- Last 7 days
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journals.plos.org journals.plos.org
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Aleta, A., Arruda, G. F. de, & Moreno, Y. (2020). Data-driven contact structures: From homogeneous mixing to multilayer networks. PLOS Computational Biology, 16(7), e1008035. https://doi.org/10.1371/journal.pcbi.1008035
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- Jul 2020
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psyarxiv.com psyarxiv.com
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Litman. L,. Hartman. R., Jaffe. S., Robinson. J. (2020) County-level recruitment in online samples: Applications to COVID-19 and beyond. PsyArXiv Preprints. Retrieved from: https://psyarxiv.com/g3xw7/
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www.youtube.com www.youtube.com
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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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osf.io osf.io
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La, V.-P., Pham, T.-H., Ho, T. M., Hoàng, N. M., Linh, N. P. K., Vuong, T.-T., Nguyen, H.-K. T., Tran, T., Van Quy, K., Ho, T. M., & Vuong, Q.-H. (2020). Policy response, social media and science journalism for the sustainability of the public health system amid the COVID-19 outbreak: The Vietnam lessons [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/cfw8x
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science.sciencemag.org science.sciencemag.org
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Barton, C. M., Alberti, M., Ames, D., Atkinson, J.-A., Bales, J., Burke, E., Chen, M., Diallo, S. Y., Earn, D. J. D., Fath, B., Feng, Z., Gibbons, C., Hammond, R., Heffernan, J., Houser, H., Hovmand, P. S., Kopainsky, B., Mabry, P. L., Mair, C., … Tucker, G. (2020). Call for transparency of COVID-19 models. Science, 368(6490), 482.2-483. https://doi.org/10.1126/science.abb8637
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docs.google.com docs.google.com
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COVID-19 Social Science Tracker - Google Sheets
Tags
- research
- uncertainty
- social science
- government
- spreadsheet
- behavior
- misinformation
- social norm
- lang:en
- COVID-19
- is:other
- analysis
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- publication
- international
- sheets
- social distancing
- mental health
- healthcare
- medicine
- infection
- tracker
- preprint
- policy
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- isolation
- community
- conspiracy theory
- social media
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Betsch, C. How behavioural science data helps mitigate the COVID-19 crisis. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-020-0866-1
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Perrott, D. (2020, May 26). Is Applied Behavioural Science reaching a Local Maximum? Medium. https://medium.com/@DavePerrott/is-applied-behavioural-science-reaching-a-local-maximum-538b536f7e7d
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twitter.com twitter.com
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Maarten van Smeden on Twitter: “This is a kind reminder that most issues with data (e.g. measurement error, incomplete data, confounding, selection) do not disappear just because you have N = ginormous” / Twitter. (n.d.). Twitter. Retrieved July 19, 2020, from https://twitter.com/MaartenvSmeden/status/1283313496382373890
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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
- research
- concern
- is:news
- transmission
- data analysis
- case increase
- lethality
- clarity
- potency
- science
- USA
- potential
- lang:en
- epidemiology
- COVID-19
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twitter.com twitter.com
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ReconfigBehSci on Twitter: “brief video describing the https://t.co/zDXjvZFtkM initiative here: https://t.co/8rJEuDj7B4” / Twitter. (n.d.). Twitter. Retrieved July 5, 2020, from https://twitter.com/scibeh/status/1279123525916405762
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twitter.com twitter.com
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ReconfigBehSci on Twitter: “SciBeh now has a video describing our initative! watch, retweet.... https://t.co/j3TF3zfdIt” / Twitter. (n.d.). Twitter. Retrieved June 29, 2020, from https://twitter.com/scibeh/status/1277260447029362688
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www.youtube.com www.youtube.com
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Supporting Open Science Data Curation, Preservation, and Access by Libraries. (2020, June 25). https://www.youtube.com/watch?v=SbmGWHpzAHs&feature=youtu.be
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twitter.com twitter.com
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Science Magazine on Twitter: “Democrats & Republicans have seemingly taken a divided stance on how to handle the #COVID19 pandemic, & their Twitter accounts might provide the best evidence to date. Watch the latest video from our #coronavirus series on research from @ScienceAdvances: https://t.co/BSCdWS011J https://t.co/sJyF357wje” / Twitter. (n.d.). Twitter. Retrieved June 27, 2020, from https://twitter.com/sciencemagazine/status/1276282194596675590
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Uribe-Tirado, A., del Rio, G., Raiher, S., & Ochoa Gutiérrez, J. (2020). Open Science since Covid-19: Open Access + Open Data [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/a5nqw
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www.sciencedirect.com www.sciencedirect.com
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Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 104063. https://doi.org/10.1016/j.respol.2020.104063
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- Jun 2020
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psyarxiv.com psyarxiv.com
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Yamada, Y. (2020). Micropublishing during and after the COVID-19 era [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/8fum4
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psyarxiv.com psyarxiv.com
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Syed, M., & Donnellan, B. (2020). Registered Reports with Developmental and Secondary Data: Some Brief Observations and Introduction to the Special Issue [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/gnhxk
