236 Matching Annotations
  1. Dec 2023
  2. Jan 2023
    1. 3.1 Guest Lecture: Lauren Klein » Q&A on "What is Feminist Data Science?"<br /> https://www.complexityexplorer.org/courses/162-foundations-applications-of-humanities-analytics/segments/15631

      https://www.youtube.com/watch?v=c7HmG5b87B8

      Theories of Power

      Patricia Hill Collins' matrix of domination - no hierarchy, thus the matrix format

      What are other broad theories of power? are there schools?

      Relationship to Mary Parker Follett's work?

      Bright, Liam Kofi, Daniel Malinsky, and Morgan Thompson. “Causally Interpreting Intersectionality Theory.” Philosophy of Science 83, no. 1 (January 2016): 60–81. https://doi.org/10.1086/684173.

      about Bayesian modeling for intersectionality


      Where is Foucault in all this? Klein may have references, as I've not got the context.


      How do words index action? —Laura Klein


      The power to shape discourse and choose words - relationship to soft power - linguistic memes

      Color Conventions Project


      20:15 Word embeddings as a method within her research


      General result (outside of the proximal research) discussed: women are more likely to change language... references for this?


      [[academic research skills]]: It's important to be aware of the current discussions within one's field. (LK)


      36:36 quantitative imperialism is not the goal of humanities analytics, lived experiences are incredibly important as well. (DK)

    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

    1. We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.

      Les mesures de qualite d'un modele de machine learning

  3. Nov 2022
    1. Dr. Miho Ohsaki re-examined workshe and her group had previously published and confirmed that the results are indeed meaningless in the sensedescribed in this work (Ohsaki et al., 2002). She has subsequently been able to redefine the clustering subroutine inher work to allow more meaningful pattern discovery (Ohsaki et al., 2003)

      Look into what Dr. Miho Ohsaki changed about the clustering subroutine in her work and how it allowed for "more meaningful pattern discovery"

    2. Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

      Look into Eamonn Keogh's papers that won "best paper awards"

    1. Of course, despite what the "data is the new oil" vendors told you back in the day, you can’t just chuck raw data in and assume that magic will happen on it, but that’s a rant for another day ;-)

      Love this analogy - imagine chucking some crude into a black box and hoping for ethanol at the other end. Then, when you end up with diesel you have no idea what happened.

    2. Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.

      absolutely right - there's also a data provenance angle here - it is useful to be able to point to a data point that is 5 or 6 transformations from the raw input and be able to say "yes I know exactly where this came from, here are all the steps that came before"

    1. okay so remind you what is a sheath so a sheep is something that allows me to 00:05:37 translate between physical sources or physical realms of data and physical regions so these are various 00:05:49 open sets or translation between them by taking a look at restrictions overlaps 00:06:02 and then inferring

      Fixed typos in transcript:

      Just generally speaking, what can I do with this sheaf-theoretic data structure that I've got? Okay, [I'll] remind you what is a sheaf. A sheaf is something that allows me to translate between physical sources or physical realms of data [in the left diagram] and the data that are associated with those physical regions [in the right diagram]

      So these [on the left] are various open sets [an example being] simplices in a [simplicial complex which is an example of a] topological space.

      And these [on the right] are the data spaces and I'm able to make some translation between [the left and the right diagrams] by taking a look at restrictions of overlaps [a on the left] and inferring back to the union.

      So that's what a sheaf is [regarding data structures]. It's something that allows me to make an inference, an inferential machine.

    1. CEO, Mike Tung was on Data science podcast. Seems to be solving problem that Google search doesn't; how seriously should you take the results that come up? What confidence do you have in their truth or falsity?

  4. Aug 2022
  5. Jul 2022
  6. Apr 2022
    1. Dr Nisreen Alwan 🌻. (2020, March 14). Our letter in the Times. ‘We request that the government urgently and openly share the scientific evidence, data and modelling it is using to inform its decision on the #Covid_19 public health interventions’ @richardhorton1 @miriamorcutt @devisridhar @drannewilson @PWGTennant https://t.co/YZamKCheXH [Tweet]. @Dr2NisreenAlwan. https://twitter.com/Dr2NisreenAlwan/status/1238726765469749248

  7. Mar 2022
    1. ReconfigBehSci on Twitter: ‘@alexdefig are you really going to claim that responses to the introduction of passports on uptake across 4 other countries are evidentially entirely irrelevant to whether or not passports are justified or not?’ / Twitter. (n.d.). Retrieved 31 March 2022, from https://twitter.com/SciBeh/status/1444358068280565764

    1. Learn Data Science from IIT Madras faculty & Industry experts and earn a Data Science certification from India's best Engineering College. Become a Data Scientist through multiple data Science courses covered in this 7-month data science certification program with hands-on exercises & Project work.

      This Data Science Course is offered by Intellipaat in collaboration with IIT Madras (one of the renowned institutes in India) to help you master Data Science skills like Python, programming, Data Visualization, Statistical analysis and computing, Deep Learning, etc.

