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",[346,353,355,361,362,368],{"start":347,"end":348,"type":349,"data":350},61,71,"hyperlink",{"id":351,"type":15,"tags":352,"lang":17,"slug":54,"first_publication_date":55,"last_publication_date":56,"uid":57,"url":58,"link_type":23,"isBroken":25},"abF1UBEAACIAchKF",[],{"start":347,"end":348,"type":354},"strong",{"start":356,"end":357,"type":349,"data":358},149,169,{"id":359,"type":15,"tags":360,"lang":17,"slug":32,"first_publication_date":33,"last_publication_date":34,"uid":35,"url":36,"link_type":23,"isBroken":25},"abA3BxEAACIAb8C9",[],{"start":356,"end":357,"type":354},{"start":363,"end":364,"type":349,"data":365},248,264,{"id":366,"type":15,"tags":367,"lang":17,"slug":44,"first_publication_date":45,"last_publication_date":46,"uid":47,"url":48,"link_type":23,"isBroken":25},"abKGpREAACMAc_6h",[],{"start":363,"end":364,"type":354},{"type":318,"text":370,"spans":371,"direction":315},"The problem isn't the science. 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That's the difference between validating ML models at the pace the science demands, and being bottlenecked by data handling.",[423,425],{"start":27,"end":424,"type":354},22,{"start":426,"end":427,"type":349,"data":428},30,42,{"id":429,"type":299,"tags":430,"lang":17,"slug":432,"first_publication_date":433,"last_publication_date":434,"uid":435,"url":436,"link_type":23,"isBroken":25},"aZW_jBEAACMAKELb",[431],"Case Study","how-cradle-scales-next-gen-lab-automation-with-unitelabs--benchling","2026-02-18T16:30:47+0000","2026-05-22T13:01:04+0000","cradle-benchling-case-study","/resources/blog/cradle-benchling-case-study/",{"type":318,"text":438,"spans":439,"direction":315},"Faster decision cycles. Machine-learning model data turnaround can drop from weeks to days with better lab data management. All labs must compete on the speed of their design-test-iterate loop, so that compression is the business outcome, not a side effect.",[440,442],{"start":27,"end":441,"type":354},23,{"start":443,"end":444,"type":349,"data":445},168,192,{"id":446,"type":15,"tags":447,"lang":17,"slug":75,"first_publication_date":65,"last_publication_date":76,"uid":77,"url":78,"link_type":23,"isBroken":25},"aMKx0xMAACIAgODy",[],{"type":318,"text":449,"spans":450,"direction":315},"Data quality by default. Removing manual handling from the data pipeline eliminates transcription errors as a category. Structured capture produces FAIR data (Findable, Accessible, Interoperable, Reusable) without additional cleanup steps. Industry benchmarks for well-implemented automated data transfer systems frequently cite error rates below 0.5%, compared to manual processes where transcription error rates of 1–4% are common (and painful).",[451,453,458],{"start":27,"end":452,"type":354},24,{"start":454,"end":455,"type":349,"data":456},148,157,{"link_type":134,"url":457,"target":137},"https://www.go-fair.org/fair-principles/",{"start":459,"end":460,"type":349,"data":461},324,328,{"link_type":134,"url":462,"target":137},"https://www.researchgate.net/publication/386427772_Error_rates_of_data_processing_methods_in_clinical_research_A_systematic_review_and_meta-analysis_of_manuscripts_identified_through_PubMed",{"type":318,"text":464,"spans":465,"direction":315},"Regulatory confidence. Automated audit trails mean compliance demonstration is a reporting exercise rather than a reconstruction project. For regulated environments, this reduces inspection preparation time significantly and removes the risk of gaps in documentation.",[466],{"start":27,"end":424,"type":354},{"type":318,"text":468,"spans":469,"direction":315},"AI-readiness as a concrete outcome. Labs where data is structured, contextualized, and consistently captured can feed models that reflect how the lab actually works. Labs where data is scattered across vendor software folders are not in that position, regardless of how capable their AI tooling is.",[470],{"start":27,"end":471,"type":354},35,{"type":381,"text":473,"spans":474,"direction":315},"3 Challenges of Lab Data Management",[],{"type":318,"text":476,"spans":477,"direction":315},"This all sounds sensible so far, but what are the biggest challenges? Let's take a look:",[],{"type":479,"text":480,"spans":481,"direction":315},"heading3","1. Vendor Lock-in and Integration Gaps",[],{"type":318,"text":483,"spans":484,"direction":315},"Instruments from different vendors speak different protocols. Most