tl;dr: So far, I can’t see any principal difference between our three kinds of intellectual output: software, data and texts. I admit I’m somewhat surprised that there appears to be a need to write this post in 2014. After all, this is not really the dawn of the digital age any more.
Endorsing Data Citation Nicely timed for the Data Citation Principles workshop at the IDCC meeting in San Francisco yesterday, the finalized Joint Declaration of Data Citation Principles has just been posted on the Force11 website.
The upcoming 2014 Galaxy Community Conference (GCC2014) has just opened early registration, and following from our series announced at the last meeting we are renewing our call for papers for our special thematic focused series on studies utilizing large-scale datasets and workflows.
In what area of scholarship are repeated replications of always the same experiment every time published and then received with surprise, only to immediately be completely ignored until the next study? Point in case from an area that ought to be relevant to almost every single scientist on the planet: research evaluation.
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.
Genetically Separable" href="https://www.slideshare.net/brembs/wcalb14" target="_blank">Pavlovian and Skinnerian Processes are Genetically Separable
“Standing on the shoulders of giants” is what scientists say to acknowledge the work they are building on. It is a statement of humility and mostly accompanied by citations to the primary literature preceding the current work. In today’s competitive scientific enterprise, however, such humility appears completely misplaced.
Push the button! GigaScience moves toward more interactive articles Research articles are being published with increasingly large and complicated supporting datasets, together with the software code used in analyses of the data.
The recent call for a GlamMag boycott by Nobel laureate Randy Shekman made a lot of headlines, but will likely have no effect whatsoever. For one, the call for boycott isn’t even close in scale to “the cost of knowledge” boycott against Elsevier and even that drew less than 15,000 measly signatures, a drop in the bucket with 970,000 board members, reviewers and authors working for Elsevier largely for free.
The other day I was alerted to an interesting evaluation of international citation data. The author, Curt Rice, mentions a particular aspect of the data: In 2000, 25% of Norwegian articles remained uncited in their first four years of life. By 2009, this had fallen to about 15%. This shows that the “bottom” isn’t pulling the average down. In fact, it’s raising it, making more room for the top to pull us even higher.