Question: I'm trying to follow some of the best practices of the "open science" movement. In my thesis, I've performed all of the analyses in R (a non-proprietary, open-source program for analyzing data), and my datasets are in the non-proprietary CSV format. 

I would like to be as transparent as possible, by sharing my datasets and R analysis/code files with my thesis committee, and ultimately with the public once my thesis is finalized and placed in a repository. How can I best do this?

I was thinking about uploading my files to the Open Science Framework (<http://osf.io>) and citing them with a regular HTTPS link. Once my thesis is finalized, I would then "freeze" them on the OSF website (as I understand, this would prevent post-hoc changes), then get a DOI that points to the frozen files and cite that.

Are there any better options?

Answer: 
First, best compliments for your intent on open and reproducible research! 

Your code and datasets ought to bring you better visibility for your research. GitHub is a good alternative to publish your code. If your datasets involve elements of machine learning you may donate it to the UCI Machine Learning Repository.
Check [figshare](https://figshare.com). I have no complaints, but I still under the free quota. 

Recently, I came across more 3 interesting data repository:

* [The Dataverse project](http://dataverse.org/);
* [mlData](http://mldata.org/); and
* [OpenMl](http://www.openml.org/).