I came across Joshua Sternfeld’s definition of ‘digital historiography’ while I was writing my thesis, and two parts of it very neatly described what I was up to – firstly, the ‘interdisciplinary study of the interaction of digital technology with historical practice’ – and secondly, seeking to understand the ‘construction, use, and evaluation of digital historical representations’.However, the size and shape of the gap between digital historiography and ‘digital history’ is where I tend to get stuck. I’ve got a draft post on the various types of ‘digital history’ that’s never quite ready to go live.* Is digital history like art history – a field with its own theoretical concerns and objects of study – or will it eventually merge into ‘history’ as everyone starts integrating digital methods/tools and digitised sources into their work, in the same way that social or economic history have influenced other fields?
The real reason for me for talking about the digital humanities is that we need to realize the humanities never were the humanities. They are the print humanities and they are conditioned by print. So the question the term “digital humanities” poses is: How must humanities disciplines change if we are no longer working in a print world? This question, to me, is crucial. It is an intellectual question. And the question being proposed is: What happens to the humanities when digital methodologies are applied to them or when they start to interrogate digital methodologies? Both of these questions are crucial and that is what this term — “digital humanities” — keeps front and center.
The Science Gossip project is one year old, and they’re asking their contributors to decide which periodicals they’ll work on next and to start new discussions about the documents and images they find interesting.
I’ve seen a few interesting studentships and jobs posted lately, hinting at research and projects to come. There’s a funded PhD in HCI and online civic engagement and a (now closed) studentship on Co-creating Citizen Science for Innovation.
Apparently you can finish a thesis but you can’t stop scanning for articles and blog posts on your topic. Sharing them here is a good way to shake the ‘I should be doing something with this’ feeling.* This is a fairly random sample of recent material, but if people find it useful I can go back and pull out other things I’ve collected.
I was in London this week for the Linked Pasts event, where I presented on trends and practices for open data in cultural heritage. Linked Pasts was a colloquium on linked open data in cultural heritage organised by the Pelagios project (Leif Isaksen, Elton Barker and Rainer Simon with Pau de Soto). I really enjoyed the other papers, which included thoughtful, grounded approaches to structured data for historical periods, places and people, recognition of the importance of designing projects around audience needs (including user research), the relationship between digital tools and scholarly inquiry, visualisations as research tools, and the importance of good infrastructure for digital history.
My discussion points are based on years of conversations with other cultural heritage technologists in museums, libraries, and archives, but inevitably I’ll have blind spots. For example, I’m focusing on the English-speaking world, which means I’m not discussing the great work that Dutch and Japanese organisations are doing. I’ve undoubtedly left out brilliant specific examples in the interests of focusing on broader trends. The point is to start conversations, to bring issues out into the open so we can collectively decide how to move forward.
The good news is that more and more open cultural data is being published. Organisations have figured out that a) nothing bad is likely to happen and that b) they might get some kudos for releasing open data.
Generally, organisations are publishing the data that they have to hand – this means it’s mostly collections data. This data is often as messy, incomplete and fuzzy as you’d expect from records created by many different people using many different systems over a hundred or more years.
Copyright restrictions mean that images mightn’t be included. Furthermore, because it’s often collections data, it’s not necessarily rich in interpretative information. It’s metadata rather than data. It doesn’t capture the scholarly debates, the uncertain attributions, the biases in collecting… It certainly doesn’t capture the experience of viewing the original object.
Licensing issues are still a concern. Until cultural organisations are rewarded by their funders for releasing open data, and funders free organisations from expectations for monetising data, there will be damaging uncertainty about the opportunity cost of open data.
Non-commercial licenses are also an issue – organisations and scholars might feel exploited if others who have not contributed to the process of creating it can commercially publish their work. Finally, attribution is an important currency for organisations and scholars but most open licences aren’t designed with that in mind.
…and the unstructured
The data that’s released is often pretty unstructured. CSV files are very easy to use, so they help more people get access to information (assuming they can figure out GitHub), but a giant dump like this doesn’t provide stable URIs for each object. Records in data dumps rarely link to external identifiers like the Getty’s Thesaurus of Geographic Names, Art & Architecture Thesaurus (AAT) or Union List of Artist Names, or vernacular sources for place and people names such as Geonames or DBPedia. And that’s fair enough, because people using a CSV file probably don’t want all the hassle of dereferencing each URI to grab the place name so they can visualise data on a map (or whatever they’re doing with the data). But it also means that it’s hard for someone to reliably look for matching artists in their database, and link these records with data from other organisations.
