248 Matching Annotations
  1. May 2020
  2. Apr 2020
  3. Feb 2020
  4. Dec 2019
  5. Nov 2019
  6. Sep 2019
  7. Aug 2019
  8. Jul 2019
  9. May 2019
    1. Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.

      Etienne's blog! Amazing resource for OSINT; particularly focused on technical attacks.

  10. Feb 2019
    1. set; if this is higher, the tree 2can be considered to fit the data less well

      To test the fit between data and more than one alternative tree, you can just do a bootstrap analysis, and map the results on a neighbour-net splits graph based on the same data.

      Note that the phangorn library includes functions to transfer information between trees/tree samples and trees and networks:<br/> Schliep K, Potts AJ, Morrison DA, Grimm GW. 2017. Intertwining phylogenetic trees and networks. Methods in Ecology and Evolution (DOI:10.1111/2041-210X.12760.)[http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12760/full] – the basic functions and script templates are provided in the associated vignette.

  11. Dec 2018
    1. Ethnographic findings are not privileged, just particular: another country heard from. To regard them as anything more (or anything less) than that distorts both them and their implications, which are far profounder than mere primitivity, for social theory.

      This tension exists in HCI as well.

      Interpreted data vs empirical data and how each is systematically analyzed.

  12. Sep 2018
  13. May 2018
  14. Apr 2018
  15. Dec 2017
  16. Nov 2017
  17. Sep 2017
    1. Organizations such as Code for America (CfA) rallied support by positioning civic hacking as a mode of direct partici-pation in improving structures of governance. However, critics objected to the involve-ment of corporations in civic hacking as well as their dubious political alignment and non-grassroots origins. Critical historian Evgeny Morozov (2013a) suggested that “civic hacker” is an apolitical category imposed by ideologies of “scientism” emanating from Silicon Valley. Tom Slee (2012) similarly described the open data movement as co-opted and neoliberalist.
  18. Jul 2017
  19. May 2017
  20. Dec 2016
  21. Sep 2016
    1. Activities such as time spent on task and discussion board interactions are at the forefront of research.

      Really? These aren’t uncontroversial, to say the least. For instance, discussion board interactions often call for careful, mixed-method work with an eye to preventing instructor effect and confirmation bias. “Time on task” is almost a codeword for distinctions between models of learning. Research in cognitive science gives very nuanced value to “time spent on task” while the Malcolm Gladwells of the world usurp some research results. A major insight behind Competency-Based Education is that it can allow for some variance in terms of “time on task”. So it’s kind of surprising that this summary puts those two things to the fore.

  22. Apr 2016
  23. Mar 2016
  24. Feb 2016
    1. Since its start in 1998, Software Carpentry has evolved from a week-long training course at the US national laboratories into a worldwide volunteer effort to improve researchers' computing skills. This paper explains what we have learned along the way, the challenges we now face, and our plans for the future.

      http://software-carpentry.org/lessons/<br> Basic programming skills for scientific researchers.<br> SQL, and Python, R, or MATLAB.

      http://www.datacarpentry.org/lessons/<br> Managing and analyzing data.

  25. Jan 2016
    1. 50 Years of Data Science, David Donoho<br> 2015, 41 pages

      This paper reviews some ingredients of the current "Data Science moment", including recent commentary about data science in the popular media, and about how/whether Data Science is really di fferent from Statistics.

      The now-contemplated fi eld of Data Science amounts to a superset of the fi elds of statistics and machine learning which adds some technology for 'scaling up' to 'big data'.

    1. paradox of unanimity - Unanimous or nearly unanimous agreement doesn't always indicate the correct answer. If agreement is unlikely, it indicates a problem with the system.

      Witnesses who only saw a suspect for a moment are not likely to be able to pick them out of a lineup accurately. If several witnesses all pick the same suspect, you should be suspicious that bias is at work. Perhaps these witnesses were cherry-picked, or they were somehow encouraged to choose a particular suspect.

  26. Jan 2014