3,440 Matching Annotations
  1. Jun 2020
    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+.

    5. Wave 1 income was collected as a continuous variable, with an average of $45,728, (N=15,351, SD=$51,616). Low-income respondents (with incomes below $25,000) had an average of $9,837 (N=3,049, SD=4,633). Wave 4 income was recorded as a categorical variable, however, where respondents indicated if they made under $5,000, between $5,000 and $10,000, between $10,000 and $15,000, etc. These categories were of different sizes, getting larger as the income grew larger. Therefore, in order to create comparable measures between Wave 1 and Wave 4, both incomes were converted to 5 groups, (1) household income of less than $25,000, (2) household income of $25,000 to $49,999, (3) household income of $50,000 to $74,000, (4) household income of $75,000 to $99,000, and (5) household income of over $100,000

      Upward mobility (dependent variable); data surrounding household incomes of Wave 1 and Wave 4

    6. stratum. This sampling method yielded a sample of 20,745 students in 7thto 12thgrade, with oversampling of some minority racialethnic groups, students with disabilities, and twins(Harris, 2018). Data were also collected from the parents of the in-home survey respondents, with an 85% success rate (Chen & Chantala, 2014).Wave 1 participants also reported their home address, which was then linked to a number of state-, county-, and Census tract-level variables from other sources. The present study used the school survey data, the in-home interview data, the parent survey data, and the data that was linked to state, county, and census-tracts, as described above. This study also used data from two subsequent waves of in-home interviews, specifically waves 3 and 4 (no new information relevant to the present study was collected in Wave 2). For each subsequent wave, AddHealth survey administrators recruited from the pool of Wave 1 respondents, no matter if they had responded to any wave since Wave 1. The present study used Wave 1 data for information about the youth’s socioeconomic status, social capital and other related variables. This wave collected from 1994 to 1995, when most respondents were between11 and 19 years old (n=20,745 youth) (Harris, 2013).This study also used information from the third wave of in-home interview data, namely all questions on informal mentoring. This wave wascollected in 2001 and 2002 when the youth (N=15,197) were 18 to 26 years old. The fourth wave of data was collected in 2008 and 2009, when the respondents were 25 to 33 years old (n=15,701). Data from the fourth wave wereused to calculate economic mobility, the key dependent variable for this study.

      Data source

    7. DataTo address these questions, this study used three wavesofthe restricted-use version of the National Longitudinal Study of Adolescent Health (AddHealth). AddHealth is a multi-wave longitudinal, nationally representative study of youth who have been followed since adolescence through to adulthood. The AddHealth data were collected by sampling 80 high schools stratified across region, school type, urbanicity, ethnic mix, and school size during the 1994-1995 academic year. Fifty-two feeder schools(commonly middle schools whose students were assumed to go to these study high schools)were also sampled, resulting in a total of 132 sample schools. (Chen & Chantala, 2014, Harris, 2013). When sample high schools had grades 7 to 12, feeder schools were not recruited, as the lower grades served the role of feeding in younger students (Chen, 2014). Seventy nine percent of schools approached agreed to be in the study (Chen & Chantala, 2014). An in-school survey was then administered to over 90,000 students from these 132 schools. This survey was given during a single day within a 45-to 60-minute class period (Chen & Chantala, 2014). Subsequent recruitment for in-home interviews was done by stratifying students in each school by grade and sex and then randomly choosing 17 students from each

      Data source

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    1. Governments’ use of purchased location data has exploded in recent months, as officials around the world have sought insights on how people are moving around during the Covid-19 pandemic. In general, governments have assured their citizens that any location data collected by the marketing industry and used by public health entities is anonymous. But the movements of a phone give strong clues to its ownership—for example, where the phone is located during the evenings and overnight is likely where the phone owner lives. The identity of the phone’s owner can further be corroborated if their workplace, place of worship, therapist’s office or other information about their real-world activities are known to investigators.

