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The conceptual, practical, and ethical issues surrounding "big data" and data in general begin at the very moment of data collection. Particularly when the data concern people, not enough attention is paid to the realities entangled within that significant moment and spreading out from it.1. Data sets are the results of their means of collection. It's easy to forget that the people collecting a data set, and how they choose to do it, directly determines the data set. An illustrative example can be found in the statistics for how many hate crimes were committed in the United States in 2012. According to the FBI Uniform Crime Reporting Program (UCR), the number was 5,796. However, the Department of Justice's Bureau of Statistics reported 293,800 hate crimes.

'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."

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.

Australian farms generate huge volumes of agricultural data. Examples include the types of crops being grown, crop yields, livestock numbers and locations, types of fertilisers and pesticides being used, soil types, rainfall and more. This data is typically collected through the use of digital farming machinery and buildings featuring robotics and digital technologies, artificial intelligence, and devices connected to the internet ("internet of things", or IoT). But a recent review from the Australian Bureau of Statistics and the Australian Bureau of Agricultural and Resource Economics highlights the patchy and fragmented nature of existing government and industry approaches to agricultural data. What that means is Australian farmers are currently not adequately protected from their farm data being collected and used without their knowledge or consent.

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.

Once your business has started collecting and combining all kinds of data, the next elusive step is to extract value from it. Your data may hold tremendous amounts of potential value, but not an ounce of value can be created unless insights are uncovered and translated into actions or business outcomes. During a 2009 interview, Google's Chief Economist Dr. Hal R.Varian stated, "The ability to take data-to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it-that's going to be a hugely important skill in the next decades." Fast forward to 2016 and many businesses would agree with Varian's astute assessment.

Protests against racial injustice and the COVID-19 pandemic have exposed racial inequalities rife within social and economic systems around the world. Fed up with police brutality and systemic racism against African Americans and other racialised groups, people staged protests against racial injustice in all 50 states across the United States.Apart from these examples, however, there is surprisingly little data or discourse about the impact of the disease on racial and ethnic minorities in the rest of Europe. This silence speaks volumes about Europe's approach to racism.The vast majority of EU member states do not use the concept of race or ethnic origin in data collection, in spite of policies like the European Racial Equality Directive and the Employment Equality Directive which prohibit racial or ethnic discrimination. France outright prohibits it.Without disaggregated data, it is virtually impossible to quantify the extent of discrimination experienced by racial and ethnic groups or the impacts of COVID-19 on their lives.

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).

But because a machine learning model learns the boundaries of its world from its input data, just three people informed how any model using that dataset would interpret if 'childbirth' was emotional. This led to a perspective that has informed all of my work since: a dataset is a worldview. It encompasses the worldview of the people who scrape and collect the data, whether they're researchers, artists, or companies. It encompasses the worldview of the labelers, whether they labeled the data manually, unknowingly, or through a third party service like Mechanical Turk, which comes with its own demographic biases. It encompasses the worldview of the inherent taxonomies created by the organizers, which in many cases are corporations whose motives are directly incompatible with a high quality of life.

journalists often write about public opinion polls, which are designed to measure the public's attitudes about an issue or idea. Some of the most high-profile polls center on elections and politics. Newsrooms tend to follow these polls closely to see which candidates are ahead, who's most likely to win and what issues voters feel most strongly about. Other polls also offer insights into how people think. For example, a government agency might commission a poll to get a sense of whether local voters would support a sales tax increase to help fund school construction. Researchers frequently conduct national polls to better understand how Americans feel about public policy topics such as gun control, immigration reform and decriminalizing drug use. When covering polls, it's important for journalists to try to gauge the quality of a poll and make sure claims made about the results actually match the data collected. Sometimes, pollsters overgeneralize or exaggerate their findings. Sometimes, flaws in the way they choose participants or collect data make it tough to tell what the results really mean. Below are 11 questions we suggest journalists ask before reporting on poll results. While most of this information probably won't make it into a story or broadcast, the answers will help journalists decide how to frame a poll's findings - or whether to cover them at all.

