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Unterman, A., Sumida, T. S., Nouri, N., Yan, X., Zhao, A. Y., Gasque, V., Schupp, J. C., Asashima, H., Liu, Y., Cosme, C., Deng, W., Chen, M., Raredon, M. S. B., Hoehn, K., Wang, G., Wang, Z., Deiuliis, G., Ravindra, N. G., Li, N., … Cruz, C. S. D. (2020). Single-Cell Omics Reveals Dyssynchrony of the Innate and Adaptive Immune System in Progressive COVID-19. MedRxiv, 2020.07.16.20153437. https://doi.org/10.1101/2020.07.16.20153437
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covid-19.iza.org covid-19.iza.org
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COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved 31 July 2020, from https://covid-19.iza.org/publications/dp13388/
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Friedson, A. I., McNichols, D., Sabia, J. J., & Dave, D. (2020). Did California’s Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects (Working Paper No. 26992; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26992
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Cotti, C. D., Engelhardt, B., Foster, J., Nesson, E. T., & Niekamp, P. S. (2020). The Relationship between In-Person Voting and COVID-19: Evidence from the Wisconsin Primary (Working Paper No. 27187; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27187
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Dave, D. M., Friedson, A. I., Matsuzawa, K., & Sabia, J. J. (2020). When Do Shelter-in-Place Orders Fight COVID-19 Best? Policy Heterogeneity Across States and Adoption Time (Working Paper No. 27091; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27091
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Alfaro, L., Becerra, O., & Eslava, M. (2020). EMEs and COVID-19: Shutting Down in a World of Informal and Tiny Firms (Working Paper No. 27360; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27360
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Bhattacharya, C., Chowdhury, D., Ahmed, N., Ozgur, S., Bhattacharya, B., Mridha, S. K., & Bhattacharyya, M. (2020). The Nature, Cause and Consequence of COVID-19 Panic among Social Media Users in India. https://doi.org/10.31234/osf.io/dgr45
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Pastor, L., & Vorsatz, M. B. (2020). Mutual Fund Performance and Flows During the COVID-19 Crisis (Working Paper No. 27551; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27551
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MCLS Virtual Brown Bag June 12, 2020: Bayesian Modelling. (2020, June 15). https://www.youtube.com/watch?v=7LLZPNLhn5o
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Starominski-Uehara, M. (2020). Brief Communication Analysis of Brazilian Presidency during COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/jr7eq
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osf.io osf.io
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Motta, M., Stecula, D., & Farhart, C. E. (2020). How Right-Leaning Media Coverage of COVID-19 Facilitated the Spread of Misinformation in the Early Stages of the Pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/a8r3p
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Pârvulescu, R. A. (2020). Engineering Your Judiciary, or How the COVID Crisis Won’t Go To Waste. https://doi.org/10.31235/osf.io/yrtfb
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osf.io osf.io
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Kubinec, R., & Carvalho, L. (2020). A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/jp4wk
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Krumpal, I. (2020). Soziologie in Zeiten der Pandemie [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yqdsu
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docs.google.com docs.google.com
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COVID-19 Social Science Tracker - Google Sheets
Tags
- social norm
- lang:en
- research
- healthcare
- analysis
- conspiracy theory
- behavior
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- COVID-19
- tracker
- spreadsheet
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- is:other
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Annotators
URL
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www.sg.uu.nl www.sg.uu.nl
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Dr. Maarten van Smeden (2020, May 11). Understanding the statistics of the coronavirus. Universiteit Utrecht. https://www.sg.uu.nl/video/2020/06/understanding-statistics-coronavirus
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Data Intersections 2020 | Heather Krause. (2020, February 21). How not to use data like a racist. https://www.youtube.com/watch?v=EGO7yevPHDk
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osf.io osf.io
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Mikolai, J., Keenan, K., & Kulu, H. (2020). Household level health and socio-economic vulnerabilities and the COVID-19 crisis: An analysis from the UK [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/4wtz8
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Basu, A., Roy, A., Hazra, A. K., & Pramanick, K. (2020). Analysis of youths’ perspective in India on and during the pandemic of Covid-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/4qhgd
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osf.io osf.io
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Payne, J. L., & Morgan, A. (2020). COVID-19 and Violent Crime: A comparison of recorded offence rates and dynamic forecasts (ARIMA) for March 2020 in Queensland, Australia [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/g4kh7
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osf.io osf.io
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Rice, W. L., Mateer, T., Taff, B. D., Lawhon, B., Reigner, N., & Newman, P. (2020). The COVID-19 pandemic continues to change the way people recreate outdoors: A second preliminary report on a national survey of outdoor enthusiasts amid the COVID-19 pandemic. https://doi.org/10.31235/osf.io/dghba
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osf.io osf.io