ship with proprietary software designed for standalone use. Every new instrument potentially means another integration project, more training time, and more fragile connections. ",[],{"type":318,"text":486,"spans":487,"direction":315},"But there is a better way. Robert Zechlin, Co-CEO of UniteLabs, says that removing vendor software from the middle and replacing it with Python code gives labs \"better observability into what the instrument is actually doing, and makes it possible to control different instruments in a more consistent way\".",[488],{"start":489,"end":490,"type":354},27,41,{"type":479,"text":492,"spans":493,"direction":315},"2. Data Without Context",[],{"type":318,"text":495,"spans":496,"direction":315},"A result file that's disconnected from its sample ID, protocol version, and instrument settings is hard to interpret, impossible to audit, and unsuitable for AI or ML models. Scattered, unstructured data that's stored in folders without consistent naming conventions is a surprisingly common problem, even in sophisticated labs. Which is why UniteLabs stores it all as a single data object in the tool of your choice.",[497],{"start":498,"end":499,"type":349,"data":500},352,365,{"id":501,"type":15,"tags":502,"lang":17,"slug":84,"first_publication_date":65,"last_publication_date":85,"uid":86,"url":87,"link_type":23,"isBroken":25},"aMKyExMAACEAgOFR",[],{"type":479,"text":504,"spans":505,"direction":315},"3. Scaling multi-stage workflows",[],{"type":318,"text":507,"spans":508,"direction":315},"Lab data management challenges compound when workflows span multiple sequential stages. With that in mind, a US-based genomics startup that set out to automate a full NGS pipeline asked UniteLabs to step in and build a data management infrastructure that could be extended without full rebuilds at each stage.",[],{"type":381,"text":510,"spans":511,"direction":315},"Lab Data Management System Types",[],{"type":318,"text":513,"spans":514,"direction":315},"The market uses overlapping acronyms for lab data management systems, which is rather confusing. Here's a quick overview of what each system actually does, and which lab situations they fit best:",[],{"type":318,"text":516,"spans":517,"direction":315},"Laboratory Information Management Systems (LIMS) center on sample tracking and chain of custody. Strong on operational management and compliance documentation; historically weaker on direct instrument integration or experimental flexibility. They're best for clinical labs, CROs, QC labs, and high-throughput testing environments.",[518],{"start":27,"end":519,"type":354},48,{"type":318,"text":521,"spans":522,"direction":315},"Scientific Data Management Systems (SDMS) archive raw instrument data in its native format alongside metadata. Good for compliance and data integrity at the point of generation. These tend to function as archives rather than active workflow tools. They're best for regulated environments that need long-term data retention and audit trails.",[523],{"start":27,"end":490,"type":354},{"type":318,"text":525,"spans":526,"direction":315},"Electronic Lab Notebooks (ELN) replace paper notebooks with structured, version-controlled, searchable records. They're often the interface that scientists interact with most, which is exactly why it matters whether they can also serve as the control surface for automation, not just documentation. They're best for research labs, academic settings, and any environment where protocol documentation and experimental records are central.",[527,528],{"start":27,"end":426,"type":354},{"start":529,"end":530,"type":349,"data":531},263,273,{"id":532,"type":299,"tags":533,"lang":17,"slug":534,"first_publication_date":535,"last_publication_date":536,"uid":534,"url":537,"link_type":23,"isBroken":25},"aeuO4BIAACsA8Q09",[302],"what-is-lab-automation","2026-04-26T12:11:22+0000","2026-05-25T16:26:05+0000","/resources/blog/what-is-lab-automation/",{"type":318,"text":539,"spans":540,"direction":315},"Integrated platforms and automation OS layers connect all of the above (instruments, LIMS, ELN, and data pipelines) through a single infrastructure layer. This is where most of the value is created, and where most implementations run into trouble when it's absent. These are best for labs running multi-instrument workflows, platform-heavy biotechs, and any other organization where throughput, data quality, and AI-readiness are strategic