Science

source: Michener, 2006 Ecoinformatics.

Open Science

Source: Wolkovich et al. GCB 2012.

Source: PLOS, 2007

R Open Science

Open Science needs open source tools

Source: Revolution Analytics, 2010, Nature editorial, 2012

Development team

Carl Boettiger  🔗

Karthik Ram  🔗

Scott Chamberlain  🔗

Advisory team

Duncan Temple Lang 🔗

Hadley Wickham  🔗

JJ Allaire 🔗

Bertram Ludascher 🔗

Matt Jones 🔗

API keys can be stored in a users.rprofile

 
	options(MendeleyKey = "uf5daib7wyil7ag5buc")
	options(MendeleyPrivateKey = "faj2os5dyd7jop2fok6")
	options(PlosApiKey = "ef3vip9yak7od3hud4g")
	options(SpringerMetdataKey = "ri9hi7woc6jax4vaf8w")
	

Manage libraries and measure impact of research

groupDocInfo(mc, 530031, 4344945792)

$abstract
[1] "SUMMARY: Modern biological experiments create vast amounts of data which are geographically distributed. These datasets consist of petabytes of raw data and billions of documents. Yet to the best of our knowledge, a search engine technology that searches and cross-links all different data types in life sciences does not exist.....

$authors
$authors[[1]]
      forename        surname
   "Dominic S" 	"L\xfctjohann" 
# ....
	

# Get the URL for a data file
dryaddat <- download_url("10255/dryad.1759")

# Get a file given the URL
file <- dryad_getfile(dryaddat)

Tracks altmetrics across various sources such as GitHub, Total impact, CitedIn, CiteULike, Stackoverflow.

GitHub(userorg = "ropensci", repo = "rmendeley")

totimp(id = "10.5061/dryad.8671")

stackexchange(ids = 16632)

Using Figshare's new API, it is now possible to share figures, data, and any other object generated in R directly to one's figshare account.

> figshare(data)
# code isn't ready yet but once it is, it will return a persistent identifier

A multi-institution consortium to build infrastructure for open science

DataONE creates all the necessary components to support persistent and secure access to earth observation data.

DataONE's upcoming R package will allow users to submit and access data to/from member nodes directly from the console.

Provenance is important for reproducibility

Source: Modified from original version by James Cheney, University of Edinburgh.

Making R provenance aware

Taking an approach similar to knitr where a user can track workflow provenance using hooks.

Using XML to track metadata and maintain provenance traces across runs

Ideas?


GitHub + Science

R packages early in development can easily be tested, rapidly deployed from GitHub using devtools and revised before submitting to a persistent repository such as CRAN.

library(devtools)
install_github("RMendeley", "ropensci")

R + collaborative writing

knitr + Markdown

Xie Y (2012). knitr: A general-purpose package for dynamic report generation in R.

knitr + Markdown + GitHub

GitHub automatically renders Markdown and even provides syntax highlighting

knitr + Markdown + GitHub = executible paper

knitr + Markdown + GitHub = pre publication review

citet(c(Halpern2006 = "10.1111/j.1461-0248.2005.00827.x"))
# then cite in your markdown file
citet("Halpern2006")


# or read citations from a bibtex file which can be automatically generated and updated from services like Mendeley

bib <- read.bibtex("example.bib")
# then cite inline
citet(bib[["knitr"]])

R talks to Dropbox, Amazon S3, Wordpress, img.ur, and elsewhere in the

Various tools in R can drive data reuse, new collaborations, new tools, novel visualization, and keep the entire research process transparent through open notebooks.