## Introducing mangal,…

Introducing mangal, a database for ecological networks

From the post:

Working with data on ecological networks is usually a huge mess. Most of the time, what you have is a series of matrices with 0 and 1, and in the best cases, another file with some associated metadata. The other issue is that, simply put, data on ecological networks are hard to get. The Interaction Web Database has some, but it's not as actively maintained as it should, and the data are not standardized in any way. When you need to pull a lot of networks to compare them, it means that you need to go through a long, tedious, and error-prone process of cleaning and preparing the data. It should not be that way, and that is the particular problem I've been trying to solve since this spring.

About a year ago, I discussed why we should have a common language to represent interaction networks. So with this idea in mind, and with great feedback from colleagues, I assembled a series of JSON schemes to represent networks, in a way that will allow programmatic interaction with the data. And I'm now super glad to announce that I am looking for beta-testers, before I release the tool in a formal way. This post is the first part of a series of two or three posts, which will give informations about the project, how to interact with the database, and how to contribute data. I'll probably try to write a few use-cases, but if reading these posts inspire you, feel free to suggest some!

So what is that about?

mangal (another word for a mangrove, and a type of barbecue) is a way to represent and interact with networks in a way that is (i) relatively easy and (ii) allows for powerful analyses. It's built around a data format, i.e. a common language to represent ecological networks. You can have an overview of the data format on the website. The data format was conceived with two ideas in mind. First, it must makes sense from an ecological point of view. Second, it must be easy to use to exchange data, send them to database, and get them through APIs. Going on a website to download a text file (or an Excel one) should be a thing of the past, and the data format is built around the idea that everything should be done in a programmatic way.

Very importantly, the data specification explains how data should be formatted when they are exchanged, not when they are used. The R package, notably, uses igraph to manipulate networks. It means that anyone with a database of ecological networks can write an API to expose these data in the mangal format, and in turn, anyone can access the data with the URL of the API as the only information.

Because everyone uses R, as I've mentionned above, we are also releasing a R package (unimaginatively titled rmangal). You can get it from GitHub, and we'll see in a minute how to install it until it is released on CRAN. Most of these posts will deal with how to use the R package, and what can be done with it. Ideally, you won't need to go on the website at all to interact with the data (but just to make sure you do, the website has some nice eye-candy, with clickable maps and animated networks).

An excellent opportunity to become acquainted with the iGraph package for R (299 pages), IGraph for Python (394 pages), and iGraph C Library (812 pages).

Unfortunately, iGraph does not support multigraphs or hypergraphs.