I guess I’ve been around bioinformatics for the best part of 15 years. In that time, I’ve seen almost no improvement in the way biologists handle and use data. If anything I’ve seen a decline, perhaps because the data have become larger and more complex with no improvement in the skills base.
It strikes me when I read questions at Biostars that the problem faced by many students and researchers is deeper than “not knowing what to do.” It’s having no idea how to figure out what they need to know in order to do what they want to do. In essence, this is about how to get people into a problem-solving mindset so as they’re aware, for example that:
- it’s extremely unlikely that you are the first person to encounter this problem
- it’s likely that the solution is documented somewhere
- effective search will lead you to a solution even if you don’t fully understand it at first
- the tool(s) that you know are not necessarily the right ones for the job (and Excel is never the right tool for the job)
- implementing the solution may require that you (shudder) learn new skills
- time spent on those skills now is almost certainly time saved later because…
- …with a very little self-education in programming, tasks that took hours or days can be automated and take seconds or minutes
It’s good (and bad) to know that these issues are not confined to Australian researchers: here is It’s time to reboot bioinformatics education by Todd Harris. It is excellent and you should go and read it as soon as possible.
What You're Doing Is Rather Desperate 
That’s a great list of tips, Neil. Tough love for sure. I will copy-and-paste as a preface for answering my next help desk request ;)
I agree with much of what the article says, like the idea that bioinformatics is a “way of thinking” and not just a collection of skills, but I strongly disagree with the idea that “algorithm design has no place” in a bioinformatics overview course. But algorithm design is exactly what it means to have bioinformatics be a “way of thinking”, at least in my opinion.
@jhbadger that was a bit of hyperbole. In essence, I think that too few biologists are receiving core skills in real-world bioinformatics that they need to do their research efficiently. In my opinion, bioinformatics courses that are too heavy on the comp bio side are missing the point. Most biologists will not never need to design a nucleotide aligner. But if they can’t parse a spreadsheet, they are seriously impaired.
I’ll preface this by saying that if “implementing the solution may require that you (shudder) learn new skills” truly scares an individual off, it seems unlikely s/he would be very happy in any scientific field.
Aside from that, I think the “time spent on those skills now is almost certainly time saved later” is a hurdle for most scientists, both junior ones reporting to a supervisor, and senior ones juggling 50 other tasks with real deadlines. But provided one can make it over that hurdle, I still think one of your points is a bit off: an “effective search” is RARELY attainable “if you don’t fully understand it at first”. The approaches to a problem can be as diverse and individualistic as the people trying to solve it, and it usually takes some appreciation of the inner workings to properly distill it down into the types of solutions that float to the top on StackOverflow. (BioStars I have less familiarity with; it seems like a true mess.) I think the difficulty of creating effective searches for help is one of the biggest contributors to learning curves perceived as steep.
I agree! One issue with search is that often, a key phrase or wording makes all the difference to the results – and initially, you do not know what that wording should be. Conundrum! There’s a process that I cannot explain fully whereby one gradually becomes aware of how to refine searches with appropriate words; I think, by scanning many results and becoming aware of their usage.
Well said! That process can take some time, and seems to be off-putting to people who aren’t into the allure of tinkering and problem solving.