I’ve written a couple of guest posts for Nieman Journalism Lab and the tech news site Memeburn. The Nieman post is part of a series looking forward to 2012. I’m never a fan of futurology so I’ve cheated a little and talked about developments already in progress: new interface conventions in news websites; the rise of collaboration; and the skilling up of journalists in data.

Memeburn asked me a few months ago to write about social media’s impact on journalism’s role as the Fourth Estate, and it took me until this month to find the time to do so. Here’s the salient passage:

“But the power of the former audience is a power that needs to be held to account too, and the rise of liveblogging is teaching reporters how to do that: reacting not just to events on the ground, but the reporting of those events by the people taking part: demonstrators and police, parents and politicians all publishing their own version of events — leaving journalists to go beyond documenting what is happening, and instead confirming or debunking the rumours surrounding that.

“So the role of journalist is moving away from that of gatekeeper and — as Axel Bruns argues — towards that of gatewatcher: amplifying the voices that need to be heard, factchecking the MPs whose blogs are 70% fiction or the Facebook users scaremongering about paedophiles.

“But while we are still adapting to this power shift, we should also recognise that that power is still being fiercely fought-over. Old laws are being used in new ways; new laws are being proposed to reaffirm previous relationships. Some of these may benefit journalists — but ultimately not journalism, nor its fourth estate role. The journalists most keenly aware of this — Heather Brooke in her pursuit of freedom of information; Charles Arthur in his campaign to ‘Free Our Data’ — recognise that journalists’ biggest role as part of the fourth estate may well be to ensure that everyone has access to information that is of public interest, that we are free to discuss it and what it means, and that — in the words of Eric S. Raymond — “Given enough eyeballs, all bugs are shallow“.”

Comments, as always, very welcome.

This is the fourth part of my inaugural lecture at City University London, ‘Is Ice Cream Strawberry?’. You can find part one here, part two here, and part three here.

Human capital

So here’s person number 4: Gary Becker, a Nobel prize-winning economist.

Fifty years ago he used the phrase ‘human capital’ to refer to the economic value that companies should ascribe to their employees.

These days, of course, it is common sense to invest time in recruiting, training and retaining good employees. But at the time employees were seen as a cost.

We need a similar change in the way we see our readers – not as a cost on our time but as a valuable part of our operations that we should invest in recruiting, developing and retaining.

Any online operation that does not incorporate its users in production is not only democratically deficient, it is commercially inefficient.

Of course some are inclined to see user generated content as a mass of ignorance, abuse and waffle. Those people should ask how much work has been put into attracting good contributors? Into developing a healthy commenting culture? And how much has been invested into giving the good users a reason to keep coming back?

Journalism’s conflicted future

I have spoken about journalism’s ego problem. This is worsened by the fact that journalism is going through an identity crisis, which will become increasingly problematic as it tries to reinvent itself for an uncertain future.

And as always, this is nothing new. In the 1920s journalism faced a similar conflict: between the journalism of information and the journalism of stories. Should we, as journalists, perform a role of providing citizens with the information that they need to make informed decisions? Or are we just in the business of great stories?

The source of that conflict then was the rise of the scientific method, as I explained at the start of this lecture. The source of today’s conflict could be traced to institutional change in news organisations becoming part of larger entertainment empires – and the melting pot of online publication.

Where you stand on the role of journalists will likely depend on whether you think you’re in the business of building cars, constructing roads or organising picnics, and what role you think journalism should perform in a democracy.

Is journalism part of a deliberative democracy, in which the media provides a public forum for debate and consensus?

Is journalism’s role is to provide citizens with information – as part of a liberal democracy?

Or should the media encourage participation and engagement as part of a participatory democracy?

The institutional history of journalism kept those views somewhat separated – as Lokman Tsui explores in his ethnography of Global Voices.

But as these cultures of journalism clash in the online space it is more important than ever that we reflect on our own views of where we stand.

And as educators we should be teaching our students to be aware of their positions and how that affects what they report on, how they report it, and who gets a voice in its coverage.

If objectivity is to remain a journalistic value, then it should be modern objectivity, not this 19th century construct that passes for objectivity in most newswriting: the setting up of an arbitrary fence, and the selection of a source from each side of it as an indication of ‘balance’.

Culture shift

In many ways culture is the way that people and institutions communicate with each other. And just as institutional culture shapes the journalism that we create, for the last couple of decades there has been a growing movement outside of news institutions that sees democracy as both participatory and information driven.