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zoom.us zoom.us
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Welcome! You are invited to join a webinar: Supporting Open Science Data Curation, Preservation, and Access by Libraries. After registering, you will receive a confirmation email about joining the webinar. (n.d.). Zoom Video. Retrieved June 28, 2020, from https://zoom.us/webinar/register/2615905946283/WN_W6dYUXQFTqGQjGAZPRB74w
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advances.sciencemag.org advances.sciencemag.org
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Green, J., Edgerton, J., Naftel, D., Shoub, K., & Cranmer, S. J. (2020). Elusive consensus: Polarization in elite communication on the COVID-19 pandemic. Science Advances, eabc2717. https://doi.org/10.1126/sciadv.abc2717
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psyarxiv.com psyarxiv.com
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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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theplosblog.plos.org theplosblog.plos.org
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Yeolekar, M. (2020, April 30). The Digital Migration: Lessons About Open Science Arising from the COVID19 Crisis. The Official PLOS Blog. https://theplosblog.plos.org/2020/04/the-digital-migration-lessons-about-open-science-arising-from-the-covid19-crisis/
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psyarxiv.com psyarxiv.com
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Antonakis, J., Bastardoz, N., & Jacquart, P. (2020). In praise of the impact factor [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/h4p9e
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www.the100.ci www.the100.ci
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Crisis research, fast and slow. (2020, March 26). The 100% CI. http://www.the100.ci/2020/03/26/crisis-research-fast-and-slow/
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twitter.com twitter.com
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Kazemi, D. (2020, May 23). "NPR is promoting this article again. Without access to the study we have no way of knowing how "bot" was estimated or measured, we simply have to go on the reputation and past research of this lab, which I dug into last night here: https://twitter.com/tinysubversion..." Twitter. https://twitter.com/tinysubversions/status/1263965246416318465
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psyarxiv.com psyarxiv.com
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Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & van Mieghem, P. (2020). BECON methodology [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/53ey9
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twitter.com twitter.com
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van Smeden, M. (2020, May 26). "RT @MaartenvSmeden: Often more than 300 COVID-19 related scientific articles published per day Data from @evidencelive." Twitter. https://twitter.com/SciBeh/status/1265657595307667457
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OSF Coronavirus Outbreak Research Collection
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www.cos.io www.cos.io
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Science, C. for O. (2020 May 5). TOP Factor to appear in Master Journal List. https://www.cos.io/about/news/cos-and-the-web-of-science-collaborate-to-bring-top-factor-to-master-journal-list
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www.bristol.ac.uk www.bristol.ac.uk
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UKRN position on covid 19 research. (2020 May 01). School of Psychological Science | University of Bristol. http://www.bristol.ac.uk/psychology/research/ukrn/news/2020/ukrn-position-on-covid-19-research.html
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featuredcontent.psychonomic.org featuredcontent.psychonomic.org
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Okan, Y. (2020, May 22). From a tweet to Reddit and beyond: The road to a global behavioral science SWAT team. Psychonomic Society Featured Content. https://featuredcontent.psychonomic.org/from-a-tweet-to-reddit-and-beyond-the-road-to-a-global-behavioral-science-swat-team/
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statmodeling.stat.columbia.edu statmodeling.stat.columbia.edu
Tags
- criticism
- relevance
- role
- review
- social science
- data analysis
- is:blog
- policy
- behavioral science
- limitation
- lang:en
- COVID-19
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Torres, L., Blevins, A. S., Bassett, D. S., & Eliassi-Rad, T. (2020). The why, how, and when of representations for complex systems. ArXiv:2006.02870 [Cs, q-Bio]. http://arxiv.org/abs/2006.02870
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www.statnews.com www.statnews.com
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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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Leplaa, H. J., Rietbergen, C., & Hoijtink, H. (2020). Bayesian evaluation of replication studies [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/49tbz
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featuredcontent.psychonomic.org featuredcontent.psychonomic.org
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Lewandowsky, S. (2020, June 1). A tale of two island nations: Lessons for crisis knowledge management. Psychonomic Society Featured Content. https://featuredcontent.psychonomic.org/a-tale-of-two-island-nations-lessons-for-crisis-knowledge-management/
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- May 2020
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Crchartier, ~. (2020, March 13). The PSA Calls for Rapid and Impactful Study Proposals on COVID-19. Psychological Science Accelerator. https://psysciacc.org/2020/03/13/the-psa-calls-for-rapid-and-impactful-study-proposals-on-covid-19/
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www.psychologicalscience.org www.psychologicalscience.org
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Social Scientists Scramble to Study Pandemic, In Real Time. (n.d.). Association for Psychological Science - APS. Retrieved April 20, 2020, from https://www.psychologicalscience.org/news/social-scientists-scramble-to-study-pandemic-in-real-time.html
