Faces offer an instant connection to history, reminding us that the past is full of people. People like us, but different. People with their own lives and stories. People we might only know through a picture, a few documentary fragments, or a newspaper article.

Eyes on the Past is an experimental interface, built in a weekend. I’m exploring whether faces can provide a way to explore more than 120 million newspaper articles available on Trove.

This collection of tweets tells the story of its development.

There’s some details about the software used in the site’s about page. You can view the harvest/detection and the website code on GitHub.

No, nothing to do with Easter or eggs, but it’s Easter Sunday and who can resist a good opportunity for a bad pun?

This is another catch-up post, pulling together some recent experiments. If nothing else, it’ll help me keep track of things I’m otherwise likely to forget.

WWI Faces

In our last instalment I was playing around with some WWI data from the State Library of South Australia. I’m really pleased to report that SLSA staff have used my experiments to help them add Trove links to more than 6,000 of their Heroes of the Great War records. Here’s an example — note the ‘article’ link which goes straight to a digitised newspaper article in Trove. With some good data and bit of API wrangling we’ve now established rich linkages between an important WWI resource and Trove. Win!

I’ve also continued my fiddling with articles from the Adelaide Chronicle as I start to think about how Trove’s newspapers might be used in a WWI exhibition being developed by the National Library. At the end of my last post I’d created a list of articles from the Chronicle that were likely to include biographical details of WWI personnel. I knew that many of these included portrait photos, so I filtered them on Trove’s built-in ‘illustrated’ facet and saved the page images for the remaining articles. You can browse the resulting collection of pages on Dropbox. As you can see there are indeed many portraits of service people.

So the next step was to try and extract the portraits from the pages. This was rather familiar territory, as I’d already used a facial detection script to create The Real Face of White Australia. But I wasn’t sure how the pattern recognition software would cope with the lower quality newspaper images. After getting all the necessary libraries installed (the hardest bit of the whole process), I pointed the script at the page images and… it worked!

From 141 pages I extracted 1,738 images, and most of them were faces. You can browse all 1,738, but be warned, I’ve just dumped them onto a single page and added a bit of Isotope magic — so they’ll take a fair while to load and your browser might object. You’ll also notice that I haven’t tried to filter out photos of non-service people, I just wanted to see if it worked. And it does. Even in this rough form you can sense some of the emotive power. What’s really amazing is the way that even small images of faces in group photographs were identified. All I was aiming for at this stage was a proof of concept — yes, I can extract photos of WWI service people from newspapers. Hmmm…

Trove in space

All the faces above were from one South Australian newspaper. Several years ago I worked on a project to map places of birth and enlistment of WWI service people, and while I have no interest in the national mythologies surrounding WWI, I do still wonder about the local impact of war — all those small communities sending off their sons and daughters…

So I’m wondering whether we might be able to use the digitised newspapers in Trove to navigate from place to face. To choose a town anywhere in Australia, and present photographs of service personnel published in nearby newspapers.

I now know I can extract the photos, but how can we navigate Trove newspapers by location? Time for a new experiment…

The Trove API provides a complete list of digitised newspaper titles. You’ll notice that some of the titles include a place name as part of the summary information in brackets, while many others will include place names in their titles, for example:

• Illawarra Daily Mercury (Wollongong, NSW : 1950 – 1954)
• Hawkesbury Herald (Windsor, NSW : 1902 – 1945)
• Kiama Examiner (NSW : 1858 – 1859)
• Narromine News and Trangie Advocate (NSW : 1898 – 1955)

I haven’t had much luck getting automated named entity extraction tools to work on short text strings like this, so I decided to roll my own using Geoscience Australia’s Gazetteer of Australia 2012. I opened up the GML file containing all Australian places and saved the populated locations to my own Mongo database. This gave me a handy place name database, complete with geo-locations.

Next I went to work on the newspaper titles. Extracting the places from the summary information was easy because they followed a regular pattern, but finding them in the body of the title was trickier. First I had to exclude those words that were obviously not place names. Aside from the usual stopwords (‘and’ and ‘the’), there are many words that commonly occur in newspaper titles — ‘Herald’, ‘Star’, ‘Chronicle’ etc. To find these words I pulled apart all the titles and calculated the frequency of every word. You can explore the raw results — ‘Advertiser’ (116) wins by a large margin, with ‘Times’ (67) in second place. From these results I could create a list of words that I knew were not places and could safely be ignored.

Then it was just a matter of tokenising the titles (breaking them up into individual words), removing all the stopwords (the standard list and my special list), and then looking up the words in my place name database. I did this in two passes, first as bigrams (pairs of words), and then as single words — this allowed me to find compound place names like ‘North Melbourne’. The Trove API gives you s ‘state’ value for each title, so I could use this in the query to increase accuracy.

If I found a place name, I added the place details, including the latitude and longitude, to the title record from the API and included it in my own newspaper title database.

So I ended up to two databases — one with geolocated places, and another with geolocated newspapers. That meant I could build a simple interface to find newspaper titles by place. It’s nothing fancy — just another proof of concept — but it works pretty well. Just type in a place name and select a state and a query is run against the place name database. If the place is found then the latitude and longitude is fed to the titles database to find the closest newspapers. After removing some duplicates, the 10 nearest newspapers are displayed.

Building some sort of map interface on top of this is pretty trivial. What’s more important is to do some analysis of my place matching to see what I might have missed. But so far so good!

Trove is…

Trove is more than newspapers. This is a message the Trove team tries to emphasise at every opportunity. The digitised newspapers are an incredible resource of course, but there’s so much other interesting stuff to explore.

To try and give a quick and easy introduction to this richness, I created a simple dashboard-type view of Trove, imaginatively titled Trove is…

Trove is… gives a basic status report on each of the 1o Trove zones, with statistics updated daily (except for the archived websites as there’s no API access at the moment). The BIG NUMBERS are counter-balanced by a single randomly-selected example from each zone. It’s a summary, an overview, a portal and a snapshot. Reload the page and the zones will be reordered and the examples will change.

It’s pretty simple, but I think it works quite well, and thanks to Twitter Bootstrap it looks really nice on my phone! But while the idea was simple, the implementation was pretty tricky — particularly the balance between randomness and performance. If all the examples were truly random, drawn from the complete holdings to Trove on every page reload, you’d spend a lot of time watching spinning arrows waiting for content to appear. I tried a number of different approaches and finally settled on a system where random selections of 100 resources per zone are made every hour by background processes and cached. When you load the page, this cache is queried and an item selected. So if you keep hitting reload you’ll probably notice that some examples reappear. It’s random, but at any moment the pool of possibilities is quite limited. Come back later in the day and everything will be different.

