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Data Science AnalyticsTop 10 Best Lab Data Software of 2026
Top 10 Lab Data Software ranking for research teams. Compare Benchling, Labguru, and DataLadder by features, data handling, and costs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Benchling
Version-controlled sample and protocol entities with audit history across linked experiment records.
Built for fits when regulated teams need governed lab data linked to automation and APIs..
Labguru
Editor pickProgrammable API plus workflow automation built around record state and entity relationships.
Built for fits when regulated lab teams need governed data capture with API-driven automation..
DataLadder
Editor pickSchema-based workflow with lineage ties transformations to validated fields and dataset provenance.
Built for fits when lab teams need schema-driven workflows with API-triggered automation..
Related reading
Comparison Table
The comparison table evaluates lab data software across integration depth, including connector coverage, API surface, and how well each tool supports automation via workflows and extensibility. It also contrasts the data model and schema design choices that affect configuration effort, provisioning, and throughput. Admin and governance controls are compared through RBAC granularity, audit log behavior, and tenant or workspace controls.
Benchling
ELN LIMSLaboratory information management and electronic lab notebook with sample and inventory tracking, protocol management, and integrations for lab workflows.
Version-controlled sample and protocol entities with audit history across linked experiment records.
Benchling ties laboratory records to a structured data model that represents samples, sequences, assays, and experiments as typed entities with relationships. Each entity supports versioning and history so changes to protocol steps, results, and metadata remain attributable. The automation surface includes workflow configuration that can move work across statuses and enforce required fields before downstream actions.
Benchling’s API and extensibility require design discipline because schema and workflow configuration directly affect integration throughput and reporting consistency. Teams gain the most when external systems can map to Benchling entities and when custom fields are governed rather than added ad hoc. A common usage situation is integrating ELN or LIMS workflows with instrument runs where samples and results must stay linked for audit-ready traceability.
- +Entity versioning preserves protocol and result history for audit trails
- +Typed data model links samples, sequences, assays, and experiments consistently
- +Configurable workflows reduce manual status tracking across teams
- +RBAC and audit logs support governed collaboration across projects
- +Extensible API enables automation against entities, metadata, and events
- +Provenance fields keep instrument and human edits traceable
- –Schema choices impact integrations and downstream reporting structure
- –Workflow configuration can add overhead for small, ad hoc labs
- –Complex custom mappings require careful API and field alignment
Best for: Fits when regulated teams need governed lab data linked to automation and APIs.
More related reading
Labguru
ELNElectronic lab notebook with experimental workflow templates, experiment planning, and data capture that supports structured lab records.
Programmable API plus workflow automation built around record state and entity relationships.
Labguru is a lab data software designed for structured capture across experiments, samples, and assays, with a configurable schema tied to those entities. The integration depth shows up through API access for reads and writes, plus automation triggers that keep downstream systems aligned when records change. Automation and configuration are oriented around record state and relationships instead of ad hoc spreadsheets, which improves data consistency across sites.
A tradeoff is that deeper customization usually means defining and maintaining schema and workflow configuration in the system rather than editing forms on the fly. Labguru fits situations where instruments, ELN workflows, and LIMS steps must share identifiers and status updates at high throughput with predictable governance. Teams also use it when auditability is required for who changed what and when across controlled studies.
- +Configurable experiment and sample data model with clear entity relationships
- +API support for programmatic reads, writes, and integration workflows
- +Automation tied to record state transitions rather than manual steps
- +Admin controls with RBAC-style permissions and auditable change history
- –Schema and workflow configuration can require sustained admin effort
- –Complex cross-lab integrations can increase dependency on API conventions
Best for: Fits when regulated lab teams need governed data capture with API-driven automation.
DataLadder
RDMResearch data management software focused on creating versioned, auditable links between data, analyses, and experiments.
Schema-based workflow with lineage ties transformations to validated fields and dataset provenance.
DataLadder’s data model centers on defining entities, fields, and validation rules so datasets remain consistent across studies. The workflow editor ties transformations to a schema-aware pipeline, which makes field-level mapping and lineage auditable. Integration depth is strongest when lab data flows between spreadsheets, instrument exports, and a governed target, because mappings and transformations are carried through the workflow graph.
