Distractions and procrastination.

Distractions and procrastination.
There are lots of things to be embraced about being a Principal Investigator in Higher Education. You are free to direct your own research, you can be creative when devising your teaching sessions, and you can indulge your curiosity and passions, for example through public-engagement or immersing oneself in the literature.
But everyone knows that there is also the less enjoyable side to academic life – marking, ticking off marking criteria, providing student feedback, filling in marks moderation forms, attending exam boards – in general, the auditing and administration of mark-awarding.
These are things that need to be done to keep the external examiners happy, but they seem to have little obvious direct impact on the education of students.
And although ‘important’, they are tedious. So tedious that many, including myself, would succumb to any temptation to procrastinate during marking season.

At the best of times I love a good dataset to pore over – they usually jump right to the top of my ‘to do’ pile. But it’s heart-breaking when they arrive during marking season, when I’m most prone to distraction and procrastination, and yet subject to tight deadlines to get the mark-awarding paperwork completed.
So why do all the best datasets arrive during that marking season?
This marking season I’ve received ten genomes of novel bacterial isolates, the results of antimicrobial activity assays for 25 novel compounds, and a large set of transcriptome analyses, all of which need urgent analysis.
It’s like being a modern Tantalus, desperate to reach up to open those spreadsheets of insight and start analysing, while the chains of administration keep you grounded with moderation forms and marksheets.
So instead, I do neither and write a blog post.

Post by Dave Whitworth

But what does it mean?!?


But what does it mean?!?

During practical-based modules, I often ask undergraduates to start their practical reports with a statement of their hypothesis. This usually throws them into a mild panic, as class practicals are primarily about generating data rather than proving/disproving a hypothesis and they cannot easily negotiate that apparent disparity.

The relationship between data-generating and hypothesis-driven research is a troubled one. Twenty years ago, a loud and often-heard cry of the experimentalist after a ‘big data’ or ’-omics’ talk was ‘but what IS the hypothesis?’. Testing a hypothesis was the mantra of every bench scientist, and even today some funding agencies and scientific publishers still insist on placing hypotheses front and centre of all submissions.

But what was the hypothesis being tested when the E. coli genome was sequenced? Should we look down our noses disapprovingly at the humble genome, denigrated as a mere ‘fishing trip’, or ‘stamp collection’, because of its lack of a noble hypothesis? Do we emulate my poor undergraduates and struggle valiantly to find a hidden rationale behind the data-collecting exercise and justify its existence? Or should we celebrate the diversity, abundance and scale of the datasets that we can now generate, with or without accompanying hypothesis?

We can’t all be the ones to discover the next cure for cancer, or the novel antibiotic to which there is no possibility of resistance. However, we can all contribute resources to aid those explorers in their search. Those resources can be new knowledge, acquired through the steadfast testing of hypotheses, or they can be collections of datasets, alongside the tools and knowhow to interrogate those data.
The genome is the ultimate blueprint of an organism’s biology, however we have barely begun learning how to look inside a genome, and from its sequence deduce salient features of the host’s biology. Hypothesis-led experimentation is one way to improve our understanding of the sequence/function relationship, and now increasingly we find ourselves testing hypotheses that have themselves come directly from big datasets.

In essence, big datasets are trying to tell us everything we want to know, but to get there we need to find out what questions to ask, for which they are the answer.

Post by Dr. Dave Whitworth.