There are a lot of extremely good arguments to defend the fact that Science (as a whole) should be more open. To summarize them very roughly: it’s the ethical thing to do as it allows everyone to access information, it’s easier for scientists to access information, it’s faster than the traditional peer-review system when you need to get your work noticed, and it’s much less expensive than closed-source science. I could also elaborate on transparency, accountability, and the refusal to put a wall around knowledge for a while as well. In short, I’m yet to find an argument that would convince me that open science is a bad thing.
What we call open science obviously varies from people to people, but the common thread is that it is a set of practices aiming a lowering the technological, financial, and legal barrier to the accessibility of scientific materials, methods, processes, and outputs. This includes a diversity of practices such as open manuscript writing, the use of preprints, data sharing, in addition to open access publishing and the use of free/open source software. Most of these practices can be applied to nearly all fields of science, as proven by the OpenWetWare wiki, that caters specifically to experimental biology and biochemistry.
An interesting question that keeps popping up whenever I discuss Open ecology, or Open biodiversity, is “Why should it be different in ecology?”. Or expressed in another way, why should ecology pose particular challenges as far as open science is concerned? It is a very valid question, and in preparation for the Open biodiversity panel that will take place at the QCBS meeting in a month and a half, I thought it was time to bring some elements to answer it.
Let’s start by recognizing that there are (broadly) two types of ecologists. The “empirical” ecologists (including the microcosm people) rely on empirical observations, and extensive field surveys, to address their questions. The “theoretical” ecologists, on the other hand, integrate different data sources, or build models, to understand “general” questions. There are obviously people working all along this continuum (I don’t consider myself a “pure” theoretician, for example), and empiricists contributing some of the most important ecological theories, but we can all agree that leaning towards one end of the continuum implies having a different set of practices. And to make things worse, the communication between the two groups is not as good as it should. While it’s easy (I would like to say natural) for theoreticians to have an entirely open workflow (put your code on GitHub, use it to write your manuscripts, push your data on figshare using the API, and voilà!), it’s slightly more complicated for empiricists. While it’s true that anyone can make data public, you can’t reasonably release your field site under a Creative Commons licence (because it doesn’t make any sense…).
There are two points at which both worlds meet, however: data and algorithms. Even for the most local and system-centered question, there is a large quantity of required data. And because data are essentially multivariate, sometimes incomplete, collected with unbalanced designs, and generally subject to the contingencies of the Harvard Law of Biology, these is often a need for elegant (read: complicated) numerical methods to make sense of them (but this is easy to solve, code should always be made open source, in non-proprietary languages). This is probably the unique challenge of open ecology: we produce a lot of data, we need a lot of data, but there are so many peculiarities attached to datasets that sharing them is by nature a difficult task. Molecular biologists do not have this problem. The
fasta format is simple because the biological reality of what it represents (sequences) is simple too (or at least, it is easy to represent the building blocks). And so it seems almost natural than sequences databases are so prominent: there is no obstacle to data sharing because anyone can use a
fasta file after two minutes of explanation of the format. I cannot remember a single case when I managed to become entirely comfortable with the structure of an Excel file in less than an afternoon.
Open ecology will probably be much like all other forms of open science, but the data heterogeneity challenge is especially problematic. There are certain ways to solve it, though.
First, we need strict data specifications. If several groups work on the same questions in similar systems, it would make sense that the data are formatted in a common way. A good opportunity to draft these specifications is large working groups, in which people sharing a common interest would think about the “core” and “satellite” properties of a dataset. This will also speed-up the development of data repositories with APIs. My personal favorite for the development of data specifications is
JSON schemes, but really any kind will do. Data are highly structured information, and it makes sense that they are used and shared in a highly structured way. It is quite obvious that there will not a be a single ecological ontology, but if we managed to get it right for a few dozens of high quality ones, it will be a strong enhancement over the current data format of “However I felt that day” (I’m guilty of this one, of course).
Second, we need stricter data review. Once the data specification has been published, it is important that its use is enforced by referees and editors, so as to make sure that data will be re-usable. This is different from evaluating the quality of data (data quality is only measurable in the light of the specific question they are used for). What I have in mind is more along the lines of a checklist with a few points: is the data conforming to the specification (this step can be automated with a
jsonlint-like tool), is the data easy to access, and are enough data released to make the dataset re-usable?
Third, and finally, we need to un-install Excel and similar software. What we need instead, is a
pandoc for data.
pandoc is a tool to convert text formats into other text formats. It’s awesome. And with strict data specifications, we should be able to write one for ecology. This will allow use to store data in their correct format (i.e., conforming to the data specification), but use them in another format when we need them in another format.
All of this will require a fair amount of community coordination, and changes in the training and teaching of ecologists, but it’s all for the greater good. Some outstanding ecological problems will only be solved when a critical mass of data is reached, and it would be extremely disappointing to realize that these data were existing, but not available due to poor practices. So in short, this is the challenge specific to open ecology: making sense of highly heterogeneous and local data, and mobilizing them to address global and general questions.