layer – or through the IIIF APIs. This enables different agents to create viewers and tools for the data held in all the stores.

Different repositories have their own data stores, but they can share content through the IIIF APIs.

There are a few different APIs that make up the IIIF standard.

Image API

This API delivers the content (or pixels). The image is delivered as a URL, and the URL is structured in an agreed way.

Presentation API

This delivers information on the presentation of the material, such as the sequence of a book, for example, or a bundle of letters, and metadata about the object.

This screenshot shows the Image API providing the zoomable image, and the presentation API providing basic information – the title and the sequence of the pages of this object.

Search API

Allows searching within the text of an object.

Authentication API

Allows materials to be restricted by audience. So, this is useful for sensitive images or images under copyright that may have restrictions.

IIIF viewer

As IIIF images are served in a standard way, any IIIF viewer can access them. Examples of IIIF viewers:

The Universal Viewer: https://universalviewer.io/
Mirador: https://mirador-dev.netlify.app/tests/integration/mirador/
Archival IIIF: https://archival-iiif.github.io/
Storiiies digital storytelling: https://storiiies.cogapp.com/#storiiies

There are a whole host of viewers available, with various functionality. Most will offer the basics of zooming and cropping. There does seem to be a question around why so many viewers are needed. It might be considered a better approach for the community to work on a limited group of viewers, but this may be a politically driven desire to own and brand a viewer. In the end, a IIIF viewer can display any IIIF content, and each viewer will have its own features and functionality.

To find out more about how researchers can benefit from IIIF, you may like to watch this presentation on YouTube (59m): Using IIIF for research 

Some Examples

In many projects, the aim is to digitise key materials, such as artworks of national importance and rare books and manuscripts, in order to provide a rich experience for end users. For instance, the Raphael Cartoons at the V&A are now available to explore different layers and detail, even enabling the infra-red view and surface view, to allow researchers to study the paintings in great depth. Images can easily be compared within your own workspace, by pulling in other IIIF images.

The V&A Raphael Cartoons can be viewed in ultra high resolution colour, exploring all of the layers

What is the Archives Hub planning to do with IIIF?

Hosting content: We are starting a 15 month project to explore options for hosting and delivering content. Integral to this project will be providing a IIIF Image API. As referenced above, this will mean that the digital content can be viewed in any IIIF viewer, because we will provide the necessary URLs to do so. One of the barriers for many archives is that images need to be on a IIIF server in order to utilise the Image API. It may be that Jisc can provide this service.

Creation of IIIF manifests: We’ll talk more about this in future blog posts, but the manifest is a part of the Presentation API. It contains a sequence (e.g. ordering of a book), as well as metadata such as a title, description, attribution, rights information, table of contents, and any other information about the objects that may be useful for presentation. We will be looking at how to create manifests efficiently and at scale, and the implications for representing hierarchical collections.

Providing an interface to manage content: This would be useful for any image store, so it does not relate specifically to IIIF. But it may have implications around the metadata provided and what we might put into a IIIF manifest.

Integrating a IIIF viewer into the Archives Hub: We will be providing a IIIF viewer so that the images that we host, and other IIIF images, can be viewed within the Archives Hub.

Assessing image quality: A key aim of this project is to assess the real-world situation of a typical archive repository in the UK, and how they can best engage with IIIF. Image resolution is one potential issue. Whilst any image can be served through the IIIF API, a lower resolution image will not give the end user the same sort of rich experience with zooming and analysing that a high resolution image provides. We will be considering the implications of the likely mix of different resolutions that many repositories will hold.

Looking at rights and IIIF: Rights are an important issue with archives, and we will be considering how to work with images at scale and ensure rights are respected.

Projects often have a finite goal of providing some kind of demonstrator showing what is possible, and they often pre-select material to work with. We are taking a different approach. We are working with a limited number of institutions, but we have not pre-selected ‘good’ material. We are simply going to try things out and see what works and what doesn’t, what the barriers are and how to overcome them. The process of ingest of the descriptive data and images will be part of the project. We are looking to consider both scalability and sustainability for the UK archive sector, including all different kinds of repositories with different resourcing and expertise, and with a whole variety of content and granularity of metadata.

Acknowledgement: This blog post cites the introductory video on IIIF which can be viewed within YouTube.