      private data is not anonymous as is purported

    1. Starr, T. N., Greaney, A. J., Hilton, S. K., Crawford, K. H., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., Veesler, D., & Bloom, J. D. (2020). Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding [Preprint]. Microbiology. https://doi.org/10.1101/2020.06.17.157982

    1. normalizing our dabatase will help us. What means normalize? Well, it simply means to separate our information as much as we can

      directly contradicts firebase's official advice: denormalize the structure by duplicating some of the data: https://youtu.be/lW7DWV2jST0?t=378

    1. Kucharski, A. J., Klepac, P., Conlan, A. J. K., Kissler, S. M., Tang, M. L., Fry, H., Gog, J. R., Edmunds, W. J., Emery, J. C., Medley, G., Munday, J. D., Russell, T. W., Leclerc, Q. J., Diamond, C., Procter, S. R., Gimma, A., Sun, F. Y., Gibbs, H. P., Rosello, A., … Simons, D. (2020). Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: A mathematical modelling study. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30457-6

    1. Chu, D. K., Akl, E. A., Duda, S., Solo, K., Yaacoub, S., Schünemann, H. J., Chu, D. K., Akl, E. A., El-harakeh, A., Bognanni, A., Lotfi, T., Loeb, M., Hajizadeh, A., Bak, A., Izcovich, A., Cuello-Garcia, C. A., Chen, C., Harris, D. J., Borowiack, E., … Schünemann, H. J. (2020). Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis. The Lancet, 0(0). https://doi.org/10.1016/S0140-6736(20)31142-9

    1. Estimates say that 83% of us will be hit with a mental health crisis in our lives, we can all make the choices to invest wisely in this area to improve our ‘mental durability’ to deal with it properly.
    1. Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I., Chong, T., Druckenmiller, H., Huang, L. Y., Hultgren, A., Krasovich, E., Lau, P., Lee, J., Rolf, E., Tseng, J., & Wu, T. (2020). The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature, 1–9. https://doi.org/10.1038/s41586-020-2404-8

    1. Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Deletaille, S., Nadai, M. D., Letouzé, E., Salah, A. A., Benjamins, R., Cattuto, C., Colizza, V., Cordes, N. de, Fraiberger, S. P., Koebe, T., Lehmann, S., Murillo, J., Pentland, A., Pham, P. N., Pivetta, F., … Vinck, P. (2020). Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances, 6(23), eabc0764. https://doi.org/10.1126/sciadv.abc0764

    1. Kempfert, K., Martinez, K., Siraj, A., Conrad, J., Fairchild, G., Ziemann, A., Parikh, N., Osthus, D., Generous, N., Del Valle, S., & Manore, C. (2020). Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil. ArXiv:2006.02483 [q-Bio, Stat]. http://arxiv.org/abs/2006.02483

  2. May 2020
    1. Betsch, C., Wieler, L., Bosnjak, M., Ramharter, M., Stollorz, V., Omer, S., Korn, L., Sprengholz, P., Felgendreff, L., Eitze, S., & Schmid, P. (2020). Germany COVID-19 Snapshot MOnitoring (COSMO Germany): Monitoring knowledge, risk perceptions, preventive behaviours, and public trust in the current coronavirus outbreak in Germany. https://doi.org/10.23668/PSYCHARCHIVES.2776

    1. The data that organizations and individuals have committed to digital memory stands to ultimately control them.
    2. by practicing data minimalism and actively considering your data decisions
    3. learn how to be a data steward or data ally. Help organizations proactively think about what data they collect and how it is governed after its collected. Help organizations get their collective head around all the data they possess, how they curate it, how they back it up, and how over time they minimize it.
    1. With a single source IP address it's possible to quickly determine the type of devices on their network, and the social networks they frequent – Google, YouTube, Facebook, Soichat.com, TikTok, Line (a chat application), among many other domains.
    1. Not necessarily. Hosting companies tend to keep your backups in the same place as your primary files. You don’t carry around a copy of your birth certificate along with the actual one – you keep the real one safe at home for emergencies. So why not do the same for your backups? CodeGuard provides safe, offsite backup that is 100% independent from your hosting provider.
    1. Register Today For Data Science Certification. Learn the Best Data Science Course from our Top Tutors. Study and Get A Certified Data Science Course. Enroll For Data Science Certification and Get 24/7 support and all time study Material. Land in your Dream Job by registering to this Course.

