We are very exited to announce a major new release of the Data Retriever, our software for making it quick and easy to get clean, ready to analyze, versions of publicly available data. The Data Retriever, automates the downloading, cleaning, and installing of ecological and environmental data into your choice of databases and flat file formats.

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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.

If you’ve every worked with scientific data, your own or someone elses, you know that you can end up spending a lot of time just cleaning up the data and getting it in a state that makes it ready for analysis. This involves everything from cleaning up non-standard nulls values to completely restructuring the data so that tools like R, Python, and database management systems (e.g., MS Access, PostgreSQL) know how to work with them.

Doing science in academia involves a lot of rejection and negative feedback. Between grant agencies single digit funding rates, pressure to publish in a few “top” journals all of which have rejection rates of 90% or higher [1], and the growing gulf between the number of academic jobs and the number of graduate students and postdocs [2], spending even a small amount of time in academia pretty much guarantees that you’ll see a lot of rejection.

I’m a big fan of preprints, the posting of papers in public archives prior to peer review. Preprints speed up the scientific dialogue by letting everyone see research as it happens, not 6 months to 2 years later following the sometimes extensive peer review process. They also allow more extensive pre-publication peer review because input can be solicited from the entire community of scientists, not just two or three individuals.

Over at Dynamic Ecology this morning Jeremy Fox has a post giving advice on how to decide where to submit a paper. It’s the same basic advice that I received when I started grad school almost 15 years ago and as a result I don’t think it considers some rather significant changes that have happened in academic publishing over the last decade and a half. So, I thought it would be constructive for folks to see an alternative viewpoint.

People find blog posts in different ways. Some visit the website regularly, some subscribe to email updates, and some subscribe using the blog’s feed. Feeds can be a huge time saver for processing the ever increasing amount of information that science generates, by placing much of that information in a single place in a simple, standardized, format.

We are pretty excited about what modern technology can do for science and in particular the potential for increasingly rapid sharing of, and collaboration on, data and ideas. It’s the big picture that explains why we like to blog, tweet, publish data and code, and we’ve benefited greatly from others who do the same. So, when we saw this great talk by Michael Nielsen about Open Science, we just had to share.

Some time ago in academia we realized that it didn’t make sense for individual scientists or even entire departments to maintain their own high performance computing resources.