The good, the bad, and the unstructured… Open data in cultural heritage

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 put on 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 talk notes are below the embedded slides.

 

Warning: generalisations ahead.

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

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.

…the bad…

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. 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.

[And if you’ve published open data from cultural heritage collections, linked open data on the classical or ancient world, or any other form of structured data about the past, please add it to the wiki page for museum, gallery, library and archive APIs and machine-readable data sources for open cultural data.]

Drink your own champagne! (Nasjonalbiblioteket image)
Drink your own champagne! (Nasjonalbiblioteket image)

Crowdsourcing the world’s heritage

It’s all too easy to forget that there are crowdsourcing projects in languages other than English so I thought I’d collect some projects related to cultural heritage, history and science here (following my definition of crowdsourcing in cultural heritage as ‘asking the public to help with tasks that contribute to a shared, significant goal or research interest related to cultural heritage collections or knowledge’). This list is drawn from my PhD research, but this is a fast-moving field and I was focusing on early modern England, so inevitably this list will be missing loads of examples. Please suggest links to help people discover new projects! Also, I’m often taking my best guess at the correct translation for terms, so please correct me if I’ve misunderstood.

  • Some quick updates from DH2015…
  • Sefaria, ‘a living library of Jewish texts‘, ‘building a free living library of Jewish texts and their interconnections, in Hebrew and in translation
  • Footprints, Jewish books through time and place
  • La Grande Collecte is collecting French records about the First World War
  • KB Kranten – Editor, help correct digitized newspapers OCR. A collaboration between Dutch national library & Meertens Institute
  • Edvard Munchs tekster
  • ‘Your project goes here’ – what have I missed?

In the last week of July I’ll be teaching ‘Crowdsourcing Cultural Heritage’ with Ben Brumfield at the HILT Summer School (Humanities Intensive Learning + Teaching) at Indiana University-Purdue University Indianapolis (IUPUI) Indianapolis, Indiana. Last year’s course was a lot of fun, and was rated very highly by participants, so check out the HILT site if you’d like to spend a week getting to grips with crowdsourcing cultural heritage!

reseau-correct.fr correction
Correcting text from the Bibliothèque nationale de France on ‘Correct’.

 

Sentiment analysis is opinion turned into code

Modern elections are data visualisation bonanzas, and the 2015 UK General Election is no exception.

Last night seven political leaders presented their views in a televised debate. This morning the papers are full of snap polls, focus groups, body language experts, and graphs based on public social media posts describing the results. Graphs like the one below summarise masses of text using a technique called ‘sentiment analysis’, a form of computational language processing.* After a twitter conversation with @benosteen and @MLBrook I thought it was worth posting about the inherent biases in the tools that create these visualisations. Ultimately, ‘sentiment analysis’ is someone’s opinion turned into code – so whose opinion are you seeing?

ChartThis is a great time to remember that sentiment analysis – mining text to see what people are talking about and how they feel about it – is based on algorithms and software libraries that were created and configured by people who’ve made a series of small, accumulative decisions that affect what we see. You can think of sentiment analysis as a sausage factory with the text of tweets as the mince going in one end, and pretty pictures as the product coming out the other end. A healthy democracy needs the list of secret ingredients added during processing, not least because this election prominently features spin rooms and party lines.

What are those ‘ingredients’? The software used for sentiment analysis is ‘trained’ on existing text, and the type of text used affects what the software assumes about the world. For example, software trained on business articles is great at recognising company names but does not do so well on content taken from museum catalogues (unless the inventor of an object went on to found a company and so entered the trained vocabulary). The algorithms used to process text change the output, as does the length of the phrase analysed. The results are riddled with assumptions about tone, intent, the demographics of the poster and more.

In the case of an election, we’d also want to know when the text used for training was created, whether it looks at previous posts by the same person, and how long the software was running over the given texts. Where was the baseline of sentiment on various topics set? Who defines what ‘neutral’ looks like to an algorithm?

We should ask the same questions about visualisations and computational analysis that we’d ask about any document. The algorithmic ‘black box’ is a human construction, and just like every other text, software is written by people. Who’s paying for it? What sources did they use? If it’s an agency promoting their tools, do they mention the weaknesses and probable error rates or gloss over it? If it’s a political party (or a company owned by someone associated with a party), have they been scrupulous in weeding out bots? Do retweets count? Are some posters weighted more heavily? Which visualisations were discarded and how did various news outlets choose the visualisations they featured? Which parties are left out?