Anyway, if anyone asks you what Trove is, you now know where to point them…

Who listens to the radio?

After a lot of hard work, the Trove team was excited to announce recently that more than 200,000 records from 54 ABC Radio National programs were available through Trove.

To make it a bit easier to explore this wonderful new content, I created a simple search interface. All it really does is help you build a query using the RN program titles, and then sends the query off to Trove. Not fancy, but useful (my family motto).

Of course, I couldn’t leave my Twitter bot family out of the action. @TroveBot has been Radio National enabled. Just tweet the tag #abcrn at him to receive a randomly-selected Radio National story. To search for something amidst the RN records, just tweet a keyword or two and add the #abcrn tag to limit the results. Consult the TroveBot manual for complete operating instructions.

In a word…

But the Radio National content is not just findable through the Trove web interface — all that lovely data is freely accessible through the Trove API. That includes just about every segment of every edition of the ABC’s flagship current affairs programs, AM, PM, and The World Today from 1999 onwards. What sort of questions could you ask of this data?

I’ll be writing something soon on the Trove blog about accessing these riches, but I couldn’t resist having a play. So I harvested all the RN data via the API and built a new thing…

It’s called In a word: Currents in Australian affairs, 2003–2013, and for once it’s quite well documented, so I won’t go into details here. I’ll just say that it’s one of my favourite creations, and I hope you find it interesting.

Addendum (21 April) — The Tung Wah Newspaper Index

See, I told you I forget things…

I recently finished resurrecting the Tung Wah Newspaper Index. Kate has described the original project on her blog, and there’s a fair bit of contextual information on the site, so I won’t go into details here. Suffice it to say it’s an important resource for Chinese Australian history that had succumbed to technological decay.

The original FileMaker database has been MySqld, Solrised, and Bootstrapped to get it all working nicely. I also took the opportunity to introduce a bit of LOD love, with plenty of machine-readable data built-in.

The whole site follows an interface as API type pattern. So if you want a resource as JSON-LD, you just change the file extension to .json. To help you out, there are links at the bottom of each page to the various serialisations, and of course you can also use content negotiation to get what you’re after. There’s some examples of all this in the GitHub repository, as well as a CSV dump of the whole database.

I can’t resist a challenge, particularly when it involves lots of new historical data and an excuse to muck around with the Trove API. So when Katie Hannan from the State Library of South Australia asked me about putting the API to work to enrich one of their World War I datasets, I had to dive in and have a play.

The dataset consists of references to South Australian WWI service personnel published in the Adelaide Chronicle between 1914 and 1919. In a massive effort starting back in 2000, SLSA staff manually extracted more than 13,000 references and grouped them under 9709 headings, mostly names. You can explore the data in the SLSA catalogue as part of the Heroes of the Great War collection.

It’s great data, but it would be even better if there was a direct link to each article in Trove — hence Katie’s interest in the possibilities of the API!

Katie sent me a spreadsheet containing the data. Each row corresponds to an individual entry and includes an identifier, a name, a year, and a list of references separated by semicolons. My plan was simple, for each row I’d construct a search based on the name, then loop through the search results to try find an article that matched the date and page number of each reference. This might seem a bit cumbersome, but currently there’s no way of searching Trove for newspaper articles published on a particular day.

You’ll find all of the code on GitHub. I’ve tried to include plenty of comments to make it easy to follow along.

Let’s look at the entry for Lieutenant Frank Rosevear. It includes the following references: ‘Chronicle, 7 September 1918, p. 27, col. c;Chronicle, 14 September 1918, p. 24, col. a p.38, col. d’. If you look closely, you’ll see that there’s two page numbers for 14 September 1918, so there’s actually three references included in this string. The first thing I had to do was to pull out all the references and format them in a standard way.

Assuming that the last name was the surname, I then constructed a query that searched for an exact match of the surname together with at least one of the other names. In Lieutenant Rosevear’s case the query would’ve been ‘fulltext:”Rosevear” AND (“Lieutenant” OR “Frank”)’. Note the use of the ‘fulltext’ modifier to indicate an exact match. To this query I added a date filter to limit the search to the specified year and an ‘l-title’ value to search only the Adelaide Chronicle.

You can see the results for this query in my Trove API console. Try modifying the query string to see what difference it makes.

Once the results came back from the API I compared them to the references, looking for matches on both the date and page number. You might notice that the second result from the API query, dated 7 September 1918, is a match for one of our references. Yay! This gets saved to a list of strong matches. But what about the other references?

Just in case there’s been a transcription error, or the page numbering differed across editions,  I relax the rules a bit in a second pass and accept matches on the date, but not the page. These are saved to a list of close matches.

This second pass doesn’t help much with  Lieutenant Rosevear’s missing references, so we have to broaden our search query a bit. This time we search on the surname only. Bingo! The first result is a match and points us to one of the ‘Heroes of the Great War’ series.

It took a while, but my script eventually worked it’s way through all 9709 entries like this, writing the results out to csv files containing the strong and close matches. It also created a summary for each entry, listing the original number of references alongside the number of strong and close matches.

Ever since I read Trevor Munoz’s post on using Pandas with data from the NYPL’s What’s on the Menu? project, I’ve wanted to have a play with it. So I decided to use Pandas to produce some quick stats from my results file.

>>> import pandas as pd
>>> df = pd.read_csv('data/slsa_results.csv')
# How many entries?
>>> len(df)
9709
# How many references?
>>> df['references'].sum()
13504
# How many entries had strong matches?
>>>len(df[df.strong > 0])
6440
# As a percentage thank you...
>>> 100 * len(df[df.strong > 0]) / len(df)
66
# In how many entries did the number of refs = the number of strong matches
>>> len(df[df.references == df.strong])
4399
# As a percentage thank you...
>>> 100 * len(df[df.references == df.strong]) / len(df)
45
# How many entries had at least one strong or close match?
>>> len(df[df.total > 0])
8989
# As a percentage thank you...
>>> 100 * len(df[df.strong > 0]) / len(df)
92

Not bad. The number of strong matches equalled the number of references in 45% of cases, and overall 66% of entries had a least one strong match. I might be able to get those numbers up by tweaking the search query a bit, but of course the main limiting factor is the quality of the OCR. If the article text isn’t good enough we’re never going to find the names we’re after.

