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iLand News

Upon publication, what to archive?

Tuesday 29 of July, 2014

The endeavor of scientific publication is changing drastically. The article published in printed periodicals has - besides books, of course - been THE single most important means of making scientific advances public since more than 100 years (see Svante Arrhenius insights on the role of CO2 in the earth’s atmosphere, published in 1896 in the Philosophical Magazine and Journal of Science). In the last decade or so, these established journals have increasingly made use of the internet, and a growing share of the ever-expanding number of new journals is solely published online today. This has not only revolutionized the way we peruse the literature, it also opens up new possibilities with regard to the depth of information that is made available through an article.

It is, for instance, common to publish a detailed account of the (increasingly complex) methods used in a paper in a (digital only) online supplement. The latter is usually not restricted in length (since the cost of server space is << the cost of page space for a journal), and particularly in highly ranked journals online supplements are frequently exceeding the actual printed article by a factor of 3 to 10 in length.

While having all the additional methodology available is nice, what is oftentimes as interesting to peers is access to the raw data that has been used in study. This not only ensures transparency and enables post-publication review, but also facilitates quantitative metaanalyses (which get increasingly important to synthesize the exponentially growing body of scientific literature). Groups such as the LTER Network have pioneered such data repositories in ecology (including the development of consistent metadata standards), and repositories such as Dryad, which not only archive datasets but make them citable by assigning a unique doi to them, are increasingly popular. In fact, two of the four journals I’ve published in in the last six months requested for us to archive the data in a public archive (a request we of course gladly complied with). I expect that this (softly forced) new openness with data and raw results will considerably advance the scientific endeavor and will both help detect fraud as well as open new views and novel perspectives on existing data.

So all is well with science in the digital age then? Well, yes and no. Although I see the direction in which publishing in the digital age is moving as largely positive, I’d argue that we ought to go much further in making use of the potential of digital information storage and management. While there are clearly many facets to this, I feel that we should increasingly also make methods (in the form of scripts, code, and/ or executables) available with our work, in order to grant peers true repeatability. For simulation studies this would mean that the model code, executable, and scripts to run the model would be available to anyone wanting to scrutinize the results. We in fact tried to archive iLand (code and software) and the respective auxiliary data alongside one of our recent application papers, and were greatly encouraged by the editor to do so. However, in the end (and after quite some back and forth between the journal, the editor, and us) we had to let it go, as the journal and its online archiving system was just not set up for handling such kind of information. I do think that in the future such options should be provided by the journals though, and should be encouraged by the editors.

For the time being we’ll continue to archive all the respective model information here on our website for those of you who’re interested. This is of course likely not going to be as permanent as a server backed by a big journal would be, but it is a start I guess. And talking about permanence opens another can of worms… will we be even able to run our software or compile our code in, say, ten years from now? Well, looking at my old 5 ¼ inch floppy disks I’m not so sure. But then again, the best way to keep software from falling into oblivion is to constantly use and improve it… so back to some more model development!

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