Why open data growth At rOpenSci we try to make it easier for people to use open data and contribute open data to the community. The question often arises: How much open data do we have? Another angle on this topic is: How much is open data growing? We provide access to dozens of data respositories through our various packages.

I’m looking for one or more graduate students to join my group next fall. In addition to the official add (below) I’d like to add a few extra thoughts. As Morgan Ernest noted in her recent ad, we have a relatively unique setup at Weecology in that we interact actively with members of the Ernest Lab. We share space, have joint lab meetings, and generally maintain a very close intellectual relationship.

We are very excited to announce the newest release of our EcoData Retriever software and the first release of a supporting R package, ecoretriever. If you’re not familiar with the EcoData Retriever you can read more here.

My BioNames project has been going for over a year now, but I hadn't gotten around to providing bulk access to the data I've been collecting and cleaning. I've gone some way towards fixing this. You can now grab a snapshot of the BioNames database as a Darwin Core Archive here.

This is guest post by Angelique Hjarding in response to discussion on this blog about the paper below.Thank you for highlighting our recent publication and for the very interesting comments. We wanted to take the opportunity to address some of the issues brought up in both your review and from reader comments. One of the most important issues that has been raised is the sharing of cleaned and vetted datasets.

A couple of weeks ago Eli Kintisch (@elikint) interviewed me for what turned out to be a great article on “Sharing in Science” for Science Careers. He also interviewed Titus Brown (@ctitusbrown) who has since posted the full text of his reply, so I thought I’d do the same thing. Definitely. Sharing code and data helps the scientific community make more rapid progress by avoiding duplicated effort and by facilitating more reproducible research.

I love experiments where the insight-to-time-taken ratio is high. This one pertains to exploring the coordination chemistry of the transition metal region of the periodic table; specifically the tetra-coordination of the series headed by Mn-Ni. Is the geometry tetrahedral, square planar, or other? One can get a statistical answer in about ten minutes. The (CCDC database) search definition required is shown above.

Editor’s note: This is a guest post by Matt Sundquist from Plot.ly. Ggplotly and Plotly’s R API let you make ggplot2 plots, add py$ggplotly(), and make your plots interactive, online, and drawn with D3. Let’s make some.1. Getting Started and Examples Here is Fisher’s iris data.library("ggplot2")ggiris <- qplot(Petal.Width, Sepal.Length, data = iris, color = Species)print(ggiris) Let’s make it in Plotly.

TL;DR By using bookmarklets and a central annotation store, we can build a system to annotate any biodiversity database, and display those annotations on those databases.A couple of weeks ago I was at GBIF meeting in Copenhagen, and there was a discussion about adding a new feature to the GBIF portal. The conversation went something like this: Resources are limited, and adding new features to a project can be difficult.

tl;dr: So far, I can’t see any principal difference between our three kinds of intellectual output: software, data and texts.   I admit I’m somewhat surprised that there appears to be a need to write this post in 2014. After all, this is not really the dawn of the digital age any more.