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This final blog of the series explores how lab automation can help scientists trust in data through real-time, reliable, traceable data transfer and agnostic integrations.

Automation was long touted as a cost-effective way to ‘deliver more, better’. It has certainly done that, enabling large-scale experimentation to generate data rapidly. 

The result is that we have almost an overabundance of data in certain areas, some of which can’t be contextualised in the way that it needs to be for it to be truly usable in a scientific setting. 

Without standardisation and traceability in particular, we’re hitting roadblocks when it comes to relatability and reproducibility.

Why is data such a challenge in life science

The convergence of technology and connectivity combined with a shift in attitudes and rife consumerism has given us unprecedented access to global behavioural, biophysical and biomedical data. 

This has given us a much better understanding of public health problems, and we’ve opened up many ways we can respond to these. However, those developing treatments have complex strings of data to rationalise, and much of that data was never collected with scientific application in mind.

Disparities in collection techniques mean commonalities are hard to find, and essential elements can often be missing from auxiliary information or metadata.  

We also still have too many manual processes – at the experiment stage and in data collection – so ensuring the quality of our data is time-consuming. 

Developing non-networked lab systems hasn’t helped, nor has locking drivers or data files. The lack of flexibility in lab automation solutions compounds this issue by generating this inoperable data at speed. 

A new approach to lab automation can help tackle some of these challenges, though. Let’s explore. 

Rethinking how data from lab automation can work harder for scientists  

This four-part series on building trust in automation has focused heavily on flexibility, usability and error handling, and all of these themes apply to data in life sciences, too. 

To reap the benefits of how much information we have access to, we believe that automation should produce high-quality, timely, usable data that is traceable, secure, and connected. 

Higher-quality data with automation

Although we have abundant data, it doesn’t mean we need less. Sometimes, we must collect more to contextualise things correctly and achieve the quality we need. We also cannot rely on manual activities for this data collection which increases the risks or transcription errors and reduces the reliability of the data.

More data points 

With lab automation that connects and transfers data from across integrated workcells or workflows, you can introduce many more data points to support analysis and promote further experimentation.

Our complete lab automation platform, LINQ, transfers data from all instruments it integrates, either directly into a LIMS or via any third-party data tools you use. 

LINQ also removes manual transcription tasks and collects much more information directly from the experiment.

Good lab automation software should help with standardising and transferring data as much as possible – systems that don’t have advanced data management options are adding to the problem.

One clinical genomics laboratory we worked with went from 3 data points per plate to 39 after automating with LINQ, highlighting how quickly the volume of data can become overwhelming without automated data management. 

339data points per plate before LINQdata points per plate after LINQ

Automated data transfer 

Lab automation is all about removing errors and improving the quality and reliability of results. This should extend to data, too. 

Automation that can facilitate the two-way sharing of information between instruments and databases – and, even better, automation that can standardise that data – is a must-have for high-throughput labs.

LINQ, for example, has been architecturally designed to work with most data repositories. 

It can connect the instruments and data generated from the workflows we automate to any ELN/LIMS, and the user dictates what they collect. 

This direct integration removes the need for manual data entry, which can lead to errors and can help contextualise the information by giving the user access to metadata files.

Timely information sharing with automation 

Data can become very old very quickly, and it can be difficult to keep track of what is the most up-to-date information available. 

This becomes a more significant challenge when you run advanced workflow automation that can test thousands of compounds in a few days. 

A high-throughput screening assay that we automated was able to test 10,000 compounds with five cell lines in just one and a half days; it would take one and a half weeks with comparable semi-automated solutions.

10,000 compounds5 cell lines1.5 days

With up to 100 plates per day running through this system per set-up, you can quickly see that the volume of data generated would be massive. Without this being delivered to a LIMS in real-time, there’s a risk of it becoming obsolete even by the end of that day. 

Because LINQ is a complete automation platform, every action is tracked without intervention and securely recorded via the software LINQ Cloud. Whatever data you tell the system to collect from components in the workflow will be transferred to your data lake in real-time for immediate use.

Improving data traceability with automation 

Data loses its value if its origin can’t be traced and the environment in which it was collected isn’t understood. 

Though often user groups of data initially start small and specialised, we’ve seen the need for sharing even the most nuanced information outside of original communities become common – our COVID-19 pandemic response, for example, saw public and private companies working together towards a vaccine.

To make it usable and preserve its lifespan, data needs to have an element of traceability, something that lab automation can provide with ease. 

