The Ecology Hackathon Almost one year ago now, ecologists filled a room for the “Ecology Hackathon: Developing R Packages for Accessing, Synthesizing and Analyzing Ecological Data” that was co-organised by rOpenSci Fellow, Nick Golding and Methods in Ecology and Evolution. This hackathon was part of the “Ecology Across Borders” Joint Annual Meeting 2017 of BES, GfÖ, NecoV, and EEF in Ghent.
A new version of pdftools has been released to CRAN. Go get it while it’s hot: install.packages("pdftools") This version has two major improvements: low level text extraction and encoding improvements. About PDF textboxes A pdf document may seem to contain paragraphs or tables in a viewer, but this is not actually true.
I never really thought I would write an R package. I use R pretty casually. Then, this year, I was invited to participate during the last week of the Analytical Paleobiology short course, an intensive month-long experience in quantitative paleontology. I was thrilled to be invited.
🎤 Dan Sholler, rOpenSci Postdoctoral Fellow 🕘 Tuesday, December 18, 2018, 10-11AM PST; 7-8PM CET (find your timezone) ☎️ Details for joining the Community Call. Everyone is welcome. No RSVP needed. Researchers use open source software for the capabilities it provides, such as streamlined data access and analysis and interoperability with other pieces of the scientific computing ecosystem.
We’ve just released a new version of rnoaa with A LOT of changes. Check outthe release notesfor a complete list of changes.
spatsoc is an R package written by Alec Robitaille, Quinn Webber and Eric Vander Wal of the Wildlife Evolutionary Ecology Lab (WEEL) at Memorial University of Newfoundland. It is the lab’s first R package and was recently accepted through the rOpenSci onboarding process with a big thanks to reviewers Priscilla Minotti and Filipe Teixeira, and editor Lincoln Mullen.
What is restez? R packages for interacting with the National Center for Biotechnology Information (NCBI) have, to-date, depended on API query calls via NCBI’s Entrez.For computational analyses that require the automated look-up of reams of biological sequence data, piecemeal querying via bandwith-limited requests is evidently not ideal.
Although there are increasing incentives and pressures for researchers to share code (even for projects that are not essentially computational), practices vary widely and standards are mostly non-existent. The practice of reviewing code then falls to researchers and research groups before publication.
A few months ago, I wasn’t sure what to expect when looking at fluorescence microscopy images in published papers. I looked at the accompanying graph to understand the data or the point the authors were trying to make. Often, the graph represents one or more measures of the so-called co-localization, but I couldn’t figure out how to interpret them. It turned out; reading the images is simple.
Imagine you are a fish ecologist who compiled a list of fish species for your country. 🐟 Your list could be useful to others, so you publish it as a supplementary file to an article or in a research repository. That is fantastic, but it might be difficult for others to discover your list or combine it with other lists of species.
