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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.

'Significance' is a magazine published by the UK Royal Statistical Society and American Statistical Association. E.g. this article discusses the data we might use to analyse food security, thinking about what sources of data are available and the questions they might help us answer. "data on food insecurity is biased towards the environment in which it was created and the priorities of those who collect or commission it. Data from schools is concerned with reimbursement; government data might be focused on budgetary constraints or accountability; grocery stores could (if willing) tell us what food is bought, but not how it is used; meanwhile, non-profits are most interested in demonstrating impact to funders. There is a wide variety of data sets available, but very few are created with the intent to understand the real drivers of hunger and poverty. The data may be repurposed, but modellers must be especially careful to moderate the assumptions of each data set. ...... It is also especially important that those experiencing hunger and poverty are consulted when designing any data analysis project. The input of domain experts is crucial to the success of data science endeavours, and those experiencing poverty know the right questions to ask."

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.

Any great story means visualization and detail. It takes the small additions of those details to build a picture in someone's mind to truly make the story complete. The same goes for analytics and data. Data is just a collection of numbers until you turn it into a story. Showing reports and dashboards can be overwhelming without adding a narrative to the data. Any great insight explains what happened, why it is important and how you can use it to turn it into something actionable. Data visualization is using data and statistics in creative ways to show patterns and draw conclusions about a hypothesis, or prove theories, that can help drive decisions in the organization.

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.

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.

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.

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).

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.

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

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'."

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...

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.

The Guardian datablog has a set of visualisations https://www.theguardian.com/technology/data-visualisation and a set of stories focused on the Australian context; useful for exploring how data analysis and visusalisation are used to tell a story.

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.

When we launched Beyond the blade earlier this year, we wanted to know how many young people and children were being killed by knives in the UK. Who are these young people being killed?, Where are they dying? Is the scale of the issue changing, and if so how? We spoke to experts about the number of children and teenagers affected in Britain and Northern Ireland. We checked with the Office for National Statistics, the Home Office, politicians, academics and thinktanks. But the answer to how many young people are dying every year, it seemed, was that nobody knows. So we started trying to find out. Until now, there has been no publicly available information about the demographic profiles of those who have died from knife attacks in the UK

Journalists often make mistakes when reporting on data such as opinion poll results, federal jobs reports and census surveys because they don't quite understand - or they ignore - the data's margin of error. Data collected from a sample of the population will never perfectly represent the population as a whole. The margin of error, which depends primarily on sample size, is a measure of how precise the estimate is. The margin of error for an opinion poll indicates how close the match is likely to be between the responses of the people in the poll and those of the population as a whole. To help journalists understand margin of error and how to correctly interpret data from polls and surveys, we've put together a list of seven tips, Look for the margin of error - and report it. It tells you and your audience how much the results can vary. Remember that the larger the margin of error, the greater the likelihood the survey estimate will be inaccurate. Make sure a political candidate really has the lead before you report it. Note that there are real trends, and then there are mistaken claims of a trend. Watch your adjectives. (And it might be best to avoid them altogether.) Keep in mind that the margin of error for subgroups of a sample will always be larger than the margin of error for the sample. Use caution when comparing results from different polls and surveys, especially those conducted by different organizations.

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.

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.

A complex look at an important issue and what data can tell us about it, and how missing data complicates the picture.