Automation and API surface support programmatic provisioning of metadata and triggering executions so pipelines can connect to scheduling and data capture systems. A tradeoff appears when workflows require extensive custom logic that is not expressed in the editor, since complex transformations may require exporting intermediate data for external processing. It fits well for laboratories standardizing enrichment, quality checks, and enrichment outputs before sharing results with an ELN, LIMS, or analytics stack.
- +Schema-aware workflow graph keeps field mapping consistent across transformations
- +API and automation hooks support programmatic pipeline configuration and execution
- +Lineage and validation rules make dataset provenance traceable
- +Configuration versioning supports reproducible reruns for new study batches
- –Highly custom transformation logic can fall outside the visual editor scope
- –Granular governance setup takes planning for role boundaries and schema changes
Best for: Fits when lab teams need schema-driven workflows with API-triggered automation.
LabVantage
Regulated LIMSLaboratory management software for regulated laboratory operations with workflow, sample, and data handling capabilities.
Workflow and form configuration with RBAC and audit logging for controlled, traceable data capture.
LabVantage centralizes lab data around configurable entities, workflows, and instrument-linked records to keep schemas consistent across teams. Integration depth centers on lab systems connectivity, data capture from controlled processes, and a documented API surface for automation and data exchange.
Automation and extensibility focus on workflow provisioning, controlled data entry, and repeatable processing steps that reduce manual variance. Admin governance relies on role-based access control, audit logging, and configuration controls that support compliance-oriented handoffs.
- +Configurable data model supports consistent schema across projects and instruments
- +Workflow automation reduces variance between manual and repeat processing steps
- +API supports programmatic provisioning and data exchange for integrations
- +RBAC and audit logs support traceability across lab roles
- +Extensibility supports connecting lab instruments and downstream systems
- –Complex configuration can slow initial schema and workflow setup
- –Integration effort increases when legacy systems need custom mappings
- –High automation needs careful governance to prevent uncontrolled data flow
- –Throughput depends on workflow design and data capture granularity
Best for: Fits when regulated labs need controlled workflows with API-driven integrations and governance.
STARLIMS
LIMSLaboratory information management system with automation workflows, sample tracking, and configurable reporting for lab operations.
Configurable workflow and report schema mapped to sample and result lifecycle states.
STarLIMS provides lab workflow execution tied to configurable sample, instrument, and result handling logic. Its data model supports assay and reporting structures designed for laboratory traceability and consistent output schemas.
Automation relies on configurable processing rules plus integrations that connect LIMS actions to external systems through an exposed API surface. Admin controls include role-based access patterns and governance features such as audit logging for controlled changes and traceability.
- +Configurable assay and reporting schema supports consistent result structures
- +Workflow automation ties sample states to processing and sign-off steps
- +API enables integration with instruments, middleware, and downstream systems
- +Governance patterns support role-based access and controlled permissions
- +Audit logging records changes for traceability during regulated workflows
- –Schema configuration can be complex for teams with highly bespoke assay models
- –Integration depth depends on external system fit and interface mapping
- –High automation requires careful rule testing to avoid throughput bottlenecks
- –Admin configuration effort rises with multi-plant or multi-tenant setups
Best for: Fits when labs need governed LIMS automation with API-driven integration to multiple systems.
JupyterLab
Notebook platformInteractive analysis environment for lab data science using notebooks, extensions, and reproducible execution workflows.
JupyterLab extension system adds custom panels, editors, and commands to the IDE.
JupyterLab targets data work that needs tight notebook integration, shared environments, and extensibility via a plugin system. It offers a file and execution model built around notebooks, terminals, kernels, and extensions that can add UI panels, editors, and workflow automation hooks.
The automation surface comes largely through the Jupyter server, kernel APIs, and extension points that can be scripted for provisioning and operational controls. For governance, it supports multi-user setups through the Jupyter Server ecosystem, including RBAC patterns, authenticated spawners, and audit-log options via surrounding components.
- +Extensible UI via JupyterLab extensions and plugin APIs
- +Kernel and notebook execution model supports repeatable interactive runs
- +Works with existing Jupyter server features and kernel management
- +Rich notebook file model integrates with git-style workflows
- +Supports terminals and non-notebook workflows in one workspace
- –Governance depends on external spawners and authentication layers
- –Automation and API depth varies by the surrounding Jupyter server stack
- –Large deployments require careful configuration for throughput and isolation
- –Notebook state can complicate reproducibility and lineage tracking
- –Role management often maps to server capabilities rather than notebook-level controls
Best for: Fits when teams need interactive notebook workflows with extensibility and scripted operations.
