- Aug 2020
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covid-19.iza.org covid-19.iza.org
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Lockdown Strategies, Mobility Patterns and COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13293/
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covid-19.iza.org covid-19.iza.org
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Does the COVID-19 Pandemic Improve Global Air Quality? New Cross-National Evidence on Its Unintended Consequences. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13480/
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covid-19.iza.org covid-19.iza.org
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Intergenerational Residence Patterns and COVID-19 Fatalities in the EU and the US. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 29, 2020, from https://covid-19.iza.org/publications/dp13452/
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covid-19.iza.org covid-19.iza.org
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Exploring the Relationship between Care Homes and Excess Deaths in the COVID-19 Pandemic: Evidence from Italy. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 27, 2020, from https://covid-19.iza.org/publications/dp13492/
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covid-19.iza.org covid-19.iza.org
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Reacting Quickly and Protecting Jobs: The Short-Term Impacts of the COVID-19 Lockdown on the Greek Labor Market. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 27, 2020, from https://covid-19.iza.org/publications/dp13516/
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stackoverflow.blog stackoverflow.blog
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Valhalla aims to revise the memory model for Java to allow for immutable types, which are more complex than primitives, but less flexible than objects. Sometimes you have more complex data that doesn’t change over the course of that object’s lifespan; burdening it with the overhead of a class is unnecessary. The initial proposal put it more succinctly: “Codes like a class, works like an int.” “For things like big data for machine learning or for natural language, Valhalla promises to represent data in a way that allows the JVM to fully take advantage of modern hardware architectures that have changed dramatically since Java was created,” said Saab.
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psyarxiv.com psyarxiv.com
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Webster, G. D., Howell, J. L., Losee, J. E., Mahar, E., & Wongsomboon, V. (2020). Culture, COVID-19, and Collectivism: A Paradox of American Exceptionalism? [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/hqcs6
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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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www.youtube.com www.youtube.com
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MyData vs. COVID-19 calls (2020, June 5) - https://www.youtube.com/playlist?list=PLbpRS19STpXSWs4kTiVEx2KN5CZh6yCYI
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Brown, C. S., Ravallion, M., & van de Walle, D. (2020). Can the World’s Poor Protect Themselves from the New Coronavirus? (Working Paper No. 27200; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27200
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www.nber.org www.nber.org
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Baqaee, D., Farhi, E., Mina, M. J., & Stock, J. H. (2020). Reopening Scenarios (Working Paper No. 27244; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27244
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public.tableau.com public.tableau.com
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Roll over each school to find out more information on their respective plans. (n.d.). Tableau Software. Retrieved August 2, 2020, from https://public.tableau.com/views/NESCACFallPlansMap/Dashboard1
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osr.statisticsauthority.gov.uk osr.statisticsauthority.gov.uk
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COVID-19 Local Area Data. (n.d.). Office for Statistics Regulation. Retrieved August 2, 2020, from https://osr.statisticsauthority.gov.uk/news/covid-19-local-area-data/
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www.nber.org www.nber.org
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Monte, F. (2020). Mobility Zones (Working Paper No. 27236; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27236
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- Jul 2020
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www.ons.gov.uk www.ons.gov.uk
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Deaths registered weekly in England and Wales, provisional—Office for National Statistics. (n.d.). Retrieved 31 July 2020, from https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsregisteredweeklyinenglandandwalesprovisional/weekending17july2020
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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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www.nber.org www.nber.org
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Dave, D. M., Friedson, A. I., Matsuzawa, K., McNichols, D., Redpath, C., & Sabia, J. J. (2020). Did President Trump’s Tulsa Rally Reignite COVID-19? Indoor Events and Offsetting Community Effects (Working Paper No. 27522; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27522
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www.nber.org www.nber.org
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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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www.nber.org www.nber.org
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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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www.nber.org www.nber.org
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Aucejo, E. M., French, J. F., Araya, M. P. U., & Zafar, B. (2020). The Impact of COVID-19 on Student Experiences and Expectations: Evidence from a Survey (Working Paper No. 27392; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27392
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www.nber.org www.nber.org
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Chang, H.-H., & Meyerhoefer, C. (2020). COVID-19 and the Demand for Online Food Shopping Services: Empirical Evidence from Taiwan (Working Paper No. 27427; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27427
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www.nber.org www.nber.org
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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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Papanikolaou, D., & Schmidt, L. D. W. (2020). Working Remotely and the Supply-side Impact of Covid-19 (Working Paper No. 27330; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27330
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www.nber.org www.nber.org
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Schmitt-Grohé, S., Teoh, K., & Uribe, M. (2020). Covid-19: Testing Inequality in New York City (Working Paper No. 27019; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27019
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epjdatascience.springeropen.com epjdatascience.springeropen.com
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Fatehkia, M., Tingzon, I., Orden, A., Sy, S., Sekara, V., Garcia-Herranz, M., & Weber, I. (2020). Mapping socioeconomic indicators using social media advertising data. EPJ Data Science, 9(1), 1–15. https://doi.org/10.1140/epjds/s13688-020-00235-w
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walker-data.com walker-data.com
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Working with Census microdata. (n.d.). Retrieved July 31, 2020, from https://walker-data.com/tidycensus/articles/pums-data.html
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Coronavirus (COVID-19): Daily data for Scotland—Gov.scot. (n.d.). Retrieved July 31, 2020, from https://www.gov.scot/publications/coronavirus-covid-19-daily-data-for-scotland/
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github.com github.com
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Ruby has some really nice libraries for working with linked data. These libraries allow you to work with the data in both a graph and resource-oriented fashion, allowing a developer to use the techniques that best suit his or her use cases and skills.
