‘If you follow @BL_DigiSchol or #DigitalHumanities hashtags on twitter, you might have seen a burst of data science, history and digital humanities jobs being advertised. In this post, Dr Mia Ridge of the Library’s Digital Scholarship team provides some background to contextualise the jobs advertised with the ‘Living with Machines’ project.
We are seeking to appoint several new roles who will collaborate on an exciting new project developed by the British Library and The Alan Turing Institute, the national centre for data science and artificial intelligence.
You may have noticed that the British Library is also currently advertising for a Curator, Newspaper Data (closes Sept 9). This isn’t related to Living with Machines, but with an approach of applying data-driven journalism and visualisation techniques to historical collections, it should have some lovely synergies and opportunities to share work in progress with the project team. There’s also a Research Software Engineer advertised that will work closely with many of the same British Library teams.
In my presentation, I responded to some of the questions posed in the workshop outline:
In this workshop we want to explore how network visualisations and infrastructures will change the research and outreach activities of cultural heritage professionals and historians. Among the questions we seek to discuss during the workshop are for example: How do users benefit from graphs and their visualisation? Which skills do we expect from our users? What can we teach them? Are SNA [social network analysis] theories and methods relevant for public-facing applications? How do graph-based applications shape a user’s perception of the documents/objects which constitute the data? How can applications benefit from user engagement? How can applications expand and tap into other resources?
A rough version of my talk notes is below. The original slides are also online.
Network visualisations and the ‘so what?’ problem
While I may show examples of individual network visualisations, this talk isn’t a critique of them in particular. There’s lots of good practice around, and these lessons probably aren’t needed for people in the room.
Fundamentally, I think network visualisations can be useful for research, but to make them more effective tools for outreach, some challenges should be addressed.
I’m a Digital Curator at the British Library, mostly working with pre-1900 collections of manuscripts, printed material, maps, etc. Part of my job is to help people get access to our digital collections. Visualisations are a great way to firstly help people get a sense of what’s available, and then to understand the collections in more depth.
I’ve been teaching versions of an ‘information visualisation 101’ course at the BL and digital humanities workshops since 2013. Much of what I’m saying now is based on comments and feedback I get when presenting network visualisations to academics, cultural heritage staff (who should be a key audience for social network analyses).
Provocation: digital humanists love network visualisations, but ordinary people say, ‘so what’?
And this is a problem. We’re not conveying what we’re hoping to convey.
When teaching datavis, I give people time to explore examples like this, then ask questions like ‘Can you tell what is being measured or described? What do the relationships mean?’. After talking about the pros and cons of network visualisations, discussion often reaches a ‘yes, but so what?’ moment.
Here are some examples of problems ordinary people have with network visualisations…
Spatial layout based on the pragmatic aspects of fitting something on the screen using physics, rules of attraction and repulsion doesn’t match what people expect to see. It’s really hard for some to let go of the idea that spatial layout has meaning. The idea that location on a page has meaning of some kind is very deeply linked to their sense of what a visualisation is.
Animated physics is … pointless?
People sometimes like the sproinginess when a network visualisation resettles after a node has been dragged, but waiting for the animation to finish can also be slow and irritating. Does it convey meaning? If not, why is it there?
Size, weight, colour = meaning?
The relationship between size, colour, weight isn’t always intuitive – people assume meaning where there might be none.
In general, network visualisations are more abstract than people expect a visualisation to be.
‘What does this tell me that I couldn’t learn as quickly from a sentence, list or table?’
Scroll down the page that contains the network graph above and you get other visualisations. Sometimes they’re much more positively received, particularly people feel they learn more from them than from the network visualisation.
Onto other issues with ‘network visualisations as communication’…
Which algorithmic choices are significant?
It’s hard for novices to know which algorithmic and data-cleaning choices are significant, and which have a more superficial impact.
Images travel extremely well on social media. When they do so, they often leave information behind and end up floating in space. Who created this, and why? What world view does it represent? What source material underlies it, how was it manipulated to produce the image? Can I trust it?
‘Can’t see the wood for the trees’
When I showed this to a class recently, one participant was frustrated that they couldn’t ‘see the wood for the trees’. The visualisations gives a general impression of density, but it’s not easy to dive deeper into detail.
Stories vs hairballs
But when I started to explain what was being represented – the ways in which stories were copied from one newspaper to another – they were fascinated. They might have found their way there if they’d read the text but again, the visualisation is so abstract that it didn’t hint at what lay underneath. (Also I have only very, very rarely seen someone stop to read the text before playing with a visualisation.)
No sense of change over time
This flattening of time into one simultaneous moment is more vital for historical networks than for literary ones, but even so, you might want to compare relationships between sections of a literary work.
No sense of texture, detail of sources
All network visualisations look similar, whether they’re about historical texts or cans of baked beans. Dots and lines mask texture, and don’t always hint at the depth of information they represent.