The campaign for Freedom of Information, the work of MySociety in opening up voting records and debate transcripts so the public could see what their representatives were doing and saying in their name. The Free Our Data campaign – which sought to give the public the right to use information that was paid for with public money. And the Linked Data and Open Data movements which have campaigned to make public bodies publish data in a form that makes it easier to interrogate.

What these people – and I want to name some of them here:

• Tom Steinberg and Tom Loosemore
• Heather Brooke
• Charles Arthur
• Tim Berners Lee and
• Chris Taggart

What these people have done – and are still doing – is making power accountable, promoting a cultural expectation that we should have access to information about how our money is spent, and that most publicly funded information should be available to the people who paid for it.

This of course is one of the first steps to ‘holding power to account’, the great argument that publishers make for their existence. That is what the Telegraph did with the MPs’ expenses; what The Guardian have done with Wikileaks data.

But too much of this groundwork is lying ignored and unsupported by the mainstream press.

When Walsall Council released their spending data last year the webpage received more visits than the rest of the council website. They received several enquiries from people like Chris Taggart asking for information about why particular items had been redacted – but they received only one enquiry from the local newspaper.

And that was to ask: ‘Why have you released the data early?’

For comparison I want to show you a video of Ellen Miller of the Sunlight Foundation in the US talking about their government’s transparency initiative.

Where is the news organisation in the UK that is lobbying like this?

A similar cultural shift is happening around public meetings and hearings, with hyperlocal blogs who want to make processes of law and democracy transparent.

Simon Perry of the Ventnor Blog was ejected from a coroner’s court last year on the grounds that he was neither a member of the press nor a member of the public. Richard Taylor was investigated by Cambridge City Council for recording public meetings – he was not told what the grounds of the investigation were. In Brighton a councillor was disciplined for posting clips of council meetings to YouTube. And Heather Brooke was told that she could not make an audio recording of a hearing because the tribunal could not “maintain the necessary degree of control over the transcript.” When Brooke asked for a copy of the ruling she was told that there was to be no written record of it.

This is one area where journalists and news organisations can be investing in their users. It should not just be bloggers pushing for these changes.

Part five can be found here.

Conrad Quilty-Harper writes about the new crime data from the UK police force – and in the process adds another straw to the groaning camel’s back of the government’s so-called transparency agenda:

“It’s useless to residents wanting to find out what was going on at the house around the corner at 3am last night, and it’s useless to individuals who want to build mobile phone applications on top of the data (perhaps to get a chunk of that £6 billion industry open data is supposed to create).

“The site’s limitations are as follows:

• No IDs for crimes: what if I want to check whether real life crimes have made it onto the map? Sorry.
• Six crime categories: including “other crimes”, everything from drug dealing to bank robberies in one handy, impossible to understand category.
• No live data: you mean I have to wait until the end of the next month to see this month’s criminality?!
• No dates or times: funny how without dates and times I can’t tell which police manager was in charge.
• Case status: the police know how many crimes go solved or unsolved, why not tell us this?”

This is why people are so concerned about the Public Data Corporation. This is why we need to be monitoring exactly what spending data councils release, and in what format. And this is why we need to continue to press for the expansion of FOI laws. This is what we should be doing. Are we?

UPDATE: Will Perrin has FOI’d all correspondence relating to ICO advice on the crime maps. Jonathan Raper has a list of further flaws including:

• Some data such as sexual offences and murder is removed – even though it would be easy to discover and locate from other police reports.
• Data covers reported crimes rather than convictions, so some of it may turn out not to be crime.
• The levels of policing are not provided, so that two areas with the “same” crime levels may in fact have “radically different” experiences of crime and policing.

Charles Arthur notes that: “Police forces have indicated that whenever a new set of data is uploaded – probably each month – the previous set will be removed from public view, making comparisons impossible unless outside developers actively store it.”

Louise Kidney says:

“What we’ve actually got with www.police.uk is neither one nor the other. Ruth looks like a crime overlord cos of all the crimes happening in her garden and we haven’t got exact point data, but we haven’t got first part of postcode data either e.g. BB5 crimes or NW1 crimes. Instead, we’ve got this weird halfway house thing where it’s not accurate, but its inaccuracy almost renders it useless because we don’t have any idea if every force uses the same parameters when picking these points, we don’t know how they pick their points, we don’t know what we don’t know in terms of whether one house in particular is causing a considerable issue with anti-social behaviour for example, allowing me to go to my local Council and demand they do something about it.”

