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Over recent years, data has become an indispensable source for journalists and news organisations, providing excellent material for investigative work as well as storytelling. This has led to the emergence of data journalism, which, broadly speaking, uses information science and analytical techniques in conjunction with journalistic workflows to produce compelling stories rooted in data. Despite the relative maturity of data journalism and the growing application of data in editorial workflows, there is still a lot to learn about the systematic, seamless and effective integration of data and computational tools in newsrooms. It is time time for a holistic assessment of this emerging field by looking deeply into the ways newsrooms across the world have adopted data in their day-to-day workflows, the formation of their data teams, their best practices for producing high quality data driven investigative work, their success and failure stories, and emerging training requirements.

In 1967, following riots in Detroit, Philip Meyer used survey research methods, powered by a computer, to show that college-educated people were just as likely to have rioted as high school drop outs. His story was one of the first examples of computer assisted reporting and precision journalism, in which journalists use social science methodologies to extract and tell stories. In recognition of his contribution to the area, each year's best computer-driven and precision stories are celebrated through the Philip Meyer Journalism Award. The Award's 2016 winners have just been announced, with the successful entries showcasing techniques derived from quantitative and qualitative methods, such as surveys using randomly-selected respondents, descriptive and inferential statistical analysis, social network analysis, content analysis, field experiments, and more.

examples of data journalism in audio / podcast form - including: Right To Remain Silent is one particularly good example, because it's about bad data: specifically. police who manipulated official statistics. You might also listen to Choosing Wrong, which includes a section about polling. Another favourite of mine is an audio story by The Economist about the prostitution industry, based on data scraped from sex trade websites: More bang for your buck (there are even worse puns in the charts). David Rhodes, a BBC data journalist, has a range of stories on his Audioboom account, including pieces on Radio 4, Radio 5 Live, and a piece discussing "Did Greece really not pay 89.5% of their taxes in 2010" from the excellent factchecking radio programme, More or Less.

Nice piece on computational thinking and data journalism. For example... This story, published in the UK tabloid newspaper The Mirror, is a great example of understanding how a computer might 'see' information and be able to help you extract a story from it. The data behind the story is a collection of over 300,000 pieces of sheet music. On paper that music would be a collection of ink on paper. But because that has now been digitised, it is now quantified. That means we can perform calculations and comparisons against it. We could: Count the number of notes Calculate the variety (number of different) of notes Identify the most common notes Identify the notes with the maximum value Identify the notes with the minimum value Calculate a 'range' by subtracting the minimum from the maximum The journalist has seen this, and decided that the last option has perhaps the most potential to be newsworthy - we assume some singers have wider ranges than others, and the reality may surprise us (a quality of newsworthiness).

BBC guidelines on reporting statistics - excellent resource for AEI! http://downloads.bbc.co.uk/rmhttp/guidelines/editorialguidelines/pdfs/ReportingStatistics.pdf The BBC "accepts recommendations [...] to improve statistical training for BBC journalists and to ensure that journalists are better placed to challenge statistical claims made by people in public office...." It also has "plans to create a 'hub' for data journalism, recruit a new head of statistics and develop guidance based on 'guidelines from, for example, the Royal Statistical Society and others'."

Alcohol and Other Drug (AOD) Media Watch is based on the same premise as the ABC show Media Watch. It aims to highlight poor examples of journalism regarding AOD-related issues in the hope that we can assist journalists to report more objectively using science and evidence rather than perpetuating myths, opinions and moral panic. Research has found moral panics in the media can actually be detrimental. Moral panics in the media can actually be detrimental by counter-intuitively leading to increased drug use since it increases the perception that more people are using the drug than actually are. It has also been found to found that moral panics reduce the degree to which some people believe that the drug being reported on is harmful. It also reduces the credibility of AOD information in the media.

some examples of how the 3 types of machine learning - supervised, unsupervised, and reinforcement - have already been used for journalistic purposes, and using those to explain what those are along the way. Examples include: supervised learning to investigate doctors and sex abuse; unsurprivsed learning to identify motifs in Wes Anderson films; reinforcement learning to create a rock-paper-scissors that can beat you...

We all do it in journalism. We are taught to write a headline that a) captures what the story is about, and b) captures the reader's attention. Nothing wrong with that. Where the problem comes in is if the headline misleads or misinforms. And, as is so often the case with healthcare topics, that sort of disconnect has the potential to do more harm than good.

Mona Chalabi (@MonaChalabi) is the Data Editor of The Guardian US and a columnist at New York Magazine. Mona uses hand-drawn visualisations and data stories to make the numbers more relatable. Before getting into journalism, Mona worked in the nonprofit sector, first at the Bank of England, then Transparency International and the International Organization for Migration. 30 minute podcast interview, with some great examples of why data matters, and how to tell stories with data.