There’s a lot to take on to really understand what’s being expressed in a network graph.
There is some hope…
Onto the positive bit!
Interactivity is engaging
People find the interactive movement, the ability to zoom and highlight links engaging, even if they have no idea what’s being expressed. In class, people started to come up with questions about the data as I told them more about what was represented. That moment of curiosity is an opportunity if they can dive in and start to explore what’s going on, what do the relationships mean?
…but different users have different interaction needs
For some, there’s that frustration expressed earlier they ‘can’t get to see a particular tree’ in the dense woods of a network visualisation. People often want to get to the detail of an instance of a relationship – the lines of text, images of the original document – from a graph.
This mightn’t be how network visualisations are used in research, but it’s something to consider for public-facing visualisations. How can we connect abstract lines or dots to detail, or provide more information about what the relationship means, show the quantification expressed as people highlight or filter parts of a graph? A harder, but more interesting task is hinting at the texture or detail of those relationships.
Proceed, with caution
One of the workshop questions was ‘Are social network analysis theories and methods relevant for public-facing applications?’ – and maybe the answer is a qualified yes. As a working tool, they’re great for generating hypotheses, but they need a lot more care before exposing them to the public.
[As an aside, I’d always taken the difference between visualisations as working tools for exploring data – part of the process of investigating a research question – and visualisation as an output – a product of the process, designed for explanation rather than exploration – as fundamental, but maybe we need to make that distinction more explicit.]
But first – who are your ‘users’?
During this workshop, at different points we may be talking about different ‘users’ – it’s useful to scope who we mean at any given point. In this presentation, I was talking about end users who encounter visualisations, not scholars who may be organising and visualising networks for analysis.
Sometimes a network visualisationisn’t the answer … even if it was part of the question.
As an outcome of an exploratory process, network visualisations are not necessarily the best way to present the final product. Be disciplined – make yourself justify the choice to use network visualisations.
No more untethered images
Include an extended caption – data source, tools and algorithms used. Provide a link to find out more – why this data, this form? What was interesting but not easily visualised? Let people download the dataset to explore themselves?
Present visualisations as the tip of the data iceberg
Lots of interesting data doesn’t make it into a visualisation. Talking about what isn’t included and why it was left out is important context.
Talk about data that couldn’t exist
Beyond the (fuzzy, incomplete, messy) data that’s left out because it’s hard to visualise, data that never existed in the first place is also important:
‘because we’re only looking on one axis (letters), we get an inflated sense of the importance of spatial distance in early modern intellectual networks. Best friends never wrote to each other; they lived in the same city and drank in the same pubs; they could just meet on a sunny afternoon if they had anything important to say. Distant letters were important, but our networks obscure the equally important local scholarly communities.’ Scott Weingart, ‘Networks Demystified 8: When Networks are Inappropriate’
Help users learn the skills and knowledge they need to interpret network visualisations in context.
How? Good question! This is the point at which I hand over to you…
Probably the biggest news is the launch of citizenscience.gov, as it signals the importance of citizen science and crowdsourcing to the US government.
From the press release: ‘the White House announced that the U.S. General Services Administration (GSA) has partnered with the Woodrow Wilson International Center for Scholars (WWICS), a Trust instrumentality of the U.S. Government, to launch CitizenScience.gov as the new hub for citizen science and crowdsourcing initiatives in the public sector.
CitizenScience.gov provides information, resources, and tools for government personnel and citizens actively engaged in or looking to participate in citizen science and crowdsourcing projects. … Citizen science and crowdsourcing are powerful approaches that engage the public and provide multiple benefits to the Federal government, volunteer participants, and society as a whole.’
There’s also work to ‘standardize data and metadata related to citizen science, allowing for greater information exchange and collaboration both within individual projects and across different projects’.
Apparently you can finish a thesis but you can’t stop scanning for articles and blog posts on your topic. Sharing them here is a good way to shake the ‘I should be doing something with this’ feeling.* This is a fairly random sample of recent material, but if people find it useful I can go back and pull out other things I’ve collected.
I was in London this week for the Linked Pasts event, where I presented on trends and practices for open data in cultural heritage. Linked Pasts was a colloquium on linked open data in cultural heritage organised by the Pelagios project (Leif Isaksen, Elton Barker and Rainer Simon with Pau de Soto). I really enjoyed the other papers, which included thoughtful, grounded approaches to structured data for historical periods, places and people, recognition of the importance of designing projects around audience needs (including user research), the relationship between digital tools and scholarly inquiry, visualisations as research tools, and the importance of good infrastructure for digital history.
