138 Matching Annotations
  1. Last 7 days
  2. Aug 2020
  3. Jul 2020
    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

  4. Jun 2020
    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. 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. 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

  5. May 2020
    1. Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., Sudre, C. H., Cardoso, M. J., Ourselin, S., Wolf, J., Spector, T. D., Chan, A. T., & Consortium§, C. (2020). Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science. https://doi.org/10.1126/science.abc0473

  6. Apr 2020
  7. Feb 2020
    1. One important aspect of critical social media research is the study of not just ideolo-gies of the Internet but also ideologies on the Internet. Critical discourse analysis and ideology critique as research method have only been applied in a limited manner to social media data. Majid KhosraviNik (2013) argues in this context that ‘critical dis-course analysis appears to have shied away from new media research in the bulk of its research’ (p. 292). Critical social media discourse analysis is a critical digital method for the study of how ideologies are expressed on social media in light of society’s power structures and contradictions that form the texts’ contexts.
    2. t has, for example, been common to study contemporary revolutions and protests (such as the 2011 Arab Spring) by collecting large amounts of tweets and analysing them. Such analyses can, however, tell us nothing about the degree to which activists use social and other media in protest communication, what their motivations are to use or not use social media, what their experiences have been, what problems they encounter in such uses and so on. If we only analyse big data, then the one-sided conclusion that con-temporary rebellions are Facebook and Twitter revolutions is often the logical conse-quence (see Aouragh, 2016; Gerbaudo, 2012). Digital methods do not outdate but require traditional methods in order to avoid the pitfall of digital positivism. Traditional socio-logical methods, such as semi-structured interviews, participant observation, surveys, content and critical discourse analysis, focus groups, experiments, creative methods, par-ticipatory action research, statistical analysis of secondary data and so on, have not lost importance. We do not just have to understand what people do on the Internet but also why they do it, what the broader implications are, and how power structures frame and shape online activities
    3. Challenging big data analytics as the mainstream of digital media studies requires us to think about theoretical (ontological), methodological (epistemological) and ethical dimensions of an alternative paradigm

      Making the case for the need for digitally native research methodologies.

    4. Who communicates what to whom on social media with what effects? It forgets users’ subjectivity, experiences, norms, values and interpre-tations, as well as the embeddedness of the media into society’s power structures and social struggles. We need a paradigm shift from administrative digital positivist big data analytics towards critical social media research. Critical social media research combines critical social media theory, critical digital methods and critical-realist social media research ethics.
    5. de-emphasis of philosophy, theory, critique and qualitative analysis advances what Paul Lazarsfeld (2004 [1941]) termed administrative research, research that is predominantly concerned with how to make technologies and administration more efficient and effective.
    6. Big data analytics’ trouble is that it often does not connect statistical and computational research results to a broader analysis of human meanings, interpretations, experiences, atti-tudes, moral values, ethical dilemmas, uses, contradictions and macro-sociological implica-tions of social media.
    7. Such funding initiatives privilege quantitative, com-putational approaches over qualitative, interpretative ones.
    8. There is a tendency in Internet Studies to engage with theory only on the micro- and middle-range levels that theorize single online phenomena but neglect the larger picture of society as a totality (Rice and Fuller, 2013). Such theories tend to be atomized. They just focus on single phenomena and miss soci-ety’s big picture
  8. Jan 2020
    1. A final word: when we do not understand something, it does not look like there is anything to be understood at all - it just looks like random noise. Just because it looks like noise does not mean there is no hidden structure.

      Excellent statement! Could this be the guiding principle of the current big data boom in biology?

  9. Jan 2019
    1. Big Data is a buzzword which works on the platform of large data volume and aggregated data sets. The data sets can be structured or unstructured.  The data that is kept and stored at a global level keeps on growing so this big data has a big potential.  Your Big Data is generated from every little thing around us all the time. It has changed its way as the people are changing in the organization. New skills are being offered to prepare the new generated power of Big Data. Nowadays the organizations are focusing on new roles, new challenges and creating a new business.  
  10. Nov 2018
    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
  11. Aug 2018
    1. hus it becomes possible to see how ques-tions around data use need to shift from asking what is in the data, to include discussions of how the data is structured, and how this structure codifies value systems and social practices, subject positions and forms of visibility and invisi-bility (and thus forms of surveillance), along with the very ideas of crisis, risk governance and preparedness. Practices around big data produce and perpetuate specific forms of social engagement as well as understandings of the areas affected and the people being served.