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Another Ruby gem, Spira, allows graph data to be used as model objects
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www.w3.org www.w3.org
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As a result, web browsers can provide only minimal assistance to humans in parsing and processing web pages: browsers only see presentation information.
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json-ld.org json-ld.org
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amp.dev amp.dev
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To verify that your structured data is correct, many platforms provide validation tools. In this tutorial, we'll validate our structured data with the Google Structured Data Validation Tool.
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Valid AMP pages do not require schema.org structured data, but some platforms like Google Search require it for certain experiences like the Top stories carousel. It's generally a good idea to include structured data. Structured data helps search engines to better understand your web page, and to better display your content in Search Engine Result Pages (e.g., in rich snippets).
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edpb.europa.eu edpb.europa.eu
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As mentioned earlier in these guidelines, it is very important that controllers assess the purposes forwhich data is actually processed and the lawful grounds on which it is based prior to collecting thedata. Often companies need personal data for several purposes, and the processing is based on morethan one lawful basis, e.g. customer data may be based on contract and consent. Hence, a withdrawalof consent does not mean a controller must erase data that are processed for a purpose that is basedon the performance of the contract with the data subject. Controllers should therefore be clear fromthe outset about which purpose applies to each element of data and which lawful basis is being reliedupon.
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If there is no other lawful basisjustifying the processing (e.g. further storage) of the data, they should be deleted by the controller.
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In cases where the data subject withdraws his/her consent and the controller wishes to continue toprocess the personal data on another lawful basis, they cannot silently migrate from consent (which iswithdrawn) to this other lawful basis. Any change in the lawful basis for processing must be notified toa data subject in accordance with the information requirements in Articles 13 and 14 and under thegeneral principle of transparency.
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Data minimization, anonymisation and datasecurity are mentioned as possible safeguards.73Anonymisation is the preferred solution as soon asthe purpose of the research can be achieved without the processing of personal data.
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www.iubenda.com www.iubenda.com
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Some vendors may relay on legitimate interest instead of consent for the processing of personal data. The User Interface specifies if a specific vendor is relating on legitimate interest as legal basis, meaning that that vendor will process user’s data for the declared purposes without asking for their consent. The presence of vendors relying on legitimate interest is the reason why within the user interface, even if a user has switched on one specific purpose, not all vendors processing data for that purpose will be displayed as switched on. In fact, those vendors processing data for that specific purpose, relying only on legitimate interest will be displayed as switched off.
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Under GDPR there are six possible legal bases for the processing of personal data.
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addons.mozilla.org addons.mozilla.org
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easy access/edit/backup and then restore saved data using the integrated Bookmark Manager (even on smartphone / tablet) even if the addon is not installed
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Why save sessions as bookmarks? - all the data saved will be there no matter what addon you may use in the feature
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www.nber.org www.nber.org
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Fernández-Villaverde, J., & Jones, C. I. (2020). Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities (Working Paper No. 27128; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27128
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www.nber.org www.nber.org
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www.nber.org www.nber.org
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Granja, J., Makridis, C., Yannelis, C., & Zwick, E. (2020). Did the Paycheck Protection Program Hit the Target? (Working Paper No. 27095; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27095
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sites.google.com sites.google.com
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drawing evidence-based conclusions
One thing that is not obvious about Hypothesis, is that you can also use it to annotate data sheets — that's easiest if they are CSV files published on the web.
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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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Follow the money: See where $380B in Paycheck Protection Program money went. (n.d.). Retrieved July 27, 2020, from https://www.cnn.com/projects/ppp-business-loans/
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Do jeszcze bardziej przytłaczających wniosków doszła Julianne Holt-Lunstad, która, posiłkując się wynikami 70 badań naukowych, ogłosiła, że samotność zwiększa śmiertelność w takim samym stopniu co otyłość czy wypalanie 15 papierosów dziennie. Z kolei Nicole Valtorty z Uniwersytetu Newcastle ustaliła, że prawdopodobieństwo ataku serca u osób osamotnionych rośnie o 29 proc., a zagrożenie udarem – o 32 proc. „To niezależny czynnik przyczyniający się do śmierci. Może cię po prostu zabić. Znajduje się na tej samej liście co choroby serca i rak – twierdzi dr Josh Klapow, psycholog kliniczny z Uniwersytetu Alabamy.
