The Data Sink

Some people call it karma, others mojo. I call it the data sink. Not a sink as in a kitchen sink with a drain, but rather a sink in the sense of something towards which things  flow. I have a theory that on a given day, there is a finite amount of data to be had in a given research environment. And this data is not evenly distributed amongst the members of said research environment. Rather it is usually distributed between a handful of data sinks. These are the people in a lab who seem to be sucking up all the data while everyone else toils around fruitlessly. Fortunately, these data sinks are not stable and can rotate around from person to person over several days. And once you have a data sink you can keep it going by taking advantage of it, much to the chagrin of your lab mates. In my postdoc lab, this seemed to be certainly true, where someone would have a good run where they would get lots of data for several days, but eventually things started to falter and it would be someone else’s turn to soak up all the available data. Occasionally a couple of people would be having good runs and then it would be truly dismal for everyone else. In the rare occasion where everyone was getting data at the same time –a mega sink–  we would hear cries and screams from the lab next door. They would knock on our doors demanding we give them back their data. Once you acquire a data sink, the way to keep it going is to work harder, to keep the data flow steady. As soon as you let up, your sink will dry up and go to someone else. So you should treat your data sink with love.

While not everyone will subscribe to my theory on data sinks, I think there are some valuable lessons to be learned from it. Probably one of the most valuable things I learned as a grad student, was learning to identify data sinks. The time when all of your equipment is working perfectly, all your experimental preparations are healthy, your mental state is groovy, and then it happens. Data starts pouring in. And if it does, it is important to fully ride with it, because you don’t know when something is going to break, and the data flow will stop and you never know when you will get it back. During these times you really have to work extra hard, you can take a break when the data sink dries up. And I find this difficult to impart to my graduate students who will often go home, or to the gym, or to a two-hour lunch, or whatever in the middle of an experiment which has been giving them good data all day. They are basically losing their chance to acquire more data and making their life more difficult in the long run. I have no problem with people having a life, but science seems to work at its own pace and you just have to have a flexible work schedule. I’d rather they stay for an extra few hours and then take off the next day, than to stop a perfectly good experiment midway.

Have you ever experienced a data sink? If so, how have you kept it going, how do you get it back once it goes away? Good data sinks  are difficult to achieve, and if you find yourself lucky enough to experience one, good care and feeding of your data sink will ensure a most pleasant and long lasting experience. Let the data flow!


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14 Responses to The Data Sink

  1. Dr. O says:

    My grad mentor would rotate a data redacted around the lab, and the labmate currently in control of the data sink would keep it at their desk. In addition to being recognition of hard work, other labmates would sacrifice to it, in hopes of getting a piece of the action.

    When I was in control of it, I would take advantage of its mojo for several days. Then I’d get cocky, and the data redacted would punish me by moving the data sink to another labmate. *sigh* I never learned how to control my pride.

  2. I totally agree with this theory! At National Lab, we used to joke when someone’s experiment was going like clockwork, everyone else should spend the time writing, since it was obviously their turn for good data.

  3. GMP says:

    🙂 Totally true!

    Although “data sink” sounds kind of negative (like a place where data is discarded/dumped). Maybe “data vortex”? Or conversely “data fountain” or “data spring” — a magical source of good data?

  4. We call it “shittin figures” and I am having a bit of a dry spell but I feel the data runs are about to kick in.

  5. becca says:

    Oh GAWD. You are an electrophysiologist aren’t you? BEGONE, BEGONE!!!!

    • Genomic Repairman says:

      Wtf you think I’m some kind of rig jockey? Hells no.

      • becca says:

        What I meant was, namnezia sounded like the ephys PI I worked for- who would have been shocked, just *shocked* if anyone left the rig while they were still ‘on a good streak’- irrespective of whether they had been there for 10 hours already or not.

      • namnezia says:

        10 hours?!? That’s for lightweights… 😉

      • a.w.e. says:

        Yeah, well the daycare charges an ***load when you’re late…

      • I built something for my PhD with someone else. When it was working, we shifted our schedules to keep it running as close to 24 hours a day as possible. Alas, that wasn’t very often.

  6. a.w.e. says:

    I think the idea is bogus. “Data sink” sounds like a new way of saying “lucky”. I’ve heard that I’m “lucky” a lot, but in reality the “sink” you describe happens much more frequently to people who do the following: 1) pay attention to what they are doing, 2) pay attention to what their experiments are telling them and 3) have a specific PLAN to achieve their GOAL.

    • namnezia says:

      awe: I never said the data sink was random. I totally agree with your points, yet these are difficult to learn and bring to practice. The more you do them, the higher the likelihood of you getting more data will be. I think my key point is learning to recognize when things are working well and to go with it, since there is a certain degree of unpredictability.

      • a.w.e. says:

        I’m not saying that when things go your way, you shouldn’t try to take advantage.. but f.f.s., scientists can be more superstitious than baseball players! The trouble comes when people adopt the attitude that things go wrong/right and it’s completely out of their control– it’s not a very “scientific” approach.

        But sure, if you’re on a roll, you’re on a roll. I ride the little victories too..

  7. kevin. says:

    I think there’s something to it, and it’s not luck. It’s that all the prep work and debugging has finally come to fruition. The dicking around is over, and data can now be collected and interpreted with impunity. The cells are right, the buffers are good, the humidity is perfect, and it’s time to just do it.

    Then something changes and sweet spot goes away. The worst is when you don’t realize that you’re in a sweet spot, and you should have just been banging away because it wasn’t going to get any better than it was. I always take something that’s working fine and fuck it up trying to make it better.

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