LabWare LIMS
LIMSA laboratory information management system that manages sample workflows, laboratory data capture, audit trails, and regulatory reporting for regulated environments.
Configurable data model with governed schemas that drive workflow, validation, and audit-traceable changes.
LabWare LIMS is built around a configurable data model that maps sample, test, and result workflows into governed schemas. Integration depth centers on instrument connectivity, ELN and ERP style data exchanges, and middleware patterns for bidirectional data movement through documented interfaces.
Automation relies on workflow configuration plus programmatic hooks, where API surface and event-driven rules support higher throughput during accessioning and result review. Admin and governance controls emphasize RBAC, provisioning, and audit logging so changes to test methods, fields, and approvals remain traceable.
- +Configurable schema models complex sample and test relationships
- +Instrument connectivity supports bidirectional workflow progress and result capture
- +Automation rules coordinate routing, approvals, and status transitions
- +RBAC and audit logging support controlled review and change traceability
- +Integration interfaces support middleware-based exchanges with lab systems
- –Deep configuration can require specialized admin effort for each lab domain
- –API extensibility may depend on vendor-supported integration patterns
- –Complex workflows can increase configuration and validation overhead
- –Instrument mapping and field alignment often require upfront data standardization
Best for: Fits when regulated labs need governed LIMS schemas, automation, and deep integration with existing systems.
eLABFTW
ELNA web ELN designed for structured experiment logging, sample and inventory tracking, and exportable lab records with role-based access.
HTTP API for programmatic lab notebook provisioning and updates.
eLABFTW centers on a strict lab notebook data model with structured records, tags, and templates, built for repeatable documentation and downstream consistency. Integration depth comes primarily through its HTTP API for creating and updating items, plus export and formatting workflows that preserve schema-like structure.
Automation is driven by server-side configuration such as templates and import paths, while extensibility is mainly achieved through API-driven provisioning and external tooling. Governance control focuses on user roles, project organization boundaries, and operational visibility via logs rather than spreadsheet-style ad hoc metadata.
- +HTTP API supports create and update of notebook entries and related objects.
- +Templates and structured fields enforce consistent schemas across experiments.
- +Tagging and project scoping improve retrieval and cross-linking in large notebooks.
- +Import and export workflows preserve item organization for migration.
- –Automation is limited compared with workflow engines and rule-based schedulers.
- –Complex RBAC policies are constrained to available role and project boundaries.
- –API coverage can require multiple calls for deep edits of connected records.
- –Custom data extensions depend on available schema fields and templating options.
Best for: Fits when teams need structured lab record capture plus API-driven automation.
SOPHiA GENETICS
Lab data platformA data platform for genomic and clinical lab operations that centralizes variant data, laboratory metadata, and reporting pipelines.
End-to-end genomics data normalization into a governed schema with automation and API-driven operations.
SOPHiA GENETICS ingests lab genomics data and normalizes it into a controlled data model for analysis-ready storage and retrieval. The solution emphasizes integration depth through connectors, import pipelines, and a documented automation surface for moving data between systems.
Workflow execution can be configured for repeatability, with schema controls that support validation and consistent interpretation across batches. Admin governance focuses on access control, auditability, and operational configuration so organizations can manage throughput across projects.
- +Genomics-first data model maps samples to analysis-ready structures
- +Integration pipelines reduce manual data wrangling between lab systems
- +Automation and API surface support programmatic workflow and data movement
- +Schema and validation controls improve consistency across runs
- +RBAC and audit logging support admin governance for regulated work
- –Model specificity can require pre-alignment of heterogeneous source schemas
- –Automation depends on available endpoints for each workflow stage
- –Throughput tuning needs careful configuration for large batch loads
- –Extensibility may require vendor-aligned patterns for custom integrations
Best for: Fits when labs need governed genomics data ingestion, automation via API, and controlled access across projects.
Archiware P5
Data archiveA data protection and archive system used by labs to retain and restore large datasets with policy-based storage tiers and storage federation.
Schema-driven metadata organization paired with policy-controlled archive and retention processing.
Archiware P5 centers on archive and lab data lifecycle control, including automated storage and retention tied to a configurable data model. Its integration depth is driven by storage backends and workflow hooks that connect instrument and IT environments through defined interfaces.