It matters because, all software has biases, and, as Brandwatch say, ‘social media will have a significant role in deciding the outcome of the general election’. And finally, as always, who’s not represented in the dataset?

* If you already know this, hopefully you’ll know the rest too. This post is deliberately light on technical detail but feel free to add more detailed information in the comments.

Creating simple graphs with Excel’s Pivot Tables and Tate’s artist data

I’ve been playing with Tate’s collections data while preparing for a workshop on data visualisation. On the day I’ll probably use Google Fusion Tables as an example, but I always like to be prepared so I’ve prepared a short exercise for creating simple graphs in Excel as an alternative.

The advantage of Excel is that you don’t need to be online, your data isn’t shared, and for many people, gaining additional skills in Excel might be more useful than learning the latest shiny web tool. PivotTables are incredibly useful for summarising data, so it’s worth trying them even if you’re not interested in visualisations. Pivot tables let you run basic functions – summing, averaging, grouping, etc – on spreadsheet data. If you’ve ever wanted spreadsheets to be as powerful as databases, pivot tables can help. I could create a pivot table then create a chart from it, but Excel has an option to create a pivot chart directly that’ll also create a pivot table for you to see how it works.

For this exercise, you will need Excel and a copy of the sample data: tate_artist_data_cleaned_v1_groupedbybirthyearandgender.xlsx
(A plain text CSV version is also available for broader compatibility: tate_artist_data_cleaned_v1_groupedbybirthyearandgender.csv.)

Work out what data you’re interested in

In this example, I’m interested in when the artists in Tate’s collection were born, and the overall gender mix of the artists represented. To make it easier to see what’s going on, I’ve copied those two columns of data from the original ‘artists’ file and copied them over to a new spreadsheet. As a row by row list of births, these columns aren’t ideal for charting as they are, so I want a count of artists per year, broken down by gender.

Insert PivotChart

On the ‘Insert’ menu, click on PivotTable to open the menu and display the option for PivotCharts.
Excel pivot table Insert PivotChart detail

Excel will select our columns as being the most likely thing we want to chart. That all looks fine to me so click ‘OK’.

Excel pivot table OK detailConfigure the PivotChart

This screen asking you to ‘choose fields from the PivotTable Field List’ might look scary, but we’ve only got two columns of data so you can’t really go wrong. Excel pivot table Choose fields

The columns have already been added to the PivotTable Field List on the right, so go ahead and tick the box next to ‘gender’ and ‘yearofBirth’. Excel will probably put them straight into the ‘Axis Fields’ box.

Leave yearofBirth under Axis Fields and drag ‘gender’ over to the ‘Values’ box next to it. Excel automatically turns it into ‘count of gender’, assuming that we want to sum the number of births per year.

The final task is to drag ‘gender’ down from the PivotTable Field List to ‘Legend Fields’ to create a key for which colours represent which gender. You should now see the pivot table representing the calculated values on the left and a graph in the middle.

Close-up of the pivot fields

 

When you click off the graph, the PivotTable options disappear – just click on the graph or the data again to bring them up.

Excel pivot table Results

You’ve made your first pivot chart!

You might want to drag it out a bit so the values aren’t so squished. Tate’s data covers about 500 years so there’s a lot to fit in.

Now you’ve made a pivot chart, have a play – if you get into a mess you can always start again!

Colophon: the screenshots are from Excel 2010 for Windows because that’s what I have.

About the data: this data was originally supplied by Tate. The full version on Tate’s website includes name, date of birth, place of birth, year of death, place of death and URL on Tate’s website. The latest versions of their data can be downloaded from http://www.tate.org.uk/about/our-work/digital/collection-data The source data for this file can be downloaded from https://github.com/tategallery/collection/blob/master/artist_data.csv This version was simplified so it only contains a list of years of birth and the gender of the artist. Some blank values for gender were filled in based on the artist’s name or a quick web search; groups of artists or artists of unknown gender were removed as were rows without a birth year. This data was prepared in March 2015 for a British Library course on ‘Data Visualisation for Analysis in Scholarly Research’ by Mia Ridge.

I’d love to hear if you found this useful or have any suggestions for tweaks.