priorities. This is where UniteLabs solutions fit best.",[541,543],{"start":27,"end":542,"type":354},45,{"start":544,"end":545,"type":349,"data":546},466,485,{"id":547,"type":15,"tags":548,"lang":17,"slug":18,"first_publication_date":19,"last_publication_date":20,"uid":21,"url":227,"link_type":23,"isBroken":25},"aXCz0BAAACEA8zfd",[],{"type":318,"text":550,"spans":551,"direction":315},"Most labs don't need to choose between these categories. They already have one or more in place. The more pressing question is how to connect what they already use to the instruments and workflows that generate the data.",[],{"type":397,"url":553,"alt":554,"copyright":326,"dimensions":555,"id":558,"edit":559},"https://images.prismic.io/unitelabs/aW-QFAIvOtkhBvVR_Integrate.png?auto=format,compress","Connect anything, automate everything",{"width":556,"height":557},1805,878,"aW-QFAIvOtkhBvVR",{"x":27,"y":27,"zoom":39,"background":330},{"type":381,"text":561,"spans":562,"direction":315},"A Note on Lab Data Analytics and Dashboards",[],{"type":318,"text":564,"spans":565,"direction":315},"Data capture is only half the picture. The other half is being able to act on what you've collected.",[566],{"start":348,"end":567,"type":354},99,{"type":318,"text":569,"spans":570,"direction":315},"Modern lab data management platforms increasingly include built-in analytics and reporting tools that go well beyond exporting a CSV. ",[],{"type":318,"text":572,"spans":573,"direction":315},"Real-time dashboards make it possible to monitor experiment status, instrument outputs, and quality metrics as they happen, and deliver alerts. Trend identification across runs helps to surface systematic issues (instrument drift, reagent variability, operator-specific patterns) that batch-level review would miss.",[574],{"start":27,"end":575,"type":354},20,{"type":318,"text":577,"spans":578,"direction":315},"Automated quality thresholds take this further: rather than a scientist reviewing every result, the system flags edge cases and only surfaces those that require human judgment. Code-based workflows can select which samples progress based on user-defined criteria, routing only qualifying samples through the pipeline. Human-in-the-loop validation steps handle the exceptions.",[579],{"start":27,"end":580,"type":354},28,{"type":318,"text":582,"spans":583,"direction":315},"For labs feeding data into AI or ML models, this analytical layer matters as much as the capture layer. ",[],{"type":318,"text":585,"spans":586,"direction":315},"High-quality, structured, contextualized data that arrives in real time (rather than batched weekly after manual cleanup) is what makes model training fast and reliable. At Cradle, the shift from manual to automated data handling reduced ML model data turnaround from weeks to days.",[],{"type":381,"text":588,"spans":589,"direction":315},"Methods of Lab Data Integration",[],{"type":318,"text":591,"spans":592,"direction":315},"Better lab data integration is easier said than done. Here's what to expect:",[],{"type":318,"text":594,"spans":595,"direction":315},"Legacy instrument integration is often the hardest part. Older instruments may lack APIs, export only proprietary file formats, or require vendor software as an intermediary. The practical options are: middleware that translates between the instrument and your platform; file-watchers that ingest exports automatically; or firmware-level access via modern connectors. The latter is significantly more robust: firmware-level connections provide real-time control and sensor data, not just end-of-run file exports.",[596],{"start":27,"end":597,"type":354},29,{"type":318,"text":599,"spans":600,"direction":315},"Cloud vs. on-premise is less binary than it used to be. Most modern platforms support hybrid architectures — cloud execution for workflow orchestration and data storage, edge deployment for running workflows locally when latency or connectivity is a constraint. For regulated environments with strict data residency requirements, private deployment options (SOC 2-compliant, on-premise or private cloud) are increasingly standard.",[601],{"start":27,"end":575,"type":354},{"type":318,"text":603,"spans":604,"direction":315},"What to expect during implementation depends heavily on how much existing infrastructure there is. A realistic timeline for onboarding a new automation platform from instrument integration through to running production workflows