Being an experimental petrologist specialising in ore deposits myself, I was particularly interested in the third and last day that had the catchy name “Metals for the Millennials“. One of the scheduled talks was about unconventional resources and rare earth elements by Carl Spandler, a professor from James Cook University in Queensland. Unfortunately, he had an unexpected appointment he had to attend, and he asked my supervisor if he could give the talk instead of him (by the way – Carl is also on my supervisory panel). Instead, my supervisor suggested I do it instead. Surprised and exhilarated by the opportunity to speak in front of important people in the symposium, I agreed.
I was fortunate to attend two field trips during my visit to Japan, both before and after the conference itself.
Fuji-Hakone: Spring, forest, cave, and volcanoes around the area
We left Yokohama to the village of Oshino, northeast of Mt Fuji, the location of Oshino Hakkai: the eight springs. This area used to be a lake, lava flows from Mt Fuji covered the lake completely and it dried up. However, groundwater coming from Mt Fuji are still feeding some ponds and springs in the village.
Goldschmidt is the largest annual geochemistry conference, held this year in Yokohama, Japan. I am not a newcomer to Goldschmidt. I attended Goldschmidt in Montreal, Canada back in 2012. That was when I was a first year M.Sc. student, presenting something I did as a side project during my final year of undergraduate studies (and special thanks to my supervisor at that time, Yaron Katzir, for sending me to Canada for such an unimportant research project).
Attending conferences, like any other academic activity, is an acquired skill. My experience in 2012 can be summed up by this quote:
…Some scientist was talking about something, then another scientist goes “hmmm… interesting…” and nods the head. Really? But I don’t understand a thing! How can this be interesting??
I often get asked by people what do I do in my Ph.D. That’s a seemingly easy question, but it is actually quite difficult to answer. How are you supposed to sum up years of work, study and research into a something that should not be longer than a “yes/no” answer? An answer will also depend on how long you’ve been in the program. Several months in, you still have no idea what you’re doing.
The lowest point on our planet is the Challenger Deep, around 12 km below sea level. There is a slight problem with it though – it is underwater. The lowest place you can actually go there without getting too wet is the Dead Sea, a hypersaline lake on the border of Israel and Jordan. You are going to get all wet and sweaty, because unlike Canberra at this time of the year, that place is incredibly hot. Here’s a cropped screen capture of today’s forecast from the Israeli Meteorological Service:
Now, I could talk to you about the environmental crises occurring there because we’re geoscientists, or I could write something about the rich history of the place because some of us may be interested in that. I was going to do it when thinking about this post, but then I decided that I will not. That’s boring stuff and you can go and find some papers or articles about it. Being a native of the Israeli desert, just looking at some pictures of this area made me feel all emotional and stuff. That place can definitely take some deep feelings from your heart and throw them in you face until you start crying like a little baby because it makes you so happy (and sad because I’m so far away).
So instead of going all academic telling you that it’s lower than 400 metres below sea level and decreasing because of human activities, or that it occasionally spits out asphalt that starts floating on the surface of the water, I’ll try to convey some of those inexplicable feelings to you. Feelings work better with sound, so here’s a song to play in the background while you’re reading this:
That song is called “tzipor midbar” (ציפור מדבר), meaning “desert bird”. Some say the lyrics are about a town called Arad, the neighbouring town to the famous Masada fortress, where the Roman army lay siege until all the Jewish inhabitants committed suicide.
My own relationship with does not go too far in time. I only got to intimately know it in my early twenties, when I had a job there. My task was to operate a barge that extracted carnallite (KMgCl3·6[H2O]) from the evaporation ponds and pumped it to the factory where they produced potash, magnesia, bromine and all kinds of chemicals from it.
Occasionally, during night shifts, the factory would shut down for maintenance, so that means that barge has to shut down as well. This means no noise from the machinery, and no lights because we don’t need it. And then it hits you – complete silence. Complete darkness. It’s you, alone (well almost, we’re two on a barge) in the middle of nowhere. You are on the sea, and the closest sign of any civilisation is the factory itself which may be several km away from you. Then you look up, and see the sky. Completely clear, millions of stars. You can almost see shadows cast by the glow of the milky way. And then the sun rises:
I worked there for only six months, but I visited the region countless more times. Easy when it’s only 1.5 hours away from where you live! Now, most tourists only visit Masada and the hotels. That’s not a bad thing, considering this is how the beach looks:
Once you go slightly deeper to the desert, you find amazing things. First of all, the entire thing exposes magnificent outcrops of sedimentary rocks. You can see beautiful patterns in clays and marls, huge ammonites, quartz geodes (locally known as תפוחי אליהו, Elijah’s apples), huge dolomite crystals and what not. One of the most peculiar things you can find there is a huge salt diapir. That’s a mountain made out of almost pure salt, called Mount Sedom (of Sedom and Gomorrah fame). It’s always hilarious making unsuspecting tourists to actually lick the mountain:
Although this place is a desert and not much (if at all) can survive in the Dead Sea (hence the name), there are some green spots around it. One of my favourite ones is Ein Bokek, a small oasis in a canyon that flows all year. There’s nothing like wetting your feet in the cool water after a long walk in the scorching sun.
