3,501 Matching Annotations
  1. May 2021
    1. ReconfigBehSci. (2020, November 18). @danielmabuse yes, we all make mistakes, but a responsible actor also factors the kinds of mistakes she is prone to making into decisions on what actions to take: I’m not that great with my hands, so I never contemplated being a neuro-surgeon. Not everyone should be a public voice on COVID [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1329002783094296577

    1. Robert Colvile. (2021, February 16). The vaccine passports debate is a perfect illustration of my new working theory: That the most important part of modern government, and its most important limitation, is database management. Please stick with me on this—It’s much more interesting than it sounds. (1/?) [Tweet]. @rcolvile. https://twitter.com/rcolvile/status/1361673425140543490

    1. ReconfigBehSci on Twitter: ‘the SciBeh initiative is about bringing knowledge to policy makers and the general public, but I have to say this advert I just came across worries me: Where are the preceding data integrity and data analysis classes? Https://t.co/5LwkC1SVyF’ / Twitter. (n.d.). Retrieved 18 February 2021, from https://twitter.com/SciBeh/status/1362344945697308674

  2. Apr 2021
    1. Mehdi Hasan. (2021, April 12). ‘Given you acknowledged...in March 2020 that Asian countries were masking up at the time, saying we shouldn’t mask up as well was a mistake, wasn’t it... At the time, not just in hindsight?’ My question to Dr Fauci. Listen to his very passionate response: Https://t.co/BAf4qp0m6G [Tweet]. @mehdirhasan. https://twitter.com/mehdirhasan/status/1381405233360814085

    1. Drop missing values from the dataframeIn this method we can see that by using dropmissing() method, we are able to remove the rows having missing values in the data frame. Drop missing values is good for those datasets which are large enough to miss some data that will not affect the prediction and it’s not good for small datasets it may lead to underfitting the models.

      Listwise Deletion

    1. Jeremy Faust MD MS (ER physician) on Twitter: “Let’s talk about the background risk of CVST (cerebral venous sinus thrombosis) versus in those who got J&J vaccine. We are going to focus in on women ages 20-50. We are going to compare the same time period and the same disease (CVST). DEEP DIVE🧵 KEY NUMBERS!” / Twitter. (n.d.). Retrieved April 15, 2021, from https://twitter.com/jeremyfaust/status/1382536833863651330

    1. The privacy policy — unlocking the door to your profile information, geodata, camera, and in some cases emails — is so disturbing that it has set off alarms even in the tech world.

      This Intercept article covers some of the specific privacy policy concerns Barron hints at here. The discussion of one of the core patents underlying the game, which is described as a “System and Method for Transporting Virtual Objects in a Parallel Reality Game" is particularly interesting. Essentially, this system generates revenue for the company (in this case Niantic and Google) through the gamified collection of data on the real world - that selfie you took with squirtle is starting to feel a little bit less innocent in retrospect...

    2. Yelp, like Google, makes money by collecting consumer data and reselling it to advertisers.

      This sentence reminded me of our "privacy checkup" activity from week 7 and has made me want to go and review the terms of service for some of the companies featured in this article- I don't use yelp, but Venmo and Lyft are definitely keeping track of some of my data.

    1. The insertion of an algorithm’s predictions into the patient-physician relationship also introduces a third party, turning the relationship into one between the patient and the health care system. It also means significant changes in terms of a patient’s expectation of confidentiality. “Once machine-learning-based decision support is integrated into clinical care, withholding information from electronic records will become increasingly difficult, since patients whose data aren’t recorded can’t benefit from machine-learning analyses,” the authors wrote.

      There is some work being done on federated learning, where the algorithm works on decentralised data that stays in place with the patient and the ML model is brought to the patient so that their data remains private.

  3. Mar 2021
    1. Visualise written content into a more dynamic way. Many people, some neurodivergent folks especially, benefit from information being distilled into diagrams, comics, or less word-dense formats. Visuals can also benefit people who might not read/understand the language you wrote it in. They can also be an effective lead-in to your long-form from visually-driven avenues like Pinterest or Instagram.

      This is also a great exercise for readers and learners. If the book doesn't do this for you already, spend some time to annotate it or do it yourself.

    1. Ashish K. Jha, MD, MPH. (2020, December 12). Michigan vs. Ohio State Football today postponed due to COVID But a comparison of MI vs OH on COVID is useful Why? While vaccines are coming, we have 6-8 hard weeks ahead And the big question is—Can we do anything to save lives? Lets look at MI, OH for insights Thread [Tweet]. @ashishkjha. https://twitter.com/ashishkjha/status/1337786831065264128

    1. The urgent argument for turning any company into a software company is the growing availability of data, both inside and outside the enterprise. Specifically, the implications of so-called “big data”—the aggregation and analysis of massive data sets, especially mobile

      Every company is described by a set of data, financial and other operational metrics, next to message exchange and paper documents. What else we find that contributes to the simulacrum of an economic narrative will undeniably be constrained by the constitutive forces of its source data.

    1. DataBeers Brussels. (2020, October 26). ⏰ Our next #databeers #brussels is tomorrow night and we’ve got a few tickets left! Don’t miss out on some important and exciting talks from: 👉 @svscarpino 👉 Juami van Gils 👉 Joris Renkens 👉 Milena Čukić 🎟️ Last tickets here https://t.co/2upYACZ3yS https://t.co/jEzLGvoxQe [Tweet]. @DataBeersBru. https://twitter.com/DataBeersBru/status/1320743318234562561

  4. Feb 2021
    1. Data on blockchains are different from data on the Internet, and in one important way in particular. On the Internet most of the information is malleable and fleeting. The exact date and time of its publication isn't critical to past or future information. On a blockchain, the truth of the present relies on the details of the past. Bitcoins moving across the network have been permanently stamped from the moment of their coinage.

      data on blockchain vs internet

    1. What this means is: I better refrain from writing a new book and we rather focus on more and better docs.

      I'm glad. I didn't like that the book (which is essentially a form of documentation/tutorial) was proprietary.

      I think it's better to make documentation and tutorials be community-driven free content

    2. ather, data is passed around from operation to operation, from step to step. We use OOP and inheritance solely for compile-time configuration. You define classes, steps, tracks and flows, inherit those, customize them using Ruby’s built-in mechanics, but this all happens at compile-time. At runtime, no structures are changed anymore, your code is executed dynamically but only the ctx (formerly options) and its objects are mutated. This massively improves the code quality and with it, the runtime stability
    1. Kit Yates. (2021, January 22). Is this lockdown 3.0 as tough as lockdown 1? Here are a few pieces of data from the @IndependentSage briefing which suggest that despite tackling a much more transmissible virus, lockdown is less strict, which might explain why we are only just keeping on top of cases. [Tweet]. @Kit_Yates_Maths. https://twitter.com/Kit_Yates_Maths/status/1352662085356937216

    1. Benford’s Law is a theory which states that small digits (1, 2, 3) appear at the beginning of numbers much more frequently than large digits (7, 8, 9). In theory Benford’s Law can be used to detect anomalies in accounting practices or election results, though in practice it can easily be misapplied. If you suspect a dataset has been created or modified to deceive, Benford’s Law is an excellent first test, but you should always verify your results with an expert before concluding your data has been manipulated.

      This is a relatively good explanation of Benford's law.

      I've come across the theory in advanced math, but I'm forgetting where I saw the proof. p-adic analysis perhaps? Look this up.