By removing manual interactions in the lab and controlling them digitally, you can benefit from the dynamic information transfer capabilities of advanced software like LINQ Cloud. 

LINQ Cloud is the software component of our LINQ end-to-end workflow automation platform, and it’s how users dictate their needs, test the parameters of their experiments and visualise the results. 

Users can input parameters and run instructions, simulate and schedule experiments for confidence in the results, and analyse every action that’s taken place in the run. 

It captures data from all events in the run, and permission-controlled access allows users to export audit logs easily.

LINQ Cloud standardises the information it receives from the workflow and its instruments and transfers it to any data lake you need, instantly improving the traceability of your experiment data.

Connect automation for better collaboration 

FAIR data is findable, accessible, interoperable and reusable, which in 2024 means it needs to be available digitally. 

With cloud-based automation solutions like LINQ, data can be piped into a centralised resource in real-time for immediate use by whoever has access – across teams or entire organisations.

LINQ Cloud has 21 CFR-approved user management capabilities for internal collaboration, promoting confidence through secure access rules. You can set permissions at admin, creator and operator levels so everyone can see what they need to see, with audit trails for transparency and traceability.

LINQ Cloud aims to provide labs within a single source of truth. If we can do this, confident collaboration becomes more straightforward, and repeatability and reproducibility are safeguarded. 

Infrastructure-wide collaboration and beyond

The benefits of centralising the design, execution, troubleshooting and data collection increase exponentially when you move to infrastructure-level automation that connects all automation systems across your lab network to one highly capable software platform like LINQ Cloud.  

This centralisation creates a digitally shared bank of data and instruments that anyone can use to adapt, simulate and analyse experiments without impacting the day-to-day activities of systems.

Life science and technology came together to generate the type of data we needed to revolutionise drug discovery and therapeutics. Now that we’re generating that data at scale, we must look to technology again to make sure it’s interoperable and ready for the next revolution of connectivity.

Automata CEO Mosfata ElSayed put it best in his most recent update: 

The rise of automation, high-throughput technologies, and sensor and data integration in wet labs represents a paradigm shift in scientific work. These technologies, coupled with the seamless integration of data production and analytics, are ushering in a new era of efficiency, precision and scale.

Automated labs can now perform repetitive tasks with unparalleled accuracy, liberating scientists to focus on more intricate aspects of their research; moreover, the data generated in these automated processes can be instantly captured, analysed and visualised – at scale. That not only accelerates the pace of scientific discovery but also opens the door to the machine learning and artificial intelligence applications that are just starting to transform the way diagnostics, drug discovery and research is carried out.

Discover how LINQ can create a central source of truth for your lab, get in touch with the team today.

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The third in our series focuses on flexibility in lab automation and how a new approach to automation development will put scientists back in control.

While life science labs are at various stages of their automation journey, most now consider it a normal part of life. For advanced labs, though, automation is the key to continued and future success. 

In drug discovery, automation can speed up complicated processes end-to-end to give faster patient results and, ultimately, a competitive advantage to the business.

Even with such clear benefits, some platforms still don’t have one of the key features an ever-changing sector like pharmaceuticals needs: flexibility.

The challenge

Why flexibility in lab automation platforms is a challenge is no secret; any time you try to build something to serve all people in all ways, it’s hard. 

In life sciences, the variables that automation developers have to consider are some of the most complicated to tackle: 

• Every lab employs multiple vendors across instruments, consumables, databases and software – and there’s little standardisation across suppliers 

• Each lab has different measures of success – accuracy rates, turnaround time, and equipment up/down time; the KPIs are endless 

• Geography plays a part, with different countries and specific sectors having laws and regulations across multiple stages of the drug development process 

• Labs focus on different types of science employing different assays

• Assays themselves differ from lab to lab

• Space is a real consideration – we have a lack of space in the UK, whereas there is no such issue in other markets 

We could go on. The point is that these nuances mean the word ‘flexible’ becomes infinitely more important when designing lab automation solutions but intrinsically much harder to do.

Julie Huxley-Jones of GSK put it best when she said: 

We need data, hardware, software, and algorithms that allow us to work in a flexible environment with different levels of digital literacy – digital innovation rather than a single standardised best-in-class.

New automation principles are needed

Automata developed a lab automation platform on a principle we’ve dubbed ‘open, integrated automation’. We lead with this ethos for precisely the reasons outlined above: the future of lab automation depends on flexibility. We don’t believe this can be achieved without developing open access, vendor-agnostic solutions.