Data on health consequences of being alone
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Z danych GUS-u i tych zebranych przez portale randkowe wynika, że w Polsce w ciągu ostatnich 10 lat liczba osób żyjących samotnie wzrosła o 34 proc.
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Wśród krajów europejskich w niechlubnym rankingu zwycięża jednak Szwecja, w stolicy której samotnie mieszka aż 58 proc.(!) populacji. Z kolei w Stanach Zjednoczonych odsetek ten wynosi 27 proc. (w Nowym Jorku prawie 50 proc.) i cały czas rośnie – dla porównania w roku 1920 jednoosobowe gospodarstwo domowe prowadziło tam 5 proc. obywateli.
Percentage of people living alone
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Luscombe, A., & McClelland, A. (2020). Policing the Pandemic: Tracking the Policing of Covid-19 across Canada [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/9pn27
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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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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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akkshaya.blog akkshaya.blog
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the market size: the global note-taking management software market is estimated to reach $1.35 billion by 2026, growing at a CAGR of 5.32% from 2019 to 2026greater scope for innovation: eg., be it creating a task list, a roadmap, or a design repository, Notion can handle it alllack of satisfaction: it’s noted that people always use a combination of note-taking apps and hardly stick to one for a long time
Three reasons why we constantly see more note-taking apps, which in return increase our paradox of choice
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URL
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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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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
- misinformation
- analysis
- social science
- social media
- unofficial
- policy
- social distancing
- publication
- medicine
- data collection
- conspiracy theory
- behavior
- tracker
- mental health
- community
- infection
- lang:en
- is:other
- preprint
- social norm
- healthcare
- spreadsheet
- international
- sheets
- isolation
- COVID-19
- government
- uncertainty
Annotators
URL
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van Rooij, B., de Bruijn, A. L., Reinders Folmer, C., Kooistra, E., Kuiper, M. E., Brownlee, M., … Fine, A. (2020, April 22). Compliance with COVID-19 Mitigation Measures in the United States. https://doi.org/10.31234/osf.io/qymu3
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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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www.thelancet.com www.thelancet.com
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The Lancet. (2020). The gendered dimensions of COVID-19. The Lancet, 395(10231), 1168. https://doi.org/10.1016/S0140-6736(20)30823-0
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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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osf.io osf.io
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Goldman, D. S. (2020). Initial Observations of Psychological and Behavioral Effects of COVID-19 in the United States, Using Google Trends Data. https://doi.org/10.31235/osf.io/jecqp
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osf.io osf.io
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Bernardi, F., Cozzani, M., & Zanasi, F. (2020). Social inequality and the risk of being in a nursing home during the COVID-19 pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/ksefy
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Metternich, N. W. (2020). Drawback before the wave?: Protest decline during the Covid-19 pandemic. https://doi.org/10.31235/osf.io/3ej72
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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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www.youtube.com www.youtube.com
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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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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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osf.io osf.io
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Stephany, F., Dunn, M., Sawyer, S., & Lehdonvirta, V. (2020). Distancing Bonus or Downscaling Loss? The Changing Livelihood of US Online Workers in Times of COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/vmg34
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osf.io osf.io
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Dudel, C., Riffe, T., Acosta, E., van Raalte, A. A., Strozza, C., & Myrskylä, M. (2020). Monitoring trends and differences in COVID-19 case fatality rates using decomposition methods: Contributions of age structure and age-specific fatality [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/j4a3d
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osf.io osf.io
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Arpino, B., Bordone, V., & Pasqualini, M. (2020). Are intergenerational relationships responsible for more COVID-19 cases? A cautionary tale of available empirical evidence [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/y8hpr
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www.youtube.com www.youtube.com
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American Philosophical Society. (2020, June 08). Evidence Symposium. YouTube. https://www.youtube.com/playlist?list=PLoKwLGnyZL4Ds5cQo5muFMg8zKXK4KobH
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wellcomeopenresearch.org wellcomeopenresearch.org
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Jeffrey, B., Walters, C. E., Ainslie, K. E. C., Eales, O., Ciavarella, C., Bhatia, S., Hayes, S., Baguelin, M., Boonyasiri, A., Brazeau, N. F., Cuomo-Dannenburg, G., FitzJohn, R. G., Gaythorpe, K., Green, W., Imai, N., Mellan, T. A., Mishra, S., Nouvellet, P., Unwin, H. J. T., … Riley, S. (2020). Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research, 5, 170. https://doi.org/10.12688/wellcomeopenres.15997.1
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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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Jul 2, N. H. / P. (2020, July 2). Urban density not linked to higher coronavirus infection rates. The Hub. https://hub.jhu.edu/2020/07/02/urban-density-not-linked-to-higher-covid-19-infection-rates/
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www.medrxiv.org www.medrxiv.org
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Fonseca, S. C., Rivas, I., Romaguera, D., Quijal-Zamorano, M., Czarlewski, W., Vidal, A., Fonseca, J. A., Ballester, J., Anto, J. M., Basagana, X., Cunha, L. M., & Bousquet, J. (2020). Association between consumption of vegetables and COVID-19 mortality at a country level in Europe. MedRxiv, 2020.07.17.20155846. https://doi.org/10.1101/2020.07.17.20155846
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www.opentable.com www.opentable.com
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OpenTable. ‘State of the Industry’. Accessed 20 July 2020. https://www.opentable.com/state-of-industry.