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Payne, J. L., & Morgan, A. (2020). Property Crime during the COVID-19 Pandemic: A comparison of recorded offence rates and dynamic forecasts (ARIMA) for March 2020 in Queensland, Australia [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/de9nc
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Sehgal, D. (2020). Analysis of Vaccines to tackle COVID-19 with Patent Review [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/q96wj
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bahri, muhamad. (2020). The nexus impacts of the Covid-19: A qualitative perspective [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yj8c9
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psyarxiv.com psyarxiv.com
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Bernard, P., St-Amour, S., Lachance, Kingsbury, C., & Lapointe. (2020). Dynamic patterns of depressive symptoms and sleep during the first month of strict lockdown in two women with major depressive disorder [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5enrq
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Gleeson, J. P., Onaga, T., Fennell, P., Cotter, J., Burke, R., & O’Sullivan, D. J. P. (2020). Branching process descriptions of information cascades on Twitter. ArXiv:2007.08916 [Physics]. http://arxiv.org/abs/2007.08916
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psyarxiv.com psyarxiv.com
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Ahn, M. H., Shin, Y. W., Kim, J. H., Kim, H. J., Lee, K.-U., & Chung, S. (2020). High Work-related Stress and Anxiety Response to COVID-19 among Healthcare Workers in South Korea: SAVE study [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9nxth
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www.thelancet.com www.thelancet.com
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Peeling, Rosanna W., Catherine J. Wedderburn, Patricia J. Garcia, Debrah Boeras, Noah Fongwen, John Nkengasong, Amadou Sall, Amilcar Tanuri, and David L. Heymann. ‘Serology Testing in the COVID-19 Pandemic Response’. The Lancet Infectious Diseases 0, no. 0 (17 July 2020). https://doi.org/10.1016/S1473-3099(20)30517-X.
Tags
- rapid serology tests
- host response
- situational analysis
- molecular diagnostics
- control programmes
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- public health
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- serology testing
- commercially available
- community
- symptomatic patients
- surveillance
- immune response
- lang:en
Annotators
URL
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www.nationalgeographic.com www.nationalgeographic.com
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How scientists know COVID-19 is way deadlier than the flu. (2020, July 2). Science. https://www.nationalgeographic.com/science/2020/07/coronavirus-deadlier-than-many-believed-infection-fatality-rate-cvd/
Tags
- science
- research
- USA
- clarity
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- case increase
- data analysis
- is:news
- lethality
- potency
- transmission
- epidemiology
- COVID-19
- lang:en
- potential
Annotators
URL
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twitter.com twitter.com
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Garmendia, A., & Alfonso, S. L. (2020). Popular Reactions To External Threats in Federations. https://doi.org/10.31235/osf.io/qyjtm
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Richards, A. D. (2020). Ethical Guidelines for Deliberately Infecting Volunteers with COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/jb7gq
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osf.io osf.io
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Rosati, G., Domenech, L., Chazarreta, A., & Maguire, T. (2020). Capturing and analyzing social representations. A first application of Natural Language Processing techniques to reader’s comments in COVID-19 news. Argentina, 2020 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/3pcdu
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Pensando a Pandemia: Tópicos em Filosofia da Mente e Psicologia. (2020, July 2). https://www.youtube.com/watch?v=CiVz0q5oRK8
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Weed, M. (2020). Models and methods to analyse the interaction of evidence and policy in the first 100 days of the UK government’s response to COVID-19 (v1.1). https://doi.org/10.31235/osf.io/f73u4
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Brooks, H. Z., Kanjanasaratool, U., Kureh, Y. H., & Porter, M. A. (2020). Disease Detectives: Using Mathematics to Forecast the Spread of Infectious Diseases [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/mvn9z
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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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Lighter, J., Phillips, M., Hochman, S., Sterling, S., Johnson, D., Francois, F., & Stachel, A. (n.d.). Obesity in Patients Younger Than 60 Years Is a Risk Factor for COVID-19 Hospital Admission. Clinical Infectious Diseases. https://doi.org/10.1093/cid/ciaa415
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Lyttelton, T., Zang, E., & Musick, K. (2020). Gender Differences in Telecommuting and Implications for Inequality at Home and Work. https://doi.org/10.31235/osf.io/tdf8c
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Meyer, B., Torriani, G., Yerly, S., Mazza, L., Calame, A., Arm-Vernez, I., Zimmer, G., Agoritsas, T., Stirnemann, J., Spechbach, H., Guessous, I., Stringhini, S., Pugin, J., Roux-Lombard, P., Fontao, L., Siegrist, C.-A., Eckerle, I., Vuilleumier, N., & Kaiser, L. (2020). Validation of a commercially available SARS-CoV-2 serological immunoassay. Clinical Microbiology and Infection, 0(0). https://doi.org/10.1016/j.cmi.2020.06.024
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Su, Q., Hu, J., Lin, H., Zhang, Z., Zhu, E. C., Zhang, C., Wang, D., Gao, Z., & Cao, B. (2020). Prevalence and risks of severe events for cancer patients with COVID-19 infection: A systematic review and meta-analysis. MedRxiv, 2020.06.23.20136200. https://doi.org/10.1101/2020.06.23.20136200