Flexibility isn’t unachievable; it just needs a new approach. 

Keep reading to understand more about flexibility in lab automation and how developers like us must respond to these challenges.

What flexible automation means for large organisations 

Scale 

Scaling up within pharmaceuticals can mean different things at different stages of the drug discovery and development cycle, but what we mean is ‘give me more’. 

Whether that ‘more’ is time or throughput, physically in terms of samples or digitally on the side of data, we need to give labs the ability to grow.

To respond, automation needs to look at barriers and concerns first. In labs, these may be:  

• A lack of space

• Inability to replace legacy systems

• Lack of knowledge or experience

• Vendor lock-in  

• Cost 

• Quality of output (results)

LINQ has been developed to overcome these barriers by creating a modular, reliable, easy-to-use automation experience. 

Start with a small automated workcell system and then add components when you want to automate more actions or include new processes. You can expand this system in weeks, not months.

Responsiveness  

Responsiveness can be about scale, but this truly means the flexibility to change anything fast. 

The COVID-19 pandemic is, unfortunately, the perfect example of this and one of the reasons Automata turned to lab automation development in the first place. Within months, it became clear that a fast response from the pharmaceutical sector was critical to controlling it. 

Though a pandemic is a worst-case scenario, changes in demand and needs happen constantly in this sector, and large-scale pharmaceutical labs can’t rebuild from scratch every time. 

LINQ can automate without discrimination for any goal, allowing you to decide what you automate and when.

Instruments can be switched out, data collection criteria can be changed, workflows can be designed and tested, and LINQ can pull on resources from across an entire connected automation network to allow for reallocation should the need arise. 

Where multiple LINQ systems are connected, users can pull on resources from across the automation infrastructure, so responding to changing demand and innovating is easy.

Facilitating innovation 

The same flexibility issues as above apply to automation in an R&D setting, but the consequences are even more significant: we’re at risk of entering a world where technology restricts what people can achieve, stifling creativity and innovation. 

Experimental design should be led by the need or hypothesis, not the capabilities or accessibilities of an automation system, or data. 

This is true at both hardware and software levels: hardware needs to be interchangeable and able to handle any array of chemistries, consumables, and sample types, while software should cope with new modalities and remove data silos. 

Data

Data ecosystems in the labs of the future will be heterogeneous, so creating automation that has strict data handling parameters that can’t be changed won’t work. We need automation platforms to consider multiple data points and integrate with any data lake. 

LINQ Cloud, the software component of the LINQ automation platform, can easily do this. 

• It transfers data in real-time for full viability and multipoint analysis

• Adapts to multiple client data formats

• Delivers data to any repository

• Integrates with any LIMS

There are no data restrictions on input or output with LINQ, so as well as allowing flexibility within a known system, it’s also ready to transfer new types of data from AI resources whenever they become scientifically suitable.

Collaboration 

Using pandemics as an example again, GSK issued a document detailing its position on pandemic preparedness in March 2022. In it, they point to collaboration as one of the underpinning principles: 

…the world’s ability to identify, contain and respond to pandemic threats requires coordinated disease surveillance, unfettered access to pathogen identification, expedited access to clinical trial networks and joint working on procurement and manufacturing readiness to enable global and domestic responses

Allowing access to the right information in a secure, measurable and traceable manner opens the door for much greater collaboration opportunities.

For internal collaboration, LINQ has 21 CFR-approved user management capabilities to promote confidence through secure access rules. You can set permissions at admin, creator and operator levels so everyone can see what they need to see. 

And because information tracked on LINQ, like audit logs, is easily accessible and can be exported, information is readily available whenever needed. 

Connectivity 

The industry needs more connectivity across the board to support this kind of collaboration. 

By connectivity, we mean: 

• The ability to connect the instrument data flows to data lakes

• The option to see the status of equipment easily

• Systems that allow interventions both digitally and physically from robot technology and software like orchestrators and schedulers. End-to-end workflow automation isn’t possible without this

• At a network level, multiple automated workcells need to communicate with a central platform and each other for real flexibility in resourcing and data collection

• Cloud connectivity should support centralised knowledge sharing and remote error handling 

Everything integrated on the LINQ platform is connected, from instruments to data to users. 

