3,435 Matching Annotations
  1. Aug 2020
    1. Cajner, T., Crane, L. D., Decker, R. A., Grigsby, J., Hamins-Puertolas, A., Hurst, E., Kurz, C., & Yildirmaz, A. (2020). The U.S. Labor Market during the Beginning of the Pandemic Recession (Working Paper No. 27159; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27159

    1. Brynjolfsson, E., Horton, J. J., Ozimek, A., Rock, D., Sharma, G., & TuYe, H.-Y. (2020). COVID-19 and Remote Work: An Early Look at US Data (Working Paper No. 27344; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27344

    1. Valhalla aims to revise the memory model for Java to allow for immutable types, which are more complex than primitives, but less flexible than objects. Sometimes you have more complex data that doesn’t change over the course of that object’s lifespan; burdening it with the overhead of a class is unnecessary. The initial proposal put it more succinctly: “Codes like a class, works like an int.” “For things like big data for machine learning or for natural language, Valhalla promises to represent data in a way that allows the JVM to fully take advantage of modern hardware architectures that have changed dramatically since Java was created,” said Saab.
  2. Jul 2020
    1. Ruby has some really nice libraries for working with linked data. These libraries allow you to work with the data in both a graph and resource-oriented fashion, allowing a developer to use the techniques that best suit his or her use cases and skills.
    2. Another Ruby gem, Spira, allows graph data to be used as model objects
    1. As a result, web browsers can provide only minimal assistance to humans in parsing and processing web pages: browsers only see presentation information.
    1. To verify that your structured data is correct, many platforms provide validation tools. In this tutorial, we'll validate our structured data with the Google Structured Data Validation Tool.
    2. Valid AMP pages do not require schema.org structured data, but some platforms like Google Search require it for certain experiences like the Top stories carousel. It's generally a good idea to include structured data. Structured data helps search engines to better understand your web page, and to better display your content in Search Engine Result Pages (e.g., in rich snippets).
    1. As mentioned earlier in these guidelines, it is very important that controllers assess the purposes forwhich data is actually processed and the lawful grounds on which it is based prior to collecting thedata. Often companies need personal data for several purposes, and the processing is based on morethan one lawful basis, e.g. customer data may be based on contract and consent. Hence, a withdrawalof consent does not mean a controller must erase data that are processed for a purpose that is basedon the performance of the contract with the data subject. Controllers should therefore be clear fromthe outset about which purpose applies to each element of data and which lawful basis is being reliedupon.
    2. If there is no other lawful basisjustifying the processing (e.g. further storage) of the data, they should be deleted by the controller.
    3. In cases where the data subject withdraws his/her consent and the controller wishes to continue toprocess the personal data on another lawful basis, they cannot silently migrate from consent (which iswithdrawn) to this other lawful basis. Any change in the lawful basis for processing must be notified toa data subject in accordance with the information requirements in Articles 13 and 14 and under thegeneral principle of transparency.
    4. Data minimization, anonymisation and datasecurity are mentioned as possible safeguards.73Anonymisation is the preferred solution as soon asthe purpose of the research can be achieved without the processing of personal data.
    1. Some vendors may relay on legitimate interest instead of consent for the processing of personal data. The User Interface specifies if a specific vendor is relating on legitimate interest as legal basis, meaning that that vendor will process user’s data for the declared purposes without asking for their consent. The presence of vendors relying on legitimate interest is the reason why within the user interface, even if a user has switched on one specific purpose, not all vendors processing data for that purpose will be displayed as switched on. In fact, those vendors processing data for that specific purpose, relying only on legitimate interest will be displayed as switched off.
    2. Under GDPR there are six possible legal bases for the processing of personal data.
    1. drawing evidence-based conclusions

      One thing that is not obvious about Hypothesis, is that you can also use it to annotate data sheets — that's easiest if they are CSV files published on the web.

    1. Do jeszcze bardziej przytłaczających wniosków doszła Julianne Holt-Lunstad, która, posiłkując się wynikami 70 badań naukowych, ogłosiła, że samotność zwiększa śmiertelność w takim samym stopniu co otyłość czy wypalanie 15 papierosów dziennie. Z kolei Nicole Valtorty z Uniwersytetu Newcastle ustaliła, że prawdopodobieństwo ataku serca u osób osamotnionych rośnie o 29 proc., a zagrożenie udarem – o 32 proc. „To niezależny czynnik przyczyniający się do śmierci. Może cię po prostu zabić. Znajduje się na tej samej liście co choroby serca i rak – twierdzi dr Josh Klapow, psycholog kliniczny z Uniwersytetu Alabamy.

      Data on health consequences of being alone

    2. Z danych GUS-u i tych zebranych przez portale randkowe wynika, że w Polsce w ciągu ostatnich 10 lat liczba osób żyjących samotnie wzrosła o 34 proc.
    3. Wśród krajów europejskich w niechlubnym rankingu zwycięża jednak Szwecja, w stolicy której samotnie mieszka aż 58 proc.(!) populacji. Z kolei w Stanach Zjednoczonych odsetek ten wynosi 27 proc. (w Nowym Jorku prawie 50 proc.) i cały czas rośnie – dla porównania w roku 1920 jednoosobowe gospodarstwo domowe prowadziło tam 5 proc. obywateli.