can now be measured in weeks, not months, if the platform is genuinely vendor-agnostic and the implementation team is experienced. The first milestone is usually a single functioning workflow on a single instrument. From there, expansion is additive rather than disruptive.",[605],{"start":27,"end":606,"type":354},36,{"type":479,"text":608,"spans":609,"direction":315},"Practical integration checklist:",[610],{"start":27,"end":611,"type":354},32,{"type":613,"text":614,"spans":615,"direction":315},"o-list-item","Does the platform connect to your existing ELN or LIMS bidirectionally, not just as a file export destination?",[],{"type":613,"text":617,"spans":618,"direction":315},"Can it connect your specific instruments, and how long does a new connector take to build?",[],{"type":613,"text":620,"spans":621,"direction":315},"How does it handle errors mid-run? Does it notify, log, and allow recovery — or does it fail silently?",[],{"type":613,"text":623,"spans":624,"direction":315},"Does data arrive already linked to sample and metadata, or does reconciliation happen downstream?",[],{"type":613,"text":626,"spans":627,"direction":315},"Is the workflow logic version-controlled and auditable?",[],{"type":381,"text":629,"spans":630,"direction":315},"How to Choose the Right Lab Data Management Solution",[],{"type":318,"text":632,"spans":633,"direction":315},"Here are a few questions that cut through vendor comparisons:",[],{"type":318,"text":635,"spans":636,"direction":315},"Where does data currently get stuck? The answer is usually somewhere specific. A particular instrument with no integration, a format that nothing downstream can read, or a manual export step. Start there, not with a comprehensive platform overhaul.",[637],{"start":27,"end":606,"type":354},{"type":318,"text":639,"spans":640,"direction":315},"What do scientists already use? Adoption matters more than features. A solution that lets scientists stay in their existing LIMS or ELN to trigger workflows will be used consistently. One that requires context-switching for every run will require workarounds.",[641],{"start":27,"end":642,"type":354},31,{"type":318,"text":644,"spans":645,"direction":315},"How vendor-agnostic is it, really? Platforms that require proprietary integrations for every new instrument create ongoing dependency. Look for bidirectional connectors that give direct instrument control across vendors, without routing through proprietary software.",[646],{"start":27,"end":647,"type":354},34,{"type":318,"text":649,"spans":650,"direction":315},"Can it scale without a rebuild? Start with one workflow. Add instruments. Expand to multiple workcells. The architecture should support incremental gains.",[651],{"start":27,"end":642,"type":354},{"type":397,"url":653,"alt":654,"copyright":326,"dimensions":655,"id":658,"edit":659},"https://images.prismic.io/unitelabs/aW-P0QIvOtkhBvVJ_Automate.png?auto=format,compress","UniteLabs is modular by design, and scalable by nature",{"width":656,"height":657},2072,1080,"aW-P0QIvOtkhBvVJ",{"x":27,"y":27,"zoom":39,"background":330},{"type":318,"text":661,"spans":662,"direction":315},"Beyond the above questions, you should look for a few recurring infrastructure characteristics, which are  achievable now, and will serve you well in the years to come:",[],{"type":318,"text":664,"spans":665,"direction":315},"Build on FAIR data principles from day one. FAIR data (Findable, Accessible, Interoperable, Reusable) is increasingly a baseline expectation. Labs that retrofit FAIR compliance incur a significant cost in time and data quality. Labs that capture structured, contextualized data by default from the start don't have to.",[666],{"start":27,"end":667,"type":354},43,{"type":318,"text":669,"spans":670,"direction":315},"Adopt industry data standards. Frameworks like Allotrope are gaining traction because they solve the interoperability problem at the schema level. Data that uses a common structure can move between instruments, platforms, and organizations without custom translation. Building on established standards now prevents proprietary lock-in later.",[671,672],{"start":27,"end":426,"type":354},{"start":673,"end":674,"type":349,"data":675},47,56,{"link_type":134,"url":676,"target":137},"https://www.allotrope.org/",{"type":318,"text":678,"spans":679,"direction":315},"Choose modular over monolithic. Write workflow logic once, in code, and deploy it across instruments. Build