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jamanetwork.com jamanetwork.com
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Weinberger, D. M., Chen, J., Cohen, T., Crawford, F. W., Mostashari, F., Olson, D., Pitzer, V. E., Reich, N. G., Russi, M., Simonsen, L., Watkins, A., & Viboud, C. (2020). Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020. JAMA Internal Medicine. https://doi.org/10.1001/jamainternmed.2020.3391
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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
- potency
- data analysis
- science
- lethality
- is:news
- concern
- case increase
- COVID-19
- potential
- transmission
- USA
- lang:en
- epidemiology
- clarity
Annotators
URL
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www.nationalgeographic.com www.nationalgeographic.com
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Coronavirus News and Coverage. (n.d.). Science. Retrieved July 18, 2020, from https://www.nationalgeographic.com/science/coronavirus-coverage/
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innovationinpolitics.eu innovationinpolitics.eu
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Coping with the Crisis | Introduction. (n.d.). The Innovation in Politics Institute. Retrieved July 18, 2020, from https://innovationinpolitics.eu/en/coping-with-the-coronavirus-crisis/introduction/
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twitter.com twitter.com
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Ed Conway on Twitter: “Breaking: UK government was routinely overstating the total number of people who’d been tested for #COVID19 by as many as 200,000 at the height of the coronavirus pandemic, according to new Sky News analysis.” / Twitter. (n.d.). Twitter. Retrieved July 17, 2020, from https://twitter.com/EdConwaySky/status/1281652670000844800
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www.gov.uk www.gov.uk
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Number of people tested for coronavirus (England): 30 January to 27 May 2020. (n.d.). GOV.UK. Retrieved July 17, 2020, from https://www.gov.uk/government/publications/number-of-people-tested-for-coronavirus-england-30-january-to-27-may-2020
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Kulu, H., & Dorey, P. (2020). Infection Rates from Covid-19 in Great Britain by Geographical Units: A Model-based Estimation from Mortality Data [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/84f3e
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osf.io osf.io
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Haman, M. (2020). The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic. https://doi.org/10.31235/osf.io/u4maf
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twitter.com twitter.com
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ReconfigBehSci on Twitter: ‘RT @ObsoleteDogma: The U.S. is averaging more new covid cases than any continent is right now https://t.co/O70nj0I4Xo’ / Twitter. (n.d.). Twitter. Retrieved 16 July 2020, from https://twitter.com/SciBeh/status/1282953988774727680
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Graham, A., Cullen, F. T., Pickett, J., Jonson, C. L., Haner, M., & Sloan, M. M. (2020). Faith in Trump, Moral Foundations, and Social Distancing Defiance During the Coronavirus Pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/fudzq
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Fitzgerald, R. M. (2020). WAKING TO NORMAL: Examining Archival Appraisal in Data-driven Society [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/2befk
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Seyitoğlu, F., & Ivanov, S. H. (2020). Service robots as a tool for physical distancing in tourism [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/k3z6m
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Sakakibara, R., & Ozono, H. (2020). Psychological Research on the COVID-19 Crisis in Japan: Focusing on Infection Preventive Behaviors, Future Prospects, and Information Dissemination Behaviors. https://doi.org/10.31234/osf.io/97zye
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Betsch, C., Korn, L., Sprengholz, P., Felgendreff, L., Eitze, S., Schmid, P., & Böhm, R. (2020). Social and behavioral consequences of mask policies during the COVID-19 pandemic. https://doi.org/10.31234/osf.io/gn6c9
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sens-public.org sens-public.org
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One of these semiotizing processes is the extraction, interpretation and reintegration of web data from and into human subjectivities.
Machine automation becomes another “subjectivity” or “agentivity”—an influential one, because it is the one filtering and pushing content to humans.
