- Sep 2024
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www.theguardian.com www.theguardian.com
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Die Fossilindustrie finanziert seit Jahrzehten Universitäten und fördert damit Publikationen in ihrem Interesse, z.B. zu false solutions wie #CCS. Hintergrundbericht anlässlich einer neuen Studie: https://www.theguardian.com/business/article/2024/sep/05/universities-fossil-fuel-funding-green-energy
Studie: https://doi.org/10.1002/wcc.904
Tags
- Data for Progress
- climate obstructionism in.higher education
- Fossil fuel industry influence in higher education: A review and a research agenda
- Princeton University’s Carbon Mitigation Initiative
- Jake Lowe
- Campus Climate Network
- Fossilindustrie
- American Petroleum Institute
- Exxon
- Jennie Stephens
- Geoffrey Supran
- negative emission technologies
- by: Dharma Noor
- disinformation
- Emily Eaton
- BP
- MIT Energy Initiative
- Accountable Allies: The Undue Influence of Fossil Fuel Money in Academia
- Favourability towards natural gas relates to funding source of university energy centres
Annotators
URL
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- May 2024
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www.theguardian.com www.theguardian.com
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In den Ländern, die sich in Paris 2015 einer Initiative gegen das Verbrennen von nicht genutztem Erdgas (flaring) angeschlossen hatten, wird das Verbrennen mit offener Flamme oft nur durch Verbrennung in geschlossenen Anlagen ersetzt, wie eine investigative journalistische Recherche ergab. Die Menge der Emissionen sinkt dadurch nicht wesentlich, aber diese Anlagen sind für Satelliten nicht äußerlich erkennbar. https://www.theguardian.com/environment/2024/may/02/methane-emissions-gas-flaring-hidden-satellite-monitors-oil-gas
Ressourcen für die Recherche zu Methan-Emissionen: https://gijn.org/resource/new-tools-investigate-methane-emissions/
Tags
- data source: Visible Infrared Imaging Radiometer Suite
- NGO: Earthworks
- institution: Journalismfund Europe
- expert: Zubin Bamji
- 2024-05-02
- by: Tim Brown
- process: methane reduction
- expert: Tim Doty
- institution: World Bank
- topic: flaring
- institution: Zero Routine Flaring 2030 initiative
- institution: Carbon Mapper
- by: Christina Last
- event: Investigative Research about methane emissions April 2024
- actor: ArcelorMittal
- actor: Fulcrum Energy Capital Funds
- NGO: Arena Climate Network
- NGO: Carbon Mapper
- actor: Ineos
- expert: Eric Kort
Annotators
URL
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- Jan 2023
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ssha.org ssha.org
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hypothes.is hypothes.is假设1
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个人学习可能取决于他人行为的主张突出了将学习环境视为一个涉及多个互动参与者的系统的重要性
Tags
Annotators
URL
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- Nov 2022
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beepb00p.xyz beepb00p.xyz
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I also think being able to self-host and export parts of your data to share with others would be great.
This might be achievable through Holochain application framework. One promising project built on Holochain is Neighbourhoods. Their "Social-Sensemaker Architecture" across "neighbourhoods" is intriguing
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- Dec 2021
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Edge computing is an emerging new trend in cloud data storage that improves how we access and process data online. Businesses dealing with high-frequency transactions like banks, social media companies, and online gaming operators may benefit from edge computing.
Edge Computing: What It Is and Why It Matters0 https://en.itpedia.nl/2021/12/29/edge-computing-what-it-is-and-why-it-matters/ Edge computing is an emerging new trend in cloud data storage that improves how we access and process data online. Businesses dealing with high-frequency transactions like banks, social media companies, and online gaming operators may benefit from edge computing.