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Olsson-Collentine, A., van Assen, M. A. L. M., & Wicherts, J. M. (2020). Postprint—Heterogeneity in direct replications in psychology and its association with effect size [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/m23v4
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Du, H., Jiang, G., & Ke, Z. (2020). A Bootstrap Based Between-Study Heterogeneity Test in Meta-Analysis [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/de4g9
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Rahman, M., Ali, G. G. M. N., Li, X. J., Paul, K. C., & Chong, P. H. J. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fz4ry
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Edgcumbe, D. (2020). PrePrint Version (Edgcumbe, 2020): The developmental trajectory of open-mindedness: from 18 to 87-years of age. [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fnrmv
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Parsons, Sam. ‘Reliability Multiverse’, 26 June 2020. https://doi.org/10.31234/osf.io/y6tcz.
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Cheung, M. W.-L. (2020). Meta-Analytic Structural Equation Modeling [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/epsqt
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Maier, M., Bartoš, F., & Wagenmakers, E.-J. (2020). Robust Bayesian Meta-Analysis: Addressing Publication Bias with Model-Averaging [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/u4cns
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Yucel, M., Sjobeck, G., Glass, R., & Rottman, J. (2020). Gossip, Sabotage, and Friendship Network Dataset [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/m6tsx
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Abdelrahman, M. K. (2020, April 14). Personality Traits, Risk Perception and Social Distancing During COVID-19. https://doi.org/10.31234/osf.io/6g7kh
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covid19.gleamproject.org covid19.gleamproject.org
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Gleam Project | COVID-19 Mobility USA
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psyarxiv.com psyarxiv.com
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Kim, L. E., Dr, & Asbury, K. (2020, June 18). Teachers' initial experiences of COVID-19. https://doi.org/10.31234/osf.io/xn9ey
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psyarxiv.com psyarxiv.com
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Atoui, S., Chevance, G., Romain, A. J., Kingsbury, C., Lachance, J., & Bernard, P. (2020, June 17). Daily associations between sleep and physical activity: A systematic review and meta-analysis. https://doi.org/10.31234/osf.io/ezusb
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Crystal, J. (2020, May 18). Mobilizing behavioral scientists to respond to COVID-19. Psychonomic Society Featured Content. https://featuredcontent.psychonomic.org/mobilizing-behavioral-scientists-to-respond-to-covid-19/
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Velásquez, N., Leahy, R., Restrepo, N. J., Lupu, Y., Sear, R., Gabriel, N., Jha, O., Goldberg, B., & Johnson, N. F. (2020). Hate multiverse spreads malicious COVID-19 content online beyond individual platform control. ArXiv:2004.00673 [Nlin, Physics:Physics]. http://arxiv.org/abs/2004.00673
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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
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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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Meylan, P. (2020, March 24). The Most Credible Journalists on COVID-19. The Factual. https://blog.thefactual.com/credible-journalists-covid-19
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statmodeling.stat.columbia.edu statmodeling.stat.columbia.edu
Tags
- data analysis
- criticism
- limitation
- role
- is:blog
- policy
- review
- relevance
- COVID-19
- lang:en
- behavioral science
- social science
Annotators
URL
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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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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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Han, H., & Dawson, K. J. (2020). JASP (Software) [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/67dcb
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www.thelancet.com www.thelancet.com
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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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bmcmedresmethodol.biomedcentral.com bmcmedresmethodol.biomedcentral.com
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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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www.thelancet.com www.thelancet.com
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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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www.tandfonline.com www.tandfonline.com
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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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www.thelancet.com www.thelancet.com
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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
Tags
- legal
- research
- healthcare
- analysis
- economy
- crisis
- health
- society
- laboratory
- implication
- COVID-19
- financial
- lab
- policy
- consequence
- is:webpage
- population
- lang:en
Annotators
URL
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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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bmcmedresmethodol.biomedcentral.com bmcmedresmethodol.biomedcentral.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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Annotators
URL
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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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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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