Leiserowitz, A., Maibach, E., Rosenthal, S. A., Kotcher, J., Bergquist, P., Ballew, M. T., Goldberg, M. H., Gustafson, A., & Wang, X. (2020). Climate change in the American Mind: April 2020 [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/8439q
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Chong, D., & Druckman, J. N. (2007). Framing Theory. Annual Review of Political Science, 10(1), 103–126. https://doi.org/10.1146/annurev.polisci.10.072805.103054
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Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo Chambers on Social Media: A comparative analysis. ArXiv:2004.09603 [Physics]. http://arxiv.org/abs/2004.09603
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www.sciencemag.org www.sciencemag.org
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Servick, K., EnserinkJun. 2, M., 2020, & Pm, 7:55. (2020, June 2). A mysterious company’s coronavirus papers in top medical journals may be unraveling. Science | AAAS. https://www.sciencemag.org/news/2020/06/mysterious-company-s-coronavirus-papers-top-medical-journals-may-be-unraveling
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arxiv.org arxiv.org
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surgisphere.com surgisphere.com
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Desai, S. (2020, May 29). Response to Widespread Reaction to Recent Lancet Article on Hydroxychloroquine. Surgisphere Corporation. https://surgisphere.com/2020/05/29/response-to-widespread-reaction-to-recent-lancet-article-on-hydroxychloroquine/
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science.sciencemag.org science.sciencemag.org
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Servick, K., & Enserink, M. (2020). The pandemic’s first major research scandal erupts. Science, 368(6495), 1041–1042. https://doi.org/10.1126/science.368.6495.1041
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says, J. C. (2020, June 4). Lancet, NEJM retract Covid-19 studies that sparked backlash. STAT. https://www.statnews.com/2020/06/04/lancet-retracts-major-covid-19-paper-that-raised-safety-concerns-about-malaria-drugs/
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psyarxiv.com psyarxiv.com
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Han, H., & Dawson, K. J. (2020). JASP (Software) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/67dcb
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Colbourn, T. (2020). Unlocking UK COVID-19 policy. The Lancet Public Health, 0(0). https://doi.org/10.1016/S2468-2667(20)30135-3
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textnets.readthedocs.io textnets.readthedocs.io
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Bail, C. A. (2016). Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Proceedings of the National Academy of Sciences, 113(42), 11823–11828. https://doi.org/10.1073/pnas.1607151113
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Boulesteix, A., Strobl, C. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction. BMC Med Res Methodol 9, 85 (2009). https://doi.org/10.1186/1471-2288-9-85
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journals.sagepub.com journals.sagepub.com
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Rosenbusch, H., Hilbert, L. P., Evans, A. M., & Zeelenberg, M. (2020). StatBreak: Identifying “Lucky” Data Points Through Genetic Algorithms. Advances in Methods and Practices in Psychological Science, 2515245920917950. https://doi.org/10.1177/2515245920917950
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Chu, D., Akl, E., El-Harakeh, A., Bognanni, A., Lotf, T., Loeb, M., ... & Chen, C. (2020). Physical Distancing, Face Masks, and Eye Protection to Prevent Person-Person COVID-19 Transmission: A Systematic Review and Meta-Analysis.
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Bell, K., & Green, J. (2020). Premature evaluation? Some cautionary thoughts on global pandemics and scholarly publishing. Critical Public Health, 0(0), 1–5. https://doi.org/10.1080/09581596.2020.1769406
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- May 2020
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twitter.com twitter.com
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🔥Kareem Carr🔥 on Twitter: “I want to talk about bugs in statistical analyses. I think many data analysts worry unnecessarily about this. I do think it’s important to put a good faith effort into avoiding bugs, but I know data analysts that live in terror of hearing there’s a bug in published work. 1/6” / Twitter. (n.d.). Twitter. Retrieved May 30, 2020, from https://twitter.com/kareem_carr/status/1266029701392412673
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Verity, R., Okell, L., Dorigatti, I., Winskill, P., Whittaker, C., Walker, P., Donnelly, C., Ferguson, N., & Ghani, A. (2020). COVID-19 and the difficulty of inferring epidemiological parameters from clinical data – Authors’ reply. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30443-6
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arxiv.org arxiv.org
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O’Keeffe, K. P., Griffith, V., Xu, Y., Santi, P., & Ratti, C. (2020). The darkweb: A social network anomaly. ArXiv:2005.14023 [Nlin, Physics:Physics]. http://arxiv.org/abs/2005.14023
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psyarxiv.com psyarxiv.com
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Hanel, P. H. P. (2020). Conducting High Impact Research With Limited Financial Resources (While Working From Home) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/s3fcu
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psyarxiv.com psyarxiv.com
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Sutin, A., Luchetti, M., Aschwanden, D., Lee, J., Sesker, A. A., Strickhouser, J., … Terracciano, A. (2020, May 6). Change in Five-Factor Model Personality Traits During the Acute Phase of the Coronavirus Pandemic. Retrieved from psyarxiv.com/ja7b5
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arxiv.org arxiv.org
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Aslak, U., & Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. ArXiv:2003.14370 [Physics]. http://arxiv.org/abs/2003.14370
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arxiv.org arxiv.org