So it’s open, but it’s often not very linked. If we’re after a ‘digital ecosystem of online open materials’, this open data is only a baby step. But it’s often where cultural organisations finish their work.
Classics > Cultural Heritage?
But many others, particularly in the classical and ancient world, have managed to overcome these issues to publish and use linked open data. So why do museums, libraries and archives seem to struggle? I’ll suggest some possible reasons as conversation starters…
Not enough time
Organisations are often busy enough keeping their internal systems up and running, dealing with the needs of visitors in their physical venues, working on ecommerce and picture library systems…
Not enough skills
Cultural heritage technologists are often generalists, and apart from being too time-stretched to learn new technologies for the fun of it, they might not have the computational or information science skills necessary to implement the full linked data stack.
Some cultural heritage technologists argue that they don’t know of any developers who can negotiate the complexities of SPARQL endpoints, so why publish it? The complexity is multiplied when complex data models are used with complex (or at least, unfamiliar) technologies. For some, SPARQL puts the ‘end’ in ‘endpoint’, and ‘RDF triples‘ can seem like an abstraction too far. In these circumstances, the instruction to provide linked open data as RDF is a barrier they won’t cross.
But sometimes it feels as if some heritage technologists are unnecessarily allergic to complexity. Avoiding unnecessary complexity is useful, but progress can stall if they demand that everything remains simple enough for them to feel comfortable. Some technologists might benefit from working with people more used to thinking about structured data, such as cataloguers, registrars etc. Unfortunately, linked open data falls in the gap between the technical and the informatics silos that often exist in cultural organisations.
And organisations are also not yet using triples or structured data provided by other organisations [with the exception of identifiers for e.g. people, places and specific vocabularies]. They’re publishing data in broadcast mode; it’s not yet a dialogue with other collections.
Not enough data
In a way, this is the collections documentation version of the technical barriers. If the data doesn’t already exist, it’s hard to publish. If it needs work to pull it out of different departments, or different individuals, who’s going to resource that work? Similarly, collections staff are unlikely to have time to map their data to CIDOC-CRM unless there’s a compelling reason to do so. (And some of the examples given might use cultural heritage collections but are a better fit with the work of researchers outside the institution than the institution’s own work).
It may be easier for some types of collections than others – art collections tend to be smaller and better described; natural history collections can link into international projects for structured data, and libraries can share cataloguing data. Classicists have also been able to get a critical mass of data together. Your local records office or small museum may have more heterogeneous collections, and there are fewer widely used ontologies or vocabularies for historical collections. The nature of historical collections means that ‘small ontologies, loosely joined’, may be more effective, but creating these, or mapping collections to them, is still a large piece of work. While there are tools for mapping to data structures like Europeana’s data model, it seems the reasons for doing so haven’t been convincing enough, so far. Which brings me to…
Not enough benefits
This is an important point, and an area the community hasn’t paid enough attention to in the past. Too many conversations have jumped straight to discussion about the specific standards to use, and not enough have been about the benefits for heritage audiences, scholars and organisations.
Many technologists – who are the ones making decisions about digital standards, alongside the collections people working on digitisation – are too far removed from the consumers of linked open data to see the benefits of it unless we show them real world needs.
There’s a cost in producing data for others, so it needs to be linked to the mission and goals of an organisation. Organisations are not generally able to prioritise the potential, future audiences who might benefit from tools someone else creates with linked open data when they have so many immediate problems to solve first.
While some cultural and historical organisations have done good work with linked open data, the purpose can sometimes seem rather academic. Linked data is not always explained so that the average, over-worked collections or digital team will that convinced by the benefits outweigh the financial and intellectual investment.
No-one’s drinking their own champagne
You don’t often hear of people beating on the door of a museum, library or archive asking for linked open data, and most organisations are yet to map their data to specific, widely-used vocabularies because they need to use them in their own work. If technologists in the cultural sector are isolated from people working with collections data and/or research questions, then it’s hard for them to appreciate the value of linked data for research projects.
The classical world has benefited from small communities of scholar-technologists – so they’re not only drinking their own champagne, they’re throwing parties. Smaller, more contained collections of sources and research questions helps create stronger connections and gives people a reason to link their sources. And as we’re learning throughout the day, community really helps motivate action.
(I know it’s normally called ‘eating your own dog food’ or ‘dogfooding’ but I’m vegetarian, so there.)
