3,748 Matching Annotations
  1. Dec 2018
  2. Nov 2018
    1. One way to think about "core" biodiversity data is as a network of connected entities, such as taxa, taxonomic names, publications, people, species, sequences, images, collections, etc. (Fig. 1)
    1. “It’s about embracing the inscrutable nature of human interactions,” says Chang. Evidence-based medicine was a massive improvement over intuition-based medicine, he says, but it only covers traditionally quantifiable data, or those things that are easy to measure. But we’re now quantifying information that was considered qualitative a generation ago.

      Biggest challenges to redesigning the health care system in a way that would work better for patients and improve health

    2. “Our biggest opportunity is leaning into that. It’s either embracing the qualitative nature of that and designing systems that can act just on the qualitative nature of their experience, or figuring how to quantitate some of those qualitative measures,” says Chang. “That’ll get us much further, because the real value in health care systems is in the human interactions. My relationship with you as a doctor and a patient is far more valuable than the evidence that some trial suggests.”

      Biggest challenges to redesigning the health care system in a way that would work better for patients and improve health

    1. Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.
    1. A KG typically spans across several domains and is built on topof a conceptual schema, orontology, which defines what types of entities (classes) are allowed inthe graph, alongside the types ofpropertiesthey can have

      Wikidata differs from typical KG as it is not build on top of classes (entity types). Any item (entity) can be connected by any property. Wikidata's only strict "classes" in the sense of KG classes are its data types (item, lemma, monolingual string...).

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    1. Does the widespread and routine collection of student data in ever new and potentially more-invasive forms risk normalizing and numbing students to the potential privacy and security risks?

      What happens if we turn this around - given a widespread and routine data collection culture which normalizes and numbs students to risk as early as K-8, what are our responsibilities (and strategies) to educate around this culture? And how do our institutional practices relate to that educational mission?

  3. Oct 2018
    1. As a recap, Chegg discovered on September 19th a data breach dating back to April that "an unauthorized party" accessed a data base with access to "a Chegg user’s name, email address, shipping address, Chegg username, and hashed Chegg password" but no financial information or social security numbers. The company has not disclosed, or is unsure of, how many of the 40 million users had their personal information stolen.

  4. Sep 2018
    1. End-Users

      Because Grafoscopio was used in critical digital literacy workshops, dealing with data activism and journalism, the intended users are people who don't know how to program necessarily, but are not afraid of learning to code to express their concerns (as activists, journalists and citizens in general) and if fact are wiling to do so.

      Tool adaptation was "natural" of the workshops, because the idea was to extend the tool so it can deal with authentic problems at hand (as reported extensively in the PhD thesis) and digital citizenship curriculum was build in the events as a memory of how we deal with the problems. But critical digital literacy is a long process, so coding as a non-programmers knowledge in service of wider populations able to express in code, data and visualizations citizen concerns is a long time process.

      Visibility, scalability and sustainablitiy of such critical digital literacy endeavors where communities and digital tools change each other mutually is still an open problem, even more considering their location in the Global South (despite addressing contextualized global problems).

  5. Aug 2018
    1. Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609 Oshawa Ontario 379,848 Ottawa–Gatineau Ontario–Quebec 1,323,783 Victoria British Columbia 367,770 Edmonton Alberta 1,321,426 Windsor Ontario 329,144 Quebec Quebec 800,296 Saskatoon Saskatchewan 295,095 Winnipeg Manitoba 778,489 Regina Saskatchewan 236,481 Hamilton Ontario 747,545 Sherbrooke Quebec 212,105 Kitchener–Cambridge–Waterloo Ontario

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    2. Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609

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    3. Largest census metropolitan areas in Canada by population (2016 Census) viewtalkedit CMA Province Population CMA Province Population Toronto Ontario 5,928,040 London Ontario 494,069 Montreal Quebec 4,098,927 St. Catharines–Niagara Ontario 406,074 Vancouver British Columbia 2,463,431 Halifax Nova Scotia 403,390 Calgary Alberta 1,392,609