reusable libraries. Version-control everything. This approach compounds: each workflow you build adds to a foundation that the next workflow can use. Monolithic systems that require full commitment upfront don't compound, they accrete technical debt.",[680],{"start":27,"end":642,"type":354},{"type":318,"text":682,"spans":683,"direction":315},"Make your lab readable and writable for AI. AI agents are coming that can design experiments, execute them, interpret results, and iterate with minimal human intervention for routine decisions. That requires infrastructure where devices expose their capabilities in machine-readable formats, workflows can be triggered programmatically, and data arrives with enough context for a model to act on it. Labs that build this infrastructure now have a head start that is difficult to replicate later.",[684],{"start":27,"end":667,"type":354},{"type":318,"text":686,"spans":687,"direction":315},"The co-founder of a US-based genomics startup working toward the most automated next-generation sequencing lab in their sector described the goal clearly: an environment where method development to production is 10x faster, and where the platform scales with the science rather than constraining it.",[],{"type":381,"text":689,"spans":690,"direction":315},"The Bottom Line",[],{"type":318,"text":692,"spans":693,"direction":315},"Lab data management isn't a back-office concern. The quality of a lab's data infrastructure directly determines how fast it can move, how reliable its results are, and how confidently it can scale. This includes moving toward the AI-driven workflows that are increasingly defining competitive drug discovery, protein engineering, and genomics.",[694],{"start":695,"end":696,"type":349,"data":697},230,249,{"id":698,"type":15,"tags":699,"lang":17,"slug":64,"first_publication_date":65,"last_publication_date":66,"uid":67,"url":68,"link_type":23,"isBroken":25},"aMKxxxMAACIAgODX",[],{"type":318,"text":701,"spans":702,"direction":315},"The gap between where most labs are and where they could be is often an integration and implementation challenge: connecting instruments that don't talk to each other, getting data out of vendor software silos, and building workflows that scientists will actually use.",[],{"type":318,"text":704,"spans":705,"direction":315},"Start with this question: where does the data actually get stuck? The answer will tell you more than any vendor comparison matrix.",[706],{"start":414,"end":707,"type":354},64,"rich_text$49a7df53-a214-4267-822b-a992a1eca783","rich_text",{"variation":335,"version":336,"items":711,"primary":712,"id":753,"slice_type":754,"slice_label":326},[],{"subheading":713,"image":717,"text":726},[714],{"type":381,"text":715,"spans":716,"direction":315},"Discover UniteLabs",[],{"dimensions":718,"alt":721,"copyright":326,"url":722,"id":723,"edit":724},{"width":719,"height":720},519,346,"Lab scientists discussing an experiment","https://images.prismic.io/unitelabs/aCybiydWJ-7kSWre_ClosetheLoop.jpg?auto=format%2Ccompress&rect=0%2C2%2C5192%2C3461&w=519&h=346","aCybiydWJ-7kSWre",{"x":27,"y":725,"zoom":39,"background":330},2,[727,735,745],{"type":318,"text":728,"spans":729,"direction":315},"Want to find out more about the UniteLabs platform? Head to our Solutions Overview.",[730],{"start":731,"end":732,"type":349,"data":733},63,82,{"link_type":134,"url":734,"target":137},"https://unitelabs.io/lab-automation-solutions/",{"type":318,"text":736,"spans":737,"direction":315},"Read our latest case study to discover how biotech startup Cradle boosted lab efficiency 4x by integrating UniteLabs with Benchling to automate data and workflows.",[738,742],{"start":739,"end":414,"type":349,"data":740},16,{"link_type":134,"url":741,"target":137},"https://unitelabs.io/resources/blog/cradle-benchling-case-study/",{"start":743,"end":744,"type":354},59,65,{"type":318,"text":746,"spans":747,"direction":315},"Or simply book a call with one of our experts to find out how we can transform your lab!",[748],{"start":749,"end":750,"type":349,"data":751},10,21,{"link_type":134,"url":752,"target":137},"https://unitelabs.io/company/contact/","uneven_text_image_pair$a96a5cf9-3d8b-4da7-8e92-31675ea3632b","uneven_text_image_pair","What Is Lab Automation?","As workloads increase and become more complex, many lab leaders are turning to automation solutions to increase throughput. But what is lab automation? We dive in.",1779728632871]