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psyarxiv.com psyarxiv.com
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Peikert, A., & Brandmaier, A. M. (2019). A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker. https://doi.org/10.31234/osf.io/8xzqy
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www.nature.com www.nature.com
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Zdeborová, L. (2020). Understanding deep learning is also a job for physicists. Nature Physics, 1–3. https://doi.org/10.1038/s41567-020-0929-2
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twitter.com twitter.com
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Carl T. Bergstrom on Twitter
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leedsunilibrary.wordpress.com leedsunilibrary.wordpress.com
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The Ethics of Online Research. (2018, August 30). Leeds University Library Blog. https://leedsunilibrary.wordpress.com/2018/08/30/the-ethics-of-online-research/
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Borgonovi, F., & Pokropek, A. (2020). Can we rely on trust in science to beat the COVID-19 pandemic? [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/yq287
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hypothes.is hypothes.is
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Register Today For Data Science Certification. Learn the Best Data Science Course from our Top Tutors. Study and Get A Certified Data Science Course. Enroll For Data Science Certification and Get 24/7 support and all time study Material. Land in your Dream Job by registering to this Course.
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www.annualreviews.org www.annualreviews.org
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Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), annurev-soc-121919-054621. https://doi.org/10.1146/annurev-soc-121919-054621
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www.sciencedirect.com www.sciencedirect.com
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Lourenco, S. F., & Tasimi, A. (2020). No Participant Left Behind: Conducting Science During COVID-19. Trends in Cognitive Sciences, S1364661320301157. https://doi.org/10.1016/j.tics.2020.05.003
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bfi.uchicago.edu bfi.uchicago.edu
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Brzezinski, A., Kecht, V., Van Dijcke, D., Wright, A. (2020) Belief in Science Influences Physical Distancing in Response to COVID-19 Lockdown Policies. BFI. https://bfi.uchicago.edu/working-paper/belief-in-science-influences-physical-distancing-in-response-to-covid-19-lockdown-policies/
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science.sciencemag.org science.sciencemag.org
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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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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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Van den Akker, O., Weston, S. J., Campbell, L., Chopik, W. J., Damian, R. I., Davis-Kean, P., Hall, A. N., Kosie, J. E., Kruse, E. T., Olsen, J., Ritchie, S. J., Valentine, K. D., van ’t Veer, A. E., & Bakker, M. (2019). Preregistration of secondary data analysis: A template and tutorial [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/hvfmr
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- Apr 2020
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psyarxiv.com psyarxiv.com
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Beitner, J., Brod, G., Gagl, B., Kraft, D., & Schultze, M. (2020, April 23). Offene Wissenschaft in der Zeit von Covid-19 – Eine Blaupause für die psychologische Forschung?. https://doi.org/10.31234/osf.io/sh8xg
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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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sciencebusiness.net sciencebusiness.net
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University of Amsterdam scientists launch website that seeks ideal COVID-19 exit strategy. (2020 April 21) Science|Business. https://sciencebusiness.net/network-updates/university-amsterdam-scientists-launch-website-seeks-ideal-covid-19-exit-strategy
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Wojcik, S., et al. (2020 March 30). Survey data and human computation for improved flu tracking. Cornell University. arXiv:2003.13822
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www.medrxiv.org www.medrxiv.org
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Klepac, P., Kucharski, A. J., Conlan, A. J., Kissler, S., Tang, M., Fry, H., & Gog, J. R. (2020). Contacts in context: Large-scale setting-specific social mixing matrices from the BBC Pandemic project [Preprint]. Epidemiology. https://doi.org/10.1101/2020.02.16.20023754
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www.cell.com www.cell.com
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Callaghan, S. (2020). COVID-19 Is a Data Science Issue. Patterns, 100022. https://doi.org/10.1016/j.patter.2020.100022
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Perakslis, E. (2020). A Primer on Biodefense Data Science for Pandemic Preparedness. Patterns, 1(1), 100018. https://doi.org/10.1016/j.patter.2020.100018
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www.thelancet.com www.thelancet.com
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Holmes, E. A., O’Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., Ballard, C., Christensen, H., Cohen Silver, R., Everall, I., Ford, T., John, A., Kabir, T., King, K., Madan, I., Michie, S., Przybylski, A. K., Shafran, R., Sweeney, A., … Bullmore, E. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. The Lancet Psychiatry, S2215036620301681. https://doi.org/10.1016/S2215-0366(20)30168-1
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twitter.com twitter.com
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ReconfigBehSci en Twitter: “‘Proper science without the drag’ – Move to the medical model of journal review: ‘Yes/No’ decision. We suggest the temporary adoption of this model for crisis-relevant material by journals. [happening already, but potentially even better models: @Meta_psy and @F1000Research?]” / Twitter. (n.d.). Twitter. Retrieved April 15, 2020, from https://twitter.com/scibeh/status/1242094075312046082