Katie tells me that the State Library intends to point volunteer text correctors towards identified articles. As the correctors start to clean things up, we should be able to find more matches simply by re-running this script at regular intervals.

But what articles should they point the volunteers to? Many of them included the title ‘Heroes of the Great War’, so they’re easy to find, but there were others as well. By analysing the matches we’ve already found we can pull out the most frequent titles and build a list of likely candidates. Something like this:

title_phrases = [
'heroes of the great war they gave their lives for king and country',
'australian soldiers died for their country',
'casualty list south australia killed in action',
'on active service',
'honoring soldiers',
'military honors australians honored',
'casualty lists south australian losses list killed in action',
'australian soldiers died for his country',
'died for their country',
'australian soldiers died for the country',
'australian soldiers died for their country photographs of soldiers',
'quality lists south australian losses list killed in action',
'list killed in action',
'answered the call enlistments',
'gallant south australians how they won their honors',
'casualty list south australia died of wounds',
]

Now we can feed these phrases into a series of API queries and automatically generate a list of articles that are likely to contain details of WWI service people. This list should provide a useful starting point for keen text correctors.

I might not have completely solved Katie’s problem, but I think I’ve shown that the Trove API can be usefully called into action for these sorts of projects. Taking this approach should certainly save a lot of manual searching, clicking, cutting and pasting. And while I’ve focused on the South Australian data, there’s no reason why similar approaches couldn’t be applied to other WWI projects.

Notes from a talk I gave at the Digital Treasures Symposium, 21 June 2013, University of Canberra.

Over the last couple of weekends I’ve been building a bot. Let me introduce you to the TroveNewsBot.

TroveNewsBot is just a simple script that periodically checks for messages from Twitter, uses those messages to create queries in Trove’s newspaper database, and tweets back the results.

@TroveNewsBot Grant Featherston

— Michelle Mortimer (@chelle_mortimer) June 17, 2013

Possibly the greatest bot ever “@TroveNewsBot: @chelle_mortimer 14 Dec 1952: 'good design' http://t.co/no39oTANTc”

— Michelle Mortimer (@chelle_mortimer) June 17, 2013

TroveNewsBot’s birth was, however, not without some pain. I ran into difficulty with Twitter’s automated spam police. At one stage everytime my bot tweeted, its Twitter account was suspended.

Twitter’s bots didn’t like my bot. [:(]

The problem has since been resolved — I think I must have done something when I was testing that upset the spam bots — but it did lead me to read in detail Twitter’s policies on spam and automation. This sentence in particular caused me to reflect:

The @reply and Mention functions are intended to make communication between users easier, and automating these processes in order to reach many users is considered an abuse of the feature.

So what is a user and what is communication? I read this sentence as suggesting that communications between individual human users were somehow more real, more authentic than automatically generated replies. But is a script tweeting someone a link to to a newspaper article that they might be interested in really less authentic than a lot of the human-generated traffic on the net?

Amongst the messages I received when I revealed TroveNewsBot to the world earlier this week was this:

@wragge I love Mr. Trove Bot – nice work! Not an object talking, its a whole database.

— Alli Burness (@alli_burnie) June 17, 2013

And later from the same person:

@wragge Bugs are ok, nice to watch Trove Bot flicker to life. Always thought clxns have a personality, look forward to seeing Trove's.

— Alli Burness (@alli_burnie) June 17, 2013

Even as we live an increasing amount of our lives ‘connected’, still there remains a tendency to assume that experiences mediated through online technologies are somehow less authentic than those that take place in this space that we often refer to as ‘the real world’.

In the realm of cultural heritage, digitisation is frequently assumed to be a process of loss. We create surrogates, or derivatives — useful, but somehow inferior representations of ‘the real thing’.

Now let’s just all admit that, yes, we like the smell of old books, and that we can’t read in the bath with our iPad, and move beyond the sort of fetishism that often accompanies these sorts of discussions.

Yes, of course, digital and physical manifestations are different, the point is whether we get anywhere by arguing that one is necessarily inferior to the other.

A recent article in the Times Literary Supplement expressed concern at the money being spent on the manuscript digitisation programs that the author argued were ‘proceeding unchecked and unfocused, deflecting students into a virtual world and leaving them unequipped to deal responsibly with real rare materials’. Yes, there may be aspects of a physical page that a digital copy cannot represent, but as Alistair Dunning pointed out in response to the article, there’s no simple binary opposition:

The digital does not replace the analogue, but augments it, sometimes in dramatic and sometimes in subtle ways.

In his keynote address to the ‘Digital Transformers’ conference, Jim Mussell similarly argued against a simplistic understanding of digital deficiencies.

The key is to reconceive loss as difference and use the way the transformed object differs to reimagine what it actually was. Critical encounters with digitized objects make us rethink what we thought we knew.

I’m very pleased to be an adjunct here at the University of Canberra, but I’ve always felt a bit of a fraud around people like Mitchell when it comes to talking about visualisation. I’m actually much more comfortable with words than pictures. So why am I here talking to you today?

I think it’s because what we’re discussing today, what the Digital Treasures Program is about, is not just visualisation. It’s about transformation. It’s about taking cultural heritage collections and changing them. Changing what we can do with them. Changing how we see them. Changing how we think about them.

It’s about creating spaces within which we can have ‘critical encounters with digitized objects’ that ‘make us rethink what we thought we knew’.

And that to me is very exciting.

What might these transformations look like? Who knows? This is research, it should take us places we don’t expect and can’t predict.

However, for the sake of convenience today I’ve tried to define a few possible categories — most, admittedly, based on my own work. But I do so in the hope that the achievements of the Digital Treasures program will soon make my categories look ridiculously inadequate.

Analysis

When we have stuff in digital form — and by stuff I mean both collection metadata and digital objects — we can isolate particular characteristics and add them up, compare them, graph them. We can start to see patterns that we couldn’t see before.

• Google Ngram viewer
• Digital humanities — growing number of tools
• Not just texts — ImagePlot
• Can be simple — history thesis titles from Trove viewed in Voyant Tools
• QueryPic
• The Front Page

Assembly

Putting a lot of similar things together in a way that enables us to see them differently.

• Series Browser
• The real face of White Australia

Juxtaposition

Putting different things together in a way that enables us to find connections or similarities.

• Sembl
• History Wall

Serendipity

Displaying something unexpected or random.