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Mancastroppa, M., Burioni, R., Colizza, V., & Vezzani, A. (2020). Active and inactive quarantine in epidemic spreading on adaptive activity-driven networks. ArXiv:2004.07902 [Cond-Mat, Physics:Physics]. http://arxiv.org/abs/2004.07902
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Lanovaz, M., & Turgeon, S. (2020). Tutorial: Applying Machine Learning in Behavioral Research [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9w6a3
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psyarxiv.com psyarxiv.com
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Farias, J. E. M., & Pilati, R. (2020). Violating social distancing amid COVID-19 pandemic: Psychological factors to improve compliance [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/apg9e
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Lobato, E. J. C., Powell, M., Padilla, L., & Holbrook, C. (2020). Factors Predicting Willingness to Share COVID-19 Misinformation. https://doi.org/10.31234/osf.io/r4p5z
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nexus.od.nih.gov nexus.od.nih.gov
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Santangelo, G. (2020, April 15). New NIH Resource to Analyze COVID-19 Literature: The COVID-19 Portfolio Tool. NIH Extramural Nexus. https://nexus.od.nih.gov/all/2020/04/15/new-nih-resource-to-analyze-covid-19-literature-the-covid-19-portfolio-tool/
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www.covidcrisislab.unibocconi.eu www.covidcrisislab.unibocconi.euAbout us1
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About us. (n.d.). Retrieved May 5, 2020, from /wps/wcm/connect/Site/CovidCrisisLab/Home/About+us
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- population
- analysis
- COVID-19
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www.preprints.org www.preprints.org
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Samuel, J.; Ali, G.G.M.N.; Rahman, M.M.; Esawi, E.; Samuel, Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Preprints 2020, 2020050015 (doi: 10.20944/preprints202005.0015.v1)
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psyarxiv.com psyarxiv.com
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Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c
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link.springer.com link.springer.com
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Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143. https://doi.org/10.1186/s12874-018-0611-x
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psyarxiv.com psyarxiv.com
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Dhami, M. K., & Mandel, D. R. (2020). UK and US policies for communicating probability in intelligence analysis: A review [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/kuyhb
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www.theguardian.com www.theguardian.com
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Davey, M. (2020, May 28). Questions raised over hydroxychloroquine study which caused WHO to halt trials for Covid-19. The Guardian. https://www.theguardian.com/science/2020/may/28/questions-raised-over-hydroxychloroquine-study-which-caused-who-to-halt-trials-for-covid-19
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www.repository.cam.ac.uk www.repository.cam.ac.uk
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Toxvaerd, F. M. O. (2020). Equilibrium Social Distancing [Working Paper]. Faculty of Economics, University of Cambridge. https://doi.org/10.17863/CAM.52489
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twitter.com twitter.com
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Jo Wood on Twitter: „Yesterday saw by far the highest ever use of London @SantanderCycles and possibly highest volume of cycling ever seen in the capital. / Twitter. (n.d.). Twitter. Retrieved May 27, 2020, from https://twitter.com/jwolondon/status/1265197657385025536
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www.digital-democracy.org www.digital-democracy.org
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If it’s a live pet, you do a little threat modeling: is the cat cute and cuddly, or will it scratch the kid’s face off?
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wintoncentre.maths.cam.ac.uk wintoncentre.maths.cam.ac.uk
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Winton Centre for Risk and Evidence Communication
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www.annualreviews.org www.annualreviews.org
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Correia, Rion Brattig, Ian B. Wood, Johan Bollen, and Luis M. Rocha. “Mining Social Media Data for Biomedical Signals and Health-Related Behavior.” Annual Review of Biomedical Data Science, May 4, 2020. https://doi.org/10.1146/annurev-biodatasci-030320-040844.
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psyarxiv.com psyarxiv.com
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Feys, F., Brokken, S., & De Peuter, S. (2020, May 22). Risk-benefit and cost-utility analysis for COVID-19 lockdown in Belgium: the impact on mental health and wellbeing. https://doi.org/10.31234/osf.io/xczb3
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www.scirp.org www.scirp.org
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Makulilo, A. B. (2016). “A Person Is a Person through Other Persons”—A Critical Analysis of Privacy and Culture in Africa. Beijing Law Review, 7(3), 720–726. https://doi.org/10.4236/blr.2016.73020
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science.sciencemag.org science.sciencemag.org
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Salje, H., Tran Kiem, C., Lefrancq, N., Courtejoie, N., Bosetti, P., Paireau, J., Andronico, A., Hozé, N., Richet, J., Dubost, C.-L., Le Strat, Y., Lessler, J., Levy-Bruhl, D., Fontanet, A., Opatowski, L., Boelle, P.-Y., & Cauchemez, S. (2020). Estimating the burden of SARS-CoV-2 in France. Science, eabc3517. https://doi.org/10.1126/science.abc3517
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analytics.google.com analytics.google.com
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Cross-channel conversion paths See the big picture. Understand the value of upper funnel ad clicks in multi-click, cross-channel journeys.
