Once again, I had to write a project’s final report. Once again, I found myself writing that ‘data have been produced, and we are carrying out data analyses’. This seems to be accepted as consolidated report. Nobody expects that, when the project is over, the data have been analysed. Yet, we all obviously claim that science is not about accumulating data, but producing and interpreting results*.
Why do we take it as granted that a research program is complete without data analyses? The answser is ridicously simple: it is because we seldom schedule or budget data analyses. In our unconscious mechanistic-positivist-reductionist-platonian mindset (yes, you too you have such a mindset. You were raised like this, as a scientist), data analyses automatically derive from first principles, so they cost no time and no effort; they are an instantaneous act of revelation of patterns and laws from the data.
This reminds me of the joke, common among physicists, about the mathematician who dies of starvation because he never actually cooks his meals: once he has verified that all ingredients are in the cupboards, he considers that the meal is done. So he never eats. [I do not think mathematicians are like this. Physicists, especially experimental physicists, do].
But when we think again, we perfectly know that there is no such thing as instantaneous, self-organising data analysis. Data analyses cost “blood, toil, tears and sweat“ and enormous amounts of time and money (think not only computers and licence fees, but also salaries).
Since I have stopped spending my days doing silly things like pouring acrylamide gels and scoring bands on an X-ray film or even peak profiles on a screen (that’s a long time ago, luckily), data analyses take about 80% of my working time (not counting for grant proposal writing, paper writing, emptying the coffee room compost tank, and writing blog posts about time spent writing blog posts).
It should be obvious to all, but a project is only over when (at least) data analyses are over. To achieve this feat, we first need to honestly schedule data analyses.
So atone, you sinner, and go back correctly scheduling the next six months of your activities.
*The publication of ‘data papers’ is becoming current, but this is a different matter: the need to publish stand-alone data sets highlights even more the need to make them available to a larger community, so that they can be more easily analysed.