• The Future of the Past
• Headline Roulette
• TroveNewsBot — use #luckydip for a random article

Mobilisation

Putting things in new contexts, new conceptual spaces, new physical spaces, new geospatial spaces. Creating interventions and explorations.

• The People Inside
• TroveNewsBot – Today’s news yesterday
• Pixtory
• Kits for Cultural History

This is a very limited catalogue of possibilities, but meagre as it is I think its enough to demonstrate that the overwhelming feature of digital cultural collections is not loss or deficiency, but opportunity and inspiration.

In fact I’m less worried about the deficiencies of digital representation than I am about the possibility that we might end up doing too much — that we might become so skilled in design and transformation that we end up overdetermining the experience of our users, that we end up doing too much of the thinking for them.

It seems to me that when it comes to digital cultural collections an important part of the transformation process is knowing where to leave the gaps and spaces that invite feeling, reflection and critique. We have to find ways of representing what is missing, of acknowledging absence and exclusion. We have to be able to expose our arguments and assumptions, to be honest about our failures and limitations. We have to be prepared to leave a few raw edges, some loose threads that encourage users to unravel our carefully-woven tapestries.

As I was developing the TroveNewsBot I realised I needed some sort of avatar. So of course I started searching in the Trove newspaper database for robots — there I found Mr George Robot.

George Robot, ‘described as the greatest electro-mechanical achievement of the age’ toured Australia in 1935 and 1936. As one newspaper described, he:

rises and sits as requested, talks, sings, delivers an address on the most abstruse topics, gnashes his electric teeth in rage or derision, while he accentuates his remarks by the most natural movements of arms and hands

But it wasn’t just George’s technical sophistication that inspired comment. Articles also appeared that described George’s love for Mae West and his admiration for Hitler.

Robots provide us with an opportunity not just to marvel at their technological wizardry, but also to think about what it really is to be human.

@wragge Bugs are ok, nice to watch Trove Bot flicker to life. Always thought clxns have a personality, look forward to seeing Trove's.

— Alli Burness (@alli_burnie) June 17, 2013

In the same way, as we start to have new types of conversations with online collections, to explore their many-faceted personalities, we will of course be exploring ourselves.

The digital transformation of cultural collections is not about showcasing technology but about creating new online spaces in which we can simply be human.

This is the text of my keynote address to the Digisam conference on Open Heritage Data in the Nordic Region held in Malmö on 25 April 2013. You can also view the video and slides of my talk, or experience the full interactive experience by playing around with my text/presentation in all its LOD-powered glory. (Use a decent browser.)

The Australian poet and writer Edwin James Brady and his family lived for many years in the isolation of far eastern Victoria — in a little town called Mallacoota.

Here, from time to time, Brady amused himself by taking a map of Australia down from the wall and sticking pins in it. The pins, Brady explained in 1938, included labels such as ‘Hydro-electric supply base’, ‘Irrigation area, and ‘Area for tropical settlement’. The map and its pins were one expression of Brady’s life-long obsession with Australia’s potential for development — for progress.

Maps and pins are probably more familiar now than they were in Brady’s time. We use them routinely for sharing our location, for plotting our travel, for finding the nearest restaurant. Maps and pins are one way that we document, understand and express our relationship to space.

Brady, however, was interested in using his pins to highlight possibilities. In the late nineteenth and early twentieth centuries size mattered. With the nations of Europe jostling for land and colonial possessions, space become an index of power. When the Australian colonies came together in 1901 to form a nation, maps and spatial calculations abounded. Australia was big and so it’s future was filled with promise.

In his travels around Australia, EJ Brady started to catalogue ways in which its vast, ’empty’ spaces might be turned to productive use. A hardy yeomanry armed with the latest science could transform these so-called ‘wastes’, and Brady was determined to bring these opportunities to the attention of the world.

This evangelical crusade reached its high point in 1918, with the publication of his huge compendium, Australia Unlimited — 1139 pages of ‘Romance History Facts & Figures’.

Space may no longer be invested with the same sense of swelling power, but our maps and pins still figure in calculations of progress. Now it is the data itself that awaits exploitation. Our carefully plotted movements, preferences and passions may well have value to governments, planners or advertisers. Data, according to some financial pundits, ‘is the new oil’.

Whereas Brady traveled the land documenting its untapped riches, we can take his work and life and mine it for data — looking for new patterns and possibilities for analysis.

Brady wasn’t the first to use the phrase ‘Australia Unlimited’, though he did much to make it familiar. By exploring the huge collection of digitised newspapers available through the National Library of Australia’s discovery service, Trove, we can track the phrase through time.

Brady was a skilled self-publicist and his research trips in 1912 were eagerly reported by the local press such as the Barrier Miner in Broken Hill and the Cairns Post.

In 1918 the book was published, receiving a generally positive reception — as an advertising leaflet showed, even King George V thought the book was ‘of special interest’. In 1926, a copy of the book was presented to a visiting delegation from Japan.

Over the years Brady sought to build on his modest successes, planning a variety of new editions and even a movie. But while his hopes were thwarted, the phrase itself lived on.

In 1938 and 1952, there are references to a radio program called ‘Australia Unlimited’, apparently featuring young musical artists. Also in 1952 came the news that the author of Australia Unlimited, EJ Brady, had died in Mallacoota at the age of 83.

Unfortunately copyright restrictions bring this analysis to a rather unnatural end in 1954. If we were able to follow it through until the present, we could see that from 1957 the phrase was used by a leading daily newspaper as the title of an annual advertising supplement detailing Australia’s possibilities for development. In 1958, it was adopted as a campaign slogan by Australia’s main Conservative party. In 1999 it was resurrected again by a major media company for a political and business symposium. Even now it provides the focus for a ‘branding’ initiative supported by the federal government.

Graphs like this are pretty easy to interpret, but of course we should always ask how they were made. In this case I simply harvested the number of matching search results from the Trove newspaper database for each year. The tool I built to do this has been through several different versions and is now a freely-accessible web application called QueryPic. Anyone can quickly and easily create this sort of analysis just by entering a few keywords.

QueryPic is one product of my experiments with the newspaper database over the last couple of years. I’ve also looked at changes to content of front pages, I’ve created a combination discovery interface and fridge poetry generator, and I’ve even built a simple game called Headline Roulette — which I’m told is strangely addictive.

