List of Posts

10 May 2014 » Working with Github

Weekend Links and Readings

Yesterday had a great session where I learned a lot about using git, and use of software like the peerJ and figshare for storing of files. In this post I present a workflow that just may work

What may Work

All scholarly articles, academic notes, etc go into figshare and all of them are filed with the github as github is one stop repository that can be relied on. Plus all goes into figshare for storage. Plus a lot of them will go into the institutional repository.

Right now I am working with the meta analysis. The stata files and the R files for the work can eminently be written and worked up in the atom editor and be prepared for the github and the figshare system. The internal computer based work can be done with Scrivener. All web based work can be done here (in this editor and shared with the ftp and other access)

I also have an access to the draft writing system that works very well with most complex writing and essays and other stuff. Draft plays very well with a range of writing environments including bibdesk. Markdown and bibdesk[1] is certainly the way to go as far as I see it.

For presentations I am going to use deck.js or reveal.js. Excellent stuff for putting together presentations for classwork. Needs a bit of tweaking but can be done. The advantage being the slides can be stored in github and then linked from github directly for people to view them on their browsers. Plus figshare can directly import them from github.

Can draft play well with git?

Point to explore.

Basically, with git, atom, and markdown, writing and storing school work is essentially a one stop solution, as everything can be sweetly synchronized.

08 May 2014 » Testing with Atom

This is a new post with atom

Atom is a great software. Quite difficult to work with but nevertheless useful. I think I am going to stick with my ghost and not change much. Too tough.

This is difficult to work with.

14 Jul 2013 » Relevance of Big Data in Health Policy Setting

Came across this article on big data mining that argues that while big data is great for business intelligence such as positioning of brands (say cereals) before a snowstorm, their assumptions that correlation == causation is a problematic one. By the same token, when you take big data in healthcare and analyze, these should not necessarily have connotations for health policy setting, as for policy you necessarily have to have a notion of cause and effect association. The other issue around that problem is the role of small numbers versus large numbers and the purpose for which these data are collected.

07 Jul 2013 » How to Blog with Jekyll

The posts are in the _posts directory - edit this post and

07 Jul 2013 » Productivity

Struggle with Scrivener, Markdown, and Github to Write Presentations and blogs for Class