      Eager to step into the field of Data Science? Explore the Page now!

  8. Feb 2022
    1. Dr Emma Hodcroft. (2022, January 28). Just to clarify some confusion about what “Omicron” is. “Omicron” has always applied to the whole family (BA.1-3—We’ve known about them all since late-Nov/early-Dec). But the prevalence of BA.1 meant that it got shorthanded as ’Omicron’—That’s causing some confusion now!🥴 https://t.co/M4FwzGbluo [Tweet]. @firefoxx66. https://twitter.com/firefoxx66/status/1486999566725656576

  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. Dr Satoshi Akima. (2022, January 8). I’ve had people mention rising case numbers in Japan and South Korea. But let’s really put that rise into perspective. Nations that have early accepted that #COVIDisAirborne simply fair better https://t.co/KaoE26gQ0N [Tweet]. @ToshiAkima. https://twitter.com/ToshiAkima/status/1479724180840988673

  10. Dec 2021
    1. AIMOS. (2021, November 30). How can we connect #metascience to established #science fields? Find out at this afternoon’s session at #aimos2021 Remco Heesen @fallonmody Felipe Romeo will discuss. Come join us. #OpenScience #OpenData #reproducibility https://t.co/dEW2MkGNpx [Tweet]. @aimos_inc. https://twitter.com/aimos_inc/status/1465485732206850054

  11. Nov 2021
    1. "The Guide to Social Science Data Preparation and Archiving is aimed at those engaged in the cycle of research, from applying for a research grant, through the data collection phase, and ultimately to preparation of the data for deposit in a public archive: " from tweet

  12. Oct 2021
  13. Sep 2021
  14. Aug 2021
  15. 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

  16. Jun 2021
    1. Woolf, K., McManus, I. C., Martin, C. A., Nellums, L. B., Guyatt, A. L., Melbourne, C., Bryant, L., Gogoi, M., Wobi, F., Al-Oraibi, A., Hassan, O., Gupta, A., John, C., Tobin, M. D., Carr, S., Simpson, S., Gregary, B., Aujayeb, A., Zingwe, S., … Pareek, M. (2021). Ethnic differences in SARS-CoV-2 vaccine hesitancy in United Kingdom healthcare workers: Results from the UK-REACH prospective nationwide cohort study [Preprint]. Public and Global Health. https://doi.org/10.1101/2021.04.26.21255788

    1. Green and Murphy,Renaissance Rhetoric; Plett,English Renaissance; Middleton,Memory Systems; British Library,Incunabula Short Title Catalogue. Green and Murphy were the primary source. Middleton and Plett, who compiled memorytreatises as a distinct category, allowed me to add extra titles to Green and Murphy’s listings. An Excel file containing the266 early modern treatises graphed here can be emailed upon request.

      Sources of data for this paper. I'd definitely love to get a copy of this Excel file. Might be worth expanding to other languages, countries, and timeperiods as well.

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  17. May 2021
  18. Apr 2021
    1. Mehdi Hasan. (2021, April 12). ‘Given you acknowledged...in March 2020 that Asian countries were masking up at the time, saying we shouldn’t mask up as well was a mistake, wasn’t it... At the time, not just in hindsight?’ My question to Dr Fauci. Listen to his very passionate response: Https://t.co/BAf4qp0m6G [Tweet]. @mehdirhasan. https://twitter.com/mehdirhasan/status/1381405233360814085

  19. Mar 2021
    1. DataBeers Brussels. (2020, October 26). ⏰ Our next #databeers #brussels is tomorrow night and we’ve got a few tickets left! Don’t miss out on some important and exciting talks from: 👉 @svscarpino 👉 Juami van Gils 👉 Joris Renkens 👉 Milena Čukić 🎟️ Last tickets here https://t.co/2upYACZ3yS https://t.co/jEzLGvoxQe [Tweet]. @DataBeersBru. https://twitter.com/DataBeersBru/status/1320743318234562561

    1. Cailin O’Connor. (2020, November 10). New paper!!! @psmaldino look at what causes the persistence of poor methods in science, even when better methods are available. And we argue that interdisciplinary contact can lead better methods to spread. 1 https://t.co/C5beJA5gMi [Tweet]. @cailinmeister. https://twitter.com/cailinmeister/status/1326221893372833793

  20. Feb 2021
  21. Jan 2021
  22. Dec 2020
  23. Oct 2020
    1. 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.
  24. Sep 2020
    1. Had it not been for the attentiveness of one person who went beyond the task of classifying galaxies into predetermined categories and was able to communicate this to the researchers via the online forum, what turned out to be important new phenomena might have gone undiscovered.

      Sometimes our attempts to improve data quality in citizen science projects can actually work against us. Pre-determined categories and strict regulations could prevent the reporting of important outliers.

  25. Aug 2020
  26. Jul 2020
  27. Jun 2020
  28. May 2020
    1. 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.

    1. 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

  29. Apr 2020