Linked open data isn’t built into collections management systems
Getting linked open data into collections management systems should mean that publishing linked data is an automatic part of sharing data online.
Chicken or the egg?
So it’s all a bit ‘chicken or the egg’ – will it stay that way? Until there’s a critical mass, probably. These conversations about linked open data in cultural heritage have been going around for years, but it also shows how far we’ve come.
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?
This 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?
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.
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.
On the ‘Insert’ menu, click on PivotTable to open the menu and display the option for PivotCharts.
Excel will select our columns as being the most likely thing we want to chart. That all looks fine to me so click ‘OK’.
Configure 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.
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.
When you click off the graph, the PivotTable options disappear – just click on the graph or the data again to bring them up.
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.
One of the most important exercises in the crowdsourcing workshops I run is the ‘speed dating’ session. The idea is to spend some time looking at a bunch of crowdsourcing projects until you find a project you love. Finding a project you enjoy gives you a deeper insight into why other people participate in crowdsourcing, and will see you through the work required to get a crowdsourcing project going. I think making a personal connection like this helps reduce some of the cynicism I occasionally encounter about why people would volunteer their time to help cultural heritage collections. Trying lots of projects also gives you a much better sense of the types of barriers projects can accidentally put in the way of participation. It’s also a good reminder that everyone is a nerd about something, and that there’s a community of passion for every topic you can think of.
If you want to learn more about designing history or cultural heritage crowdsourcing projects, trying out lots of project is a great place to start. The more time you can spend on this the better – an hour is ideal – but trying just one or two projects is better than nothing. In a workshop I get people to note how a project made them feel – what they liked most and least about a project, and who they’d recommend it to. You can also note the input and output types to help build your mental database of relevant crowdsourcing projects.
The list of projects I suggest varies according to the background of workshop participants, and I’ll often throw in suggestions tailored to specific interests, but here’s a generic list to get you started.
NB: crowdsourcing is a dynamic field, some sites may be temporarily out of content or have otherwise settled in transit. Some sites require registration, so you may need to find another site to explore while you’re waiting for your registration email.
Here’s the back page blurb: “Crowdsourcing, or asking the general public to help contribute to shared goals, is increasingly popular in memory institutions as a tool for digitising or computing vast amounts of data. This book brings together for the first time the collected wisdom of international leaders in the theory and practice of crowdsourcing in cultural heritage. It features eight accessible case studies of groundbreaking projects from leading cultural heritage and academic institutions, and four thought-provoking essays that reflect on the wider implications of this engagement for participants and on the institutions themselves.
Crowdsourcing in cultural heritage is more than a framework for creating content: as a form of mutually beneficial engagement with the collections and research of museums, libraries, archives and academia, it benefits both audiences and institutions. However, successful crowdsourcing projects reflect a commitment to developing effective interface and technical designs. This book will help practitioners who wish to create their own crowdsourcing projects understand how other institutions devised the right combination of source material and the tasks for their ‘crowd’. The authors provide theoretically informed, actionable insights on crowdsourcing in cultural heritage, outlining the context in which their projects were created, the challenges and opportunities that informed decisions during implementation, and reflecting on the results.
This book will be essential reading for information and cultural management professionals, students and researchers in universities, corporate, public or academic libraries, museums and archives.”
Massive thanks to the following authors of chapters for their intellectual generosity and their patience with up to five rounds of edits, plus proofing, indexing and more…
Crowdsourcing in Brooklyn, Shelley Bernstein;
Old Weather: approaching collections from a different angle, Lucinda Blaser;
‘Many hands make light work. Many hands together make merry work’: Transcribe Bentham and crowdsourcing manuscript collections, Tim Causer and Melissa Terras;
Build, analyse and generalise: community transcription of the Papers of the War Department and the development of Scripto, Sharon M. Leon;
What’s on the menu?: crowdsourcing at the New York Public Library, Michael Lascarides and Ben Vershbow;
What’s Welsh for ‘crowdsourcing’? Citizen science and community engagement at the National Library of Wales, Lyn Lewis Dafis, Lorna M. Hughes and Rhian James;
Waisda?: making videos findable through crowdsourced annotations, Johan Oomen, Riste Gligorov and Michiel Hildebrand;
Your Paintings Tagger: crowdsourcing descriptive metadata for a national virtual collection, Kathryn Eccles and Andrew Greg.
