Nathan Clark

Labs need a sophisticated data stack. In fact, an advanced lab data tech stack is just as important as advanced scientific equipment. Modern lab data tech stacks should help labs answer key questions about their data, including: * What did a person do, or what should they do? To understand human

How often are your analytical instruments used? How often have you wished that you could figure out the sequence of errors that cause your automation runs to fail? How does freezer sample storage temperature affect your assays? All these questions come down to data. You’ve scaled to dozens or

Instruments such as mass spectrometers and flow cytometers form the backbone of every scientific lab. However, despite how important these assets are to every experiment and discovery, scientific instruments also pose challenges. They stop working for mysterious reasons, are slow to operate, and are often opaque. Labs don’t really

One of the most common points of failure in lab data automation is deceptively simple: things stop working, and labs don’t know why.  While one or two failures may be fixable by hand, once a lab has scaled up to dozens of workcells or hundreds of analytical instruments, that

ELN. LIMS. QMS. When it comes to lab data, there are a lot of three to four-letter acronyms. For many people, an ELN, shorthand for an electronic lab notebook, is arguably the most important. In fact, at Ganymede, many of the biotech companies we talk with view their ELN as