Beyond The Lab


TFG: What’s the story behind Labstep?

Schofield: The project stemmed out of my own personal frustrations with how experimental data in lab-based science was recorded, captured and shared. I think there’s a lot of personal frustrations that you feel as an individual, but then these pain points have much larger global knock-on effects. When you have a look at that, we produce stability within science. More than half of all scientific research cannot be reproduced. People can’t demonstrate which steps result in findings. And the real understanding of those wider issues is really what lit a fire for me to go and try and tackle this.

TFG: The definition of science is something that can be reproduced so it’s shocking that this is such a huge problem. As a researcher at the University of Oxford, you must have seen this first-hand. 

Schofield: It’s madness when you look at the time and money spent on research that can’t be reproduced. And that’s often not the fault of the scientists, it’s just that this information maybe isn’t captured or can’t be properly shared. I know a lot of people have horror stories, like if a postdoc leaves the lab and it turns out they were writing their notes in Japanese so there’s this big black hole in knowledge. What we want to do is provide a much better alternative for scientists, which is a net positive addition to their day-to-day. But it also means that you’re tackling some of these bigger issues around reproducibility, and those advancements will trickle down onto a wider world impact in the future. 

TFG: Why have previous attempts to tackle this problem failed? 

Schofield: In the past, companies have raised hundreds of millions to go and build products that address this. There are companies that have been doing electronic lab notebooks, inventory management tools, etc. These digital solutions for science have been around for 10, 20, 30 years, yet the statistics show that adoption is incredibly low. 

Only 7% of scientists actually use a dedicated digital solution for capturing and recording their science, which is crazy. Science is advancing at an incredible rate with amazing analytical techniques and new hardware instruments, yet the way science is carried out hasn’t changed. It’s hugely inefficient. We started Labstep in response to these problems, but also in response to these other digital solutions that had tried to go and create a problem but hadn’t been adopted.

They haven’t been adopted because if you’re in a lab environment, and you’re working every day until 10 pm, the last thing you want to do is manually have to spend hours typing up a diary of what you’ve done, adding to your workload. And so we wanted to fundamentally turn that on its head to build a completely different experience around it.

More than half of all scientific research cannot be reproduced.

TFG: You’ve described Labstep in the past as “GitHub for lab experiments”. Can you expand on this idea?

Schofield: I’ve always liked that analogy because I think it strikes at the core of what we’re doing. And what we’re doing is really different from a lot of these other legacy solutions or different approaches that haven’t been adopted. Ultimately, they just bolted onto it a notebook diary, where you’re expected to type something out and keep a written record.

At the core of what we’re really trying to do at Labstep is allow you to focus on doing your science, executing your lab-based procedures, and then tie your inventory via an API to your experiments, automatically building these great records of what you’ve done. This record is generated for you for compliance, but also in case you go on to make these great discoveries, you can recreate those findings and make sure that your research is reproducible. Or if you’re collaborating with another lab group across the world, you can see in real-time what they’re doing, and you can comment and you can shift things around.

It’s a much more intuitive way of carrying out experiments and it kind of moves the emphasis away from manual curation and manual data entry. People have huge amounts of existing inventory and huge amounts of legacy material. And a big part of what we’re trying to do is making it as easy as possible for you to get all that information and lower that activation energy. GitHub is an amazing tool, and it does amazing things for the computational software development space and that’s really what we’re trying to emulate with science.

TFG: Academia can sometimes be a field that’s resistant to change. How has the scientific community responded to your tools?

Schofield: It’s interesting, isn’t it? Because science is very open-minded and progressive when it comes to the adoption of new technologies. But you’re right, there has been this big resistance to adopting new software solutions when it comes to actually the nuts and bolts of recording their experimentation. And I think a big part of that is because you’re incredibly busy and overworked, and you don’t want to have to invest mass amounts of time and effort in uploading stuff into a new system, and that’s really what we’ve tried to strip back.