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www.medrxiv.org www.medrxiv.org
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Buitrago-Garcia, D. C., Egli-Gany, D., Counotte, M. J., Hossmann, S., Imeri, H., Salanti, G., & Low, N. (2020). The role of asymptomatic SARS-CoV-2 infections: Rapid living systematic review and meta-analysis [Preprint]. Epidemiology. https://doi.org/10.1101/2020.04.25.20079103
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psychologicalsciences.unimelb.edu.au psychologicalsciences.unimelb.edu.au
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White, J. (2020, May 8). Attitudes of Australians to the Government’s COVIDSafe contact tracing app. Melbourne School of Psychological Sciences. https://psychologicalsciences.unimelb.edu.au/chdh/news/attitudes-of-australians-to-the-governments-covidsafe-contact-tracing-app
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science-sciencemag-org.ezproxy.redlands.edu science-sciencemag-org.ezproxy.redlands.edu
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Aspesi, C., & Brand, A. (2020). In pursuit of open science, open access is not enough. Science, 368(6491), 574–577. https://doi.org/10.1126/science.aba3763
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Kennedy, B., Atari, M., Davani, A. M., Hoover, J., Omrani, A., Graham, J., & Dehghani, M. (2020, May 7). Moral Concerns are Differentially Observable in Language. https://doi.org/10.31234/osf.io/uqmty
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ai.googleblog.com ai.googleblog.com
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Tsitsulin, A. & Perozzi B. Understanding the Shape of Large-Scale Data. (2020 May 05). Google AI Blog. http://ai.googleblog.com/2020/05/understanding-shape-of-large-scale-data.html
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psycnet.apa.org psycnet.apa.org
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Can we count on parents to help their children learn at home? (2020, May 8). Evidence for Action. https://blogs.unicef.org/evidence-for-action/can-we-count-on-parents-to-help-their-children-learn-at-home/
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academic.oup.com academic.oup.com
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While somewhat modest in size, the literature on chronic tolerance to nicotine in humans is reasonably consistent in showing clear evidence of tolerance to subjective mood effects but little or no tolerance to cardiovascular, performance or other nicotine effects
This is what I'd expect for tobacco, but it tells me little about nicotine. Most of the subjective effects are not from tobacco, so It's still plausible that nicotine does not develop tolerance. Indeed, the effects that don't go away are the effects expected from nicotine.
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psyarxiv.com psyarxiv.com
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Cartaud, A., François, Q., & Coello, Y. (2020). Beware of virus! Wearing a face mask against COVID-19 results in a reduction of social distancing [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ubzea
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science.sciencemag.org science.sciencemag.org
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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
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Zoder-Martell, K., Markelz, A., Floress, M. T., Skriba, H. A., & Sayyah, L. E. N. (2020, May 6). Technology to Facilitate Telehealth in Applied Behavior Analysis. https://doi.org/10.31234/osf.io/nz5s7
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Du, H., Yang, J., King, R. B., Yang, L., & Chi, P. (2020). COVID-19 Increases Online Emotional and Health-Related Searches [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5gskw
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onlinelibrary.wiley.com onlinelibrary.wiley.com
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Dey, S. K., Rahman, M. M., Siddiqi, U. R., & Howlader, A. (n.d.). Analyzing the epidemiological outbreak of COVID-19: A visual exploratory data analysis approach. Journal of Medical Virology, n/a(n/a). https://doi.org/10.1002/jmv.25743
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leoferres.info leoferres.info
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Ferres, L. (2020 April 10). COVID19 mobility reports. Leo's Blog. https://leoferres.info/blog/2020/04/10/covid19-mobility-reports/
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easystats.github.io easystats.github.io
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psyarxiv.com psyarxiv.com
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Haaf, J. M., Hoogeveen, S., Berkhout, S., Gronau, Q. F., & Wagenmakers, E. (2020, April 14). A Bayesian Multiverse Analysis of Many Labs 4: Quantifying the Evidence against Mortality Salience. https://doi.org/10.31234/osf.io/cb9er
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Interdonato, R., Magnani, M., Perna, D., Tagarelli, A., & Vega, D. (2020). Multilayer network simplification: Approaches, models and methods. ArXiv:2004.14808 [Physics]. http://arxiv.org/abs/2004.14808
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epjdatascience.springeropen.com epjdatascience.springeropen.com
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Vilella, S., Paolotti, D., Ruffo, G. et al. News and the city: understanding online press consumption patterns through mobile data. EPJ Data Sci. 9, 10 (2020). https://doi.org/10.1140/epjds/s13688-020-00228-9
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Laban, G., George, J., Morrison, V., & Cross, E. S. (2020, May 6). Tell Me More! Assessing Interactions with Social Robots From Speech. Retrieved from psyarxiv.com/jkht2
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www.medrxiv.org www.medrxiv.org
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Modi, C., Boehm, V., Ferraro, S., Stein, G., & Seljak, U. (2020). Total COVID-19 Mortality in Italy: Excess Mortality and Age Dependence through Time-Series Analysis. MedRxiv, 2020.04.15.20067074. https://doi.org/10.1101/2020.04.15.20067074
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www.sciencedirect.com www.sciencedirect.com
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Hart, O. E., & Halden, R. U. (2020). Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: Feasibility, economy, opportunities and challenges. Science of The Total Environment, 730, 138875. https://doi.org/10.1016/j.scitotenv.2020.138875
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statmodeling.stat.columbia.edu statmodeling.stat.columbia.edu
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Statistical Modeling, Causal Inference, and Social Science. (2020 April 22). Blog Post: New analysis of excess coronavirus mortality; also a question about poststratification. https://statmodeling.stat.columbia.edu/2020/04/22/analysis-of-excess-coronavirus-mortality-also-a-question-about-poststratification/
