The start of a New Year is traditionally a time when people take stock of what’s going on in their lives and plan ways to improve in the coming year. At Artel, we think that these New Year’s resolutions are relevant to business life as well as one’s personal life. So as we welcome 2016, why not inject a little of that improvement energy into your lab practices too?
Cleaner work areas; better, more efficiently organized labs; updated SOPs, methods, and protocols; improved data quality—all of these activities would fit well in any labs’ New Year’s resolution list. But as we all know, creating a new year of change means putting all those good intentions into practice. While we have a few things to say about the various components of quality in the lab (see our recent blog on LEAN approaches to lab organization, and stay tuned for an upcoming Digest post featuring expert customers’ best practices documentation), what really drives us is the end game: high data quality.
To help jump-start your new year of better data quality, we’ve gathered all of our lab reports—newly updated and revised—into a convenient, single eBook format.
Here’s a quick look at what you’ll find in its virtual pages, where good data quality begins with good liquid handling. (Note that while some of these lab reports focus on handheld pipettes (Lab Reports 2, 3, and 5), three are relevant to both handheld and automated liquid handling instruments (Lab Reports 1, 4, and 6), and Lab Report 7 covers the intersection of handheld and automated during assay transfer).
When testing the trueness and precision of liquid handling instruments, how many data points should you test for each volume? Two? Five? Ten? Find out how to determine the number of data points you need to ensure good performance in Lab Report 1.
It’s all in the wrist…and thumb…and posture…and a number of other factors that may seem like small details about how you operate a pipette, but can actually have a large impact on your data reproducibility and accuracy. Find out how to improve your pipetting technique in Lab Report 2.
Like office workers who get tendonitis from typing too much when in suboptimal postures, scientists and technologists who spend a lot of time pipetting can experience repetitive use injuries. In Lab Report 3, we recommend steps that you can take into consideration to reduce ergonomic issues caused when pipetting.
Do you know the difference between accuracy and precision? And what about this “trueness” term that keeps popping up in regulatory documents? Find out the important distinctions between these different measures of data quality in Lab Report 4.
Have you recently thought about your assays and how much variability in liquid handling they can tolerate? Do these observations influence your pipette calibration methods? If not, you’re not alone. It’s never too late to start thinking about these issues. You can learn more about figuring out what your pipette tolerances should be in Lab Report 5.
How often should you calibrate your pipettes? While there is no universal answer to this seemingly simple question—because everyone’s assays and risk-appetites are different—you can find out how to figure it out in Lab Report 6. Note that while the wording of this lab report covers handheld pipettes, many of the same principles also apply to automated liquid handlers.
If you’re doing an assay transfer—whether it’s automating a manual assay or pushing an assay from development to production—it’s not always a straightforward process. However, there are a number of different factors you can consider to smooth your way forward. Three of these are explained in Lab Report 7.
Wishing you all a good, productive New Year full of high-quality data!
Pia Abola is a scientist who walked out of the lab five years ago and stumbled into the world of marketing. She never had to look back because it turns out that she’s mostly doing the same things–both her lab work and her marketing work revolve around signalling and information transfer. Chemical, biochemical, behavioral, or digital signals, the math is the same — it’s just scale and medium that differs.