Automation and extensibility rely on job control, schema-driven handling of metadata, and an API surface meant for repeatable operations. Admin governance is oriented around roles, controlled provisioning, and auditability for regulated lab workflows.
- +Archive and retention workflows tied to a configurable data model
- +Automation supports repeatable ingest and storage operations across datasets
- +Integration with IT storage tiers supports predictable throughput and placement
- +Metadata handling aligns with schema-based structures for consistent queries
- –Automation surface can require upfront configuration of schemas and workflows
- –Extensibility depends on understanding the platform’s defined integration patterns
- –Admin governance features may feel heavier than small lab data catalogs
- –Instrument connectivity scope can require additional setup per source system
Best for: Fits when regulated labs need governed archiving with automation and structured metadata across systems.
How to Choose the Right Lab Data Software
This buyer's guide covers Lab Data Software tools including Benchling, Labguru, DataLadder, LabVantage, STARLIMS, JupyterLab, LabWare LIMS, eLABFTW, SOPHiA GENETICS, and Archiware P5. It focuses on integration depth, data model control, automation and API surface, and admin and governance controls across lab workflows, sample lifecycles, analyses, and data lifecycle retention.
Lab Data Software for governed lab artifacts, workflows, and data lifecycle control
Lab Data Software manages structured lab records such as samples, assays, experiments, and results inside a controlled data model with traceable relationships and change history. It solves recurring problems in lab operations like status drift, inconsistent field mapping, fragile manual re-keying, and missing audit trails across protocol and result evolution.
Benchling and Labguru exemplify tools that combine entity versioning, configurable schemas, and workflow automation tied to record state transitions. DataLadder exemplifies schema-driven transformations where lineage ties field-validated steps to dataset provenance for reproducible reruns.
Evaluation criteria for integration, governed data models, automation APIs, and admin governance
Integration depth determines whether data moves through APIs and connectors without manual export and re-import loops. Tools like Benchling and LabVantage center entity and workflow connectivity through documented APIs and instrument-linked records. A governed data model matters because schema choices set the structure for reporting, downstream mapping, and validation rules.
Automation and API surface matters because teams need programmatic provisioning, record creation and updates, and repeatable execution for throughput. Admin and governance controls determine auditability and controlled collaboration through RBAC patterns, audit logs, and configuration controls.
Version-controlled entities for samples, protocols, and experiment artifacts
Benchling preserves protocol and result history through version-controlled sample and protocol entities linked to experiment records. LabVantage also relies on workflow and form configuration that supports controlled, traceable capture, while STarLIMS ties automation to sample lifecycle states for consistent sign-off history.
Typed schema and governed data model linkages across experiments, samples, and assays
Benchling uses typed data model links that connect samples, sequences, assays, and experiments consistently across governed artifacts. DataLadder and LabWare LIMS emphasize schema-driven workflows and governed schemas that drive validation and audit-traceable changes.
Automation tied to record state transitions and workflow provisioning
Labguru ties workflow execution to record state transitions rather than manual steps so automation follows governed lifecycle logic. LabVantage and STARLIMS reduce variance by provisioning repeatable processing steps that route sample states and approvals through configurable workflow rules.
Documented API surface for programmatic reads, writes, and pipeline configuration
Benchling exposes extensible APIs that enable automation against entities, metadata, and events across linked records. Labguru also provides API support for programmatic reads and writes that drive integration workflows, while eLABFTW offers an HTTP API for creating and updating notebook items and related objects.
Lineage, provenance, and validation rules for traceable transformations
DataLadder ties transformations to validated fields with lineage ties that attach dataset provenance to schema-aware workflow steps. Benchling uses provenance fields to keep instrument and human edits traceable, while SOPHiA GENETICS normalizes genomics data into an analysis-ready governed schema with validation controls.
RBAC, audit logs, and configuration controls for controlled collaboration and compliance
Benchling includes RBAC, audit logs, and provenance for regulated traceability across governed collaboration. LabVantage and LabWare LIMS also rely on RBAC and audit logging so changes to fields, approvals, and test methods remain reviewable, while STARLIMS records changes for traceability in regulated workflows.