Adrian Short argues that “What we’re looking at here isn’t a value-neutral scientific exercise in helping people to live their daily lives a little more easily, it’s an explicitly political attempt to shape the terms of a debate around the most fundamental changes in British policing in our lifetimes.”

He adds:

“It’s derived data that’s already been classified, rounded and lumped together in various ways, with a bit of location anonymising thrown in for good measure. I haven’t had a detailed look at it yet but I would caution against trying to use it for anything serious. A whole set of decisions have already transformed the raw source data (individual crime reports) into this derived dataset and you can’t undo them. You’ll just have to work within those decisions and stay extremely conscious that everything you produce with it will be prefixed, “as far as we can tell”.

“£300K for this? There ought to be a law against it.”

UPDATE 2: One frustrated developer has launched CrimeSearch.co.uk to provide “helpful information about crime and policing in your area, without costing 300k of tax payers’ money”

In writing last week’s Guardian Data Blog piece on How to be a data journalist I asked various people involved in data journalism where they would recommend starting. The answers are so useful that I thought I’d publish them in full here.

The Telegraph’s Conrad Quilty-Harper:

Start reading:

http://www.google.com/reader/bundle/user%2F06076274130681848419%2Fbundle%2Fdatavizfeeds

Keep adding to your knowledge and follow other data journalists/people who work with data on Twitter.

Look for sources of data:

ONS stats release calendar is a good start http://www.statistics.gov.uk/hub/release-calendar/index.html Look at the Government data stores (Data.gov, Data.gov.uk, Data.london.gov.uk etc).

Check out What do they know, Freebase, Wikileaks, Manyeyes, Google Fusion charts.

Find out where hidden data is and try and get hold of it: private companies looking for publicity, under appreciated research departments, public bodies that release data but not in a granular form (e.g. Met Office).

Test out cleaning/visualisation tools:

You want to be able to collect data, clean it, visualise it and map it.

Obviously you need to know basic Excel skills (pivot tables are how journalists efficiently get headline numbers from big spreadsheets).

For publishing just use Google Spreadsheets graphs, or ManyEyes or Timetric. Google MyMaps coupled with http://batchgeo.com is a great beginner mapping combo.

Further on from that you want to try out Google Spreadsheets importURL service, Yahoo Pipes for cleaning data, Freebase Gridworks and Dabble DB.

More advanced stuff you want to figure out query language and be able to work with relational databases, Google BigQuery, Google Visualisation API (http://code.google.com/apis/charttools/), Google code playgrounds (http://code.google.com/apis/ajax/playground/?type=visualization#org_chart) and other Javascript tools. The advanced mapping equivalents are ArcGIS or GeoConcept, allowing you to query geographical data and find stories.

You could also learn some Ruby for building your own scrapers, or Python for ScraperWiki.

Get inspired:

Get the data behind some big data stories you admire, try and find a story, visualise it and blog about it. You’ll find that the whole process starts with the data, and your interpretation of it. That needs to be newsworthy/valuable.

Look to the past!

Edward Tufte’s work is very inspiring: http://www.edwardtufte.com/tufte/ His favourite data visualisation is from 1869! Or what about John Snow’s Cholera map? http://www.york.ac.uk/depts/maths/histstat/snow_map.htm

And for good luck here’s an assorted list of visualisation tutorials.

• Visualisation of types of visualisations
• How to make a heatmap
• How to create successful infographics
• How to create a timeline using Simile
• 10 useful Google Spreadsheet tricks
• 10 tips for designing infographics
• Graphing and charting solutions in javascript and flash
• How to create outstanding modern infographics in Illustrator

The Times’ Jonathan Richards

I’d say a couple of blogs.

• Hacks/Hackers: http://hackshackers.com/
• and more importantly their Q&A site: http://help.hackshackers.com/ - which answers a ton of questions about which computer language/book/manual/online tutorial to get started with.

Others that spring to mind are:

• Michelle Minkoff: http://michelleminkoff.com/ (Hack/hacker who’s now at the LA Times. Provides a nice ‘not too geeky’ take on the area) and
• 10,000 words: http://10000words.net/

If people want more specific advice, tell them to come to the next London Hack/Hackers and track me down!

The Guardian’s Charles Arthur:

Obvious thing: find a story that will be best told through numbers. (I’m thinking about quizzing my local council about the effects of stopping free swimming for children. Obvious way forward: get numbers for number of children swimming before, during and after free swimming offer.)