My 25 most popular posts on Open Objects / Open Objects has moved

Sign saying 'Move on nothing to see here'
Time to move on…

After nine years with Blogger/Blogspot, the little niggles have become too much and I’ve moved Open Objects over to a self-hosted WordPress blog. If you’ve been redirected from there, use the search box to find specific posts, check out tags or categories, or check out the 25 most popular posts (since 2008, when I added stats):

And two bonus favourite posts:

A New Year’s resolution for start-ups, PRs and journalists writing about museums

Some technology in a museum

Dear journalists, start-ups, agencies and PR folk,

I get that you want to talk about how amazing some new app, product or company is, but can you please do so without resorting to lazy, outdated cliches?

I’ve seen far too many articles make un-evidenced claims like ‘museums don’t realise people have different preferences in their galleries’ or that museums are ‘repeatedly turning a blind eye to technology, rather than recognizing it could be used to deliver an experience unique to every visitor’. If your app, product or company is good enough, you shouldn’t need to do the ‘competition’ down to stand out, and besides, sometimes my eyes hurt from rolling so hard.

I know that traditionally everyone makes New Years resolutions for themselves, but in the spirit of disruption (ha! not really) I’d like to suggest a New Years resolution for you:  leave those cliches about dusty old museums behind and find out what people in your city love about their museums. Find a new angle for your piece, one that recognises that museums don’t always get it right but that they’ve probably been thinking about the best uses of technology for their audiences longer than you have.

Museums have been experimenting with new technologies for decades. The post-2008 financial cuts might have reduced the number of digital pilot projects across the sector as a whole but most museums are still investing in improving the visitor experience, engaging wider audiences and making a difference in the lives of their communities. You probably don’t need to lecture them on what they could be doing – they already know, and wish they had more resources to do cool things.

You could even check out past papers and discussions at conferences and groups like the Museum Computer Network (MCN), Museums and the Web, the Museums Computer Group (MCG), MuseumNext, the Visitor Studies Group (VSG), the many fantastic museum technology, design and audience research blogs, the #musetech hashtag (when agencies aren’t spamming it) and much, much more if you wanted some inspiration or to learn what’s been tried in the past and how it worked out…

Yours in museums,

Mia

The rise of interpolated content?

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.

Three ways you can help with ‘In their own words: collecting experiences of the First World War’ (and a CENDARI project update)

Somehow it’s a month since I posted about my CENDARI research project (in Moving forward: modelling and indexing WWI battalions) on this site. That probably reflects the rhythm of the project – less trying to work out what I want to do and more getting on with doing it. A draft post I started last month simply said, ‘A lot of battalions were involved in World War One’. I’ll do a retrospective post soon, and here’s a quick summary of on-going work.

First, a quick recap. My project has two goals – one, to collect a personal narrative for each battalion in the Allied armies of the First World War; two, to create a service that would allow someone to ask ‘where was a specific battalion at a specific time?’. Together, they help address a common situation for people new to WWI history who might ask something like ‘I know my great-uncle was in the 27th Australian battalion in March 1916, where would he have been and what would he have experienced?’.

I’ve been working on streamlining and simplifying the public-facing task of collecting a personal narrative for each battalion, and have written a blog post, Help collect soldiers’ experiences of WWI in their own words, that reduces it to three steps:

  1. Take one of the diaries, letters and memoirs listed on the Collaborative Collections wiki, and
  2. Match its author with a specific regiment or battalion.
  3. Send in the results via this form.

If you know of a local history society, family historian or anyone else who might be interested in helping, please send them along to this post: Help collect soldiers’ experiences of WWI in their own words.

Work on specifying the relevant data structures to support a look-up service to answer questions about a specific units location and activities at a specific time largely moved to the wiki:

You can see the infobox structures in progress by flipping from the talk to the Template tabs. You’ll need to request an account to join in but more views, sample data and edge cases would be really welcome.

Populating the list of battalions and other units has been a huge task in itself, partly because very few cultural institutions have definitive lists of units they can (or want to) share, but it’s necessary to support both core goals. I’ve been fortunate to have help (see ‘Thanks and recent contributions’ on ‘How you can help‘) but the task is on-going so get in touch if you can help!

