En este seminario se discutirán distintas aproximaciones metodológicas (Smith et al. 2018) para intentar mejorar nuestras estimaciones de nicho ecológico teniendo en cuenta la posible divergencia intraespecífica o adaptación local de las poblaciones, …
Earlier this year a few colleagues (Ignasi Bartomeus, Sara Varela, Antonio J. Pérez-Luque, and myself) created a new working group on Ecoinformatics within the Spanish Terrestrial Ecology Association (AEET). Our main goals are to promote knowledge and training and exchange experiences on all aspects of ecoinformatics, including data management, statistical modelling, programming, etc.
Reproducibility is a hot topic in science nowadays (e.g. see this Nature special). Some argue that we are in the middle of a ‘reproducibility crisis’, and thus scientists are being strongly encouraged to increase the reproducibility of their research.
As a side product (or trailer) of our paper on reproducible science, we made a video promoting reproducible workflows. Particularly, showing how using Git and Rmarkdown make your research and scientific collaboration way much easier and better, compared to a typical (non-reproducible) workflow involving Excel, Word, some figure production software, and a lot of manual steps.
Rmarkdown is a great tool for reproducible science. You can combine text and code to produce dynamic reports that generate updated results with a single click, as in the example below.
I have supervised my first master project this year. The project is coming to an end, and I am very happy with the results as well as the fully reproducible workflow we have followed: all developed on GitHub using R package structure and Rmarkdown.
Science has a big reproducibility problem: hardly anyone can reproduce (i.e. re-run, re-obtain) the results of most published papers (including authors themselves!). That is a big problem not only for science as a collective enterprise but also for scientists' everyday life (‘how did I do this?