      How data structure influences value systems and social practices is a much-needed topic of inquiry.

    2. Big data is not just about knowing more. It could be – and should be – about knowing better or about changing what knowing means. It is an ethico- episteme-ontological- political matter. The ‘needle in the haystack’ metaphor conceals the fact that there is no such thing as one reality that can be revealed. But multiple, lived are made through mediations and human and technological assemblages. Refugees’ realities of intersecting intelligences are shaped by the ethico- episteme-ontological politics of big data.

      Big, sweeping statement that helps frame how big data could be better conceptualized as a complex, socially contextualized, temporal artifact.

    3. Burns (2015) builds on this to investigate how within digital humanitarianism discourses, big data produce and perform subjects ‘in need’ (individuals or com-munities affected by crises) and a humanitarian ‘saviour’ community that, in turn, seeks answers through big data

      I don't understand what Burns is arguing here. Who is he referring to claims that DHN is a "savior" or "the solution" to crisis response?

      "Big data should therefore be be conceptualized as a framing of what can be known about a humanitarian crisis, and how one is able to grasp that knowledge; in short, it is an epistemology. This epistemology privileges knowledges and knowledge- based practices originating in remote geographies and de- emphasizes the connections between multiple knowledges.... Put another way, this configuration obscures the funding, resource, and skills constraints causing imperfect humanitarian response, instead positing volunteered labor as ‘the solution.’ This subjectivity formation carves a space in which digital humanitarians are necessary for effective humanitarian activities." (Burns 2015: 9–10)

    4. Crises are often not a crisis of information. It is often not a lack of data or capacity to analyse it that prevents ‘us’ from pre-venting disasters or responding effectively. Risk management fails because there is a lack of a relational sense of responsibility. But this does not have to be the case. Technologies that are designed to support collaboration, such as what Jasanoff (2007) terms ‘technologies of humility’, can be better explored to find ways of framing data and correlations that elicit a greater sense of relational responsibility and commitment.

      Is it "a lack of relational sense of responsibility" in crisis response (state vs private sector vs public) or is it the wicked problem of power, class, social hierarchies, etc.?

      "... ways of framing data and correlations that elicit a greater sense of responsibility and commitment."

      That could have a temporal component to it to position urgency, timescape, horizon, etc.

    5. In some ways this constitutes the production of ‘liquid resilience’ – a deflection of risk to the individuals and communities affected which moves us from the idea of an all-powerful and knowing state to that of a ‘plethora of partial projects and initiatives that are seeking to harness ICTs in the service of better knowing and governing individuals and populations’ (Ruppert 2012: 118)

      This critique addresses surveillance state concerns about glue-ing datasets together to form a broader understanding of aggregate social behavior without the necessary constraints/warnings about social contexts and discontinuity between data.

      Skimmed the Ruppert paper, sadly doesn't engage with time and topologies.

    6. Indeed, as Chandler (2015: 9) also argues, crowdsourcing of big data does not equate to a democratisation of risk assessment or risk governance:

      Beyond this quote, Chandler (in engaging crisis/disaster scenarios) argues that Big Data may be more appropriately framed as community reflexive knowledge than causal knowledge. That's an interesting idea.

      *"Thus, It would be more useful to see Big Data as reflexive knowledge rather than as causal knowledge. Big Data cannot help explain global warming but it can enable individuals and household to measure their own energy consumption through the datafication of household objects and complex production and supply chains. Big Data thereby datafies or materialises an individual or community’s being in the world. This reflexive approach works to construct a pluralised and multiple world of self-organising and adaptive processes. The imaginary of Big Data is that the producers and consumers of knowledge and of governance would be indistinguishable; where both knowing and governing exist without external mediation, constituting a perfect harmonious and self-adapting system: often called ‘community resilience’. In this discourse, increasingly articulated by governments and policy-makers, knowledge of causal connections is no longer relevant as communities adapt to the real-time appearances of the world, without necessarily understanding them."