All of these tools and experiments take advantage of Trove’s API. I think it’s important to note that the delivery of cultural heritage resources in a machine-readable form, whether through a custom API or as Linked Open Data, provides more than just improved access or possibilities for aggregation. It opens those resources to transformation. It empowers us to move beyond ‘discovery’ as a mode of interaction to analyse, extract, visualise and play.

Using freely available tools we can extract named entities from a text, we can look for topic clusters across a collection of documents, we can find places and pin them to a map. With a little bit of code I can take the newspaper reports of Brady’s travels in 1912 and map them. With a bit more time I could take another of Brady’s travel books, River Rovers, available in digitised form through the Internet Archive, and plot his journey along Australia’s longest river, the Murray.

Such transformations help us see resources in different ways — we can find new patterns, new problems, new questions. But transformation is a lossy business. I can put a pin in a map to show that Brady stopped off in Mildura on his voyage along the Murray. What is much harder to represent are the emotions that surrounded that visit. While he was there Brady received news of a friend’s death. ‘Bad news makes hateful the most pleasant place of abiding’, he wrote mournfully, ‘I strained to open the gate to go forth again into a wilderness of salt bush and sere sand’. Travel can be a form of escape.

Digital humanist and designer Johanna Drucker has written about the problems of representing the human experience of time and space using existing digital tools. ‘If I am anxious’, she notes, ‘spatial and temporal dimensions are distinctly different than when I am not’. We do not experience our journeys in a straightforward linear fashion — as the accumulation of metres and minutes. We invest each footstep with associations and meanings, with hopes for the future and memories of the past. Drucker calls on humanities scholars to articulate these complexities and work towards the development of new techniques for modelling and visualising our data.1

‘If human beings matter, in their individual and collective existence, not as data points in the management of statistical information, but as persons living actual lives, then finding ways to represent them within the digital environment is important.’2

In a similar way, Australia Unlimited is not just a catalogue of potentialities, or a passionate plea for national progress. It’s also the story of a struggling poet trying to find some way of supporting his family. Proceeds from the book enabled Brady to buy a plot of land in Mallacoota and build a modest home — I suspect that it wasn’t the same home that was painted by his wife in the 1950s.

But even this small success was undermined. Distribution of the book was beset with difficulties and disappointments and and despite all his plans financial security remained elusive.

Brady’s youngest daughter, Edna June, was born to his third wife Florence in 1946. What could he leave he leave her? A ‘modern edition’ of Australia Unlimited lay completed but unpublished. ‘If it fails to find a publisher’, he remarked wistfully, ‘the MSS will be a liberal education for her after she has outgrown her father’s nonsense rhymes’. It was, he pondered, ‘a sort of heritage’.

One of the things I love about being a historian is that the more we focus in on the past the more complicated it gets. People don’t always do what we expect them to, and that’s both infuriating and wonderful.

Likewise, while we often have to clean up or ‘normalise’ cultural heritage data in order to do things with it, we should value its intrinsic messiness as a reminder that it is shot through with history. Invested with the complexities of human experience it resists our attempts at reduction, and that too is both infuriating and wonderful.

The glories of messiness challenge the extractive metaphors that often characterise our use of digital data. We’re not merely digging or mining or drilling for oil, because each journey into the data offers new possibilities — our horizons are opened, because our categories refuse to be closed. These are journeys of enrichment, interpretation and creation, not extraction.

We’re putting stuff back, not taking it out.

Cultural institutions have an exciting opportunity to help us work with this messiness. The challenge is not just to pump out data, anyone can do that. The challenge is to enrich the contexts within which we meet this data — to help us embrace nuance and uncertainty; to prevent us from taking the categories we use for granted.

For all it’s exuberant optimism, a current of fear ran through Australia Unlimited. The publisher’s prospectus boldly announced that it was a ‘Book with a Mission’. ‘A mere handful of White People’, perched uncomfortably near Asia’s ‘teeming centres of population’, could not expect to maintain unchallenged ownership of the continent and its potential riches. Australia’s survival as a white nation depended upon ‘Effective Occupation’, secured by a dramatic increase in population and the development of its vast, empty lands — ‘The Hour of Action is Now!’.

In 1901, one of the first acts of the newly-established nation of Australia was to introduce legislation designed to keep the country ‘white’. Restrictions on immigration, administered through a complex bureaucratic system, formed the basis of what became known as the White Australia Policy.

While the legislation was designed to keep non-white people out, an increase of the white population was seen as essential to strengthen security and legitimise Australia’s claim to the continent. Australia Unlimited was an exercise in national advertising aimed at filling the unsettling emptiness with sturdy, white settlers.

But White Australia always a myth. As well as the indigenous population there were, in 1901, many thousands of people classified as non-white living in Australia. They came from China, India, Indonesia, Turkey and elsewhere. A growing number had been born in Australia. They were building lives, making families and contributing to the community.

Here are some of them…

I built this wall of faces using records held by the National Archives of Australia. If a non-white person resident in Australia wanted to travel overseas they needed to carry special documents. Without them they could be prevented from re-entering the country — from coming home. Many, many thousands of these documents are preserved within the National Archives.

Kate Bagnall, a historian of Chinese-Australia, and I are exploring ways of exposing these records through an online project called Invisible Australians.

To build the wall I downloaded about 12,000 images from the Archives’ website — representing just a fraction of one series relating to the administration of the White Australia Policy. Unfortunately there’s no machine-readable access to this data, so I had to resort to cruder means — reverse-engineering interfaces and screen-scraping.

Once I had the images I ran them through a facial detection script to find and crop out the portraits. What we ended up with was a different way of accessing those records — an interface that brings the people to the front; an interface which is compelling, discomfiting, and often moving.

The wall of faces also raises interesting questions about context. Some people might be concerned by the loss of context when images are presented in this way, although each portrait is linked back to the document it was derived from, and to the Archive’s own collection database. What is more important, I think, are the contexts that are gained.

If you’re viewing digitised files on the National Archives’ own website, you can only do so one page at a time. Each document is separate and isolated. What changes when you can see the whole of the file at once? I’ve built another tool that lets you do just that with any digitised file in the Archives’ collection. You see the whole as well as the parts. You have a sense of the physical and conceptual shape of the file.

In the case of the wall of faces, bringing the images together, from across files, helps us understand the scale of the White Australia Policy and how it impacted on the lives of individuals and communities. These were not isolated cases, these were thousands of ordinary people caught up in the workings of a vast bureaucratic system. The shift of context wrought by these digital manipulations allows us to see, and to feel, something quite different.