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.

The eBook contains 16 short stories about brilliant Australian research data projects that have led to real-life impacts for Australia and beyond. It is intentionally very punchy and image-led. The stories were collected during ANDS' #dataimpact campaign which ran through 2016.

As an epidemiologist, I am interested in disease - and more specifically, who in a population currently has or might get that disease. What is their age, sex, or socioeconomic status? Where do they live? What can people do to limit their chances of getting sick? Questions exploring whether something is likely to happen or not can be answered with quantitative research. By counting and measuring, we quantify (measure) a phenomenon in our world, and present the results through percentages and averages. We use statistics to help interpret the significance of the results. While this approach is very important, it can't tell us everything about a disease and peoples' experiences of it. That's where qualitative data becomes important.

On the importance of having data, in order to understand and tackle an issue... The most recent U.S. estimate...concluded approximately 513,000 women and girls at risk of genital mutilation...But that number should be taken with a big grain of salt...the data doesn't account for immigrants from countries where female genital cutting isn't studied or widely practiced...."You also can't assume that people who come to the U.S. are a representative sample of their country of origin," Clark said. That's especially problematic for estimating rates of female genital cutting, since it's not practiced uniformly within countries. It's also possible, he said, that some immigrants abandon the procedure as they assimilate....some advocates point out that although the estimates focus on immigrants, ...female genital cutting isn't new to the U.S. Female circumcision was performed as a treatment for masturbation by American physicians as recently as the mid-20th century...

Last month, CNBC reported on a page in Google's account settings titled "Purchases" - a month-by-month list of items you've bought across online services like Amazon and other apps that are collected via Google services like Gmail. Purchases is a jarring example of how leaky our data really is and how large companies can aggregate that information unbeknown to the consumer. I, for one, was unaware that almost every concert ticket, Domino's pizza and Amazon purchase (including a 2014 accidental purchase of the film "Tango & Cash") was being logged by Google. Equally troubling: The purchases can't easily be deleted from the page without also deleting the receipt emails from your Gmail account.

Since the census is the ultimate measure of population in the U.S., one might wonder how we could even know if its count was off. In other words, who recounts the count? Well, the Census Bureau itself, but using a different data source. After each modern census, the bureau carries out research to gauge the accuracy of the most recent count and to improve the survey for the next time around. The best method for determining the scope of the undercount is refreshingly simple: The bureau compares the total number of recorded births and deaths for people of each birth year, then adds in an estimate of net international migration and … that's it. With that number, the bureau can vet the census - which missed 4.6 percent of kids under 5 in 2010, according to this check.

Most people pass through some type of public space in their daily routine - sidewalks, roads, train stations. Thousands walk through Bryant Park every day. But we generally think that a detailed log of our location, and a list of the people we're with, is private. Facial recognition, applied to the web of cameras that already exists in most cities, is a threat to that privacy. To demonstrate how easy it is to track people without their knowledge, we collected public images of people who worked near Bryant Park (available on their employers' websites, for the most part) and ran one day of footage through Amazon's commercial facial recognition service.

An introduction to a set of deep dive articles an important issue....When we tell people that we work at the intersection of statistics and human rights, the reaction is often surprise. Everyone knows that lawyers and journalists think about human rights problems … but statisticians? Yet, documenting and proving human rights abuses frequently involves the need for quantification. In the case of war crimes and genocide, guilt or innocence can hinge on questions of whether violence was systematic and widespread or one group was targeted at a differential rate compared to others. Similar issues can arise in assessing violations of civil, social, and economic rights. Sometimes the questions can be answered through simple tabulations, but often, more-complex methods of data collection and analysis are required.

The census has been used for hundreds of years to determine how many U.S. House members each state will have, and it currently helps determine how hundreds of billions of dollars in federal spending is divvied up. "The risk that really troubles me is that there's a big undercount and then there's a big lack of representation," said John Thompson, who was director of the U.S. Census Bureau until he resigned last year (the bureau is still without a director).