123 Matching Annotations
  1. Dec 2023
  2. Jul 2023
  3. Jan 2023
    1. https://www.complexityexplorer.org/courses/162-foundations-applications-of-humanities-analytics/segments/15630

      https://www.youtube.com/watch?v=HwkRfN-7UWI


      Seven Principles of Data Feminism

      • Examine power
      • Challenge power
      • Rethink binaries and hierarchies
      • Elevate emotion an embodiment
      • Embrace pluralism
      • Consider context
      • Make labor visible

      Abolitionist movement

      There are some interesting analogies to be drawn between the abolitionist movement in the 1800s and modern day movements like abolition of police and racial justice, etc.


      Topic modeling - What would topic modeling look like for corpuses of commonplace books? Over time?


      wrt article: Soni, Sandeep, Lauren F. Klein, and Jacob Eisenstein. “Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers.” Journal of Cultural Analytics 6, no. 1 (January 18, 2021). https://doi.org/10.22148/001c.18841. - Brings to mind the difference in power and invisible labor between literate societies and oral societies. It's easier to erase oral cultures with the overwhelm available to literate cultures because the former are harder to see.

      How to find unbiased datasets to study these?


      aspirational abolitionism driven by African Americans in the 1800s over and above (basic) abolitionism

  4. Nov 2022
    1. with Prisma you never create application models in your programming language by manually defining classes, interfaces, or structs. Instead, the application models are defined in your Prisma schema
  5. Aug 2022
  6. Apr 2022
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  8. Feb 2022
    1. Adam Kucharski. (2022, January 18). Below analysis was two years ago (https://bbc.co.uk/news/health-51148303). As well as providing an early warning about the COVID threat, it’s a good illustration of what is often an under-appreciated point: If we want to make sense of epidemic data and dynamics in real-time, we need models… 1/ https://t.co/ZdpzOq3Bzp [Tweet]. @AdamJKucharski. https://twitter.com/AdamJKucharski/status/1483368504392880128

  9. Jan 2022
    1. Zimmerman, M. I., Porter, J. R., Ward, M. D., Singh, S., Vithani, N., Meller, A., Mallimadugula, U. L., Kuhn, C. E., Borowsky, J. H., Wiewiora, R. P., Hurley, M. F. D., Harbison, A. M., Fogarty, C. A., Coffland, J. E., Fadda, E., Voelz, V. A., Chodera, J. D., & Bowman, G. R. (2021). SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nature Chemistry, 13(7), 651–659. https://doi.org/10.1038/s41557-021-00707-0

    1. Budak, C., Soroka, S., Singh, L., Bailey, M., Bode, L., Chawla, N., Davis-Kean, P., Choudhury, M. D., Veaux, R. D., Hahn, U., Jensen, B., Ladd, J., Mneimneh, Z., Pasek, J., Raghunathan, T., Ryan, R., Smith, N. A., Stohr, K., & Traugott, M. (2021). Modeling Considerations for Quantitative Social Science Research Using Social Media Data. PsyArXiv. https://doi.org/10.31234/osf.io/3e2ux

    1. Prof. Debby Bogaert 💙. (2021, December 20). @chrischirp NL is slightly behind the UK re #omicron and based lockdown on a.o. This model (source @MarionKoopmans). Despite uncertainty the ‘continue as is’ effect on ICU beds occupied (red) is chilling. Green model is with lockdown after and blue is before Christmas. Decisiveness matters! Https://t.co/2IODZGnNJ6 [Tweet]. @DebbyBogaert. https://twitter.com/DebbyBogaert/status/1472845880411758592

    1. Helen Branswell. (2022, January 11). 1. #Omicron’s takeover was stunningly rapid and is now nearly complete, at least in the U.S. The latest “Nowcast” from @CDCgov (which uses recent data to model what’s happen now) suggests most of what is circulating here now is omicron. Https://t.co/6w3e8Ut5NW [Tweet]. @HelenBranswell. https://twitter.com/HelenBranswell/status/1480970453313277954