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sciencebusiness.net sciencebusiness.net
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Viewpoint: COVID-19, open science, and a ‘red alert’ health indicator. (n.d.). Science|Business. Retrieved April 17, 2020, from https://sciencebusiness.net/viewpoint/viewpoint-covid-19-open-science-and-red-alert-health-indicator
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www.reddit.com www.reddit.com
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r/BehSciResearch—Behavioural science research for guiding societies out of lockdown. (n.d.). Reddit. Retrieved April 20, 2020, from https://www.reddit.com/r/BehSciResearch/comments/g2bm09/behavioural_science_research_for_guiding/
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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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en.wikipedia.org en.wikipedia.org
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Data visualization is both an art and a science
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- Feb 2020
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danmackinlay.name danmackinlay.name
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A statistician is the exact same thing as a data scientist or machine learning researcher with the differences that there are qualifications needed to be a statistician, and that we are snarkier.
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- Jan 2020
- 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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www.cleveroad.com www.cleveroad.com
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What’s the Difference Between AI, Machine Learning and Data Science?
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- Jun 2019
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Success ina data science project comes not from access to any one exotic tool, but from having quantifiablegoals, good methodology, crossdiscipline interactions, and a repeatable workflow.
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- Apr 2019
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www.go-fair.org www.go-fair.org
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blog.socialcops.com blog.socialcops.com
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Interesting data science / development / technology blog from an Indian Start up
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- Dec 2018
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bid.berkeley.edu bid.berkeley.edu
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bcourses.berkeley.edu bcourses.berkeley.edu
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Annotators
URL
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- Nov 2018
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multithreaded.stitchfix.com multithreaded.stitchfix.com
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Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.
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- Oct 2018
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www.springboard.com www.springboard.com
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tl;dr: data engineer = software, coding, cleaning data sets data architects = structure the technology to manage data models and database admin data scientist = stats + math models business analysts = communication and domain expertise
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- May 2018
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www.audit.vic.gov.au www.audit.vic.gov.au
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Negative values included when assessing air quality In computing average pollutant concentrations, EPA includes recorded values that are below zero. EPA advised that this is consistent with NEPM AAQ procedures. Logically, however, the lowest possible value for air pollutant concentrations is zero. Either it is present, even if in very small amounts, or it is not. Negative values are an artefact of the measurement and recording process. Leaving negative values in the data introduces a negative bias, which potentially under represents actual concentrations of pollutants. We noted a considerable number of negative values recorded. For example, in 2016, negative values comprised 5.3 per cent of recorded hourly PM2.5 values, and 1.3 per cent of hourly PM10 values. When we excluded negative values from the calculation of one‐day averages, there were five more exceedance days for PM2.5 and one more for PM10 during 2016.
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- Sep 2017
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blog.dmptool.org blog.dmptool.org
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We’re delighted to announce that the California Digital Library has been awarded a 2-year NSF EAGER grant to support active, machine-actionable data management plans (DMPs).
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- Mar 2017
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cs231n.github.io cs231n.github.io
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Great course
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URL
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- Feb 2017
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wiki.dbpedia.org wiki.dbpedia.orgDBpedia1
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URL
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semanticweb.org semanticweb.org
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demo.dbpedia-spotlight.org demo.dbpedia-spotlight.org
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lod-cloud.net lod-cloud.net
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URL
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cognonto.com cognonto.com
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en.lodlive.it en.lodlive.it
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www.nytimes.com www.nytimes.com
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After a brief training session, participants spent six hours archiving environmental data from government websites, including those of the National Oceanic and Atmospheric Administration and the Interior Department.