The means of this automated subjectivity is feeding data capitalism: more content, more interaction, more behavioral data produced by the users—data which is then captured (“dispossessed”), extracted, and transformed into prediction services, which render human behavior predictable, and therefore monetizable (Shoshana Zuboff, The Age of Surviellance Capitalism, 2019).
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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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Dunn, M., Stephany, F., Sawyer, S., Munoz, I., Raheja, R., Vaccaro, G., & Lehdonvirta, V. (2020). When Motivation Becomes Desperation: Online Freelancing During the COVID-19 Pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/67ptf
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Hossain, M. M., McKyer, E. L. J., & Ma, P. (2020). Applications of artificial intelligence technologies on mental health research during COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/w6c9b
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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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all the subcolletions must have the same name, for instance tags
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Breznau, N. (2020). The Welfare State and Risk Perceptions: The Novel Coronavirus Pandemic and Public Concern in 70 Countries. https://doi.org/10.31235/osf.io/96fd2
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Burgess, M. G., Langendorf, R. E., Ippolito, T., & Pielke, R. (2020). Optimistically biased economic growth forecasts and negatively skewed annual variation [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/vndqr
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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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Fontanet, A., Tondeur, L., Madec, Y., Grant, R., Besombes, C., Jolly, N., Pellerin, S. F., Ungeheuer, M.-N., Cailleau, I., Kuhmel, L., Temmam, S., Huon, C., Chen, K.-Y., Crescenzo, B., Munier, S., Demeret, C., Grzelak, L., Staropoli, I., Bruel, T., … Hoen, B. (2020). Cluster of COVID-19 in northern France: A retrospective closed cohort study. MedRxiv, 2020.04.18.20071134. https://doi.org/10.1101/2020.04.18.20071134
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Sapoval, N., Mahmoud, M., Jochum, M. D., Liu, Y., Elworth, R. A. L., Wang, Q., Albin, D., Ogilvie, H., Lee, M. D., Villapol, S., Hernandez, K., Berry, I. M., Foox, J., Beheshti, A., Ternus, K., Aagaard, K. M., Posada, D., Mason, C., Sedlazeck, F. J., & Treangen, T. J. (2020). Hidden genomic diversity of SARS-CoV-2: Implications for qRT-PCR diagnostics and transmission. BioRxiv, 2020.07.02.184481. https://doi.org/10.1101/2020.07.02.184481
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Sperrin, M., Martin, G. P., Sisk, R., & Peek, N. (2020). Missing data should be handled differently for prediction than for description or causal explanation. Journal of Clinical Epidemiology, 0(0). https://doi.org/10.1016/j.jclinepi.2020.03.028
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Introducing JASP 0.13. (2020, July 2). JASP - Free and User-Friendly Statistical Software. https://jasp-stats.org/?p=6483
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Natalie E. Dean, PhD on Twitter: “THINK LIKE AN EPIDEMIOLOGIST: There are more new confirmed cases each day in the US than at any time during the earlier April peak. But is it really meaningful to compare those numbers? How do epidemiologists decide when to sound the alarm? A thread. 1/11 https://t.co/rPelzIvcxs” / Twitter. (n.d.). Twitter. Retrieved July 3, 2020, from https://twitter.com/nataliexdean/status/1278868210385915904
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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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www.nature.com www.nature.com
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Lavezzo, E., Franchin, E., Ciavarella, C., Cuomo-Dannenburg, G., Barzon, L., Del Vecchio, C., Rossi, L., Manganelli, R., Loregian, A., Navarin, N., Abate, D., Sciro, M., Merigliano, S., De Canale, E., Vanuzzo, M. C., Besutti, V., Saluzzo, F., Onelia, F., Pacenti, M., … Crisanti, A. (2020). Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’. Nature, 1–1. https://doi.org/10.1038/s41586-020-2488-1
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psyarxiv.com psyarxiv.com
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Levita, L., Gibson Miller, J., Hartman, T. K., Murphy, J., Shevlin, M., McBride, O., Mason, L., Martinez, A. P., bennett, kate m, Stocks, T. V. A., McKay, R., & Bentall, R. (2020). Report2: Impact of Covid-19 on young people aged 13-24 in the UK- preliminary findings [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/s32j8
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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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medium.com medium.com
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Caching is dangerous if not done correctly. For example, making decisions based on outdated data as if it was current.