    1. Keeling, M. J., Brooks-Pollock, E., Challen, R. J., Danon, L., Dyson, L., Gog, J. R., Guzman-Rincon, L., Hill, E. M., Pellis, L. M., Read, J. M., & Tildesley, M. (2021). Short-term Projections based on Early Omicron Variant Dynamics in England. (p. 2021.12.30.21268307). https://doi.org/10.1101/2021.12.30.21268307

  10. Dec 2021
    1. Art Poon. (2021, November 28). Our first https://filogeneti.ca/CoVizu update with B.1.1.529. As expected, number of mutations is well over molecular clock prediction (~13 diffs). Relatively low numbers of identical genomes implies large number of unsampled infections. We update every two days from GISAID. https://t.co/m8w2CjL1c0 [Tweet]. @art_poon. https://twitter.com/art_poon/status/1465001066194481162

  11. Nov 2021
    1. Jeffrey Barrett. (2021, October 19). Proportion of AY.4.2 (now on http://covid19.sanger.ac.uk) has been steadily increasing in England, which is a pattern that is quite different from other AY lineages. Several of them rose when there was still Alpha to displace, but none has had a consistent advantage vs other Delta. Https://t.co/mD5gQzKxgV [Tweet]. @jcbarret. https://twitter.com/jcbarret/status/1450408485829718039

  12. Sep 2021
  13. Jul 2021
    1. 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

  14. Jun 2021
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  19. Oct 2020
  20. Sep 2020
  21. Aug 2020
    1. Ray, E. L., Wattanachit, N., Niemi, J., Kanji, A. H., House, K., Cramer, E. Y., Bracher, J., Zheng, A., Yamana, T. K., Xiong, X., Woody, S., Wang, Y., Wang, L., Walraven, R. L., Tomar, V., Sherratt, K., Sheldon, D., Reiner, R. C., Prakash, B. A., … Consortium, C.-19 F. H. (2020). Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S. MedRxiv, 2020.08.19.20177493. https://doi.org/10.1101/2020.08.19.20177493

    1. Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., ... & Visconti, A. (2020). Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine, 1-4.

  22. Jul 2020
  23. Jun 2020
    1. 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

    1. Kempfert, K., Martinez, K., Siraj, A., Conrad, J., Fairchild, G., Ziemann, A., Parikh, N., Osthus, D., Generous, N., Del Valle, S., & Manore, C. (2020). Time Series Methods and Ensemble Models to Nowcast Dengue at the State Level in Brazil. ArXiv:2006.02483 [q-Bio, Stat]. http://arxiv.org/abs/2006.02483

  24. May 2020
  25. Apr 2020
  26. Jan 2020
  27. Nov 2018
    1. For the second, we could try to detect inconsistencies, eitherby inspecting samples of the class hierarchy

      Yes, that's what I do when doing quality work on the taxonomy (with the tool wdtaxonomy)

    2. Possible relations between Items

      This only includes properties of data-type item?! It should be made more clear because the majority of Wikidata classes has other data types.

    3. A KG typically spans across several domains and is built on topof a conceptual schema, orontology, which defines what types of entities (classes) are allowed inthe graph, alongside the types ofpropertiesthey can have

      Wikidata differs from typical KG as it is not build on top of classes (entity types). Any item (entity) can be connected by any property. Wikidata's only strict "classes" in the sense of KG classes are its data types (item, lemma, monolingual string...).

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  28. Aug 2017
  29. Jun 2015
    1. The comparison between the model and the experts is based on the species distribution models (SMDs), not on actual species occurrences, so the observed difference could be due to weakness in the SDM predictions rather than the model outperforming the experts. The explanation for this choice in Footnote 4 is reasonable, but I wonder if it could be addressed by rarifying the sampling appropriately.