What is the best way of reporting on data related to the environment? Where do you find the data in the first place? How do you make it relatable to the public and which challenges do you face along the way? Last October, seven experts got together on the Data Journalism Awards Slack team on to tackle these questions.

Q&A episode on misinformation and fake news. What's true, how do we distinguish high quality journalism from populism, should we worry about being in an 'echo-chamber' where we're only exposed to views that agree with us, is mainstream media fake news, etc.

A list of "happy data stories" or stories related to the arts...including: Pup Inflation: Good Dogs Getting Better. "Oh, but for a much purer example of silly data journalism that I remembered as soon as I hit send, earlier this year I scraped and analyzed the dog ratings from the Twitter account WeRateDogs." -David Henry Montgomery Sneakin' Toward the Weekend, Workers Fill Roadways "How about using hourly traffic counting stations to show how people are cheating out of the office earlier and earlier on Fridays? Mental note: we're four years away from that link being older than my incoming freshmen. Also, note the wonder at phones able to receive email being someday widely available in the story." -Matt Waite

How are your intuitions shaped by the immediate world around you? The importance of evidence over anecdote and considering the bigger picture! If it's hot outside, you're more likely to believe in climate change. The public perception of climate change is shaped by the weather that people experience, according to a study published yesterday in the Proceedings of the National Academy of Sciences journal.

During the 2016 EU referendum campaign, both sides used statistics pretty freely to back their arguments. Understandably, UK broadcasters felt compelled to balance competing perspectives, giving audiences the opportunity to hear the relative merits of leaving or remaining in the EU. In doing so, however, the truth of these statistical claims was not always properly tested. This might help explain some of the public's misconceptions about EU membership. So, for example, although independent sources repeatedly challenged the Leave campaign's claim that the UK government spent £350m per week on EU membership, an IPSOS MORI survey found that almost half of respondents believed this was true just days before the election. Of the 6,916 news items examined in our research, more than 20% featured a statistic. Most of these statistical references were fairly vague, with little or limited context or explanation. Overall, only a third provided some context or made use of comparative data. Statistics featured mostly in stories about business, the economy, politics and health. So, for example, three-quarters of all economics items featured at least one statistic, compared to almost half of news about business. But there were some areas - where statistics might play a useful role in communicating trends or levels of risk - that statistics were rarely used.

One way of working out if the data you're gathering is particularly sensitive is to do a thought experiment: what would happen if this data got into the hands of a malicious actor? Who would be keen to get their hands on it? What are the worst things that they could do with this data? Sometimes, though, it can be hard to put yourself in the shoes of your enemies, or to envision potential future actions. As a result, practising data minimisation is a keystone of a rights-based, responsible data approach. And sadly, it's the opposite of the approach we're seeing governments around the world take.

Journalists Need to Do the Math Numbers still make many watchdogs whimper

Life is one long string of decisionmaking, even if none of them is major. This is certainly the impression we get from reading cognitive neuroscience journals. A great many studies these days seem to involve 'decisionmaking under uncertainty' (otherwise known as gambling). As a married couple, we have now clocked up just over 50 years of decisionmaking together. We still frequently avoid or delay decisions, but we know that this does not pay off in the long run. And, when we do make decisions, we usually make them jointly. In case this sounds too good to be true, we hasten to add that it's not always easy and often involves arguments - despite, or perhaps because, we are both cognitive neuroscientists ourselves. Actually, argument turns out to be a well-kept secret in group decisionmaking. But before we turn to the value of acrimony, let's look at some of the reasons why we believe that people can make better decisions together than they can on their own.

Journalists know they need to get better with data and statistics, but they have a long way to go Only 25 percent of journalists surveyed said they were "very" well equipped to interpret statistics from sources, and only 11 percent said the same about doing statistical analysis themselves.

What's the most efficient way to communicate a large amount to a reader? We ran an experiment to find out. The results show that we must give up with senseless "football fields" comparisons and focus on finding out if a number matters or not.

Almost one in four cancers detected in men were overdiagnosed in 2012, according to our new research, published today in the Medical Journal of Australia. In the same year, we found that approximately one in five cancers in women were overdiagnosed. Overdiagnosis is when a person is diagnosed with a "harmless" cancer that either never grows or grows very slowly. These cancers are sometimes called low or ultra-low-risk cancers and wouldn't have spread or caused any problems even if left untreated. Cancer overdiagnosis can result in people having unnecessary treatments, such as surgery, radiotherapy and hormone therapy. Being diagnosed with cancer and having cancer treatments can cause physical, psychological and financial harms.