Posts Tagged ‘R’

Software Carpentry Workshop at SCSU Python

Registration is now open for the workshop: 

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The Unix Shell

  • Files and directories
  • History and tab completion
  • Pipes and redirection
  • Looping over files
  • Creating and running shell scripts
  • Finding things
  • Reference…

Programming in Python

  • Using libraries
  • Working with arrays
  • Reading and plotting data
  • Creating and using functions
  • Loops and conditionals
  • Defensive programming
  • Using Python from the command line
  • Reference…

@software carpentry @scsu #python getting ready w @Gaurav Vaidya and @John Liu

Posted by InforMedia Services on Saturday, June 2, 2018


#Python Programming from @Software Carpentry at St. Cloud State University

Posted by InforMedia Services on Saturday, June 2, 2018

Jupyter is IDE

JSON file format where Jupiter data is stored. HMTL and Markdown (simplified HTML).


React OS (JS)


#git and #github

Posted by InforMedia Services on Sunday, June 3, 2018

more on Software Carpentry workshops on this iMS blog

Reproducibility Librarian

Reproducibility Librarian? Yes, That Should Be Your Next Job
Vicky Steeves (@VickySteeves) is the first Research Data Management and Reproducibility Librarian
Reproducibility is made so much more challenging because of computers, and the dominance of closed-source operating systems and analysis software researchers use. Ben Marwick wrote a great piece called ‘How computers broke science – and what we can do to fix it’ which details a bit of the problem. Basically, computational environments affect the outcome of analyses (Gronenschild et. al (2012) showed the same data and analyses gave different results between two versions of macOS), and are exceptionally hard to reproduce, especially when the license terms don’t allow it. Additionally, programs encode data incorrectly and studies make erroneous conclusions, e.g. Microsoft Excel encodes genes as dates, which affects 1/5 of published data in leading genome journals.
technology to capture computational environments, workflow, provenance, data, and code are hugely impactful for reproducibility.  It’s been the focus of my work, in supporting an open source tool called ReproZip, which packages all computational dependencies, data, and applications in a single distributable package that other can reproduce across different systems. There are other tools that fix parts of this problem: Kepler and VisTrails for workflow/provenance, Packrat for saving specific R packages at the time a script is run so updates to dependencies won’t break, Pex for generating executable Python environments, and o2r for executable papers (including data, text, and code in one).
plugin for Jupyter notebooks), and added a user interface to make it friendlier to folks not comfortable on the command line.

I would also recommend going to conferences:

more on big data in an academic library in this IMS blog
academic library collection data visualization

more on library positions in this IMS blog:

on university library future:

librarian versus information specialist