Crowdsourcing: Crowding out the archivist? Locating crowdsourcing within the broader landscape of participatory archives, Alexandra Eveleigh;
How the crowd can surprise us: humanities crowdsourcing and the creation of knowledge, Stuart Dunn and Mark Hedges;
The role of open authority in a collaborative web, Lori Byrd Phillips;
Making crowdsourcing compatible with the missions and values of cultural heritage organisations, Trevor Owens.
I’m Mia, I was dev/design team lead on Serendipomatic, and I’ll be talking about how play shaped both what you see on the front end and the process of making it.
How did play shape the process?
The playful interface was a purposeful act of user advocacy – we pushed against the academic habit of telling, not showing, which you see in some form here. We wanted to entice people to try Serendipomatic as soon as they saw it, so the page text, graphic design, 1 – 2 – 3 step instructions you see at the top of the front page were all designed to illustrate the ethos of the product while showing you how to get started.
How can a project based around boring things like APIs and panic be playful? Technical decision-making is usually a long, painful process in which we juggle many complex criteria. But here we had to practice ‘rapid trust’ in people, in languages/frameworks, in APIs, and this turned out to be a very freeing experience compared to everyday work. First, two definitions as background for our work…
Just in case anyone here isn’t familiar with APIs, APIs are a set of computational functions that machines use to talk to each other. Like the bank in Monopoly, they usually have quite specific functions, like taking requests and giving out information (or taking or giving money) in response to those requests. We used APIs from major cultural heritage repositories – we gave them specific questions like ‘what objects do you have related to these keywords?’ and they gave us back lists of related objects. The term ‘UX‘ is another piece of jargon. It stands for ‘user experience design’, which is the combination of graphical, interface and interaction design aimed at making products both easy and enjoyable to use. Here you see the beginnings of the graphic design being applied (by team member Amy) to the underlying UX related to the 1-2-3 step explanation for Serendipomatic.
The ‘feed’ part of Serendipomatic parsed text given in the front page form into simple text ‘tokens’ and looked for recognisable entities like people, places or dates. There’s nothing inherently playful in this except that we called the system that took in and transformed the text the ‘magic moustache box’, for reasons lost to time (and hysteria).
These terms were then mixed into database-style queries that we sent to different APIs. We focused on primary sources from museums, libraries, archives available through big cultural aggregators. Europeana and the Digital Public Library of America have similar APIs so we could get a long way quite quickly. We added Flickr Commons into the list because it has high-quality, interesting images and brought in more international content. [It also turns out this made it more useful for my own favourite use for Serendipomatic, finding slide or blog post images.] The results are then whirled up so there’s a good mix of sources and types of results. This is the heart of the magic moustache.
User-focused design was key to making something complicated feel playful. Amy’s designs and the Outreach team work was a huge part of it, but UX also encompasses micro-copy (all the tiny bits of text on the page), interactions (what happened when you did anything on the site), plus loading screens, error messages, user documentation.
We knew lots of people would be looking at whatever we made because of OWOT publicity; you don’t get a second shot at this so it had to make sense at a glance to cut through social media noise. (This also meant testing it for mobiles and finding time to do accessibility testing – we wanted every single one of our users to have a chance to be playful.)
Without all this work on the graphic design – the look and feel that reflected the ethos of the product – the underlying playfulness would have been invisible. This user focus also meant removing internal references and in-jokes that could confuse people, so there are no references to the ‘magic moustache machine’. Instead, ‘Serendhippo’ emerged as a character who guided the user through the site.
But how does a magic moustache make a process playful?
The moustache was a visible signifier of play. It appeared in the first technical architecture diagram – a refusal to take our situation too seriously was embedded at the heart of the project. This sketch also shows the value of having a shared physical or visual reference – outlining the core technical structure gave people a shared sense of how different aspects of their work would contribute to the whole. After all, if there aren’t any structure or rules, it isn’t a game.
This playfulness meant that writing code (in a new language, under pressure) could then be about making the machine more magic, not about ticking off functions on a specification document. The framing of the week as a challenge and as a learning experience allowed a lack of knowledge or the need to learn new skills to be a challenge, rather than a barrier. My role was to provide just enough structure to let the development team concentrate on the task at hand.
In a way, I performed the role of old-fashioned games master, defining the technical constraints and boundaries much as someone would police the rules of a game. Previous experience with cultural heritage APIs meant I was able to make decisions quickly rather than letting indecision or doubt become a barrier to progress. Just as games often reduce complex situations to smaller, simpler versions, reducing the complexity of problems created a game-like environment.