• LINQ Cloud communicates run instructions to the platform and integrated instruments, and it pulls information back from those elements into your data lake

• Anyone with user permissions can access LINQ Cloud online so global teams can utilise one source of truth

• Scara robot arms and a magnetic transport superhighway connect instruments and samples, and those actions are driven by a scheduling engine that bridges the physical and digital worlds 

The flexibility challenge will only worsen as AI introduces more data, our population becomes increasingly diverse, and new lab tech continues to launch. Automation can step up to this challenge; we just need to listen to what scientists tell us and work on building OUT all the restrictions we previously built IN. 

Put the power back in the hands of scientists by seeking solutions built with those open, integrated automation principles like Automata. Get in touch to explore LINQ today.

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The second in our series focuses on improving usability and functionality to support better adoption, future-proofing of investments, and faster innovation.

Apple has found the perfect recipe for making a luxury piece of technology into an everyday essential. In fact, over 21% of the world’s population uses its leading product, the iPhone.

Though incredibly complicated in its functionality, people with various needs from a variety of educational backgrounds and age groups choose the iPhone for its sleek design, reliability and user-friendly interface. And though it’s a smartphone at its core, but it serves everyone’s unique needs with minimal customisation required. 

Good automation technology is just as – if not even more – essential for ambitious labs as the iPhone is to modern-day life. Still, the level of advocacy for lab automation pales into insignificance in comparison. 

Automation developers have missed part of the memo; functional lab automation systems don’t need to be beautiful (although ours is), but they do need to be simple, adaptable, and helpful.

This second in our series on building trust in automation explores how it needs to balance familiarity with customisation to become part of everyday lab life – now and in the longer term.  

Reducing cognitive load for scientists

Aside from a growing skills gap that sees the sector struggling to recruit scientists (let alone those with automation experience), there’s also a cognitive load barrier when using some automation platforms: people are overwhelmed. 

Source: buildfire.com

According to Buildfire.com, an average person downloads 60 – 80 apps to their phone, using around nine daily and up to 30 every month. 

If the average person uses nine apps per day, that’s potentially nine different interfaces they’ve learned to operate; if you double that to include programmes used in lab life – ELNs, data studios, schedulers – that’s nearly 20 independent pieces of software being used by lab staff every day. 

Designing technology with poor usability is, at best, time-consuming and tedious for the end user and, at worst, a hindrance to productivity and discovery in the lab.

In his PhD thesis Students’ learning experience in the chemistry laboratory and their views of science…, Hendra Agustian, University of Copenhagen, suggests that improperly managing cognitive load – or experiencing too much unimportant ‘noise’ – inhibits deep learning, particularly for novice scientists. 

His figure below outlines how anything not essential – like learning to use an excessively complicated piece of automation software, for example,… – takes away a person’s ability to learn lab fundamentals successfully.

Source: Agustian, Hendra. 2020/02/21

When you consider how many instruments are in regular use and combine that with a steep on-the-job learning curve, it’s no surprise there’s little enthusiasm for complicated automation technology, too. 

Simple design for improving confidence  

Our complete automation platform – LINQ – has been designed and engineered with the cognitive load of people in the lab in mind, at both a hardware and a software level. 

It’s easy to see all the moving components on the bench, and it uses lighting hints to connect what’s going on between the bench and the associated operating software, LINQ Cloud. 

We know that it’s typically the digital components of lab automation that lack user-focused design, so let’s focus on software.

Making automation software user-friendly

LINQ Cloud’s UI has a canvas-style workflow designer that enables a whiteboarding experience where users (regardless of automation experience) can ‘sketch out’ their experiments, build in parameters, conditionals and more with confidence.

Building a workflow with LINQ Cloud

Tabbed navigation, colour and simple profiling cues immediately inspire recognition, building familiarity, confidence and enthusiasm from the outset. 

LINQ Cloud also doesn’t require coding skills (though it’s Python-friendly if you need it) and has user-based permissions; operators aren’t overwhelmed with administration tools, and administrators don’t have access to the customisation tools that power users do.

Customisation when needed rather than as standard

Though the fastest way to remove usability barriers in most instances is to allow minimal customisation, 100% no-code software isn’t the way to future-proof lab automation solutions. 

The sector is all about challenging the norm, so why does so much lab automation software get it wrong? It’s either too customisable, making standardisation, traceability, and repeatability a nightmare, or so inflexible that it becomes unusable in too many circumstances. 

Programming can automate time-intensive tasks like data sorting and documentation; it can update models and results quickly and facilitate collaboration, with even entire protocols being shared worldwide on platforms like protocols.io. So, it makes no sense to remove customisation and data accessibility features from automation software altogether. 

