Concours Antarctique

Nous organisons un concours d’histoires sur l’Antarctique destiné aux élèves de la 5e primaire à la 1ère secondaire!

Le principe est simple : choisissez l’une des 22 images proposées et écrivez une histoire courte de 2 à 3 pages sur le thème « l’Antarctique ».
10 histoires francophones et 10 histoires néerlandophones seront rassemblées dans en livre et imprimées. Chaque école participante et les élèves dont l’histoire aura été sélectionnée recevront un exemplaire. Les gagnants recevront également la visite d’un scientifique polaire dans leur classe.

Bon amusement!!



R: Visualize occurrence records by binning

Let’s get a dataset from OBIS using robis, R client for OBIS API.

occ <- occurrence(resourceid = 2296)

The function occurrence() get occurrence records from OBIS which has resourceid = 2296 and transform the results into an R dataframe. To quickly visualize the occurrences, we can do



There are a lot of points overlapping each other.  We can bin these points into hexagons/squares and colour these hexagons/squares based on the number of points in each hexagon. To achieve this, we can use the ly_hexbin function from rbokeh.

# hexbin with rbokeh
figure(width = 900, height = 450) %>%
 ly_map("world", color = "#707070", alpha = 0.8) %>% 
 ly_hexbin(decimalLongitude, decimalLatitude, xbins = 100, shape = 0.2, 
 alpha = 0.8, palette = "Spectral6", trans = log, inv = exp) %>% 
 theme_plot(background_fill_color = "black") %>% # dark background
 theme_grid(grid_line_alpha = 0) # remove grid


Warmer colour means more occurrences within that hexagon. The colour is based on log scale of the count.

It is nicer, but we could have done better! A lot of points are scatter around antarctic/subantarctic zone. It could be nicer to make the plot in polar projection. In this case, we can do that with ggplot!

world <- map_data("world")
ggplot() + 
 geom_bin2d(data = occ, aes(x = decimalLongitude, y = decimalLatitude), bins = 100) +
 geom_path(data = world, aes(x = long, y = lat, group = group), colour = "#c0c0c0") +
 scale_y_continuous(name = "latitude", breaks = (-2:2) * 30) + 
 scale_x_continuous(name = "longitude", breaks = (-4:4) * 45) + 
 coord_map("ortho", orientation = c(-90, 0, 0)) + # orthographic projection from South Pole
 scale_fill_viridis(option = "viridis", trans = "log") + # log scale for bin count
 theme(panel.background = element_rect("black"), # dark background
 panel.grid = element_blank(), # remove panel grid
 axis.text.x = element_blank()) # remove x-axis value


Tada!! 🙂 We will post more interesting tutorials later! Thanks for reading!!


British Antarctic Survey. SOMBASE PYCNOGONIDS. Occurrence Dataset accessed via on 2018-02-11.


Data, data, data, …

We live in the area of big data. As scientists, but also communicators it is important to be able to handle and understand large (and small…) amounts of data. APECS Belgium therefore introduces a new category on our website: data tutorials.

Yi Ming Gan has written before about how data science can aid polar research. Now she’s contributed our first data tutorial. Check it out!

Travel Awards for POLAR2018

Abstract notifications will be sent out very soon, much of the program stands already. So, it’s time to make further arrangements, if you’re planning to attend the must-go conference for all polar researchers in 2018. In order to help Early Career Scientists get there, APECS in association with IASC and SCAR offers some travel support. Find all details here! Deadline 28 February.