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link.aps.org link.aps.org
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Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., & Donges, J. F. (2020). Dynamics of tipping cascades on complex networks. Physical Review E, 101(4), 042311. https://doi.org/10.1103/PhysRevE.101.042311
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psyarxiv.com psyarxiv.com
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Olthof, M., Hasselman, F., & Lichtwarck-Aschoff, A. (2020, May 1). Complexity In Psychological Self-Ratings: Implications for research and practice. Retrieved from psyarxiv.com/fbta8
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psyarxiv.com psyarxiv.com
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Horstmann, K. T., Rauthmann, J. F., Sherman, R. A., & Ziegler, M. (2020, April 30). Unveiling an Exclusive Link: Predicting Behavior with Personality, Situation Perception, and Affect in a Pre-Registered Experience Sampling Study. Retrieved from psyarxiv.com/ztw2n
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- Apr 2020
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psyarxiv.com psyarxiv.com
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Tarbox, C., Silverman, E. A., Chastain, A. N., Little, A., Bermudez, T. L., & Tarbox, J. (2020, April 30). Taking ACTion: 18 Simple Strategies for Supporting Children with Autism During the COVID-19 Pandemic. Retrieved from psyarxiv.com/96whj
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psyarxiv.com psyarxiv.com
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Edelsbrunner, P. A., & Thurn, C. (2020, April 22). Improving the Utility of Non-Significant Results for Educational Research. https://doi.org/10.31234/osf.io/j93a2
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Moyers, S. A., & Hagger, M. S. (2020, April 20). Physical activity and sense of coherence: A meta-analysis. https://doi.org/10.31234/osf.io/d9e3k
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psyarxiv.com psyarxiv.com
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Fischer, R., Karl, J. A., Bortolini, T., Zilberberg, M., Robinson, K., Rabelo, A. L. A., … Mattos, P. (2020, April 22). Rapid review and meta-meta-analysis of self-guided interventions to address anxiety, depression and stress during COVID-19 social distancing. https://doi.org/10.31234/osf.io/ndyf4
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psyarxiv.com psyarxiv.com
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Dai, B., Fu, D., Meng, G., Qi, L., & Liu, X. (2020, April 25). The effects of governmental and individual predictors on COVID-19 protective behaviors in China: a path analysis model. https://doi.org/10.31234/osf.io/hgzj9
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psyarxiv.com psyarxiv.com
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Derks, K., de swart, j., van Batenburg, P., Wagenmakers, E., & wetzels, r. (2020, April 28). Priors in a Bayesian Audit: How Integration of Existing Information into the Prior Distribution Can Increase Transparency, Efficiency, and Quality. Retrieved from psyarxiv.com/8fhkp
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Chaves, M. S., Mattos, T. G., & Atman, A. P. F. (2020). Characterizing network topology using first-passage analysis. Physical Review E, 101(4), 042123. https://doi.org/10.1103/PhysRevE.101.042123
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psyarxiv.com psyarxiv.com
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Hallford, D. J., & D'Argembeau, A. (2020, April 15). Why We Imagine Our Future: Introducing the Functions of Future Thinking Scale (FoFTS). https://doi.org/10.31234/osf.io/bez4u
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psyarxiv.com psyarxiv.com
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Jarynowski, A., Wójta-Kempa, M., & Belik, V. (2020, April 22). TRENDS IN PERCEPTION OF COVID-19 IN POLISH INTERNET. https://doi.org/10.31234/osf.io/dr3gm
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Wang, T., Chen, X., Zhang, Q., & Jin, X. (2020, April 26). Use of Internet data to track Chinese behavior and interest in COVID-19. https://doi.org/10.31234/osf.io/j6m8q
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www.medrxiv.org www.medrxiv.org
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Jefferson, T., Jones, M., Al Ansari, L. A., Bawazeer, G., Beller, E., Clark, J., Conly, J., Del Mar, C., Dooley, E., Ferroni, E., Glasziou, P., Hoffman, T., Thorning, S., & Van Driel, M. (2020). Physical interventions to interrupt or reduce the spread of respiratory viruses. Part 1 - Face masks, eye protection and person distancing: Systematic review and meta-analysis [Preprint]. Public and Global Health. https://doi.org/10.1101/2020.03.30.20047217
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psyarxiv.com psyarxiv.com
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Fronapfel, B. H., & Demchak, M. (2020, April 12). School’s Out for COVID-19: 50 Ways BCBA Trainees in Special Education Settings Can Accrue Independent Fieldwork Experience Hours During the Pandemic. https://doi.org/10.31234/osf.io/cr3uv
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psyarxiv.com psyarxiv.com
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Colombo, R., Wallace, M., & Taylor, R. S. (2020, April 11). An Essential Service Decision Model for Applied Behavior Analytic Providers During Crisis. https://doi.org/10.31234/osf.io/te8ha
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Source: Office for National Statistics - United Kingdom
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Wölfel, R., Corman, V.M., Guggemos, W. et al. Virological assessment of hospitalized patients with COVID-2019. Nature (2020). https://doi.org/10.1038/s41586-020-2196-x
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www.nationalreview.com www.nationalreview.com
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Verbruggen, R. (2020 March 24). Another COVID Cost-Benefit Analysis. National Review. https://www.nationalreview.com/corner/another-covid-cost-benefit-analysis/
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doi.org doi.org
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Hossain, M. A. (2020). Is the spread of COVID-19 across countries influenced by environmental, economic and social factors? [Preprint]. Epidemiology. https://doi.org/10.1101/2020.04.08.20058164
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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journals.plos.org journals.plos.org