If someone already has the skills for data journalism (which I’d put at (1) understanding statistics and relevance (2) understanding how to manipulate data (3) understanding how to make the data visual) the key, I’d say, is always being able to spot a story that can be told through data – and only makes sense that way, and where being able to manipulate the data is key to extracting the story. It’s like interviewing the data. Good interviewers know how to get what they want out from the conversation. Ditto good data journalists and their data.

The New York Times’ Aron Pilhofer:

I would start small, and start with something you already know and already do. And always, always, always remember that the goal here is journalism. There is a tendency to focus too much on the skills for the sake of skills, and not enough on how those skills help enable you to do better journalism. Be pragmatic about it, and resist the tendency to think you need to know everything about the techy stuff before you do anything — nothing could be further from the truth.

Less abstractly, I would start out learning some basic computer-assisted reporting skills and then moving from there as your interests/needs dictate. A lot of people see the programmer/journalism thing as distinct from computer-assisted reporting, but I don’t. I see it as a continuum. I see CAR as a “gateway drug” of sorts: Once you start working with small data sets using tools like Excel, Access, MySQL, etc., you’ll eventually hit limits of what you can do with macros and SQL.

Soon enough, you’ll want to be able to script certain things. You’ll want to get data from the web. You’ll want to do things you can only do using some kind of scripting language, and so it begins.

But again, the place to start isn’t thinking about all these technologies. The place to start is thinking about how these technologies can enable you to tell stories you otherwise would never be able to tell otherwise. And you should start small. Look for little things to start, and go from there.

In researching my book chapter (UPDATE: now published) I asked a group of journalists who worked with data what led them to do so. Here are their answers:

Jonathon Richards, The Times:

The flood of information online presents an amazing opportunity for journalists, but also a challenge: how on earth does one keep up with; make sense of it? You could go about it in the traditional way, fossicking in individual sites, but much of the journalistic value in this outpouring, it seems, comes in aggregation: in processing large amounts of data, distilling them, and exploring them for patterns. To do that – unless you’re superhuman, or have a small army of volunteers – you need the help of a computer.

I ‘got into’ data journalism because I find this mix exciting. It appeals to the traditional journalistic instinct, but also calls for a new skill which, once harnessed, dramatically expands the realm of ‘stories I could possibly investigate…’

Mary Hamilton, Eastern Daily Press:

I started coding out of necessity, not out of desire. In my day-to-day work for local newspapers I came across stories that couldn’t be told any other way. Excel spreadsheets full of data that I knew was relevant to readers if I could break it down or aggregate it up. Lists of locations that meant nothing on the page without a map. Timelines of events and stacks of documents. The logical response for me was to try to develop the skills to parse data to get to the stories it can tell, and to present it in interactive, interesting and – crucially – relevant ways. I see data journalism as an important skill in my storytelling toolkit – not the only option, but an increasingly important way to open up information to readers and users.

Charles Arthur, The Guardian:

When I was really young, I read a book about computers which made the point – rather effectively – that if you found yourself doing the same process again and again, you should hand it over to a computer. That became a rule for me: never do some task more than once if you can possibly get a computer to do it.

Obviously, to implement that you have to do a bit of programming. It turns out all programming languages are much the same – they vary in their grammar, but they’re all about making the computer do stuff. And it’s often the same stuff (at least in my ambit) – fetch a web page, mash up two sets of data, filter out some rubbish and find the information you want.

I got into data journalism because I also did statistics – and that taught me that people are notoriously bad at understanding data. Visualisation and simplification and exposition are key to helping people understand.

So data journalism is a compound of all those things: determination to make the computer do the slog, confidence that I can program it to, and the desire to tell the story that the data is holding and hiding.

I don’t think there was any particular point where I suddenly said “ooh, this is data journalism” – it’s more that the process of thinking “oh, big dataset, stuff it into an ad-hoc MySQL database, left join against that other database I’ve got, see what comes out” goes from being a huge experiment to your natural reaction.

It’s not just data though – I use programming to slough off the repetitive tasks of the day, such as collecting links, or resizing pictures, or getting the picture URL and photographer and licence from a Flickr page and stuffing it into a blogpost.

Data journalism is actually only half the story. The other half is that journalists should be **actively unwilling** to do repetitive tasks if it’s machine-like (say, removing line breaks from a piece of copy, or changing a link format).