LINQ Cloud is customisable with Python for out-of-the-box thinking, and with those all-important user permissions built-in as standard, it’s suitable for tech novices and experts alike.

Flexible experiment design, even with automation 

In her keynote at last year’s Lab of the Future Europe conference, Julie Huxley-Jones, VP Scientific Digital and Tech at GSK said: 

Each new concept that we test, experiment we do, piece of equipment we use, or modality that comes along allows us to challenge what we thought was possible beforehand…we cannot have an ecosystem where software doesn’t recognise these conjugates; we cannot have an ecosystem where hardware in our labs isn’t able to deliver the capability to test multifactorial medicines that we’re trying to discover and develop; we cannot be in a place where our physical constraints about what we can do in the lab hinder the human mind

Because LINQ has been designed without a particular lab or experiment type in mind, it’s entirely physically and digitally agnostic. We can integrate any instrumentation via LINQ Bench, and LINQ Cloud works with any data or tech stack. 

Multipurpose automation 

Because LINQ Bench is modular and vendor agnostic, you can combine more robotic benches in any configuration as you grow. Add and move instruments as needed, change reagents and parameters easily, and maintain access to equipment for manual experimentation too. 

Flexible growth is possible with modular systems like LINQ

With solutions like LINQ, automation becomes possible for lab spaces too small for traditional large systems, opening up the ability to conduct exciting experimentation at scale in even the smallest R&D facilities.

Risk-free creativity 

LINQ Cloud’s interface is so user-friendly that anyone from complete novices to programmers can design robust experiments and simulate them for risk-free creativity in the lab.

Once executed, data from each facility/experiment/instrument and user is standardised and transferred to your data lake with full traceability – in real-time – so anyone interested in exploring these results has access from anywhere in the world.

Future-proofing investments

No one is questioning the importance of automation in the lab (or at least we hope not in 2024), but many people are asking how to ensure any investment made now will stay usable for years to come.  

The automation market is complicated, but there is one truth regardless of what your lab needs are: if it’s too intimidating, too inflexible, and too unreliable, people will keep choosing the more manual option, even if it takes more time.

Find a long-term automation partner like Automata that understands how to design solutions fit for science and scientists. 

We have ex-scientists and lab leaders working alongside engineers and programmers to build next-generation automation platforms like LINQ. Get in touch to see whether we’re the right partner for you.

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The first in our series focuses on error handling and covers not only how automation can reduce experiment failure but recover when issues occur. 

In a recent keynote at the Lab of the Future Europe event, VP Scientific Digital & Tech at GSK Julie Huxley-Jones said: 

No matter what transformation we have in the lab, we still have too high a failure rate when it comes to discovering new medicine.

This highlights one of the most essential parts of successfully integrating automation into a lab: if people don’t trust technology or its results, they won’t use it.

For automation to become a partner in experiment design and part of lab infrastructure, we need to improve reliability, provide better ways for users to understand points of failure, and build solutions that can respond to errors intelligently – and fast.

Automation that reduces experiment failure

Removing human variables from experimentation is one of the reasons automation exists, and it does it really well.

One clinical genomics lab we worked with reduced the risk of manual errors by 88% while tripling their output across a fully automated, next-generation sequencing sample preparation workflow. 

This system involved our lab automation platform, LINQ, seamlessly integrating 16 individual hardware components robotically and digitally to automate both pre and post-PCR testing workflows. 

A pre-PCR system on LINQ

A post-PCR system on LINQ

This level of integration may seem completely unmanageable for a workforce without trust in automation, but with user controls in place, access to transparent data, and pre-execution testing, trust can quickly be built.

Transparent data

For labs to undertake root cause failure analysis, data files need to be accessible. 

With LINQ we don’t lock audit logs, so understanding anomalies and errors becomes easy. And because we’re vendor agnostic, any equipment within the workflows we automate with LINQ is digitally connected – whether it’s one workflow in one lab or many workcells across an entire global network, you can analyse and compare run time data from them all.

We also allow the import and export of information like run instructions, so integrated SOPs reduce the risk of operator error even for new automation users. 

Run logs are visible and downloadable in LINQ Cloud for transparent and remote error analysis and handling

Pre-execution testing

To build trust in technology, it can also be helpful to allow people to see what may happen before they commit to using valuable resources. 