So there are three different ways you can help with ‘In their own words: collecting experiences of the First World War':

Finally, last week I was in New Zealand to give a keynote on this work at the National Digital Forum. The video for ‘Collaborative collections through a participatory commons‘ is online, so you can catch up on the background for my project if you’ve got 40 minutes or so to spare. Should you be in Dublin, I’m giving a talk on ‘A pilot with public participation in historical research: linking lived experiences of the First World War’ at the Trinity Long Room Hub today (thus the poster).

And if you’ve made it this far, perhaps you’d like to apply for a CENDARI Visiting Research Fellowships 2015 yourself?

All the things I didn’t say in my welcome to UKMW14 ‘Museums beyond the web’…

Here are all the things I (probably) didn’t say in my Chair’s welcome for the Museums Computer Group annual conference… Other notes, images and tweets from the day are linked from ‘UKMW14 round-up: posts, tweets, slides and images‘.

Welcome to MCG’s UKMW14: Museums beyond the web! We’ve got great speakers lined up, and we’ve built in lots of time to catch up and get to know your peers, so we hope you’ll enjoy the day.

It’s ten years since the MCG’s Museums on the Web became an annual event, and it’s 13 years since it was first run in 2001. It feels like a lot has changed since then, but, while the future is very definitely here, it’s also definitely not evenly distributed across the museum sector. It’s also an interesting moment for the conference, as ‘the web’ has broadened to include ‘digital’, which in turn spans giant distribution networks and tiny wearable devices. ‘The web’ has become a slightly out-dated shorthand term for ‘audience-facing technologies’.

When looking back over the last ten years of programmes, I found myself thinking about planetary orbits. Small planets closest to the sun whizz around quickly, while the big gas giants move incredibly slowly. If technology start-ups are like Mercury, completing a year in just 88 Earth days, and our audiences are firmly on Earth time, museum time might be a bit closer to Mars, taking two Earth years for each Mars year, or sometimes even Jupiter, completing a circuit once every twelve years or so.

But museums aren’t planets, so I can only push that metaphor so far. Different sections of a museum move at different speeds. While heroic front of house staff can observe changes in audience behaviours on a daily basis and social media platforms can be adopted overnight, websites might be redesigned every few years, but galleries are only updated every few decades (if you’re lucky). For a long time it felt like museums were using digital platforms to broadcast at audiences without really addressing the challenges of dialogue or collaborating with external experts.

But at this point, it seems that, finally, working on digital platforms like the web has pushed museums to change how they work. On a personal level, the need for specific technical skills hasn’t changed, but more content, education and design jobs work across platforms, are consciously ‘multi-channel’ and audience rather than platform-centred in their focus. Web teams seem to be settling into public engagement, education, marketing etc departments as the idea of a ‘digital’ department slowly becomes an oxymoron. Frameworks from software development are slowly permeating organisations that use to think in terms of print runs and physical gallery construction. Short rounds of agile development are replacing the ‘build and abandon after launch’ model, voices from a range of departments are replacing the disembodied expert voice, and catalogues are becoming publications that change over time.

While many of us here are comfortable with these webby methods, how will we manage the need to act as translators between digital and museums while understanding the impact of new technologies? And how can we help those who are struggling to keep up, particularly with the impact of the cuts?

Today is a chance to think about the technologies that will shape the museums of the future. What will audiences want from us? Where will they go looking for information and expertise, and how much of that information and expertise should be provided by museums? How can museums best provide access to their collections and knowledge over the next five, ten years?

We’re grateful to our sponsors, particularly as their support helps keep ticket prices affordable. Firstly I’d like to thank our venue sponsors, the Natural History Museum. Secondly, I’d like to thank Faversham & Moss for their sponsorship of this conference. Go chat to them and find out more about their work!

Moving forward: modelling and indexing WWI battalions

A super-quick update from my CENDARI Fellowship this week. I set up the wiki for In their own words: linking lived experiences of the First World War a week ago but only got stuck into populating it with lists of various national battalions this week. My current task list, copied from the front page is to:

If you can help with any of that, let me know! Or just get stuck in and edit the site.
I’ve started another Google Doc with very sketchy Notes towards modelling information about World War One Battalions. I need to test it with more battalion histories and update it iteratively. At this stage my thinking is to turn it into an InfoBox format to create structured data via the wiki. It’s all very lo-fi and much less designed than my usual projects, but I’m hoping people will be able to help regardless.
So, in this phase of the project, the aim is find a personal narrative – a diary, letters, memoirs or images – for each military unit in the British Army. Can you help?