And we can go the other way. In another experiment I created a userscript to insert faces back into Archives’ website. A userscript is just a little bit of code that rewrites web pages as they load in your browser. In this case the script grabs images relating to the files that you’re looking at from Invisible Australians.

So instead of this typical view of search results.

You see something quite different.

Instead of just the record metadata for an individual item, you see that there are people inside.

We also have to remember that the original context of these records was the administration of a system of racial surveillance and exclusion. The Archives preserves not only the records, but the recordkeeping systems that were used to monitor people’s movements. The remnants of that power adhere to the descriptive framework. There is power in the definition of categories and the elaboration of significance.

Thinking about this I came across Wendy Duff and Verne Harris’s call to develop ‘liberatory standards’ for archival description. Standards, like categories, are useful. They enable us to share information and integrate systems. But standards also embody power. How can we take advantage of the cooperative utility of standards while remaining attuned to the workings of power?

A liberatory descriptive standard, Duff and Harris argue: ‘would posit the record as always in the process of being made, the record opening out of the future. Such a standard would not seek to affirm the keeping of something already made. It would seek to affirm a process of open-ended making and re-making’.3

‘Holes would be created to allow the power to pour out.’

‘Making and re-making’ — sounds a lot like the open data credo of ‘re-use and re-mix’ doesn’t it? I think it’s important to carry these sorts of discussions about power over into the broader realm of open data. After all, open data must always, to some extent, be closed. Categories have been determined, data has been normalised, decisions made about what is significant and why. There is power embedded in every CSV file, arguments in every API.

This is inevitable. There is no neutral position. All we can do is encourage re-use of the data, recognising that every such use represents an opening out into new contexts and meanings. Beyond questions of access or format, data starts to become open through its use. In Duff and Harris’s words, we should see open data ‘as always in the process of being made’.

What this means for cultural institutions is that the sharing of open data is not just about letting people create new apps or interfaces. It’s about letting people create new meanings. We should be encouraging them to use our APIs and LOD to poke holes in our assumptions to let the power pour out.

There’s no magic formula for this beyond, perhaps, building confidence and creating opportunities. But I do think that Linked Open Data offers interesting possibilities as a framework for collaboration and contestation — for making and challenging meanings.

We tend to think about Linked Open Data as a way of publishing — of pushing our data out. But in fact the production and consumption of Linked Open Data are closely entwined. The links in our data come from re-using identifiers and vocabularies that others have developed. The linked data cloud grows through a process of give and take, by many small acts of creation and consumption.

There’s no reason why that process should be confined to cultural institutions, government departments, business, or research organisations. Linked Open Data enables any individual to talk about what’s important to them, while embedding their thoughts, collections, passions or obsessions within a global conversation. By sharing identifiers and vocabularies we create a platform for communication. Anyone can join in.

So, if we want people to engage with our data, perhaps we need to encourage them to create their own.

I’ve just been working on a project with the Mosman public library in Sydney aimed at gathering information about the experiences of local servicepeople during World War One. There are many such projects happening around the world at the moment, but I think ours is interesting in a couple of ways. The first is a strong emphasis on linking things up.

The are records relating to Australian service people available through the National Archives, the Australian War Memorial, and the Commonwealth War Graves Commission, but there are currently no links between these databases. I’ve created a series of screen scrapers that allow structured data to be easily extracted from these sources. That means that people can, for the first time, search across all these databases in one hit. It’s a very simple tool that I started coding to ease the boredom of a long bus trip — but it has proved remarkably popular with family historians.

Once you’ve found entries in these databases, you can just cut and paste the URL into a form on the Mosman website and a script will retrieve the relevant data and attach it to the record of the the person you’re interested in. Linking to a service record in the National Archives, for example, will automatically create entries for the person’s birthplace and next-of-kin.

The process of linking builds structures, and these structures will themselves all be available as Linked Open Data. Even more exciting is that the links will not only be between the holdings of cultural institutions. The stories, memories, photographs and documents that people contribute will also be connected, providing personal annotations on the official record.

None of this is particularly hard, it’s just about getting the basics right. Remembering that structure matters and that links can have meaning. It’s also about recognising that ‘crowd sourcing’ or user-generated content can be made anywhere. Using Linked Open Data people can attach meanings to your stuff without visiting your website. Through the process of give and take, creation and consumption, we can build layers of description, elaboration, and significance across the web.

What excites me most about open cultural data is not the possibility of shiny new apps or collection visualisations, but the possibility of doing old things better. The possibility of reimagining the humble footnote, for example, as a re-usable container of structured contextual data — as a form of distributed collection description. The possibility of developing new forms of publication that immerse text and narrative within a rich contextual soup brewed from the holdings of cultural institutions.

I want every online book or article to be a portal. I want every blog or social media site to be a collection interface.

What might this look like? Perhaps something like this presentation.

My slides today are embedded within a HTML document that incorporates all sorts of extra goodies. The full text of my talk is here, and as you scroll through it you’ll see more information about the people, places and resources I mention pop up in the sidebar. Alternatively you can explore just the people or the resources, looking at the connections between them and the contexts in which they’re mentioned within my text.

This is part of an ongoing interest to explore different ways of presenting historical narrative, that build a relationship between the text and the structured data that underlies the story.

All of the structured data is available in machine-readable form as RDF — it is, in itself, a source of LOD. In fact the interface is built using an assortment of JavaScript libraries that read the RDF into a little temporary triplestore, and then query it to create the different views. So the whole thing is really powered by LOD.

It’s still very much an experiment, but I think it raises some interesting possibilities for thinking about how we might consume and create LOD simply by doing what we’ve always done — telling stories.

EJ Brady’s dreams were never realised. Australia’s vast spaces remained largely empty, and the poet continued to wrestle with personal and financial disappointment. ‘After nearly eight decades association with the man’, Brady wrote of himself in 1949, ‘I have come to look upon him as the most successful failure in literary history’. This energetic booster of Australia’s potentialities was well aware of his own life’s mocking irony. ‘He has not… made the wages of a wharf laborer out of book writing yet he persists in asserting Australia is the best country in the world!’.

But still Brady continued to add pins to his map.’For half a century I’ve been heaping up notes, reports, clippings, pamphlets, etc. on… all phases of the country’s life and development’. ‘What in hell I accumulate such stuff for I don’t know’, he complained in 1947. As the elderly man surveyed the ‘bomb blasted pile of rubbish’ strewn about his writing tent, he admitted that ‘this collecting is a sort of mania’.