Time spent doing those sorts of tasks is time lost to journalism and given up to being a machine. Let the damn machines do it. Humans have better things to do.

Stijn Debrouwere, Belgian information designer:

I used to love reading the daily newspaper, but lately I can’t seem to be bothered anymore. I’m part of that generation of people news execs fear so much: those that simply don’t care about what newspapers and news magazines have to offer. I enjoy being an information designer because it gives me a chance to help reinvent the way we engage and inform communities through news and analysis, both offline and online. Technology doesn’t solve everything, but it sure can help. My professional goal is simply this: make myself love news and newspapers again, and thereby hopefully getting others to love it too.

This is a draft from a book chapter on data journalism (the first, on gathering data, is here). I’d really appreciate any additions or comments you can make – particularly around ways of spotting stories in data, and mistakes to avoid.

UPDATE: It has now been published in The Online Journalism Handbook.

“One of the most important (and least technical) skills in understanding data is asking good questions. An appropriate question shares an interest you have in the data, tries to convey it to others, and is curiosity-oriented rather than math-oriented. Visualizing data is just like any other type of communication: success is defined by your audience’s ability to pick up on, and be excited about, your insight.” (Fry, 2008, p4)

Once you have the data you need to see if there is a story buried within it. The great advantage of computer processing is that it makes it easier to sort, filter, compare and search information in different ways to get to the heart of what – if anything – it reveals.

The first stage in this process, then, is making sure the data is in the right format to be interrogated. Quite often this will be a spreadsheet or CSV (comma-separated values) file. If your information is in a PDF you will not be able to do a great deal with it other than re-type the values into a new spreadsheet (making sure to check you have not made any errors). A Word or Powerpoint document is likely to require the same work.

If the information is already online you can sometimes ‘scrape’ it – that is, automatically copy the relevant information into a separate document. How easy this is to do depends on how structured the information is. A table in a Wikipedia entry, for example, can be ‘scraped’ into a Google spreadsheet relatively easily (Tony Hirst gives instructions on how to do this at http://ouseful.wordpress.com/2008/10/14/data-scraping-wikipedia-with-google-spreadsheets/) and an online CSV file and certain other structured data can be scraped with Yahoo! Pipes (see below for more on using Yahoo! Pipes) but most scraping will involve programming (the online tool ScraperWiki provides one environment to help you do this).

Insert: Cleaning up data

Whether you have been given data, had to scrape it, or copied it manually, you will probably need to clean it up. All sorts of things can ‘dirty’ your data, from misspellings and variations in spelling, to odd punctuation, mixtures of numbers and letters, unnecessary columns or rows, and more. Computers, for example, will see ‘New Town’, ‘Newtown’ and ‘newtown’ as three separate towns when they may be one.

This can cause problems later on when analysing your data – for example, calculations not working or results not being accurate.

Some tips for cleaning your data include:

• Use a spellchecker to check for misspellings. You will probably have to add some words to the computer’s dictionary.
• Use ‘find and replace’ (normally in the Edit menu) to remove double-spaces and other common punctuation errors. Alternatively, if you are in Excel you can create a new column and use the =TRIM() function, which will copy the contents of the cell in the brackets and remove any spaces.
• Remove duplicate entries – if you are using Excel there are a few ways to do this under the Data tab – search for duplicates in Help.

For more tips on Excel specifically see this guide: http://office.microsoft.com/en-us/excel/HA102218401033.aspx

For cleaning up very large sets of data you might want to use a data cleaning tool like Google Refine.

Spotting the story

Once your data is cleaned you can start to look for anything newsworthy in it. There are some obvious places to start: if you are dealing with numbers, for example, you can work out what the ‘average’ is (the average bonus paid to council employees, for example). Similarly, you might look for the term which appears most often (e.g. the most common reason given for arresting terrorist suspects).

However, Kaiser Fung, a statistician whose blog Junk Charts is essential reading on the field, notes the dangers in lazily reaching for the average when you want to make an editorial point:

“Averaging stamps out diversity, reducing anything to its simplest terms. In so doing, we run the risk of oversimplifying, of forgetting the variations around the average. Hitching one’s attention to these variations rather than the average is a sure sign of maturity in statistical thinking. One can, in fact, define statistics as the study of the nature of variability. How much do things change? How large are these variations? What causes them?” (Fung, 2010, p4)

So while averages and modes (a mode is the number or term which appears most often) can be interesting discoveries, they should most often be used as a starting point for more illuminating investigation – normally involving leaving your computer to make phonecalls and speak to sources.

