2025-06-06 00:19:44
A couple years ago, Harvard professor Francesca Gino was accused of faking data, ironically for research on honesty. Gino recently lost tenure:
A Harvard professor who has written extensively about honesty was stripped of her tenure this month, a university spokesman said on Tuesday, after allegations that she had falsified data.
The scholar, Francesca Gino, a professor of business administration at Harvard Business School and a prominent behavioral scientist, has studied how small changes can influence behavior and been published in a number of peer-reviewed journals. Among the studies in which Dr. Gino has been a co-author are, for example, one showing that counting to 10 before deciding what to eat can lead to choosing healthier food.
2025-06-05 23:08:16
Hi folks. It’s Nathan. Welcome to the Process, the newsletter for FlowingData members on visualizing and analyzing data beyond the defaults and averages. Some suggest that AI is the way to get there. While you can use it for the technical parts of visualization, the parts with intent and context are still for you, and you shouldn’t have it any other way.
Become a member for access to this — plus tutorials, courses, and guides.
2025-06-04 15:31:07
There is always ample discussion about progressive tax rates in the United States. For those unfamiliar, income earned within certain ranges are taxed differently. Higher income is taxed higher. For Datawrapper, Luc Guillemot charted the rates for countries in Western Europe.
The x-axes represent income levels as a percentage of average income in each country. The y-axes represent the tax rate for the income level. The black bars show averages for the European Union. Belgium, with the steepest climb, increases taxes the most, whereas Hungary and Bulgaria use a flat rate across income levels.
Tags: Datawrapper, Europe, taxes
2025-06-03 19:04:23
For MIT Technology Review, James O’Donnell and Casey Crownhart ran numbers and interviewed experts go piece together a projection for how much energy AI will use. The takeaway is that it’s impossible to know with any certainty, because companies don’t disclose what they’re building.
The Lawrence Berkeley researchers offered a blunt critique of where things stand, saying that the information disclosed by tech companies, data center operators, utility companies, and hardware manufacturers is simply not enough to make reasonable projections about the unprecedented energy demands of this future or estimate the emissions it will create. They offered ways that companies could disclose more information without violating trade secrets, such as anonymized data-sharing arrangements, but their report acknowledged that the architects of this massive surge in AI data centers have thus far not been transparent, leaving them without the tools to make a plan.
“Along with limiting the scope of this report, this lack of transparency highlights that data center growth is occurring with little consideration for how best to integrate these emergent loads with the expansion of electricity generation/transmission or for broader community development,” they wrote. The authors also noted that only two other reports of this kind have been released in the last 20 years.
Tags: data center, energy, MIT Technology Review
2025-06-03 16:58:20
Hardware for AI uses a whole lot of energy while training on data from the internets, processing queries, and hallucinating surprising solutions. Alex de Vries-Gao, from the Institute for Environmental Studies in the Netherlands, published estimates for how much energy and compared to energy demand for countries.
Over the full year of 2025, a power demand of 5.3–9.4 GW could result in 46–82 TWh of electricity consumption (again, without further production output in 2025). This is comparable to the annual electricity consumption of countries such as Switzerland, Austria, and Finland (see Figure 2; Data S1, sheet 6). As the International Energy Agency estimated that all data centers combined (excluding crypto mining) consumed 415 TWh of electricity in 2024, specialized AI hardware could already be representing 11%–20% of these figures.
There are many assumptions behind the estimates, and they could be lower or higher depending on the unknowns, but most signs appear to point to steep increases.
We should probably plan for that. It doesn’t seem like this AI train is going to slow down any time soon. (via Wired)
Tags: Alex de Vries-Gao, energy, Institute for Environmental Studies
2025-06-02 17:18:29
Downloading survey microdata from public resources can be tricky. Sometimes the documentation is sparse, the tools are outdated, or the datasets are tucked away in obscure FTP subdirectories. This is annoying when you just want to work with the data.
Analyze Survey Data for Free, maintained by Anthony Damico, aims to streamline the download process via R. From a decade ago:
Governments spend billions of dollars each year surveying their populations. If you have a computer and some energy, you should be able to unlock it for free, with transparent, open-source software, using reproducible techniques. We’re in a golden era of public government data, but almost nobody knows how to mine it with technology designed for this millennium. I can change that, so I’m gonna. Help. Use it.
The site received an update to make downloading easier across 49 public datasets. Given the data takedowns these days, now seems like a good time to make quick use of the resource.
Tags: Anthony Damico, public, R, surveys