A worthwhile effort.
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An anonymous donor has provided storage on Amazon servers, and the information can be searched from a website at the University of Pennsylvania called Data Refuge. Though the Federal Records Act theoretically protects government data from deletion, scientists who rely on it say would rather be safe than sorry.
Data refuge.
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- Oct 2016
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m.pnas.org m.pnas.org
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(courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf)
nice reference !
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URL
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- Sep 2016
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www.sr.ithaka.org www.sr.ithaka.org
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Activities such as time spent on task and discussion board interactions are at the forefront of research.
Really? These aren’t uncontroversial, to say the least. For instance, discussion board interactions often call for careful, mixed-method work with an eye to preventing instructor effect and confirmation bias. “Time on task” is almost a codeword for distinctions between models of learning. Research in cognitive science gives very nuanced value to “time spent on task” while the Malcolm Gladwells of the world usurp some research results. A major insight behind Competency-Based Education is that it can allow for some variance in terms of “time on task”. So it’s kind of surprising that this summary puts those two things to the fore.
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- Jul 2016
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books.google.ca books.google.ca
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Initially, the digital humanities consisted of the curation and analysis of data that were born digital, and the digitisation and archiving projects that sought to render analogue texts and material objects into digital forms that could be organised and searched and be subjects to basic forms of overarching, automated or guided analysis, such as summary visualisations of content or connections between documents, people or places. Subsequently, its advocates have argued that the field has evolved to provide more sophisticated tools for handling, searching, linking, sharing and analysing data that seek to complement and augment existing humanities methods, and facilitate traditional forms of interpretation and theory building, rather than replacing traditional methods or providing an empiricist or positivistic approach to humanities scholarship.
summary of history of digital humanities
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- Apr 2016
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mitpress.mit.edu mitpress.mit.edu
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Great Principles of Computing<br> Peter J. Denning, Craig H. Martell
This is a book about the whole of computing—its algorithms, architectures, and designs.
Denning and Martell divide the great principles of computing into six categories: communication, computation, coordination, recollection, evaluation, and design.
"Programmers have the largest impact when they are designers; otherwise, they are just coders for someone else's design."
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- Mar 2016
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amstat.tandfonline.com amstat.tandfonline.com
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American Statistical Association statement on p-values
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- Feb 2016
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leanpub.com leanpub.com
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Books on data science and R programming by Roger D. Peng of Johns Hopkins.
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blog.cloudera.com blog.cloudera.com
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Great explanation of 15 common probability distributions: Bernouli, Uniform, Binomial, Geometric, Negative Binomial, Exponential, Weibull, Hypergeometric, Poisson, Normal, Log Normal, Student's t, Chi-Squared, Gamma, Beta.
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f1000research.com f1000research.com
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Since its start in 1998, Software Carpentry has evolved from a week-long training course at the US national laboratories into a worldwide volunteer effort to improve researchers' computing skills. This paper explains what we have learned along the way, the challenges we now face, and our plans for the future.
http://software-carpentry.org/lessons/<br> Basic programming skills for scientific researchers.<br> SQL, and Python, R, or MATLAB.
http://www.datacarpentry.org/lessons/<br> Managing and analyzing data.
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URL
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- Jan 2016
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courses.csail.mit.edu courses.csail.mit.edu
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50 Years of Data Science, David Donoho<br> 2015, 41 pages
This paper reviews some ingredients of the current "Data Science moment", including recent commentary about data science in the popular media, and about how/whether Data Science is really di fferent from Statistics.
The now-contemplated fi eld of Data Science amounts to a superset of the fi elds of statistics and machine learning which adds some technology for 'scaling up' to 'big data'.
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quoracast.quora.com quoracast.quora.com
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"A friend of mine said a really great phrase: 'remember those times in early 1990's when every single brick-and-mortar store wanted a webmaster and a small website. Now they want to have a data scientist.' It's good for an industry when an attitude precedes the technology."
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phys.org phys.org
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paradox of unanimity - Unanimous or nearly unanimous agreement doesn't always indicate the correct answer. If agreement is unlikely, it indicates a problem with the system.
Witnesses who only saw a suspect for a moment are not likely to be able to pick them out of a lineup accurately. If several witnesses all pick the same suspect, you should be suspicious that bias is at work. Perhaps these witnesses were cherry-picked, or they were somehow encouraged to choose a particular suspect.
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