Open Access

Note: This post is cross-posted on Sandra Chung’s blog here. The rise of the unconference The Ecological Society of America meeting is holding its 98th annual meeting next year in Minneapolis, MN. Several thousand students and professionals in ecological science and education will gather to hear and read the latest work and ideas in ecology in the familiar poster and lecture formats that are the core of every major scientific conference. But a subset of these people will get a taste of something a little bit different: an unconference within the conference. ...

It can be a pain in the ass to get taxonomic names. For example, I sometimes need to get all the Class names for a set of species. This is a relatively easy problem using the ITIS API (example below). The much harder problem is getting all the taxonomic names downstream. ITIS doesn’t provide an API method for this - well, they do (getHirerachyDownFromTSN), but it only provides direct children (e.g., the genera within a tribe - but it won’t give all the species within each genus). ...

UPDATE: In response to Jarrett’s query I laid out a separate use case in which you may want to query by higher taxonomic rankings than species. See below. In addition, added examples of querying by location in reply to comments by seminym. We have been working on an R package to get GBIF data from R, with the stable version available through CRAN, and the development version available on GitHub at https://github.com/rgbif ...

We (rOpenSci) started a repo to wrap the API for VertNet, an open access online database of vertebrate specimen records across many collection holders. Find the open source code here - please contribute if you are so inclined. We had a great Google Summer of Code student, Vijay Barve contributing to the repo this summer, so it is getting close to being CRAN-able. Most of the functions in the repo get you the raw data, but there were no functions to visualize the data. Since much of the data records of latitude and longitude data, maps are a natural visualization to use. ...

The problem: There are a lot of figures in published papers in the scholarly literature, like the below, from (Attwood et. al. 2012)): At some point, a scientist wants to ask a question for which they can synthesize the knowledge on that question by collecting data from the published literature. This often requires something like the following workflow: Search for relevant papers (e.g., via Google Scholar). Collect the papers. Decide which are appropriate for inclusion. Collect data from the figures using software on a native application. Examples include GraphClick and ImageJ. Proof data. Analyze data & publish paper. This workflow needs revamping, particularly in step number 3 - collecting the data. This data remains private, moving from one closed source (original publication) to another (personal computer). We can surely do better. ...

Metadata! Metadata is very cool. It’s super hot right now - everybody is talking about it. Okay, maybe not everyone, but it’s an important part of archiving scholarly work. We are working on a repo on GitHub rmetadata to be a one stop shop for querying metadata from around the web. Various repos on GitHub we have started - rpmc, rdatacite, rdryad, rpensoft, rhindawi - will at least in part be folded into rmetadata. As a start we are writing functions to hit any metadata services that use the OAI-PMH: “Open Archives Initiative Protocol for Metadata Harvesting” framework. OAI-PMH has six methods (or verbs as they are called) for data harvesting that are the same across different metadata providers: ...