Brady’s map and pins told a complex story of hope and disappointment, of confidence and fear. A story that combined national progress with an individual’s attempts merely to live.

There are stories in our data too — complex and contradictory stories full of emotion and drama, disappointment and achievement, violence and love. Let’s find ways to tell those stories.

1. Johanna Drucker, ‘Humanistic Theory and Digital Scholarship’, in Matthew K. Gold (ed.), Debates in the Digital Humanities, University of Minnesota Press, 2012.
2. Johanna Drucker, ‘Representation and the digital environment: Essential challenges for humanists’, University of Minnesota Press Blog, http://www.uminnpressblog.com/2012/05/representation-and-digital-environment.html
3. Wendy M. Duff and Verne Harris, ‘Stories and Names: Archival Description as Narrating Records and Constructing Meanings’, Archival Science, vol. 2, 2002, pp. 263–285.

Yep, there’s a new version of QueryPic.

About 18 months ago I created a little Python script to visualise search results in Trove’s collection of digitised newspapers. After a bit more tweaking. I christened it QueryPic. People started to use it. It was even reviewed in the Journal of Digital Humanities. With the release of the Trove API earlier this year I rewrote the whole thing in Javascript and let it loose on the web. People could make graphs without having to download any code or fire up the command line. Anyone could play.

And now?

The latest version lets you save your QueryPics. As new features go it’s not very revolutionary. But it meant another significant shift. From Python script, to web page, to web app. The Javascript-enabled interface now connects to a Django-powered backend. Save a graph and you can access it via a lovely, short, persistent url (like this). It’s as much a platform as a tool. But to be persistent, the urls need to work for ummm… a long time. Is this even possible for a project that has no funding and a support team of one?

I don’t know.

My enthusiasm for making tools is punctuated by regular bouts of doubt and disillusionment. With millions of dollars being spent on industrial-strength digital research infrastructure why should I devote my evenings to hand-crafting pretty little widgets like QueryPic?

My grandfather made this brass dish. He owned an engineering workshop and forge. My dad was a draftsman, engineer and builder. My mum made fine dresses in the fashion houses of Melbourne. I make things too. It’s what I do. It took me quite a few years to work this out. Years spent wondering why I felt out of place in academia. I’m also a historian, so I research and I write, but without some time to tinker, well… I’m just not happy. Making things is not separate — for me it’s all part of being a historian. I make things that let people connect to the past in different ways. And along the way I learn.

And by people I mean people. Just last week I took part in an online question and answer session organised by Inside History Magazine. It was a lot of fun. Amidst the questioning I unveiled the latest version of QueryPic. Considerable excitement ensued. QueryPic graphs are starting to be included in research publications, but anyone can make and understand them. Local and family historians are enthusiastic users of digital technologies and I’m excited to see them playing around with tools that I’ve made. I want to create things that other people use. Things that help them, and sometimes surprise them.

QueryPic has graduated from WraggeLabs to dhistory — my platform for digital history research. There it joins The Front Page and Archives Viewer. As usual, I have big plans. Are they practical? Probably not. Are they sustainable? I doubt it. Will I keeping making things anyway? Of course.

So please accept this gift. I made it for you. I hope you find it useful.

QueryPic — explore digitised newspapers from Australia & New Zealand.

http://dhistory.org/querypic/

Features include:

• Save and date-stamp your graphs with persistent urls — perfect for citing and sharing
• Copy and paste query urls from Trove or Digital NZ, or connect automatically with a handy bookmarklet
• Easily regenerate saved graphs to draw in updated data
• Explore QueryPics created by others — use them as the starting point for your own visualisations
• Combine any number of queries, either from Australia or New Zealand
• Click on the graphs to preview matching articles

All this and more documented on QueryPic’s extensive help page. Code on Github.

On 15 April 1944 the Sydney Morning Herald turned inside out. For more than a hundred years, the front page had been dominated by advertisements, but this changed suddenly in 1944 as the newspaper took on a completely new look. In place of the ads were the day’s top stories, headlines and photographs — a ‘front page’ design familiar to modern readers.

The change was, the newspaper explained, partly a response to the demands of war. Advertising had been cut due to the rationing of newsprint and ‘an urgent public demand in these critical days for more papers and more news’. But they were also looking forward to the problems of peace:

It is essential… that we should not only provide the space, but also adopt the manner and methods of presentation which will spread knowledge of these problems yet more widely, and bring them home yet more deeply, among the people of this country.

But the Sydney Morning Herald wasn’t breaking new ground. The design of front pages had been changing across the first half of the twentieth century as advertisements gradually gave way to news. This graph shows the average number of words per issue on the front pages of Australian newspapers devoted to advertising.

You can see a clear decline from about the turn of the century. News articles, on the other hand, were on the way up.

Not all the changes were as sudden as the Sydney Morning Herald‘s. The Barrier Miner entered the First World War with the ads on top, but by war’s end the position was reversed. In between was a period of transition as you can see from this graph which plots advertising against news.

If you dig a bit deeper, you find that the amount of advertising follows a regular pattern.

These peaks and troughs in June 1916 are a week apart — Saturday’s front page was all advertising, but the next day brought a ‘Special Sunday Issue’ focused on the ‘Latest War News’.

It’s clear just from these two examples that there are stories behind these changes. There are subtleties and contingencies to be explored along with dramatic shifts.

And now you can explore them…

The Front Page

The Front Page is a database containing details of more than 4 million front page newspaper articles harvested from the National Library of Australia’s Trove service.

Trove divides articles into a series of categories:

• articles (news)
• advertising
• detailed lists, results, guides
• family notices
• literature

I’ve simply gone through and added up the numbers of articles and the numbers of words in each category for each issue, and aggregated this across months, years and the full run of each newspaper.

These totals are presented as a series of linked tables and graphs. Just click on a point to zoom in, or use the navigation controls to go directly to the issue of your choice. It’s pretty straightforward.

Why?

We’re lucky to have rich resources like Trove, but if we’re going to make best use of them we have to move beyond the search box to find new ways of exploring and contexualising their content. That’s why I’ve developed tools like QueryPic, Headline Roulette and even The future of the past. Each lets you engage with the newspaper database in a different way.

But not all newspaper articles are created equal. I’d like to be able to aggregate and analyse the ‘top’ stories for each day, but to do this I need to know more about the structure of the newspapers themselves. I’ve already made a few attempts to find and extract editorials. This is useful because before the main news moved to the front page it was often directly after the editorials. But when did the news shift to the front page?