What’s been really nice is that we’ve spent nothing on marketing. We’ve just focused on building an amazing product. And that growth has come organically just from individuals using it and sharing it with other colleagues and collaborators. And this is really positive and this is going to actually have huge value. We have over 100% retention of everybody that uses the platform, which is amazing. 

It's a much more intuitive way of carrying out experiments and it moves the emphasis away from manual curation and manual data entry

TFG: The product must speak for itself! And they say data is the currency of the future so having such easy access to it must be very valuable for researchers. 

Schofield: Data is so important, and it really is more so in science than in anything else. Because of this, we’ve had to navigate how to make sure that we can provide that security and that safety blanket. There’s full encrypting in transit, full encryption at rest and watertight audit trails in terms of timestamps with an uneditable audit trail for compliance pieces because we’re dealing with very valuable data here. We’ve kind of got best-in-class security put in place from the technical point of view, but also, what’s really unique about what we’re able to do is that we can give you that granularity of permissions and control and access codes so that you can share it securely rather than just sending stuff via email, and PDFs. 

When you look at labs of the future, you’re going to want to be able to leverage AI to be able to mine these datasets to get more insights and extract more innovation. To be able to do that you need to make sure that the data is incredibly well-annotated. What’s really unique about Labstep is that we’re able to tie that dataset to this sample and to these experimental conditions and these people. It means that you have an asset that can be mined and leveraged in the future, which is a real driver for some of the large organisations that we work with.

TFG: Labstep has seen a great adoption within academia but also in the wider industry. How has been the process of branching out?

Schofield: It has been very exciting. Initially, my co-founder was still doing a PhD at Oxford University, and we got traction within the university. We then followed that success with investment from Seedcamp and by going through the Google residency programme. The guidance we received there helped us build this amazing product and really work out how can we create a model that can scale and the platform has now grown to be used at 900 universities globally, including some massive real figurehead institutional deployments like the Francis Crick Institute, one of the largest life sciences research centres in Europe. But I think that what’s really exciting is that we’ve managed to branch out not just from having a massively positive impact in academia, but also to try and have that positive impact in the industry as well.

This is often where some other tools have fallen down in the past. In sciences, you need to have a solution that can work with both industry and academia, you need a solution that can work with chemists, with biologists, with material scientists, and you need something that can work for that really cool company that’s making alternative milk. I think the flexibility in the configurability of Labstep allows you to build these connected environments to meet your specific needs, which means that it works really well in both industry and academia.

There’s a lot of innovation in science and it’s really nice that labs that can have a positive impact in helping commercial companies tackle some of the global challenges. Our client Sherlock Biosciences was one of the first companies to develop a rapid diagnostic test for COVID, which was really exciting. And one of our biggest commercial customers works in alternative proteins and lab-grown meats. It’s really nice that Labstep can help with solving those issues. Our active users doubled in the last couple of months and have got a lot of really exciting traction, especially within the commercial sector. 

Ultimately, I feel like we're almost the best-kept secret within the industry

TFG: Obviously, you have a lot of very exciting things coming up, including a future funding round. Where do you see Labstep going in the next three to five years?

Schofield: I feel that we can really position ourselves to become the global tool for scientific experimentation. I think we feel like we’ve built this incredible tool, we’ve got these amazing metrics, and we deliver real value to the customers that we do have, and we now want to really start getting it out there to the wider community and having a much broader impact. And so that’s why we’re going to be eyeing up this funding round coming up, which will allow us to really turn up the dial and put Labstep in front of more people.

Ultimately, I feel like we’re almost the best-kept secret within the industry. From a scale up point of view, I think when you have a look at science, that has huge opportunities. I think that lab automation, AI, these technologies are here and they will be incredibly disruptive. We want to provide a springboard and a stepping stone to allow scientists to start using these and incorporating them more in their day-to-day R&D activities. And so I feel like if we can build this amazing ecosystem and this operating system for science, which allows scientists to run, execute, share and collaborate, e can also provide a springboard to give exposure to some of these other technologies to really change the way sciences is carried out for the better.

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