1/ Because my lab's about to undergo a full changeover—all the folks who started with me in 2014 are about to move on—I decided to formalize some of the lab's (neuro)scientific process / ethos / approach in today's lab meeting. Here are some of the slides about how we think.
2/ We begin with the idea of "technical debt" in software development, and how we can think about that extending to "research debt".
2/ We begin with the idea of "technical debt" in software development, and how we can think about that extending to "research debt".
2/ We begin with the idea of "technical debt" in software development, and how we can think about that extending to "research debt".
2/ We begin with the idea of "technical debt" in software development, and how we can think about that extending to "research debt".
2/ We begin with the idea of "technical debt" in software development, and how we can think about that extending to "research debt".
3/ Under- / over-fitting is when you use a non-optimal model to fit your data, one that doesn't generalize to new data. By analogy, using analysis approaches that are too general—or too specific—to your given dataset mean they don't generalize well to new data(sets).
3/ Under- / over-fitting is when you use a non-optimal model to fit your data, one that doesn't generalize to new data. By analogy, using analysis approaches that are too general—or too specific—to your given dataset mean they don't generalize well to new data(sets).
3/ Under- / over-fitting is when you use a non-optimal model to fit your data, one that doesn't generalize to new data. By analogy, using analysis approaches that are too general—or too specific—to your given dataset mean they don't generalize well to new data(sets).
3/ Under- / over-fitting is when you use a non-optimal model to fit your data, one that doesn't generalize to new data. By analogy, using analysis approaches that are too general—or too specific—to your given dataset mean they don't generalize well to new data(sets).
3/ Under- / over-fitting is when you use a non-optimal model to fit your data, one that doesn't generalize to new data. By analogy, using analysis approaches that are too general—or too specific—to your given dataset mean they don't generalize well to new data(sets).
4/ Modular, reusable, extendable, well-documented and designed approaches, with good testing on simulated data with known ground-truth (hard in biology!), that include detailed tutorials and troubleshooting is... difficult and time-consuming. But makes *everyone's* work better.
4/ Modular, reusable, extendable, well-documented and designed approaches, with good testing on simulated data with known ground-truth (hard in biology!), that include detailed tutorials and troubleshooting is... difficult and time-consuming. But makes *everyone's* work better.
4/ Modular, reusable, extendable, well-documented and designed approaches, with good testing on simulated data with known ground-truth (hard in biology!), that include detailed tutorials and troubleshooting is... difficult and time-consuming. But makes *everyone's* work better.
5/ Similarly, there's a lot of open data out there. None will be "perfect" for your needs, but if you analyze lots of it, you'll be working in parallel, learning analyses while *also* collecting your "perfect" dataset. Plus, there is power in combining many datasets!
5/ Similarly, there's a lot of open data out there. None will be "perfect" for your needs, but if you analyze lots of it, you'll be working in parallel, learning analyses while *also* collecting your "perfect" dataset. Plus, there is power in combining many datasets!
5/ Similarly, there's a lot of open data out there. None will be "perfect" for your needs, but if you analyze lots of it, you'll be working in parallel, learning analyses while *also* collecting your "perfect" dataset. Plus, there is power in combining many datasets!
5/ Similarly, there's a lot of open data out there. None will be "perfect" for your needs, but if you analyze lots of it, you'll be working in parallel, learning analyses while *also* collecting your "perfect" dataset. Plus, there is power in combining many datasets!
5/ Similarly, there's a lot of open data out there. None will be "perfect" for your needs, but if you analyze lots of it, you'll be working in parallel, learning analyses while *also* collecting your "perfect" dataset. Plus, there is power in combining many datasets!
6/ That's it. That's pretty much what we do. If you read this far, you're basically an honorary, virtual lab member now! Oscar the Oscillating Orca welcomes you to San Diego 😎⛱️ https://voyteklab.com/lab-members/ 
You can follow @bradleyvoytek.
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