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Czarnek, G., Szwed, P., & Kossowska, M. (2020). Political ideology and attitudes toward vaccination: Study report. https://doi.org/10.31234/osf.io/uwehk
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zoom.us zoom.us
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Rosenberg, E. S., Tesoriero, J. M., Rosenthal, E. M., Chung, R., Barranco, M. A., Styer, L. M., Parker, M. M., John Leung, S.-Y., Morne, J. E., Greene, D., Holtgrave, D. R., Hoefer, D., Kumar, J., Udo, T., Hutton, B., & Zucker, H. A. (2020). Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Annals of Epidemiology. https://doi.org/10.1016/j.annepidem.2020.06.004
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Parsons, Sam. ‘Reliability Multiverse’, 26 June 2020. https://doi.org/10.31234/osf.io/y6tcz.
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Groarke, J., Berry, E., Wisener, L.-G., McKenna-Plumley, P., McGlinchey, E., & Armour, C. (2020). Loneliness in the UK during the COVID-19 pandemic: Cross-sectional results from The COVID-19 Psychological Wellbeing Study [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/j2pce
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Brik, A. B. (2020). COVID 19 FAMILY LIFE STUDY [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/gcqhp
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EU Datathon 2020—Webinar on COVID-19 and media and data monitoring. (2020, April 22). https://www.youtube.com/watch?v=wyNgmEfi_vk&feature=youtu.be
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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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Minocher, R., Atmaca, S., Bavero, C., McElreath, R., & Beheim, B. (2020, June 24). Reproducibility of social learning research declines exponentially over 63 years of publication. https://doi.org/10.31234/osf.io/4nzc7
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psyarxiv.com psyarxiv.com
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Romero, E., López-Romero, L., Domínguez-Álvarez, B., Villar, P., & Gómez-Fraguela, J. A. (2020). Testing the effects of COVID-19 confinement in Spanish children: The role of parents’ distress, emotional problems and specific parenting [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/spxtw
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psyarxiv.com psyarxiv.com
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Bulbulia, J., Barlow, F., Davis, D. E., Greaves, L., Highland, B., Houkamau, C., Milfont, T. L., Osborne, D., Piven, S., Shaver, J., Troughton, G., Wilson, M., Yogeeswaran, K., & Sibley, C. G. (2020). National Longitudinal Investigation of COVID-19 Lockdown Distress Clarifies Mechanisms of Mental Health Burden and Relief [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/cswde
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Tags
- qualitative research
- pandemic
- study design
- COVID-19
- technique
- ethics
- data collection
- is:blog
- lang:en
- scientific practice
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blogs.lse.ac.uk blogs.lse.ac.uk
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Using LinkedIn for Social Research. (2019, July 9). Impact of Social Sciences. https://blogs.lse.ac.uk/impactofsocialsciences/2019/07/09/using-linkedin-for-social-research/
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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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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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n this project we see a shift from a citizen-based model to a consumer model for urban planning, where all citizens’ ‘personal and environmental data is an economic resource.’
Called survillance capitlism
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reutersinstitute.politics.ox.ac.uk reutersinstitute.politics.ox.ac.uk
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Newman, N. (n.d.). Reuters Institute Digital News Report 2020. 112.
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Local file Local file
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Informal mentorship was captured using the following retrospective question from Wave 3 of the AddHealth data: "Other than your parents or step-parents, has an adult made an important positive difference in your life at any time since you were 14 years old?" Based on this question, I created a binary indicator for mentorship coded 1 if the young person had an informal mentor and 0 if they did not. Respondents were then asked "How is this person related to you?", and given response options like "family,""teacher/counselor,""friend's parent,""neighbor,"and "religious leader.
Defining informal mentorship in the survey data
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Middle-income subsample 3,158
Middle-income subsample for analysis was 3,158
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1. "Middle-income" is defined as anyone living in a household making two-thirds to double the median income (Pew Research Center, 2016). In 1994, the median income for a family of four was $46,757(US Bureau of Statistics, 1996). Thus, "middle-income" families would be those making between $30,860 and $93,514. Because I only have data available in $25,000 increments, I am defining middle-income families as those making between $25,000 and $100,000 a year in Wave 1.
Middle-income = families making $25k-$100k a year in Wave 1
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Defining low-,middle-, and high-income groupsDue to the limitation in the data described above, all incomes had to be converted in to categorical responses, with the smallest possible category size of $25,000 dollars. This created five categories for all incomes:
Defining income groups: under $25k, $25k-$49999, $50k-$74999, $75k-$99999, and $100k+.