Ultimately, a focus on the end user experience drove all the decisions about the backend functionality, the graphic design and micro-copy and how the site responded to the user.
It’s easy to forget that every pixel, line of code or text is there either through positive decisions or decisions not consciously taken. User experience design processes usually involve lots of conversation, questions, analysis, more questions, but at OWOT we didn’t have that time, so the trust we placed in each other to make good decisions and in the playful vision for Serendipomatic created space for us to focus on creating a good user experience. The whole team worked hard to make sure every aspect of the design helps people on the site understand our vision so they can get with exploring and enjoying Serendipomatic.
Some possible real-life lessons I didn’t include in the paper
One Week One Tool was an artificial environment, but here are some thoughts on lessons that could be applied to other projects:
Conversations trump specifications and showing trumps telling; use any means you can to make sure you’re all talking about the same thing. Find ways to create a shared vision for your project, whether on mood boards, technical diagrams, user stories, imaginary product boxes.
Find ways to remind yourself of the real users your product will delight and let empathy for them guide your decisions. It doesn’t matter how much you love your content or project, you’re only doing right by it if other people encounter it in ways that make sense to them so they can love it too (there’s a lot of UXy work on ‘on-boarding’ out there to help with this). User-centred design means understanding where users are coming from, not designing based on popular opinion.you can use tools like customer journey maps to understand the whole cycle of people finding their way to and using your site (I guess I did this and various other UXy methods without articulating them at the time).
Document decisions and take screenshots as you go so that you’ve got a history of your project – some of this can be done by archiving task lists and user stories.
Having someone who really understands the types of audiences, tools and materials you’re working with helps – if you can’t get that on your team, find others to ask for feedback – they may be able to save you lots of time and pain.
Design and UX resources really do make a difference, and it’s even better if those skills are available throughout the agile development process.
A quick post triggered by an article on the role of domain knowledge (knowledge of a field) in critical thinking, Deep in thought:
Domain knowledge is so important because of the way our memories work. When we think, we use both working memory and long-term memory. Working memory is the space where we take in new information from our environment; everything we are consciously thinking about is held there. Long-term memory is the store of knowledge that we can call up into working memory when we need it. Working memory is limited, whereas long-term memory is vast. Sometimes we look as if we are using working memory to reason, when actually we are using long-term memory to recall. Even incredibly complex tasks that seem as if they must involve working memory can depend largely on long-term memory.
When we are using working memory to progress through a new problem, the knowledge stored in long-term memory will make that process far more efficient and successful. … The more parts of the problem that we can automate and store in long-term memory, the more space we will have available in working memory to deal with the new parts of the problem.
There’s a long history of hard-won wisdom, design patterns and knowledge (whether about ways not to tender for or specify software, reasons why proposed standards may or may not work, translating digital methods and timelines for departments raised on print, etc – I’m sure you all have examples) contained in the individual and collective memory of individual technologists and teams. Some of it is represented in museum technology mailing lists, blogs or conference proceedings, but the lessons learnt in the past aren’t always easily discoverable by people encountering digital heritage issues for the first time. And then there’s the issue of working out which knowledge relates to specific, outdated technologies and which still holds while not quashing the enthusiasm of new people with a curt ‘we tried that before’…
Something in the juxtaposition of the 20th anniversary of BritPop and the annual wave of enthusiasm and discovery from the international Museums and the Web (#MW2014) conference prompted me to look at what the Museums Computer Group (MCG) and Museum Computer Network (MCN) lists were talking about in April five and ten years ago (i.e. in easily-accessible archives):
Some of the conversations from that random sample are still highly relevant today, and more focused dives into various archives would probably find approaches and information that’d help people tackling current issues.
So how can we help people new to the sector find those previous conversations and get some of this long-term memory into their own working memory? Pointing people to search forms for the MCG and MCN lists is easy, some of the conference proceedings are a bit trickier (e.g. search within the museumsandtheweb.com) and there’s no central list of museum technology blogs that I know of. Maybe people could nominate blog posts they think stand the test of time, mindful of the risk of it turning into a popularity/recency thing?
If you’re new(ish) to digital heritage, how did you find your feet? Which sites or communities helped you, and how did you find them? Or if you have a new team member, how do you help them get up to speed with museum technology? Or looking further afield, which resources would you send to someone from academia or related heritage fields who wanted to learn about building heritage resources for or with specialists and the public?