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- Oct 2021
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www.mdpi.com www.mdpi.com
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Mazumdar, S., & Thakker, D. (2020). Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet, 12(12), 210. https://doi.org/10.3390/fi12120210
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- Sep 2021
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Taylor, L. (2021). The Venezuelan health-care workers secretly collecting COVID stats. Nature, 597(7874), 20–21. https://doi.org/10.1038/d41586-021-02276-1
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- Jul 2021
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journals.sagepub.com journals.sagepub.com
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Sheetal, A., Feng, Z., & Savani, K. (2020). Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors. Psychological Science, 31(10), 1222–1235. https://doi.org/10.1177/0956797620959594
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twitter.com twitter.comTwitter1
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Leah Keating on Twitter: “This work with @DavidJPOS and @gleesonj is now on arXiv (https://t.co/hxjZnCmKcM): ‘A multi-type branching process method for modelling complex contagion on clustered networks’ Here is a quick overview of our paper: (1/6) https://t.co/3jQ2flhk71” / Twitter. (n.d.). Retrieved July 23, 2021, from https://twitter.com/leahakeating/status/1418150117106978816
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- Mar 2021
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arxiv.org arxiv.org
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Gupta, Prateek, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, et al. ‘COVI-AgentSim: An Agent-Based Model for Evaluating Methods of Digital Contact Tracing’. ArXiv:2010.16004 [Cs], 29 October 2020. http://arxiv.org/abs/2010.16004.
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McCabe, Stefan, Leo Torres, Timothy LaRock, Syed Arefinul Haque, Chia-Hung Yang, Harrison Hartle, and Brennan Klein. ‘Netrd: A Library for Network Reconstruction and Graph Distances’. ArXiv:2010.16019 [Physics], 29 October 2020. http://arxiv.org/abs/2010.16019.
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- Sep 2020
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iriss.stanford.edu iriss.stanford.edu
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2020 Conference on Computational Sociology | IRiSS. (n.d.). Retrieved 30 September 2020, from https://iriss.stanford.edu/css/conferences/2020-conference-computational-sociology
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Romanini, Daniele, Sune Lehmann, and Mikko Kivelä. ‘Privacy and Uniqueness of Neighborhoods in Social Networks’. ArXiv:2009.09973 [Physics], 21 September 2020. http://arxiv.org/abs/2009.09973.
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www.visualcapitalist.com www.visualcapitalist.com
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Ali, A. (2020, August 28). Visualizing the Social Media Universe in 2020. Visual Capitalist. https://www.visualcapitalist.com/visualizing-the-social-media-universe-in-2020/
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- Aug 2020
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Holtz, D., Zhao, M., Benzell, S. G., Cao, C. Y., Rahimian, M. A., Yang, J., Allen, J., Collis, A., Moehring, A., Sowrirajan, T., Ghosh, D., Zhang, Y., Dhillon, P. S., Nicolaides, C., Eckles, D., & Aral, S. (2020). Interdependence and the cost of uncoordinated responses to COVID-19. Proceedings of the National Academy of Sciences, 117(33), 19837–19843. https://doi.org/10.1073/pnas.2009522117
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Young, J.-G., Cantwell, G. T., & Newman, M. E. J. (2020). Robust Bayesian inference of network structure from unreliable data. ArXiv:2008.03334 [Physics, Stat]. http://arxiv.org/abs/2008.03334
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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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www.youtube.com www.youtube.comYouTube1
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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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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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arxiv.org arxiv.org
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Gupta, H., & Porter, M. A. (2020). Mixed Logit Models and Network Formation. ArXiv:2006.16516 [Physics, Stat]. http://arxiv.org/abs/2006.16516
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arxiv.org arxiv.org
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Lovato, J., Allard, A., Harp, R., & Hébert-Dufresne, L. (2020). Distributed consent and its impact on privacy and observability in social networks. ArXiv:2006.16140 [Physics]. http://arxiv.org/abs/2006.16140
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- Jun 2020
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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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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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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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Murphy, C., Laurence, E., & Allard, A. (2020). Deep learning of stochastic contagion dynamics on complex networks. ArXiv:2006.05410 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.05410
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Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo Chambers on Social Media: A comparative analysis. ArXiv:2004.09603 [Physics]. http://arxiv.org/abs/2004.09603
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dataforgood.fb.com dataforgood.fb.com
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Our Work on COVID-19. (n.d.). Facebook Data for Good. Retrieved April 20, 2020, from https://dataforgood.fb.com/docs/covid19/