My discussion points are based on years of conversations with other cultural heritage technologists in museums, libraries, and archives, but inevitably I’ll have blind spots. For example, I’m focusing on the English-speaking world, which means I’m not discussing the great work that Dutch and Japanese organisations are doing. I’ve undoubtedly left out brilliant specific examples in the interests of focusing on broader trends. The point is to start conversations, to bring issues out into the open so we can collectively decide how to move forward.
The good news is that more and more open cultural data is being published. Organisations have figured out that a) nothing bad is likely to happen and that b) they might get some kudos for releasing open data.
Generally, organisations are publishing the data that they have to hand – this means it’s mostly collections data. This data is often as messy, incomplete and fuzzy as you’d expect from records created by many different people using many different systems over a hundred or more years.
Copyright restrictions mean that images mightn’t be included. Furthermore, because it’s often collections data, it’s not necessarily rich in interpretative information. It’s metadata rather than data. It doesn’t capture the scholarly debates, the uncertain attributions, the biases in collecting… It certainly doesn’t capture the experience of viewing the original object.
Licensing issues are still a concern. Until cultural organisations are rewarded by their funders for releasing open data, and funders free organisations from expectations for monetising data, there will be damaging uncertainty about the opportunity cost of open data.
Non-commercial licenses are also an issue – organisations and scholars might feel exploited if others who have not contributed to the process of creating it can commercially publish their work. Finally, attribution is an important currency for organisations and scholars but most open licences aren’t designed with that in mind.
…and the unstructured
The data that’s released is often pretty unstructured. CSV files are very easy to use, so they help more people get access to information (assuming they can figure out GitHub), but a giant dump like this doesn’t provide stable URIs for each object. Records in data dumps rarely link to external identifiers like the Getty’s Thesaurus of Geographic Names, Art & Architecture Thesaurus (AAT) or Union List of Artist Names, or vernacular sources for place and people names such as Geonames or DBPedia. And that’s fair enough, because people using a CSV file probably don’t want all the hassle of dereferencing each URI to grab the place name so they can visualise data on a map (or whatever they’re doing with the data). But it also means that it’s hard for someone to reliably look for matching artists in their database, and link these records with data from other organisations.
So it’s open, but it’s often not very linked. If we’re after a ‘digital ecosystem of online open materials’, this open data is only a baby step. But it’s often where cultural organisations finish their work.
Classics > Cultural Heritage?
But many others, particularly in the classical and ancient world, have managed to overcome these issues to publish and use linked open data. So why do museums, libraries and archives seem to struggle? I’ll suggest some possible reasons as conversation starters…
Not enough time
Organisations are often busy enough keeping their internal systems up and running, dealing with the needs of visitors in their physical venues, working on ecommerce and picture library systems…
Not enough skills
Cultural heritage technologists are often generalists, and apart from being too time-stretched to learn new technologies for the fun of it, they might not have the computational or information science skills necessary to implement the full linked data stack.
Some cultural heritage technologists argue that they don’t know of any developers who can negotiate the complexities of SPARQL endpoints, so why publish it? The complexity is multiplied when complex data models are used with complex (or at least, unfamiliar) technologies. For some, SPARQL puts the ‘end’ in ‘endpoint’, and ‘RDF triples‘ can seem like an abstraction too far. In these circumstances, the instruction to provide linked open data as RDF is a barrier they won’t cross.
But sometimes it feels as if some heritage technologists are unnecessarily allergic to complexity. Avoiding unnecessary complexity is useful, but progress can stall if they demand that everything remains simple enough for them to feel comfortable. Some technologists might benefit from working with people more used to thinking about structured data, such as cataloguers, registrars etc. Unfortunately, linked open data falls in the gap between the technical and the informatics silos that often exist in cultural organisations.
And organisations are also not yet using triples or structured data provided by other organisations [with the exception of identifiers for e.g. people, places and specific vocabularies]. They’re publishing data in broadcast mode; it’s not yet a dialogue with other collections.
Not enough data
In a way, this is the collections documentation version of the technical barriers. If the data doesn’t already exist, it’s hard to publish. If it needs work to pull it out of different departments, or different individuals, who’s going to resource that work? Similarly, collections staff are unlikely to have time to map their data to CIDOC-CRM unless there’s a compelling reason to do so. (And some of the examples given might use cultural heritage collections but are a better fit with the work of researchers outside the institution than the institution’s own work).
It may be easier for some types of collections than others – art collections tend to be smaller and better described; natural history collections can link into international projects for structured data, and libraries can share cataloguing data. Classicists have also been able to get a critical mass of data together. Your local records office or small museum may have more heterogeneous collections, and there are fewer widely used ontologies or vocabularies for historical collections. The nature of historical collections means that ‘small ontologies, loosely joined’, may be more effective, but creating these, or mapping collections to them, is still a large piece of work. While there are tools for mapping to data structures like Europeana’s data model, it seems the reasons for doing so haven’t been convincing enough, so far. Which brings me to…
Not enough benefits
This is an important point, and an area the community hasn’t paid enough attention to in the past. Too many conversations have jumped straight to discussion about the specific standards to use, and not enough have been about the benefits for heritage audiences, scholars and organisations.
