Curating a menu of our stopping points that others can carry forward
We have science news to share. But it’s not the type of update you might expect. It’s kind of an anti-announcement, if you will. We are excited to tell you about all the latest research we don’t plan to work on — and why.
Because we are focused on exploration, Arcadia’s research is biased towards risk and breadth. For us, failing fast and often is a feature, not a bug. We see our “misses” as knowledge wins. With every roadblock, there is a lesson. If we’re doing it right, we should be churning out lots of lessons.
We know it’s not the norm to put time into sharing work we don’t plan to pursue. But we’ve decided that these pieces of work are more useful in the public domain than stored away in our freezer. Not only could this approach benefit other researchers, but there are also advantages to Arcadia as a company. Sharing science accelerates collective progress to move past mutual bottlenecks, brings in users for future tools we may build, gives potential job candidates clearer insights into what we do, and fosters a strong scientific community around us. We hope other companies consider adopting open science as a win-win strategy.
So today we’re announcing a new way we’d like to highlight our library of lessons, which we have coined our “Icebox.” These are literature searches, discussions, and research pilots that we worked on for a few months and decided to pause.
It’s easy to assume that when projects don’t move forward in science, it’s because of bad ideas. But as most scientists know, there are typically lots of practical bottlenecks to navigate before you have the privilege to exercise taste in choosing whether an idea is “worth” pursuing. We will describe these bottlenecks in our iced pubs.
The usefulness of this effort depends on other scientists, which could be you. We hope our Icebox pubs help de-risk your ideas. Maybe you are building a technology perfectly suited to address a critical hurdle. Or maybe it helps you replicate our work, avoid our mistakes, or bring renewed attention to a problem as fields evolve.
You will also note that sometimes we release efforts that did, in fact, technically pan out. These are opportunities that we believe other scientists would be better positioned to move on. It’s not a signal for you to back off — it’s an explicit invitation. We are stating our intent to not pursue it for various reasons, such as technical expertise, mission alignment, commercial viability, or some combination of these.
To ease interpretation, we’ve come up with eight common reasons that we may ice a project at Arcadia. We’ve listed and defined them below. Look out for our tags and explainers in actual pubs in case you hold the key to unlocking their progress. We are excited to see if this experiment accelerates our work or yours.
If you want to revisit one of our ideas and take it off ice, go for it. Comment publicly if there’s anything we can do to assist. We’ve tried to flesh out our pubs so that they’re actionable to the next set of researchers. Give us feedback. Let us know how we can help you heat something up.
To make an impactful advancement, we’d need further technological or conceptual development.
We've decided not to pursue this area of science at the moment because it doesn't play to our unique strengths as a company.
We learned what we needed to learn from the project and there is no reason to continue.
What we discovered during the project lands us in an area where we lack internal expertise, or taking the project to the next step would require expertise we don’t have (yet).
We tried to derisk a project, but could not overcome barriers that would enable us to move forward, or our hypothesis was wrong in a way that the remaining possibilities are not of interest for us to pursue.
The market is not appropriate, big enough, well-scoped enough, or is in an area where we are not well positioned as a company to have a competitive advantage. The time horizon to make this translationally actionable is too long or the challenges are too layered to align with translational goals.
The project can’t be done without massive resources, it’s inefficient, or we don’t have a high-throughput approach to scan enough search space and have confidence we’ll be successful.
We lack the facilities, equipment, or tangible resources to act on this project.