On the occasion of the first “BigDataDay” at our university, I have summarized on the below poster our two main efforts to automate the publication of our tiny raw data. On the left is our project automating Buridan data deposition at FigShare using the ROpenSci plugin and the consequence of just sending the links to the data and the evaluation code to a publisher, instead of pixel-based figures, when submitting a paper.

For the last few years, we have been working on the development of new Drosophila flight simulators. Now, finally, we are reaching a stage where we are starting to think about how to store the data we’ll be capturing both with Open Science in mind, but particularly keeping in mind that this will likely be the final major overhaul of this kind of data until I retire in 20 years.

This is a post written jointly by Nelson Lau from Brandeis and me, Björn Brembs. In contrast to Nelson’s guest post, which focused on the open data aspect of our collaboration, this one describes the science behind our paper and a second one by Nelson, which just appeared in PLoS Genetics. Laboratories around the world are generating a tsunami of deep-sequencing data from nearly every organism, past and present.

Why our Open Data project worked, (and how Decorum can allay our fears of Open Data). I am honored to Guest Post on Björn’s blog and excited about  the interest in our work from Björn’s response to Dorothy Bishop’s first post. As corresponding author on our paper, I will provide more context to our successful Open Data experience with Björn’s and Casey’s labs.

This is a response to Dorothy Bishop’s post “Who’s afraid of open data?“. After we had published a paper on how Drosophila strains that are referred to by the same name in the literature (Canton S), but came from different laboratories behaved completely different in a particular behavioral experiment, Casey Bergman from Manchester contacted me, asking if we shouldn’t sequence the genomes of these five fly strains to find out how they