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Wave 1 income was collected as a continuous variable, with an average of $45,728, (N=15,351, SD=$51,616). Low-income respondents (with incomes below $25,000) had an average of $9,837 (N=3,049, SD=4,633). Wave 4 income was recorded as a categorical variable, however, where respondents indicated if they made under $5,000, between $5,000 and $10,000, between $10,000 and $15,000, etc. These categories were of different sizes, getting larger as the income grew larger. Therefore, in order to create comparable measures between Wave 1 and Wave 4, both incomes were converted to 5 groups, (1) household income of less than $25,000, (2) household income of $25,000 to $49,999, (3) household income of $50,000 to $74,000, (4) household income of $75,000 to $99,000, and (5) household income of over $100,000
Upward mobility (dependent variable); data surrounding household incomes of Wave 1 and Wave 4
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stratum. This sampling method yielded a sample of 20,745 students in 7thto 12thgrade, with oversampling of some minority racialethnic groups, students with disabilities, and twins(Harris, 2018). Data were also collected from the parents of the in-home survey respondents, with an 85% success rate (Chen & Chantala, 2014).Wave 1 participants also reported their home address, which was then linked to a number of state-, county-, and Census tract-level variables from other sources. The present study used the school survey data, the in-home interview data, the parent survey data, and the data that was linked to state, county, and census-tracts, as described above. This study also used data from two subsequent waves of in-home interviews, specifically waves 3 and 4 (no new information relevant to the present study was collected in Wave 2). For each subsequent wave, AddHealth survey administrators recruited from the pool of Wave 1 respondents, no matter if they had responded to any wave since Wave 1. The present study used Wave 1 data for information about the youth’s socioeconomic status, social capital and other related variables. This wave collected from 1994 to 1995, when most respondents were between11 and 19 years old (n=20,745 youth) (Harris, 2013).This study also used information from the third wave of in-home interview data, namely all questions on informal mentoring. This wave wascollected in 2001 and 2002 when the youth (N=15,197) were 18 to 26 years old. The fourth wave of data was collected in 2008 and 2009, when the respondents were 25 to 33 years old (n=15,701). Data from the fourth wave wereused to calculate economic mobility, the key dependent variable for this study.
Data source
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DataTo address these questions, this study used three wavesofthe restricted-use version of the National Longitudinal Study of Adolescent Health (AddHealth). AddHealth is a multi-wave longitudinal, nationally representative study of youth who have been followed since adolescence through to adulthood. The AddHealth data were collected by sampling 80 high schools stratified across region, school type, urbanicity, ethnic mix, and school size during the 1994-1995 academic year. Fifty-two feeder schools(commonly middle schools whose students were assumed to go to these study high schools)were also sampled, resulting in a total of 132 sample schools. (Chen & Chantala, 2014, Harris, 2013). When sample high schools had grades 7 to 12, feeder schools were not recruited, as the lower grades served the role of feeding in younger students (Chen, 2014). Seventy nine percent of schools approached agreed to be in the study (Chen & Chantala, 2014). An in-school survey was then administered to over 90,000 students from these 132 schools. This survey was given during a single day within a 45-to 60-minute class period (Chen & Chantala, 2014). Subsequent recruitment for in-home interviews was done by stratifying students in each school by grade and sex and then randomly choosing 17 students from each
Data source
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www.wsj.com www.wsj.com
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Governments’ use of purchased location data has exploded in recent months, as officials around the world have sought insights on how people are moving around during the Covid-19 pandemic. In general, governments have assured their citizens that any location data collected by the marketing industry and used by public health entities is anonymous. But the movements of a phone give strong clues to its ownership—for example, where the phone is located during the evenings and overnight is likely where the phone owner lives. The identity of the phone’s owner can further be corroborated if their workplace, place of worship, therapist’s office or other information about their real-world activities are known to investigators.
private data is not anonymous as is purported
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www.biorxiv.org www.biorxiv.org
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Starr, T. N., Greaney, A. J., Hilton, S. K., Crawford, K. H., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., Veesler, D., & Bloom, J. D. (2020). Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding [Preprint]. Microbiology. https://doi.org/10.1101/2020.06.17.157982
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www.jclinepi.com www.jclinepi.com
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van Smeden, M., Groenwold, R.H.H., & Moons, K. GM. (2020, June 18). A cautionary note on the use of the missing indicator method for handling missing data in prediction research. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2020.06.007
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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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Giles, C., & Burn-Murdoch, J. (2020, May 28). UK suffers second-highest death rate from coronavirus | Free to read. https://www.ft.com/content/6b4c784e-c259-4ca4-9a82-648ffde71bf0
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psyarxiv.com psyarxiv.com
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Yamagata, M., Teraguchi, T., & Miura, A. (2020, April 10). The Relationship between Infection-Avoidance Tendency and Exclusionary Attitudes towards Foreigners: A Case Study of the COVID-19 Outbreak in Japan. https://doi.org/10.31234/osf.io/vhrqn
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royalsociety.org royalsociety.org