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Sumner, P., Vivian-Griffiths, S., Boivin, J., Williams, A., Bott, L., Adams, R., Venetis, C. A., Whelan, L., Hughes, B., & Chambers, C. D. (2016). Exaggerations and Caveats in Press Releases and Health-Related Science News. PLOS ONE, 11(12), e0168217. https://doi.org/10.1371/journal.pone.0168217
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post.parliament.uk post.parliament.uk
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Hill-Cawthorne, G. (2020). COVID-19: Insights from behavioural science. https://post.parliament.uk/analysis/covid-19-insights-from-behavioural-science/, https://post.parliament.uk/analysis/covid-19-insights-from-behavioural-science/
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www.imperial.ac.uk www.imperial.ac.uk
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The economic impact of coronavirus: Analysis from Imperial experts | Imperial News | Imperial College London. (n.d.). Imperial News. Retrieved April 8, 2020, from https://www.imperial.ac.uk/news/196514/the-economic-impact-coronavirus-analysis-from/
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www.cmu.edu www.cmu.edu
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Fischhoff, B., de Bruin, W. B., Güvenç, Ü., Caruso, D., & Brilliant, L. (2006). Analyzing disaster risks and plans: An avian flu example. Journal of Risk and Uncertainty, 33(1–2), 131–149. https://doi.org/10.1007/s11166-006-0175-8
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twitter.com twitter.com
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ReconfigBehSci en Twitter: “https://t.co/DN7ajEZkiJ and Fischhoff, B., Wong-Parodi, G., Garfin, D., Holman, E.A., & Silver, R. (2018). Public understanding of Ebola risks: Mastering an unfamiliar threat. Risk Analysis, 38(1), 71-83. doi: 10.1111/risa.12794 2)” / Twitter. (n.d.). Twitter. Retrieved April 16, 2020, from https://twitter.com/scibeh/status/1243547189248393218
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datascience.udd.cl datascience.udd.cl
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Ferres, L., Schifanella, R., Perra, N., Vilella, S., Bravo, L., Paolotti, D., Ruffo, G., & Sacasa, M. (n.d.). Measuring Levels of Activity in a Changing City. 11.
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onlinelibrary.wiley.com onlinelibrary.wiley.com
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Giangreco, G. (n.d.). Case fatality rate analysis of Italian COVID-19 outbreak. Journal of Medical Virology, n/a(n/a). https://doi.org/10.1002/jmv.25894
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trello.com trello.com
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Collective Intelligence and COVID-19 | Trello. (n.d.). Retrieved April 20, 2020, from https://trello.com/b/STdgEhvX/collective-intelligence-and-covid-19
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arxiv.org arxiv.org
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Alam, F., Sajjad, H., Imran, M., & Ofli, F. (2020). Standardizing and Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing. ArXiv:2004.06774 [Cs]. http://arxiv.org/abs/2004.06774
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www.troyhunt.com www.troyhunt.com
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It might be contrary to traditional thinking, but writing unique passwords down in a book and keeping them inside your physically locked house is a damn sight better than reusing the same one all over the web. Just think about it - you go from your "threat actors" (people wanting to get their hands on your accounts) being anyone with an internet connection and the ability to download a broadly circulating list Collection #1, to people who can break into your house - and they want your TV, not your notebook!
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time.com time.com
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“Even if experts are saying it’s really not going to make a difference, a little [part of] people’s brains is thinking, well, it’s not going to hurt. Maybe it’ll cut my risk just a little bit, so it’s worth it to wear a mask,” she says.
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- Mar 2020
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www.cmswire.com www.cmswire.com
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One MailChimp user tweeted this week that it seems the EU has "effectively killed newsletter with GDPR." He said he sent "get consent" emails through MailChimp and reported these numbers: 100 percent delivery rate, 37 percent open rate, 0 percent given consent.
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en.wikipedia.org en.wikipedia.org
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URL
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stats.idre.ucla.edu stats.idre.ucla.edu
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Factors that affect power
Factors that affect power.
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Cohen’s recommendations: Jacob Cohen has many well-known publications regarding issues of power and power analyses, including some recommendations about effect sizes that you can use when doing your power analysis. Many researchers (including Cohen) consider the use of such recommendations as a last resort, when a thorough literature review has failed to reveal any useful numbers and a pilot study is either not possible or not feasible. From Cohen (1988, pages 24-27):
Recommendations from Cohen about choosing the effect size when doing a power analysis.
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Obtaining the necessary numbers to do a power analysis
Obtaining the necessary numbers to do a power analysis
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Power is the probability of detecting an effect, given that the effect is really there. In other words, it is the probability of rejecting the null hypothesis when it is in fact false. For example, let’s say that we have a simple study with drug A and a placebo group, and that the drug truly is effective; the power is the probability of finding a difference between the two groups. So, imagine that we had a power of .8 and that this simple study was conducted many times. Having power of .8 means that 80% of the time, we would get a statistically significant difference between the drug A and placebo groups. This also means that 20% of the times that we run this experiment, we will not obtain a statistically significant effect between the two groups, even though there really is an effect in reality.
Power analysis definition
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www.lexology.com www.lexology.com
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Are any sectors experiencing significant M&A activity? The following sectors are experiencing significant M&A activity: manufacturing; financial services; IT and information technology enabled services; oil and gas; pharmaceuticals; life sciences; and healthcare.