LINQ Cloud, the software component of our platform, allows people to simulate runs before executing them. By enabling users to see the workflow schedule beforehand, they can also see where bottlenecks may occur, building trust in the sequence of automated actions and reducing avoidable failures. 

Simulating an experiment with LINQ Cloud

Dynamic replanning scheduling

There are various scheduling programmes on the market for labs; however, traditionally, customers have had to choose between static schedulers – that give run predictions based on known constraints – and dynamic schedulers that can adapt to failures but don’t offer the same upfront view.

We’ve developed a scheduler that delivers both, so users can trust the automation to replan without intervention if it encounters an issue. 

Our unique architecture enables real-time dynamic execution while constantly looking to the future and planning the best course of action to complete your workflow successfully.

A visual example of LINQ amending a run schedule

Parameterisation

Parameterisation with LINQ is easy, as instrument methods, time constraints, data transfer events, conditionals, and customisations are visible and can be amended during a workflow build.  

LINQ uses this information to determine the most effective route to completion, and the scheduling engine will respond if any of these change during a live run, dynamically rerouting to keep delivery on track.

By building tools like this users will gradually trust automation to deliver alongside them, secure in the knowledge that their needs are being considered without intervention and monitoring.

Automation that responds to failure 

Many modern automation solutions – robotic, digital or combined – have some level of the previously discussed error tracking and mitigation, but this hasn’t been enough. 

In reality, it’s what a system actively does to overcome errors that’s important. Failure to keep a run on track can quickly impact timelines, and in high-throughput environments, the consequences of this can be costly.

We believe in providing scientists with true walkaway time, so there are several in-built ways our automation platform recovers when issues occur without needing people to monitor it. 

Remote handling

To ensure our system is suitable for even global lab networks, LINQ uses cloud-based software that enables users to design, build, simulate, and execute workflows remotely. 

Crucially, it also has built-in error notification and handling capabilities, inspiring trust in the system to deliver whether operators are in the room or not.

An example error notification a user may receive

 In the event of an issue, LINQ Cloud instantly:  

• Delivers in-app and email notifications of any issues encountered 

• Allows users to triage and react to errors as soon as they occur. Abort, repeat, skip and pause any part of the workflow remotely

• Facilitates collaboration so the right people from your team, business, and your Automata success team can explore problems and quickly help through cloud accessibility 

Reallocating resources

Based on operators’ instructions, instrument parameters, or a scheduler’s decisions, workflows may be rerouted and actions reallocated to instruments with the capability and capacity to step in.

The Scara robotic arms featured on LINQ work in harmony with the unique transport layer, mimicking the flexibility of actions you’d see if a scientist were tending the workflow. 

The transport layer delivers the right sample to the right instrument at the right time, all while being tracked and logged by LINQ Cloud software.

Networked resources

Because all automation provided by LINQ is connected on both a physical and digital level, every element across every automated workcell or workflow becomes a usable resource. 

When errors occur, LINQ has the ability to explore the whole interconnected network, giving many more ways to overcome errors and bottlenecks. 

Even if one workcell is out of action, the run can continue by reallocating the at-risk actions to other systems with capacity.

Three LINQ systems connected to one central cloud-based control hub

The more your automation with LINQ the more connected your labs become; the journey from automating single workflows to creating a fully automated lab network becomes easy.

Field support

There will inevitably be times when the automation developer can help solve issues that operators cannot. When you work with us to automate your lab, you’ll have access to our field services team of scientists, programmers and engineers, all ready to provide fast on-site and remote support should you need it. 

We can triage issues remotely or on-site and have developed secure on-site backup solutions should internet outages occur, so you’ll never be offline or too far from help.

Honesty from automation vendors

Automation reduces the risk of human errors like contamination, mislabelling, using the wrong agents, and misrecording results, but it’s not infallible.

End-to-end automated workflows like that which LINQ can deliver still require human input, and they include integrations between many physical and digital components.

We have clients running LINQ with no issues; however, to claim a 100% run success rate would be misleading. So, as much as we focus on building reliable systems and maintaining uptime for our clients, we’ve also put the aforementioned features in place to help people understand when and why errors occur and, more importantly, in-built ways LINQ can overcome these. 

We need tech-scientist relationships to become partnerships, where instruments take on manual, time-consuming elements and people have the data and time they need to research, analyse, and discover. 

As developers, we refuse to shy away from the problems that still exist; we need a combined effort between scientists, industry, vendors and automation designers to make adoption not just easier, not just possible, but critical. Because to respond to the challenges we’re facing as a population, it is.