I will stop now because just writing this blog post makes me home sick. I will finish with another song. Don’t worry – this time it’s in English. The filming location, the vibe of the song, the choreography, the effects, and everything else in this song makes me feel like I am actually standing there, 40°C, facing a strong and dry western wind. Put it on full screen and plug in your headphones.
Last time we had an introduction to colour in scientific graphs, where we had two categorical data series. What happens if we have continuous or sequential data, plotted over an area?
Here are two X-ray maps using the classic ‘rainbow’ colour scheme, the first of Ti in allanite:
Looks good, no? We can see the background that has very little Ti compared to the allanite. The allanite has a Ti rich “core”, intermediate “mantle”, and a Ti-poor “crust”. It is also possible to see some “hotspots” of Ti in the mantle.
Let’s see another example, this time of Mn in epidote:
Not as good as before, but we can see the important things. The epidote is mostly in the ~40 area (green), with some Mn-rich zones (blue and violet). The other mineral in the top is low on Mn (yellow).
If you’ve read my last post, then you can already see the problems in this. First of all, this will go bad in black and white printing. Let’s simulate that:
See how the colour scale makes absolutely no sense? The second obvious problem is the accessibility: the use of both red and green makes it difficult for people with colour deficiency to properly understand the figure.
In plots such as this, there is a third problem – the way our eye perceives colour. The computer “sees” the data as continuous. However, when plotted with different colours (hues) our brain interprets what it sees as discrete colours. You see green, red, blue, violet, etc. The problem is exacerbated because not all colours have the same numerical range. If you look at the scale, you can see that you have only one orange whereas you have several greens. Therefore, differences within different shades of the same colour are lost.
In order to make better colour schemes, luminosity should be used instead of hue. The human eye and brain can detect subtle differences in luminosity, especially when it is the same colour. Let’s see how it works for our two X-ray maps:
Oh my, now that’s beautiful. And clear. I used two different schemes – white to dark colour and black to bright colour. Both work fantastically. You can now see that the allanite is actually sector zoned in respect to Ti, and it has a weird Ti-poor ring surrounding its core. You can also discern the delicate details of Mn zoning in the epidote. Notice that I used only one colour! This also works great in grey scale printing.
What happens when you have a certain middle value (such as 0) where the values are diverging from it? It is a good idea to use two different colours and a neutral middle colour:
Even though it doesn’t make much sense to use it in mineral chemistry X-ray map, the result is surprisingly beautiful. It is, however, quite useful in topography (below and above water), geophysics (magnetic anomalies), meteorology (temperature), etc. Just be sure that you’re using colour blind friendly colours.
In the past years, there is a surge in the amount of colour that appears in scientific papers. There are several reasons for it. The ease of creating coloured figures increases with every new release of a software version. Journals are accepting (and encouraging) colour more than ever. Additionally, in the world of online publishing, there are no added costs for colour.
In the past, people had only black and white (and grey scale if lucky) to use. This forced them to think about line types (dashed vs dotted), symbols (circles and squares), etc. to make the graph easy to understand. Today, many people submit their pretty colour figures without putting too much thought into the act.
Let’s take a look at an example:
Seemingly, there’s nothing wrong with that graph. There are two data series, both of which are coloured (red and green) and appear in the legend. However, this figure fails in two critical aspects:
Black and white photocopying and printing
Even though it’s possible to view articles online nowadays, many people still print out papers. Just take a look at any desk here at RSES. While you may think that the green looks brighter than the red so when printing one will be dark grey and the other will be light grey, it is not the case. The two colours have the exact same luminosity and thus will appear identical in print. It is then left to the reader to try and figure out which are better, kittens or spiders. And it is open to subjective interpretation.
There is a simple solution to that: use different symbols in addition to colour:
This way, people with no access to colour printing can still understand your figure, while people with colour printing or screen viewing can benefit from your use of colour. If you prefer to use only lines and no symbols, there is a solution for you as well – line types:
Accessibility and colour blindness
Colour blindness affects mostly males (in some countries close to 10% of the population) but also females. This means that you can be confident that some of your readers are certain to be colour blind. Most commonly, people cannot distinguish red from green. This makes our figure a great example of how not to do it. While people can use the symbols to distinguish the two data series, the entire point of the colour is reduce the time the reader needs to understand the figure. Which colours should we use then? This is a hard question because of the multitude of colour blindness forms. Generally, blue and orange are a good combination:
However, with increasing data series this quickly becomes a hard task. Fortunately, there are online tools to assist in colour choice. One of my favourites is ColorBrewer: http://colorbrewer2.org/ but there are others (just search online for ‘colorblind safe colors’).
Remember – colour in scientific figures is a tool to assist understand the figure better and faster. It is not to make it pretty, although that is usually a welcome side effect.
There are two ways to test your figures after you’ve created them. First of all, print it black and white. Can you still understand the different data series? Second, you should run your figure through a simulator (or ask a colour blind friend). My favourite is the one at http://colortest.it/, but it requires that your figure already exists somewhere online. Finding other simulators should be easy using a search engine. Try to run the red-green and the blue-orange versions through the simulator!
Final note about legends
Legends are terrible in my opinion: your eyes keep moving around from the actual data to the legend. I always try to avoid using them. A better alternative is quite obvious in our case:
That’s it for now. Stay tuned for part 2 where I will discuss colour gradients. There plots were produced using R. If anyone is interested in seeing the code I used, feel free to ask me.