Thoughts from the team

The Environment’s Invisible Hand

I wanted to take a break from our regularly scheduled programming to highlight a recent true user success story, which illustrates the importance of collecting all the data – whether you think you need it or not.

This particular academic user- a student researcher – was doing growth studies on a specific model plant organism (Pisum sativum var. saccharatum), testing the effect of various experimental soil amendments on germination and seedling growth. The experiment was conducted indoors under ostensibly climate controlled conditions, using natural light exposure. Germination in non-dormant seeds is triggered by the presence of water – which re-hydrates stored food within the seed and activates hydrolytic enzymes – and oxygen. Under normal conditions, P. sativum is expected to germinate between 7-9 days after planting. The entire experiment was scheduled with a 9-day germination budget as this was a time-critical study and previous trials had yielded germination times in as little as 6 days. Almost as an afterthought, an Elemental Machines Element-A already in the general lab area was placed directly in the experiment pod to monitor light, humidity and temperature. (For reference, Element-As monitor ambient temperature, humidity, air pressure and light levels. )


Why Invest in a Smarter Lab?

Every single day, lab managers and scientists ask me why they should invest in a ‘smart lab’ when they already have solutions in place that “work just fine.” So let’s talk about what ‘just fine’ really means.

In most labs, ‘just fine’ refers to the tried-and-true–albeit antiquated–techniques used to support cutting-edge research. It often involves a lab tech walking around to manually check, record and transcribe temperature readings in the mistaken belief that a single data point over the course of the day is sufficient. In other cases, it’s the false comfort from a Chart Recorder that it is tracking readouts continuously. Except, upon closer inspection, it’s overwriting valuable data because someone forgot to change the paper disc at the beginning of the month.


Debugging the Lab

Since the very first line of code was written, there have been software bugs (one apocryphal story traces the origin of the term to 1946 when an actual moth was found trapped in the relays of a Harvard mainframe – and like all good code, was dutifully documented by being taped into a notebook). As a result, the development of debugging tools has closely mirrored the rise of modern software. From symbol tables and breakpoints to the sophisticated predictive code profilers of the 21st century, better debugging tools have enabled us to create sophisticated and smoothly functional software.


2017 | The Year of the Smart Lab

Now that 2017 resolutions have been made (and, perhaps, already broken), I’m going to go on record with the prediction that 2017 is going to be the year of the Smart Lab. From industrial and commercial sectors, to the smart home, smart technologies have made major inroads in every category except one — scientific research.


Good News, Bad News | New Generation of Solutions for the Smart Lab and Beyond

As a technologist and a scientist, it is very gratifying to see the technologies that have transformed business and consumer markets being applied to the challenges in the laboratory and beyond. Miniaturization of components, sensors, machine learning, wireless communications, and big data have shaped other industries, and are finally positioned to make a huge impact in the science-based organizations that are doing the most for human-kind.


Your lab is talking. Are you listening?

If you’ve ever spent time around children, you know that when all is quiet that something’s not right. You expect a baseline level of comforting noise – chattering, thumping, and a general buzz of activity and engagement – sometimes punctuated by the occasional happy shriek or sad wail. The same is true in the research lab.


Disrupting the Lab Not the Scientist

Disruptive innovation has made its way through most industries as a driving force for advancement and change.
But, the scientific research sector, whether public or private, has remained largely untouched by the benefits of disruptive innovation. The result? Two of life science’s biggest hurdles – astoundingly high lack of experimental reproducibility, and the fact that it continues to take 10+ years and over $2 billion to develop a life saving drug in the year 2016 – are still ongoing challenges.


The Case of the Missing Data: Metadata

Research scientists are swimming in data. Every day. Every experiment. The data and the integrity of the data are paramount.They provide documentation of scientific processes and hold keys to success, failure and reproducibility. But there is always missing data in most experimental research – information that you should have collected that gives context to the experimental data itself.