      "Rather than engaging in external understandings of causality in the world, Big Data works on changing social behaviour by enabling greater adaptive reflexivity. If, through Big Data, we could detect and manage our own biorhythms and know the effects of poor eating or a lack of exercise, we could monitor our own health and not need costly medical interventions. Equally, if vulnerable and marginal communities could ‘datafy’ their own modes of being and relationships to their environments they would be able to augment their coping capacities and resilience without disasters or crises occurring. In essence, the imaginary of Big Data resolves the essential problem of modernity and modernist epistemologies, the problem of unintended consequences or side-effects caused by unknown causation, through work on the datafication of the self in its relational-embeddedness.42 This is why disasters in current forms of resilience thinking are understood to be ‘transformative’: revealing the unintended consequences of social planning which prevented proper awareness and responsiveness. Disasters themselves become a form of ‘datafication’, revealing the existence of poor modes of self-governance."*

      Downloaded Chandler paper. Cites Meier quite a bit.

    7. However, with these big data collections, the focus becomes not the individu-al’s behaviour but social and economic insecurities, vulnerabilities and resilience in relation to the movement of such people. The shift acknowledges that what is surveilled is more complex than an individual person’s movements, communica-tions and actions over time.

      The shift from INGO emergency response/logistics to state-sponsored, individualized resilience via the private sector seems profound here.

      There's also a subtle temporal element here of surveilling need and collecting data over time.

      Again, raises serious questions about the use of predictive analytics, data quality/classification, and PII ethics.

    8. Andrejevic and Gates (2014: 190) suggest that ‘the target becomes the hidden patterns in the data, rather than particular individuals or events’. National and local authorities are not seeking to monitor individuals and discipline their behaviour but to see how many people will reach the country and when, so that they can accommodate them, secure borders, and identify long- term social out-looks such as education, civil services, and impacts upon the host community (Pham et al. 2015).

      This seems like a terribly naive conclusion about mass data collection by the state.

      Also:

      "Yet even if capacities to analyse the haystack for needles more adequately were available, there would be questions about the quality of the haystack, and the meaning of analysis. For ‘Big Data is not self-explanatory’ (Bollier 2010: 13, in boyd and Crawford 2012). Neither is big data necessarily good data in terms of quality or relevance (Lesk 2013: 87) or complete data (boyd and Crawford 2012)."

    9. as boyd and Crawford argue, ‘without taking into account the sample of a data set, the size of the data set is meaningless’ (2012: 669). Furthermore, many tech-niques used by the state and corporations in big data analysis are based on probabilistic prediction which, some experts argue, is alien to, and even incom-prehensible for, human reasoning (Heaven 2013). As Mayer-Schönberger stresses, we should be ‘less worried about privacy and more worried about the abuse of probabilistic prediction’ as these processes confront us with ‘profound ethical dilemmas’ (in Heaven 2013: 35).

      Primary problems to resolve regarding the use of "big data" in humanitarian contexts: dataset size/sample, predictive analytics are contrary to human behavior, and ethical abuses of PII.

  12. May 2018
    1. We showhow the rise of large datasets, in conjunction with arising interest in data as scholarly output, contributesto the advent of data sharing platforms in a field trad-itionally organized by infrastructures.

      What does this paper mean by infrastructures? Perhaps this is a reference to the traditional scholarly journals and monographs.

    1. Human cognition loses its personal character. Individuals turn into data, and data become regnant

      Reminds me of The End of Theory. But if we lose the theory, the human understanding, what will be the consequences?

    1. Get the best Explanation on Talend Training and Tutorial Course with Real time Experience and Exercises with Real time projects for better Hands on from the scratch to advance level

      so check this link and learn :- https://www.youtube.com/watch?v=lhTPrpBvakw

  13. Jan 2018
    1. reliability and accessibility of big data will help facilitate increased reliance upon outcomes-based contracting and alternative payment models.

      reliability and accessibility of big data will help facilitate increased reliance upon outcomes-based contracting and alternative payment models.

  14. Oct 2017
    1. We also downloaded Twitter user profiles, such as the size offollowers, along with their profile description.

      I wonder how many profiles in the 3,389 tweets? Did the automate the review and capture of the details? Or did they review each profile by hand?

    1. Thesedigitaltracesareoftenreferredtoasbigdataandarepopularlydiscussedasaresource,arawmaterialwithqualitiestobeminedandcapitalized,thenewoiltobetappedtospureconomies.Throughavarietyofpracticesofvaluation,corporationsnotonlyexploitthedigitaltracesoftheircustomerstomaximizetheiroperationsbutalsosellthosetracestoothers.Forthatreason,citizensubjectswhouseplatformssuchasGooglearesometimesreferredtonotasitscustomersbutasitsproduct.

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    1. The company's API for scoring toxicity in online discussions already behaves like a racist hand dryer.

      this is just so awful.