Many technologists – who are the ones making decisions about digital standards, alongside the collections people working on digitisation – are too far removed from the consumers of linked open data to see the benefits of it unless we show them real world needs.
There’s a cost in producing data for others, so it needs to be linked to the mission and goals of an organisation. Organisations are not generally able to prioritise the potential, future audiences who might benefit from tools someone else creates with linked open data when they have so many immediate problems to solve first.
While some cultural and historical organisations have done good work with linked open data, the purpose can sometimes seem rather academic. Linked data is not always explained so that the average, over-worked collections or digital team will that convinced by the benefits outweigh the financial and intellectual investment.
No-one’s drinking their own champagne
You don’t often hear of people beating on the door of a museum, library or archive asking for linked open data, and most organisations are yet to map their data to specific, widely-used vocabularies because they need to use them in their own work. If technologists in the cultural sector are isolated from people working with collections data and/or research questions, then it’s hard for them to appreciate the value of linked data for research projects.
The classical world has benefited from small communities of scholar-technologists – so they’re not only drinking their own champagne, they’re throwing parties. Smaller, more contained collections of sources and research questions helps create stronger connections and gives people a reason to link their sources. And as we’re learning throughout the day, community really helps motivate action.
(I know it’s normally called ‘eating your own dog food’ or ‘dogfooding’ but I’m vegetarian, so there.)
Linked open data isn’t built into collections management systems
Getting linked open data into collections management systems should mean that publishing linked data is an automatic part of sharing data online.
Chicken or the egg?
So it’s all a bit ‘chicken or the egg’ – will it stay that way? Until there’s a critical mass, probably. These conversations about linked open data in cultural heritage have been going around for years, but it also shows how far we’ve come.
One thing that might stand out when we look back at 2014 is the rise of interpolated content. We’ve become used to translating around auto-correct errors in texts and emails but we seem to be at a tipping point where software is going ahead and rewriting content rather than prompting you to notice and edit things yourself.
iOS doesn’t just highlight or fix typos, it changes the words you’ve typed. To take one example, iOS users might use ‘ill’ more than they use ‘ilk’, but if I typed ‘ilk’ I’m not happy when it’s replaced by an algorithmically-determined ‘ill’. As a side note, understanding the effect of auto-correct on written messages will be a challenge for future historians (much as it is for us sometimes now).
And it’s not only text. In 2014, Adobe previewed GapStop, ‘a new video technology that eases transitions and removes pauses from video automatically’. It’s not just editing out pauses, it’s creating filler images from existing images to bridge the gaps so the image doesn’t jump between cuts. It makes it a lot harder to tell when someone’s words have been edited to say something different to what they actually said – again, editing audio and video isn’t new, but making it so easy to remove the artefacts that previously provided clues to the edits is.
Photoshop has long let you edit the contrast and tone in images, but now their Content-Aware Move, Fill and Patch tools can seamlessly add, move or remove content from images, making it easy to create ‘new’ historical moments. The images on extrapolated-art.com, which uses ‘[n]ew techniques in machine learning and image processing […] to extrapolate the scene of a painting to see what the full scenery might have looked like’ show the same techniques applied to classic paintings.
But photos have been manipulated since they were first used, so what’s new? As one Google user reported in It’s Official: AIs are now re-writing history, ‘Google’s algorithms took the two similar photos and created a moment in history that never existed, one where my wife and I smiled our best (or what the algorithm determined was our best) at the exact same microsecond, in a restaurant in Normandy.’ The important difference here is that he did not create this new image himself: Google’s scripts did, without asking or specifically notifying him. In twenty years time, this fake image may become part of his ‘memory’ of the day. Automatically generated content like this also takes the question of intent entirely out of the process of determining ‘real’ from interpolated content. And if software starts retrospectively ‘correcting’ images, what does that mean for our personal digital archives, for collecting institutions and for future historians?
Interventions between the act of taking a photo and posting it on social media might be one of the trends of 2015. Facebook are about to start ‘auto-enhancing’ your photos, and apparently, Facebook Wants To Stop You From Uploading Drunk Pictures Of Yourself. Apparently this is to save your mum and boss seeing them; the alternative path of building a social network that don’t show everything you do to your mum and boss was lost long ago. Would the world be a better place if Facebook or Twitter had a ‘this looks like an ill-formed rant, are you sure you want to post it?’ function?
So 2014 seems to have brought the removal of human agency from the process of enhancing, and even creating, text and images. Algorithms writing history? Where do we go from here? How will we deal with the increase of interpolated content when looking back at this time? I’d love to hear your thoughts.