    1. Now personal data exports include users session information and users location data from the community events widget. Plus, a table of contents!See progress as you process export and erasure requests through the privacy tools.
    1. They collect very little data so their "export" feature is very simplistic: just an in-browser JSON dump of localStorage and cookies.

      Browser Data

      We use data on your browser to offer features on this website. We do not store this data, but we can offer a view of your browser data at any time.

      View Browser Data

    1. users must also be informed of the breach (within the same time frame) unless the data breached was protected by encryption (data rendered unreadable for the intruder), or, in general, the breach is unlikely to result in a risk to individuals’ rights and freedoms.
    2. The GDPR permits data transfers of EU resident data outside of the European Economic Area (EEA) only when in compliance with set conditions.
    3. Because consent under the GDPR is such an important issue, it’s mandatory that you keep clear records and that you’re able to demonstrate that the user has given consent; should problems arise, the burden of proof lies with the data controller, so keeping accurate records is vital.
    1. If you’re switching from another cookie management solution to ours, you may want to migrate the consents you’ve already collected. This is useful for ensuring that users who have already given their consent under the previous solution are not presented with the cookie banner, and the related request for consent, again.
    1. If, for example, you want to migrate consents from a previous provider, you could call this method inside the onBeforePreload callback when consent is already given by another platform.
    1. Services generally fall into two categories: Services related to your own data collection activities (eg. contact forms)Services related to third-party data collection activities (eg. Google Analytics)
    1. “If you are a non-college graduate man you have a less than 50/50 shot of ever being married in your life” – Andrew YangIn the 1970s and ‘80s, there were about 17 million manufacturing jobs in the USToday, there are about 12 million of those jobsMore women are graduating from college than men58% of college graduates in the US are women
    2. Today, 40% of children are born to unmarried mothersBack in the 70s and 80s, it was only 15%
    1. In practice, the TCF provides a standardized process for getting users’ informed consent and allows the seamless signaling of users’ s consent preferences across the advertising supply chain.
    1. The goal of the W3C Semantic Web Education and Outreach group's Linking Open Data community project is to extend the Web with a data commons by publishing various open datasets as RDF on the Web and by setting RDF links between data items from different data sources.
    2. The above diagram shows which Linking Open Data datasets are connected, as of August 2014.
    1. Hartman, T. K., Stocks, T. V. A., McKay, R., Gibson Miller, J., Levita, L., Martinez, A. P., Mason, L., McBride, O., Murphy, J., Shevlin, M., bennett, kate m, & Bentall, R. (2020). The Authoritarian Dynamic During the COVID-19 Pandemic: Effects on Nationalism and Anti-Immigrant Sentiment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/4tcv5

    1. Sure, anti-spam measures such as a CAPTCHA would certainly fall under "legitimate interests". But would targeting cookies? The gotcha with reCAPTCHA is that this legitimate-interest, quite-necessary-in-today's-world feature is inextricably bundled with unwanted and unrelated Google targeting (cookiepedia.co.uk/cookies/NID) cookies (_ga, _gid for v2; NID for v3).
    2. Many 3rd parties has some magic parameter which blocks the cookie, but doesn't block the functionality of the element, and I'm looking for something like that. For example brightcove player has a data attribute. Video is working, cookies are not set.
    1. Google encouraging site admins to put reCaptcha all over their sites, and then sharing the resulting risk scores with those admins is great for security, Perona thinks, because he says it “gives site owners more control and visibility over what’s going on” with potential scammer and bot attacks, and the system will give admins more accurate scores than if reCaptcha is only using data from a single webpage to analyze user behavior. But there’s the trade-off. “It makes sense and makes it more user-friendly, but it also gives Google more data,”
    2. For instance, Google’s reCaptcha cookie follows the same logic of the Facebook “like” button when it’s embedded in other websites—it gives that site some social media functionality, but it also lets Facebook know that you’re there.
    3. But this new, risk-score based system comes with a serious trade-off: users’ privacy.
    1. Lots of definitions. Pretty good, but a lot of it is obvious.