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www.mca.gov.in www.mca.gov.in
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Form No INC-5 : One Person Company- Intimation of exceeding threshold Form No INC-21 : Declaration prior to the commencement of business or exercising borrowing powers Form No. PAS-3 : Return of allotment Form No. SH-8 : letter of offer Form No SH-11 : Return in respect of buy-back of securities Form No MGT-14 : Filing of Resolutions and agreements to the Registrar Form No DIR-11 : Notice of resignation of a director to the Registrar Form No. MR-1 : Return of appointment of managing director or whole time director or manager Form No FC-4 : Annual Return of a Foreign company Form No MSC-3 : Return of dormant companies Form 5INV : Statement of unclaimed and unpaid amounts Form I-XBRL : Form for filing XBRL document in respect of cost audit report and other documents with the Central Government Form A-XBRL : Form for filing XBRL document in respect of compliance report and other documents with the Central Government
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- Feb 2020
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journals.sagepub.com journals.sagepub.com
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Discourses tend to be intertextual and interdiscursive (Reisigl and Wodak, 2001: 39). They interlink various texts, discourses and contexts. Social media data are therefore not independent from other media but tend to be multimodal and connected with texts in traditional media. An example is that many political tweets tend to link to articles in the online versions of mainstream newspapers. Studying social media therefore does not substitute the study of other media but often requires studying various media’s intercon-nection. Discourses are texts that stand in particular societal, political-economic, histori-cal, cultural contexts. Understanding them requires taking a holistic point of view, that is, to situate them in history and society.
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moodle.cpce-polyu.edu.hk moodle.cpce-polyu.edu.hk
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Introduction
The first two sentences briefly summarise the scenario; the last sentence of the paragraph highlights the problem -- the topic to be elaborated in the subsequent section.
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ttu.blackboard.com ttu.blackboard.com
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What do you envision these people will do over the next two,three, and four years? How is it different from what they do now?
optimal questions
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wouldn’t it makesense to make that program widely available? Jobs are changing. We need best-in-breed practices here. What can we do to move that dispersed and diversegroup forward?”
YES I THOUGHT THE EXACT SAME FUCKING THING
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chrisbateman.github.io chrisbateman.github.io
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webpack.js.org webpack.js.orgConcepts1
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Bundle Analysis
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- Jan 2020
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quantum.country quantum.country
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What does it mean for a matrix UUU to be unitary? It’s easiest to answer this question algebraically, where it simply means that U†U=IU^\dagger U = IU†U=I, that is, the adjoint of UUU, denoted U†U^\daggerU†, times UUU, is equal to the identity matrix. That adjoint is, recall, the complex transpose of UUU:
Starting to get a little bit more into linear algebra / complex numbers. I'd like to see this happen more gradually as I haven't used any of this since college.
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diposit.ub.edu diposit.ub.edu
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) +)
Krippendorff, aquí en la bibliografía: Content analysis. An introduction to its methodology.
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- Dec 2019
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medium.com medium.comtayloR1
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“A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).”
What is valence in music according to Spotify?
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www.civilsociety.co.uk www.civilsociety.co.uk
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Foundation finds its grantees 'significantly outperform' similar charities
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unixwiz.net unixwiz.net
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Before each election, I have traditionally written up an analysis of the California ballot measures and send it to my friends. It's not always obvious what the "real" agenda is on each one, and even with clear purposes there are often competing interests at play. These writings are the result of my own analysis, which comes from a libertarian perspective, and I'm not knowingly affiliated with any party behind any ballot measure. I believe that mere lists of "vote yes" or "vote no" are not very helpful except for sheep: it's important to know why one is urged to vote in any given direction. I would rather you vote against my position because you had an opposing view than vote with my position because you flipped a coin.
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- Nov 2019
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rstudio-pubs-static.s3.amazonaws.com rstudio-pubs-static.s3.amazonaws.com
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1. Introduction to eXtensible Time Series, using xts and zoo for time series Introducing xts and
question?
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github.com github.com
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a-little-book-of-r-for-time-series.readthedocs.io a-little-book-of-r-for-time-series.readthedocs.io
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This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.
what is time series?
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- Sep 2019
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lateraleconomics.com.au lateraleconomics.com.au
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Estimated economic benefit of data linkage
the potential value from linking Census data to administrative data sets is only beginning to be realised and holds immense potential.(In other work for the Population Health Research Network, Lateral Economics concluded that data linkage generated over $16 for every dollar invested).
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Economic benefit of the Census.
Our estimates suggest the benefits of running the Census easily outweigh its costs in the order of$6 of economic value for each $1 it costs. On this reckoning, the cost of the Census would have to rise to six times its current cost –to around $3 billion every five years –before it startedto become cost ineffective
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- Aug 2019
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labsblog.f-secure.com labsblog.f-secure.com
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Security Issues, Dangers And Implications Of Smart Systems
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www.pysurvival.io www.pysurvival.io
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so that instead of predicting the time of event, we are predicting the probability that an event happens at a particular time .
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- Jul 2019
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www.bloomberg.com www.bloomberg.com
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Children are going hungry too. Almost 14% of kids, or some 3.5 million in all, are estimated to live in poverty -- and that’s already down from a peak of more than 16% in 2012. To combat the problem, local governments around the country are opening thousands of cafeterias where children can eat for free.
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Given all of this good behavior, conservatives might expect that Japan’s poverty rate would be very low. But the opposite is true; Japan has a relatively high number of poor people for an advanced country.
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archimede.mat.ulaval.ca archimede.mat.ulaval.ca
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In practice, we found that it is not appropriate to use Aalen’s additive hazardsmodel for all datasets, because when we estimate cumulativeregression functionsB(t),they are restricted to the time interval where X (X has been defined in Chapter 3) is offull rank, that meansX0Xis invertible. Sometimes we found that X is not of full rank,which was not a problem with the Cox model.