Now I can find out.

But why create a public web resource? Well, it’s just what I do. I build and I share. It’s what motivates me. It’s how I understand things. It’s where I find both my questions and my answers. Hey, I’m a digital humanist ok?

How?

Everything’s up on GitHub, so you can follow along with my ugly coding. It was all a bit of an experiment, because I simply didn’t know whether I could harvest and use 4 million articles. How long would it take? Would MySQL grind to a halt? Would my laptop blow up?

In my Harold White lecture I wondered whether what I was trying to do was really beyond the reach of ‘an ordinary bloke and his laptop’. I suspect the day is rapidly coming where my work will be superceded by well-funded academic projects with access to supercomputers and a pool of bright young graduate students. But for now I’ll just keep pushing the boundaries of what’s possible over a dodgy home broadband connection.

Of course, this project was only possible because of the Trove API. My screen-scrapers of yore would have been impossibly slow and wasteful of bandwith. With the API I could simply construct a query and then loop through the 4 million articles in batches of a hundred. These were then fed into MySql via Django. I quickly worked out that I needed to keep my Django models simple. My clever relational model linking newspapers, issues, pages and articles was just too complex for this sort of operation. I flattened everything out to store all the metadata in a single ‘article’ model.

The harvesting operation took about 5 days. Once I had all the metadata I ran a couple of processes to do all the adding up and saved the results to a separate ‘totals’ table.

Then it was just a matter of building a front end. Using Django, Twitter Bootstrap and HighCharts made this amazingly easy. Really. Really truly.

What now?

I built this because I wanted to track changes in the design of front pages, but now I’m wondering what else I can find. The role of war in the examples above is intriguing. Are there other changes in our relationship to ‘news’ that these graphs might reveal?

I hope other people will wonder about this as well.

I have some ideas for future developments. For example, I’d like to add tagging to make it easy to construct timelines of significant changes. But first I just want to see if anybody’s actually interested. If you have any ideas, suggestions or comments please let me know.

Ok, off you go — explore.

It seems a bit late to be introducing the newest version of QueryPic. Folks are already using it to explore the contents of digitised newspapers made available through Trove and Papers Past. Some, like the National Library of New Zealand, Andrew S. Bowman and the Carnamah Historical Society are already blogging about it. But I suppose I’d better document a few things…

As I noted in my post about QueryPicNZ (yes I now have a rather confusing proliferation of QueryPics), I was waiting for the Trove API to become public. Last week I noticed a little ‘API’ link pop up in the Trove footer and so I set to work…

My original version of QueryPic (recently reviewed in the Journal of the Digital Humanities) used a series of Python scripts to harvest and scrape content from the Trove web pages. This meant that you had to download the scripts and be code-confident enough to run them in a terminal. It’s still a useful tool and I’ll be updating it as well, but I wanted to create something quicker and simpler that encouraged people to explore and play.

The latest version of QueryPic (QueryPic+, QueryPic Web, QueryPic 2.0?) simply runs in your browser. It uses JQuery to grab data on the fly from the Trove and DigitalNZ APIs. Like previous versions, it uses the HighCharts library to turn the data into pretty graphs.

What does it do? It’s really pretty basic. QueryPic just displays the number of articles matching your search query over time. By default, these are displayed as a proportion of the total articles available for that year, but a dropdown field lets you switch to view the raw numbers. It’s simple, but it’s also remarkably evocative, suggestive and fun. Just try it!

Why stop at just one query? To compare frequency patterns you can add as many as you like. Just keep entering new words or phrases.

If you notice an interesting peak or trough you can just click on it and another API request will be fired off to retrieve the first 20 matching articles. So it’s also a new way of exploring the newspaper databases themselves.

There are plenty of limitations — not all newspapers are digitised, for example, and the quality of the OCR is patchy. The National Library of New Zealand’s post does a great job summing up a number of issues relating to Papers Past. It’s not magic, it’s not perfect, but is it useful? I think so.

Tasks for the future:

• Create some sort of backend that makes it easy to save , share and cite your query data. The ‘share’ link just regenerates the graph which, of course, might change as new articles are added to the databases.
• Make it possible to add more complex queries — I want to keep the interface simple, so I’ll probably create a bookmarklet to take any Trove or Papers Past query and display it using QueryPic.
• As I mentioned over at the WraggeLabs Emporium, I intend to rewrite my various Trove tools to work with the new API. This will include the classic Python version of QueryPic. I still think it’s useful for harvesting your own data.

You may have noticed I have a bit on an interest in exploring ways of using digitised historical newspapers. In the last year or so I’ve spent a lot of time scraping, mining, processing and visualising content from the Trove collection of digitised Australian newspapers. But what about other countries?

Recently I was invited to a digital history workshop organised by Sydney Shep (@nzsydney) at the Victoria University of Wellington. In between sessions I started to play with the DigitalNZ API guided by Chris McDowall (@fogonwater). In anticipation of the forthcoming Trove API I’d already done a bit of work converting QueryPic to run in the browser. It didn’t take long to adapt this to work with New Zealand newspapers available through Papers Past.

So presenting for your enjoyment and education… QueryPicNZ.

Like QueryPic, the New Zealand version graphs newspaper search results over time. But thanks to the DigitalNZ API it has a number of advantages:

• it runs in your browser — no need to download or run any scripts
• results appear almost instantly
• easy to combine queries — just search on a new word or phrase
• easy to remove queries — just use the ‘Clear last’ button
• easy to share — just copy the provided link or use the Tweet button

It’s limited to simple word or phrase searches at the moment, but eventually I’ll add the ability to process more sophisticated queries. I also want to add a way of saving, sharing and citing graphs. For now the ‘share’ link simply regenerates the graph, so if the content has changed the result could well be different.

The code is available on GitHub.

Ultimately, I want to combine Trove and Papers Past so that you can query and combine content from either Australia or New Zealand… perhaps even other countries?

In February I made a quick dash to Melbourne to talk at VALA2012.

The paper I originally submitted, ‘Mining the treasures of Trove: New approaches and new tools’, provided a general introduction to the use of digitised historical newspapers and the possibilities of digital history. You can download the pdf from the VALA2012 proceedings, or view online at Scribd.

I ended up presenting something a little different, focusing on my recent work around 1913 and extracting editorials from the Trove newspaper database. You can view the slides on Slideshare or watch a video of the whole presentation on the VALA2012 site.