If you are looking at data over time, you can look to see what has increased over that period, or decreased – or disappeared.

But you will need to gather further data to provide context to your figures. If, for example, more council staff are receiving bonuses, is that simply because more staff have been employed? How much is spent on wages, and how do your figures compare? If you are comparing one city with another, understand how their populations differ – not just in aggregate, but in relevant details such as age, ethnicity, life expectancy, etc. You will need to know where to access basic statistics like these – the National Statistics website is often a good place to start.

Sometimes a change in the way data is gathered or categorised can produce a dramatic change in the data itself. In one example, designer Adrian Short obtained information (via an FOI request) on parking tickets from Transport for London that showed the numbers of tickets issued against a particular offence plummeted from around 8,000 to 8 in the space of one month (Arthur, 2009). Had people suddenly stopped committing that parking offence, or was there another explanation? A quick phonecall to Transport for London revealed that traffic wardens were issued with new handsets around the same time. Guardian journalist Charles Arthur hypothesised:

“Could it be that s46 [another offence which had a steep rise at the same time] is the default on the screen to issue a new ticket, and that wardens don’t bother to change it? Whatever it is, there’s a serious problem for TfL if those aren’t all s46 offences which have been ticketed since August 2006. Because if the ticket isn’t written out to the correct offence, then the fine isn’t payable. Theoretically, TfL might have to pay back millions in traffic fines for people who have been ticketed for s46 offences when they were actually committing s25 or s30 offences.”

This particular story came about at least in part because that information was easy to visualise.

The next section covers visualisation. In the meantime, once again I’d really appreciate any additions or comments – particularly around ways of spotting stories in data, and mistakes to avoid.

You may know about The Guardian’s Datastore: a compilation of “publicly-available data for you to use free” that’s been around for a few months now. You know the sort of thing: university tables; MPs’ expenses; tax paid by the FTSE 100.

It has already produced some great work from what I once described as the “Technician” variant of distributed journalism. 

But a column by Charles Arthur recently was the first example I’ve seen of Datastore being used for, well, more ordinary data – the sort of information journalists deal with every week. Here’s how it appeared in print:

“I dug up the figures from the UK music industry: the British record industry’s trade association (the BPI), and the UK games industry (via its trade body, Elspa) as well as the DVD industry (through the UK Film Council and the British Video Association). The results are over on the Guardian Data Store (http://bit.ly/data01), because they are the sort of numbers that should be available to everyone to chew over.

“What did I find? Total spending has grown – but music spending is being squeezed. The games industry – hardware and software – has grown from £1.4bn in 1999 (the year Napster started, and the music business stood rabbit-transfixed) to £4.04bn in 2008. That’s 12% annual compound growth. You’d kill for an endowment like that. Even DVD sales and rental take a £2.5bn bite out of consumers’ available funds, double that of 1999.

“So the music industry’s deadliest enemy isn’t filesharing – it’s the likes of Nintendo, Microsoft and Sony, and a zillion games publishers.”

That link (which frustratingly isn’t active in the online article) takes you to a Datablog post by Arthur which in turn links to a rather simple spreadsheet. 

And it’s the simplicity that I think is important.

It’s one thing to link to huge datasets that benefit from lots of eyeballs looking for stories, or perming the data in different ways.

But it’s something else to link to the more everyday figures journalists deal with; to show your sums, in short.

Is this a natural extension of the blogging culture of linking to your sources? I think it is. And the more journalists get used to publishing their work on the likes of Google Spreadsheets, the better journalism we will get.

So why aren’t more journalists doing it? And why aren’t more news organisations providing a place for them to do it? Or are they? I’d love to know of any other individual or organisational examples.

Charles Arthur of The Guardian makes his point pretty plain: “If I had one piece of advice to a journalist starting out now, it would be: learn to code”

“Let’s be clear that I’m not saying “code” as in “get deep into C++ or Java” … I mean it in the sense of having a nodding acquaintance with methods of programming, and perhaps a few languages, so that when something comes along where you’ll need, say, to transform data from one form to another, you can. Or where you need to make your own life easier by automating some process or other.

” … None of which is saying you shouldn’t be talking to your sources, and questioning what you’re told, and trying to find other means of finding stuff out from people. But nowadays, computers are a sort of primary source too. You’ve got to learn to interrogate them effectively – and quote them meaningfully – too.”

Amen to that.