It's a nice feeling when work that one did ages ago seems relevant again. Markus Döring has been working on a new backbone classification of all the species which occur in taxonomic checklists harvested by GBIF. After building a new classification the obvious question arises "how does this compare to the previous GBIF classification?" A simple question, answering it however is a little tricky.

I spent last Friday and Saturday at ( Research in the 21st Century: Data, Analytics and Impact , hashtag #ReCon_15) in Edinburgh. Friday 19th was conference day, followed by a hackday at CodeBase. There's a Storify archive of the tweets so you can get a sense of the meeting. Sitting in the audience a few things struck me. No identifier wars, DOIs have won and are everywhere.

I've published a short note on my work on geophylogenies and GeoJSON in PLoS Currents Tree of Life : At the time of writing the DOI hasn't registered, so the direct link is here. There is a GitHub repository for the manuscript and code. I chose PLoS Currents Tree of Life because it is (supposedly) quick and cheap.

There are two things that make R such a wonderful programming environment - the vast number of packages to access, process and interpretdata, and the enthusiastic individuals and subcommunities (of which rOpenSci is a great example). One, of course, flows from the other:R programmers write R packages to provide language users with more features, which makes everyone’s jobs easier and (hopefully!)attracts more users and more contributions.

Below I sketch what I believe is a straightforward way GBIF could tackle the issue of annotating and cleaning its data. It continues a series of posts Annotating GBIF: some thoughts, Rethinking annotating biodiversity data, and More on annotating biodiversity data: beyond sticky notes and wikis on this topic. Let's simplify things a little and state that GBIF at present is essentially an aggregation of Darwin Core Archive files.

Following on from the discussion of the African chameleon data, I've started to explore Angelique Hjarding's data in more detail. The data is available from figshare (doi:10.6084/m9.figshare.1141858), so I've grabbed a copy and put it in github. Several things are immediately apparent. There is a lot of ungeoreferenced data. With a little work this could be geotagged and hence placed on a map.

Note to self for upcoming discussion with JournalMap. As of Monday August 25th, BioStor has 106,617 articles comprising 1,484,050 BHL pages. From the full text for these articles, I have extracted 45,452 distinct localities (i.e., geotagged with latitude and longitude). 15,860 BHL pages in BioStor pages have at least one geotag, these pages belong to 5,675 BioStor articles. In summary, BioStor has 5,675 full-text articles that are geotagged.

As announced on phylobabble I've started to revisit visualising large phylogenies, building on some work I did a couple of years ago (my how time flies). This time, there is actual code (see https://github.com/rdmpage/deep-tree) as well as a live demo http://iphylo.org/~rpage/deep-tree/demo/. You can see the amphibian tree below at http://iphylo.org/~rpage/deep-tree/demo/show.php?id=5369171e32b7a: You can upload or paste a tree (for now in

 Over the past few weeks there has been a sudden increase in the amount of financial data on scholarly communications in the public domain. This was triggered in large part by the Wellcome Trust releasing data on the prices paid for Article Processing Charges by the institutions it funds.

Today I managed to publish some data from a GitHub repository directly to GBIF. Within a few minutes (and with Tim Robertson on hand via Skype to debug a few glitches) the data was automatically indexed by GBIF and its maps updated. You can see the data I uploaded here. The data I uploaded came from this paper: This is the data I used to build the geophylogeny for Banza using Google Earth.