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An overall conclusion is that the two models give different pieces of informationand should not be viewed as alternatives to each other, but ascomplementary methodsthat may be used together to give a fuller and more comprehensive understanding ofdata
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The effect ofthe covariates on survival is to act multiplicatively on some unknown baseline hazardrate, which makes it difficult to model covariate effects that change over time. Secondly,if covariates are deleted from a model or measured with a different level of precision, theproportional hazards assumption is no longer valid. These weaknesses in the Cox modelhave generated interest in alternative models. One such alternative model is Aalen’s(1989) additive model. This model assumes that covariates act in an additive manneron an unknown baseline hazard rate. The unknown risk coefficients are allowed to befunctions of time, so that the effect of a covariate may vary over time.
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www.sthda.com www.sthda.com
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Note that, three often used transformations can be specified using the argument fun: “log”: log transformation of the survivor function, “event”: plots cumulative events (f(y) = 1-y). It’s also known as the cumulative incidence, “cumhaz” plots the cumulative hazard function (f(y) = -log(y))
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Note that, the confidence limits are wide at the tail of the curves, making meaningful interpretations difficult. This can be explained by the fact that, in practice, there are usually patients who are lost to follow-up or alive at the end of follow-up. Thus, it may be sensible to shorten plots before the end of follow-up on the x-axis (Pocock et al, 2002).
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www.khanacademy.org www.khanacademy.org
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our sum of squares is 41.187941.187941.1879
Just considering the Y, and not the X. Calculating the residuals from the average/mean Y.
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summarizing and visualizing a complex data table in which individuals are described by several sets of variables (quantitative and /or qualitative) structured into groups.
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www.sthda.com www.sthda.com
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it acts as PCA quantitative variables and as MCA for qualitative variables.
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factominer.free.fr factominer.free.fr
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MFA is a weighted PCA
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Study the similarity between individuals with respect to thewhole set of variables AND the relationships between variables
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pedroconcejero.wordpress.com pedroconcejero.wordpress.com
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RF is now a standard to effectively analyze a large number of variables, of many different types, with no previous variable selection process. It is not parametric, and in particular for survival target it does not assume the proportional risks assumption.
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www.cscu.cornell.edu www.cscu.cornell.edu
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Thesurvival function gives,for every time,the probability of surviving(or not experiencing the event) up to that time.The hazard function gives the potential that the event will occur, per time unit, given that an individual has survived up to the specified time.
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- May 2019
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www.randhome.io www.randhome.io
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Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.
Etienne's blog! Amazing resource for OSINT; particularly focused on technical attacks.
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- Mar 2019
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www.nwlink.com www.nwlink.com
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This is Bloom's taxonomy of cognitive objectives. I selected this page because it explains both the old and new versions of the taxonomy. When writing instructional objectives for adult learning and training, one should identify the level of learning in Blooms that is needed. This is not the most attractive presentation but it is one of the more thorough ones. rating 4/5
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iphysresearch.github.io iphysresearch.github.io
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A Deep Generative Model of Speech Complex Spectrograms
对傅里叶变换建模~~
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www.thedevelopersconference.com.br www.thedevelopersconference.com.br
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Você consegue visualizar a saúde da sua aplicação?
Ainda que aqui os tópicos da certificação não cubram exatamente esse assunto, monitorar a saúde de um sistema e suas aplicações é missão do profissional DevOps. Atente para os tópicos:
701 Software Engineering 701.1 Modern Software Development (weight: 6)
e
705.2 Log Management and Analysis (weight: 4)
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- Feb 2019
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paleorxiv.org paleorxiv.org
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set; if this is higher, the tree 2can be considered to fit the data less well
To test the fit between data and more than one alternative tree, you can just do a bootstrap analysis, and map the results on a neighbour-net splits graph based on the same data.
Note that the phangorn library includes functions to transfer information between trees/tree samples and trees and networks:<br/> Schliep K, Potts AJ, Morrison DA, Grimm GW. 2017. Intertwining phylogenetic trees and networks. Methods in Ecology and Evolution (DOI:10.1111/2041-210X.12760.)[http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12760/full] – the basic functions and script templates are provided in the associated vignette.
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framework.thoughtvectors.net framework.thoughtvectors.net
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especially at a time when many (perhaps most) computer technologies appear untethered to any philosophy besides the pursuit of maximum profit
This is why I am here. As we have become more and more specialized, we have become less capable of understanding the consequences, good or ill, of new technologies. Looking back at foundational documents like this with a critical eye is a first step. We can't divorce science and technology from history, ethics and critical analysis without suffering the consequences. Looking back and understanding how we got here will provide clues in how to fix things. I am Geoff Cain - I started out life as a writer and English teacher and eventually went into elearning. I am VERY interested in projects like this because we need to stop being passive consumers of information. I want to help end the Era of the Guilty By-Stander: shared thought can lead to shared action. I will be blogging my experiences with this project at http://geoffcain.com
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- Jan 2019
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www.at-the-intersection.com www.at-the-intersection.com
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Kind of the technical philosophy is everything that happens in the market is captured in the data and so any headline moves will be captured pretty much instantaneously or in a few minutes in the charts.
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Yeah, uh, I would say for reallocating, I'm, yeah. So I would say on Gemini I do Bitcoin, ethereum, and that's kind of like the longer term things.
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Uh, I definitely have some other, you know, mostly it's mostly I use ta very, very ta heavy. Um, I will, but I'll always keep the fundamentals in mind, especially for the medium to long term.
