957 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. The Chinese place a higher value on community good versus individual rights, so most feel that, if social credit will bring a safer, more secure, more stable society, then bring it on
    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. For the second, we could try to detect inconsistencies, eitherby inspecting samples of the class hierarchy

      Yes, that's what I do when doing quality work on the taxonomy (with the tool wdtaxonomy)

    2. Possible relations between Items

      This only includes properties of data-type item?! It should be made more clear because the majority of Wikidata classes has other data types.

    3. 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. Entscheidend ist, dass sie Herren des Verfahrens bleiben - und eine Vision für das neue Maschinenzeitalter entwickeln.

      Es sieht für mich nicht eigentlich so aus als wären wir jemals die "Herren des Verfahrens" gewesen. Und auch darum geht es ja bei Marx. Denke ich.

    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.

    1. tl;dr: data engineer = software, coding, cleaning data sets data architects = structure the technology to manage data models and database admin data scientist = stats + math models business analysts = communication and domain expertise

    1. research publications are not research data

      they could be, if used as part of a text mining corpus, for example

  4. Sep 2018
    1. Third, the post-LMS world should protect the pedagogical prerogatives and intellectual property rights of faculty members at all levels of employment. This means, for example, that contingent faculty should be free to take the online courses they develop wherever they happen to be teaching. Similarly, professors who choose to tape their own lectures should retain exclusive rights to those tapes. After all, it’s not as if you have to turn over your lecture notes to your old university whenever you change jobs.

      Own your pedagogy. Send just like anything else out there...

    1. I love the voice of their help page. Someone very opinionated (in a good way) is building this product. I particularly like this quote: Your data is a liability to us, not an asset.
    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).

    1. In October 2014 the Open Knowledge Foundation recommends the Creative Commons CC0 license to dedicate content to the public domain,[51][52] and the Open Data Commons Public Domain Dedication and License (PDDL) for data.[53]
    1. predictive analysis

      Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.

  5. Aug 2018
    1. this possibility of increased ownership and agency over technology and a somewhat romantic idea I have that this can transfer to inspire ownership and agency over learning
    1. A file containing personal information of 14.8 million Texas residents was discovered on an unsecured server. It is not clear who owns the server, but the data was likely compiled by Data Trust, a firm created by the GOP.