    2. One of the GDPR's principles of data processing is storage limitation. You must not store personal data for longer than you need it in connection with a specified purpose.
    3. But it also requires that you keep personal data well-organized and accessible to those who require access to it.
    4. Don't collect personal data that you don't need. "Data minimization" is a crucially important principle under the GDPR, and can also make you less susceptible to data breaches,
    1. there’s no need to send consent request emails — provided that this basis of processing was stated in your privacy policy and that users had easy access to the notice prior to you processing their data. If this information was not available to users at the time, but one of these legal bases can currently legitimately apply to your situation, then your best bet would be to ensure that your current privacy notice meets requirements, so that you can continue to process your user data in a legally compliant way.
    2. Here’s why sending GDPR consent emails is tricky and should be handled very carefully.
    1. they sought to eliminate data controllers and processors acting without appropriate permission, leaving citizens with no control as their personal data was transferred to third parties and beyond
    1. the data subject has explicitly consented to the proposed transfer, after having been informed of the possible risks of such transfers for the data subject due to the absence of an adequacy decision and appropriate safeguards;
    2. In the absence of an adequacy decision pursuant to Article 45(3), or of appropriate safeguards pursuant to Article 46, including binding corporate rules, a transfer or a set of transfers of personal data to a third country or an international organisation shall take place only on one of the following conditions:

      These conditions are individually sufficient and jointly necessary (https://hyp.is/e0RRFJCfEeqwuR_MillmPA/en.wikipedia.org/wiki/Necessity_and_sufficiency).

      Each of the conditions listed is a sufficient (but, by itself, not necessary) condition for legal transfer (T) of personal data to a third country or an international organisation. In other words, if any of those conditions is true, then legal transfer is also true.

      On the other hand, the list of conditions (C; let C be the disjunction of the conditions a-g: a or b or c ...) are jointly necessary for legal transfer (T) to be true. That is:

      • T cannot be true unless C (one of a or b or c ...) is true
      • if C is false (there is not one of a or b or c ... that is true), then T is false
      • T ⇒ C
      • C ⇐ T
    1. “Until CR 1.0 there was no effective privacy standard or requirement for recording consent in a common format and providing people with a receipt they can reuse for data rights.  Individuals could not track their consents or monitor how their information was processed or know who to hold accountable in the event of a breach of their privacy,” said Colin Wallis, executive director, Kantara Initiative.  “CR 1.0 changes the game.  A consent receipt promises to put the power back into the hands of the individual and, together with its supporting API — the consent receipt generator — is an innovative mechanism for businesses to comply with upcoming GDPR requirements.  For the first time individuals and organizations will be able to maintain and manage permissions for personal data.”
    2. CR 1.0 is an essential specification for meeting the proof of consent requirements of GDPR to enable international transfer of personal information in a number of applications.
    1. generic-sounding term may be interpreted as something more specific than intended: I want to be able to use "data interchange" in the most general sense. But if people interpret it to mean this specific standard/protocol/whatever, I may be misunderstood.

      The definition given here

      is the concept of businesses electronically communicating information that was traditionally communicated on paper, such as purchase orders and invoices.

      limits it to things that were previously communicated on paper. But what about things for which paper was never used, like the interchange of consent and consent receipts for GDPR/privacy law compliance, etc.?

      The term should be allowed to be used just as well for newer technologies/processes that had no previous roots in paper technologies.