      Percentage of people living alone

    1. the market size: the global note-taking management software market is estimated to reach $1.35 billion by 2026, growing at a CAGR of 5.32% from 2019 to 2026greater scope for innovation: eg., be it creating a task list, a roadmap, or a design repository, Notion can handle it alllack of satisfaction: it’s noted that people always use a combination of note-taking apps and hardly stick to one for a long time

      Three reasons why we constantly see more note-taking apps, which in return increase our paradox of choice

    1. Jeffrey, B., Walters, C. E., Ainslie, K. E. C., Eales, O., Ciavarella, C., Bhatia, S., Hayes, S., Baguelin, M., Boonyasiri, A., Brazeau, N. F., Cuomo-Dannenburg, G., FitzJohn, R. G., Gaythorpe, K., Green, W., Imai, N., Mellan, T. A., Mishra, S., Nouvellet, P., Unwin, H. J. T., … Riley, S. (2020). Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research, 5, 170. https://doi.org/10.12688/wellcomeopenres.15997.1

    1. One of these semiotizing processes is the extraction, interpretation and reintegration of web data from and into human subjectivities.

      Machine automation becomes another “subjectivity” or “agentivity”—an influential one, because it is the one filtering and pushing content to humans.

      The means of this automated subjectivity is feeding data capitalism: more content, more interaction, more behavioral data produced by the users—data which is then captured (“dispossessed”), extracted, and transformed into prediction services, which render human behavior predictable, and therefore monetizable (Shoshana Zuboff, The Age of Surviellance Capitalism, 2019).

    1. Fontanet, A., Tondeur, L., Madec, Y., Grant, R., Besombes, C., Jolly, N., Pellerin, S. F., Ungeheuer, M.-N., Cailleau, I., Kuhmel, L., Temmam, S., Huon, C., Chen, K.-Y., Crescenzo, B., Munier, S., Demeret, C., Grzelak, L., Staropoli, I., Bruel, T., … Hoen, B. (2020). Cluster of COVID-19 in northern France: A retrospective closed cohort study. MedRxiv, 2020.04.18.20071134. https://doi.org/10.1101/2020.04.18.20071134

    1. Sapoval, N., Mahmoud, M., Jochum, M. D., Liu, Y., Elworth, R. A. L., Wang, Q., Albin, D., Ogilvie, H., Lee, M. D., Villapol, S., Hernandez, K., Berry, I. M., Foox, J., Beheshti, A., Ternus, K., Aagaard, K. M., Posada, D., Mason, C., Sedlazeck, F. J., & Treangen, T. J. (2020). Hidden genomic diversity of SARS-CoV-2: Implications for qRT-PCR diagnostics and transmission. BioRxiv, 2020.07.02.184481. https://doi.org/10.1101/2020.07.02.184481

    1. Lavezzo, E., Franchin, E., Ciavarella, C., Cuomo-Dannenburg, G., Barzon, L., Del Vecchio, C., Rossi, L., Manganelli, R., Loregian, A., Navarin, N., Abate, D., Sciro, M., Merigliano, S., De Canale, E., Vanuzzo, M. C., Besutti, V., Saluzzo, F., Onelia, F., Pacenti, M., … Crisanti, A. (2020). Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’. Nature, 1–1. https://doi.org/10.1038/s41586-020-2488-1

  3. Jun 2020
    1. Levita, L., Gibson Miller, J., Hartman, T. K., Murphy, J., Shevlin, M., McBride, O., Mason, L., Martinez, A. P., bennett, kate m, Stocks, T. V. A., McKay, R., & Bentall, R. (2020). Report2: Impact of Covid-19 on young people aged 13-24 in the UK- preliminary findings [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/s32j8

    1. Rosenberg, E. S., Tesoriero, J. M., Rosenthal, E. M., Chung, R., Barranco, M. A., Styer, L. M., Parker, M. M., John Leung, S.-Y., Morne, J. E., Greene, D., Holtgrave, D. R., Hoefer, D., Kumar, J., Udo, T., Hutton, B., & Zucker, H. A. (2020). Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Annals of Epidemiology. https://doi.org/10.1016/j.annepidem.2020.06.004

    1. n this project we see a shift from a citizen-based model to a consumer model for urban planning, where all citizens’ ‘personal and environmental data is an economic resource.’

      Called survillance capitlism

    1. Informal mentorship was captured using the following retrospective question from Wave 3 of the AddHealth data: "Other than your parents or step-parents, has an adult made an important positive difference in your life at any time since you were 14 years old?" Based on this question, I created a binary indicator for mentorship coded 1 if the young person had an informal mentor and 0 if they did not. Respondents were then asked "How is this person related to you?", and given response options like "family,""teacher/counselor,""friend's parent,""neighbor,"and "religious leader.

      Defining informal mentorship in the survey data

    2. Middle-income subsample 3,158

      Middle-income subsample for analysis was 3,158

    3. 1. "Middle-income" is defined as anyone living in a household making two-thirds to double the median income (Pew Research Center, 2016). In 1994, the median income for a family of four was $46,757(US Bureau of Statistics, 1996). Thus, "middle-income" families would be those making between $30,860 and $93,514. Because I only have data available in $25,000 increments, I am defining middle-income families as those making between $25,000 and $100,000 a year in Wave 1.

      Middle-income = families making $25k-$100k a year in Wave 1

    4. Defining low-,middle-, and high-income groupsDue to the limitation in the data described above, all incomes had to be converted in to categorical responses, with the smallest possible category size of $25,000 dollars. This created five categories for all incomes:

      Defining income groups: under $25k, $25k-$49999, $50k-$74999, $75k-$99999, and $100k+.

    Tags

    Annotators