Tiny life sticking to growing green things

Communicating science to non-scientists is important, but often the jargon scientists use makes their work impenetrable, even to other scientists. So how can scientific writing become less obscure and more approachable? Randall Monroe, the creator of xkcd webcomics, gave it a go with his annotation of a Saturn V rocket blueprint. The annotation used only the 1000 most commonly used words, so instead of Saturn V the name of the rocket became Up Goer Five.

So can scientific communication in my field (microbiology and genetics) be effective using only the 1000 most commonly used words? In the interests of simplifying my writing, I wrote a summary of my PhD project using only the 1000 most commonly used words (using this text editor):

This study wants to find the ‘small pieces’ which are important for tiny life (the helping ones) to stick to growing green things. Pseudomonas tiny life are some of the best helping tiny life and one of the most well-known ones, Pseudomonas protegens Pf-5, can control problems in growing green things used for food. But in the field, helping tiny life show does not stick to growing green things very often or very well. This study will look at the whole set of ‘small pieces’ important for P. protegens Pf-5 to stick to growing green things. Making tiny life stick better to growing green things will help lower problems with growing green things and better the return from growing green things used for food, which are important both here and around the world.

This is hilarious and obviously oversimplified (to the point of not making sense in a lot of places). For comparison, this is the ‘normal’ version of my project summary:

The project aims to identify the essential genes for colonisation of plant surfaces by biocontrol bacteria. Pseudomonas bacteria are some of the most successful biocontrol bacteria and one of the most well-known strains, Pseudomonas protegens Pf-5, has the ability to control diseases that affect cotton, wheat, pea, maize, tomatoes and potatoes. Despite this, field trials of biocontrol bacteria show a lack of reliability and persistence on plant surfaces. This project will conduct a genome-wide study of genes essential for P. protegens Pf-5 colonisation of plant surfaces. Enabling reliable colonisation of crop roots by biocontrol bacteria will contribute to lowering plant disease and increasing crop yields, which are important both in Australia and internationally.

From this exercise I learned that some level of complicated language is important to communicate a precise meaning (important in science), but not every complicated word is necessary. Sometimes the language I choose can be off-putting to the reader, make my work harder to understand and appear pretentious even when I don’t mean it to.

So overall, science writing in my field using the 1000 most used words is not practical and makes it harder, not easier to understand (even nonsensical in places). But it’s an interesting exercise to see just how much jargon you’ve used or if a simpler word will do in place of a complicated one. And wouldn’t we all like simpler rather than complex!

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A minefield of data issues?

Even creating a small amount of data for my Masters project has brought home to me some of the issues around data – how to store it, where to store it, in what format to store it, how to ensure the appropriate people have access to it, how to stop people accessing it if they shouldn’t have access to it, how to future proof the storage, how to ensure the data and method used to collect it remain linked, who gets to keep it.

And that’s just for a few small plant biology experiments for my Masters. I’m sure there are many more levels of complexity for confidential data and big data. Some of these issue were discussed briefly with my Masters cohort, but it seems like that short conversation was only scratching the surface. I’m sure that as a (very) early career researcher there are a lot of things I don’t know and even more things I’m not even aware that I need to know.

 

Brightfield microscope image of Australian wild cotton (G. australe) leaf cross-section – one of the types of data from my Masters project (Photo: Belinda Fabian)

During my research break between my Masters and my PhD I’m working on up-skilling in a variety of areas; some directly related to my potential research topic(s), some which are generally related to study and/or my career (e.g. learning to code and using R) and others that just broaden my horizons (both scientifically and personally). One of the general study/career areas I’m learning about is data management through the 23 [research data] things program (see below for more information).

I see the 23 [research data] things program as helping me with generic study/career knowledge and skills and ideally it will will form part of a firm footing for me as a researcher. Awareness of the issues related to data management is important for researchers (and keeping digital data adds more concerns), but from my experience an understanding of it comes in a very piecemeal fashion during research training (as with many other things). So hopefully participating in this program will help me get out in front of the curve and make me aware of issues, solutions and strategies for managing data and where to find information down the track when the need becomes pressing.

Things you need to know:

The 23 [research data] things is a program run through ANDS (Australian National Data Service). More information can be found here. There’s an introductory webinar on tomorrow 1 March, 12.30-1.30 AEDT.

The program is free and runs from March to November 2016 (I know that sounds like a lot, but the FAQs suggest that it will only take about an hour a week and there will be breaks and catch-up opportunities during the year).

The program is advertised as being of interest to lots of different types of people – from the 23 [research data] things website: “If you are a person who cares for, and about, research data and want to fill in some gaps, learn more, find out what others are thinking… then this may be for you!” I’m getting involved as a research who will deal with data in my career, but if you’re a person who creates or cares for data then the program may be of interest to you too.

There’s a Meetup group for discussing the activities and other thoughts about the program and search #23RDThings on Twitter for all the buzz.