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DELVE group publishes evidence paper on the use of face masks in tackling Coronavirus (COVID-19) pandemic | Royal Society. (2020 May 04). https://royalsociety.org/news/2020/05/delve-group-publishes-evidence-paper-on-use-of-face-masks/
Tags
- Data Evaluation and Learning for Viral Epidemics
- DELVE
- Royal Society
- policy
- is:webpage
- social distancing
- publication
- management
- SAGE
- droplet
- behavioral change
- face mask
- infection
- asymptomatic
- evidence
- learning
- COVID-19
- transmission reduction
- public health
- lang:en
- physical distancing
Annotators
URL
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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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Raihani, N., & de-Wit, L. (2020, April 21). Factors Associated With Concern, Behaviour & Policy Support in Response to SARS-CoV-2. https://doi.org/10.31234/osf.io/8jpzc
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doi.org doi.org
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Willem, L., Hoang, T. V., Funk, S., Coletti, P., Beutels, P., & Hens, N. (2020). SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19 [Preprint]. Epidemiology. https://doi.org/10.1101/2020.03.03.20030627
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DaveLagnado on Reddit
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firebase.google.com firebase.google.com
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An index of groups can help a great deal here:
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proandroiddev.com proandroiddev.com
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normalizing our dabatase will help us. What means normalize? Well, it simply means to separate our information as much as we can
directly contradicts firebase's official advice: denormalize the structure by duplicating some of the data: https://youtu.be/lW7DWV2jST0?t=378
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 18, 2020, from https://twitter.com/devisridhar/status/1273528564793643008
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 18, 2020, from https://twitter.com/scibeh/status/1273519745762037765
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www.dkv.global www.dkv.global
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COVID-19 Regional Safety Assessment | DKG. (n.d.). DKV. Retrieved June 17, 2020, from https://www.dkv.global/covid-safety-assessment-200-regions
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www.reddit.com www.reddit.com
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r/BehSciMeta—Comment by u/UHahn on ”What is the impact of retraction of scientific studies reported in news media?”. (n.d.). Reddit. Retrieved June 17, 2020, from https://www.reddit.com/r/BehSciMeta/comments/gyw43b/what_is_the_impact_of_retraction_of_scientific/ftp5w1p
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www.thelancet.com www.thelancet.com
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Kucharski, A. J., Klepac, P., Conlan, A. J. K., Kissler, S. M., Tang, M. L., Fry, H., Gog, J. R., Edmunds, W. J., Emery, J. C., Medley, G., Munday, J. D., Russell, T. W., Leclerc, Q. J., Diamond, C., Procter, S. R., Gimma, A., Sun, F. Y., Gibbs, H. P., Rosello, A., … Simons, D. (2020). Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: A mathematical modelling study. The Lancet Infectious Diseases, 0(0). https://doi.org/10.1016/S1473-3099(20)30457-6
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Zelner, J., Riou, J., Etzioni, R., & Gelman, A. (2020). Accounting for Uncertainty During a Pandemic. ArXiv:2006.08745 [Physics, q-Bio, Stat]. http://arxiv.org/abs/2006.08745
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zoom.us zoom.us
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Oxford Internet Institute. Webinar Registration - "Data, data (science), get us out of here! Recommendations for resilient and fair policy-making in a crisis"
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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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twitter.com twitter.com
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Nivi Mani on Twitter. "I cannot stop smiling! Here is a first peek at the data from our online browser-based intermodal preferential looking set-up! We replicate the prediction effect (boy eats big cake, Mani & Huettig, 2012) using our online webcam testing software @julien__mayor @Kindskoepfe_Lab" / Twitter. (n.d.). Twitter. Retrieved June 15, 2020, from https://twitter.com/nivedita_mani/status/1265556217486815232
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Raleigh, V. S. (2020). Tackling UK’s mortality problem: Covid-19 and other causes. BMJ, 369. https://doi.org/10.1136/bmj.m2295
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assets.publishing.service.gov.uk assets.publishing.service.gov.uk
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COVID-19: Review of disparities in risks and outcomes. (n.d.). GOV.UK. Retrieved June 15, 2020, from https://www.gov.uk/government/publications/covid-19-review-of-disparities-in-risks-and-outcomes
Tags
- big data
- inequality
- ethnicity
- occupation
- risk factor
- age
- socioeconomic status
- is:pdf
- geography
- COVID-19
- comorbidity
- is:report
- UK
- lang:en
Annotators
URL
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rviews.rstudio.com rviews.rstudio.com
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Views, R. (2020, May 20). An R View into Epidemiology. /2020/05/20/some-r-resources-for-epidemiology/
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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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psyarxiv.com psyarxiv.com
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Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., & Weber, R. (2020). The Extended Moral Foundations Dictionary (eMFD): Development and Applications of a Crowd-Sourced Approach to Extracting Moral Intuitions from Text [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/924gq
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arxiv.org arxiv.org
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Ziems, C., He, B., Soni, S., & Kumar, S. (2020). Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis. ArXiv:2005.12423 [Physics]. http://arxiv.org/abs/2005.12423
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 12, 2020, from https://twitter.com/robertoge/status/1270710014135676928
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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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