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I really try to focus on technicals cause I mean, yeah, the technicals is, is supposed to be representative, at least from an historical standpoint of the sentiment, right? Like if it's, if it's losing, if people are losing faith in it, then you'll probably see where did it go down? You'll see the price get affected by it. Um, and I tried to just trade on that. I try to minimize my sources all over the place.
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So depending on where you're trading, you could put more emphasis on where the other, when when you're doing fundamental analysis on a stock, there's a lot more information going into that, you know, potential company valuation. Um, whereas I would argue most cryptocurrencies heavily lack fundamentals at all.
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I personally try to trade based on technicals only. I'll read stuff for more general and for information. Um, but I guess the way I look at is like technical is this more short term? And fundamentals is more longterm.
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Uh, yeah, I'm in a few groups. There's a couple of the crypto focused, uh, the also have been just, I wouldn't say [inaudible], but have put more emphasis on, you know, since we're technical traders, there's a reason not to take advantage of, uh, the market opportunities and traditional as they pop up. So we've been focused mainly on just very few inverse etfs to short the s&p to short some major Chinese stocks, um, doing some stuff with, uh, oil, gas. And then there's some groups that I'm in that are specifically focused on just traditional, uh, that are broken up or categorized by what they're trading.
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inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
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This is because most authors usually did not report a plausible theoretical model for the structure of their observed variables, and there was often insufficient information for us to create our own plausible non-g models that could be compared with a theory of the existence of Spearman’s g in the data
The EFA vs. CFA question was a stickler for one peer reviewer, and I can understand why. When measurement is based on strong theory, then I believe that CFA is preferable to EFA. But that was rarely the case in these datasets.
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the strongest first factor accounted for 86.3% of observed variable variance
I suspect that this factor was so strong because it consisted of only four observed variables, and three of them were written measures of verbal content. All of the verbal cariables correlated r = .72 to .89. Even the "non-verbal" variable (numerical ability) correlates r = .72 to .81 with the other three variables (Rehna & Hanif, 2017, p. 25). Given these strong correlations, a very strong first factor is almost inevitable.
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The weakest first factor accounted for 18.3% of variance
This factor may be weak because the sample consists of Sudanese gifted children, which may have restricted the range of correlations in the dataset.
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The mean sample size of the remaining data sets was 539.6 (SD = 1,574.5). The large standard deviation in relationship to the mean is indicative of the noticeably positively skewed distribution of sample sizes, a finding supported by the much smaller median of 170 and skewness value of 6.297. There were 16,559 females (33.1%), 25,431 males (48.6%), and 10,350 individuals whose gender was unreported (19.8%). The majority of samples—62 of 97 samples (63.9%)—consisted entirely or predominantly of individuals below 18. Most of the remaining samples contained entirely or predominantly adults (32 data sets, 33.0%), and the remaining 3 datasets (3.1%) had an unknown age range or an unknown mix of adults and children). The samples span nearly the entire range of life span development, from age 2 to elderly individuals.
My colleague, Roberto Colom, stated in his blog (link below) that he would have discarded samples with fewer than 100 individuals. This is a legitimate analysis decision. See his other commentary (in Spanish) at https://robertocolom.wordpress.com/2018/06/01/la-universalidad-del-factor-general-de-inteligencia-g/
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wendynorris.com wendynorris.com
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Overall, recovery studies suggest that subcategories of the recovery process exist. However, different units of analysis (e.g., individual versus group) or different types of groups (e.g., based on ethnicity or social class) may experience the phases of recovery at differing rates. Thus, patterns, phrases or cycles of recovery are not linear.
Strong statement on how the unit of analysis can influence disaster research beyond theoretical frameworks and the need to look at temporality differently.
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- Dec 2018
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inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
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Ethnographic findings are not privileged, just particular: another country heard from. To regard them as anything more (or anything less) than that distorts both them and their implications, which are far profounder than mere primitivity, for social theory.
This tension exists in HCI as well.
Interpreted data vs empirical data and how each is systematically analyzed.
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- Nov 2018
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iphysresearch.github.io iphysresearch.github.io
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On the Spectral Bias of Deep Neural Networks
就是这么一句话：“we study deep networks using Fourier analysis.” 让我立马收藏此文，好好读读看！
Paper Summary
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futurepress.github.io futurepress.github.io
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The transition is a keen one, I assure you, from a schoolmaster to a sailor, and requires a strong decoction of Seneca and the Stoics to enable you to grin and bear it. But even this wears off in time.
This phrase is typical of Melville's sentence construction. Comparison is the mean and reason is the goal.
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- Oct 2018
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www.poetryfoundation.org www.poetryfoundation.org
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ghosts
These ghosts are representative of past lovers who are haunting her, which is especially disturbing to the speaker because she cannot remember them. Perhaps she cannot remember the past lovers because she was promiscuous rather than trying to find real love. I think Millay chose the word ghost because usually things that haunt you are things that you feel guilty about, and I think Millay feels guilty for her past behavior of being promiscuous.
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A little while, that in me sings no more.
This line also contributes to the overall gloominess of the text because it does not indicate any sort of hope for the future or incline that something will change for the speaker, whether that be her getting her memories back or her companionship and and presumable happiness back. They are saying that they were only happy for a little while and will never be happy again.
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I have forgotten, and what arms have lain
The speaker feels as though she should have deeper associations and more vivid memories of these past experiences.
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