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Eroglu, D. (2020). Revealing Dynamics, Communities, and Criticality from Data. Physical Review X, 10(2). https://doi.org/10.1103/PhysRevX.10.021047
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Cantwell, G. T., Liu, Y., Maier, B. F., Schwarze, A. C., Serván, C. A., Snyder, J., & St-Onge, G. (2020). Thresholding normally distributed data creates complex networks. Physical Review E, 101(6), 062302. https://doi.org/10.1103/PhysRevE.101.062302
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- May 2020
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Rosenblatt, S. F., Smith, J. A., Gauthier, G. R., & Hébert-Dufresne, L. (2020). Immunization Strategies in Networks with Missing Data. ArXiv:2005.07632 [Physics, q-Bio]. http://arxiv.org/abs/2005.07632
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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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www.thelancet.com www.thelancet.com
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Rybniker, J., & Fätkenheuer, G. (2020). Importance of precise data on SARS-CoV-2 transmission dynamics control. The Lancet Infectious Diseases, S1473309920303595. https://doi.org/10.1016/S1473-3099(20)30359-5
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Dahl Fitjar, R. (2020, May 9). The density and connectedness of cities now appear as weaknesses. LSE Business Review. https://blogs.lse.ac.uk/businessreview/2020/05/09/the-density-and-connectedness-of-cities-now-appear-as-weaknesses/
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www.nature.com www.nature.com
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Vespignani, A., Tian, H., Dye, C. et al. Modelling COVID-19. Nat Rev Phys (2020). https://doi.org/10.1038/s42254-020-0178-4
Tags
- superspreading
- containment measures
- modeling
- public health
- COVID-19
- antibody testing
- emergency
- computational modeling
- contact tracing
- transmission dynamics
- mathematics
- open data
- war time
- prediction
- forecast
- lang:en
- complex network
- policy
- pharmaceutical
- epidemiology
- quarentine
- intervention
- isolation
- infection
- is:article
- challenge
- China
Annotators
URL
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arxiv.org arxiv.org
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Qian, Y., Expert, P., Panzarasa, P., & Barahona, M. (2020). Geometric graphs from data to aid classification tasks with graph convolutional networks. ArXiv:2005.04081 [Physics, Stat]. http://arxiv.org/abs/2005.04081
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www.economist.com www.economist.com
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Countries are using apps and data networks to keep tabs on the pandemic. (2020 March 26). The Economist. https://www.economist.com/briefing/2020/03/26/countries-are-using-apps-and-data-networks-to-keep-tabs-on-the-pandemic?fsrc=newsletter&utm_campaign=the-economist-today&utm_medium=newsletter&utm_source=salesforce-marketing-cloud&utm_term=2020-05-07&utm_content=article-link-1
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www.tandfonline.com www.tandfonline.com
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Fenton, N. E., Neil, M., Osman, M., & McLachlan, S. (2020). COVID-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing. Journal of Risk Research, 0(0), 1–4. https://doi.org/10.1080/13669877.2020.1756381
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link.aps.org link.aps.org
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Krönke, J., Wunderling, N., Winkelmann, R., Staal, A., Stumpf, B., Tuinenburg, O. A., & Donges, J. F. (2020). Dynamics of tipping cascades on complex networks. Physical Review E, 101(4), 042311. https://doi.org/10.1103/PhysRevE.101.042311
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- Apr 2020
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security.googleblog.com security.googleblog.com
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Our approach strikes a balance between privacy, computation overhead, and network latency. While single-party private information retrieval (PIR) and 1-out-of-N oblivious transfer solve some of our requirements, the communication overhead involved for a database of over 4 billion records is presently intractable. Alternatively, k-party PIR and hardware enclaves present efficient alternatives, but they require user trust in schemes that are not widely deployed yet in practice. For k-party PIR, there is a risk of collusion; for enclaves, there is a risk of hardware vulnerabilities and side-channels.
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Jiang, C., Gao, J., & Magdon-Ismail, M. (2020). Inferring Degrees from Incomplete Networks and Nonlinear Dynamics. ArXiv:2004.10546 [Physics]. http://arxiv.org/abs/2004.10546
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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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haveibeenpwned.com haveibeenpwned.com
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github.com github.com
- Feb 2020
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www.geospatialworld.net www.geospatialworld.net
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GIS and Spatial Analytics Market: Global Size
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- Sep 2017
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digital.hbs.edu digital.hbs.edu
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Marc Rysman - BU
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- Apr 2016
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googleguacamole.wordpress.com googleguacamole.wordpress.com
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followed a TAGS Explorer of a conference hashtag
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- Dec 2015
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mfeldstein.com mfeldstein.com
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increased investment in professional development and teaching-friendly tenure and promotion practices
Even those who adopt a taylorist model to education may understand that “it takes money to save money”.
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