A decision framework for selecting lab data platforms by integration, schema control, automation, and governance
Start with the integration depth required for existing systems like instruments, ERP, middleware, and downstream pipelines. Benchling and LabVantage focus on instrument-linked records and documented API surfaces that support data exchange and automation.
Next confirm the data model control needs so schema decisions match reporting, validation, and field mapping workflows. Finally, validate automation and governance requirements by checking how tools tie automation to lifecycle states, how APIs support provisioning, and how RBAC and audit logs cover the workflows that matter most.
Map integration points to the tool's API and connector model
If automated entity and metadata operations must run across linked records, Benchling and Labguru provide extensible APIs for programmatic automation. If integrations require schema-driven transformation pipelines, DataLadder offers API and automation hooks for pipeline configuration and execution.
Define the governed data model that must survive reporting and rework
If sample, protocol, and result histories must remain queryable, Benchling supports version-controlled sample and protocol entities with audit history across linked experiment records. If field mapping consistency must survive transformation steps, DataLadder uses a schema-aware workflow graph with validation ties.
Choose automation that attaches to lifecycle states or validated execution graphs
If automation should execute when records transition between states, Labguru runs workflow execution tied to record state transitions. If automation should be reproducible across batches using controlled configuration, DataLadder supports versioned and re-executed runs against controlled schema-driven workflow graphs.
Verify governance coverage with RBAC, audit logs, and provenance fields
If controlled traceability is required for instrument and human edits, Benchling includes provenance fields along with RBAC and audit logs. If auditability must cover form and workflow configuration changes, LabVantage and LabWare LIMS emphasize RBAC plus audit logging for controlled review and change traceability.
Assess workload shape for notebook-centric work or LIMS-scale accessioning
If the core workflow is interactive analysis with custom panels and scripted operations, JupyterLab extension points add UI panels, editors, and commands inside the IDE. If the core workflow is accessioning and regulated result handling at scale, STARLIMS and LabWare LIMS emphasize configurable workflow execution tied to sample, instrument, and result lifecycles.
Lab Data Software fit by workflow type and governance depth
Different lab data problems map to different platform shapes in this set. Some tools prioritize governed ELN and API-driven automation, others prioritize schema-driven transformations with lineage, and others prioritize regulated LIMS workflows or governed genomics ingestion.
Selection should follow the operational center of gravity, not only the interface type. The best-fit list below uses each tool's stated best-for focus on governance, schema, automation, and integration.
Regulated teams that must keep versioned sample and protocol history for audit trails
Benchling fits when governed lab data must stay linked to automation and APIs with version-controlled sample and protocol entities and audit history across linked experiment records. LabVantage also fits when regulated labs need controlled workflows with RBAC and audit logging for traceable data capture.
Regulated labs that require configurable experiment capture with API-driven workflow automation
Labguru fits when schema control and governance matter alongside data capture, and when workflow automation should trigger from record state transitions via a programmable API. STarLIMS fits when regulated labs need governed LIMS automation with API-driven integration to multiple systems and configurable processing rules tied to sample states.
Teams that need schema-driven transformations with validated lineage for repeatable reruns
DataLadder fits when lab workflows must keep field mapping consistent across transformations through a schema-based workflow graph with lineage and validation rules. SOPHiA GENETICS fits when genomics ingestion must normalize heterogeneous sources into an analysis-ready governed schema with automation and API-driven operations.
Labs that need deep LIMS schema control and bidirectional integration through middleware patterns
LabWare LIMS fits when regulated labs need governed LIMS schemas, automation, and deep integration with existing systems via documented interfaces and middleware-based exchanges. LabVantage fits when instrument-linked records and workflow and form configuration must stay consistent across teams with governance controls.
Teams that emphasize interactive notebooks or governed archival retention
JupyterLab fits when teams need interactive notebook workflows with extension-driven custom panels and IDE automation hooks for scripted operations. Archiware P5 fits when regulated labs need governed archiving with policy-controlled retention workflows tied to a configurable data model.
Pitfalls that break schema control, automation reliability, and governance coverage
Several tools show recurring friction points that show up during rollout. Schema configuration can add overhead, and workflow configuration can become a governance bottleneck when roles, validation rules, and mappings are not planned.
Treating schema and workflow configuration as a quick setup task
Benchling notes that schema choices can impact integrations and downstream reporting structure, so schema governance must be planned before building mappings. Labguru and DataLadder both highlight that schema and workflow configuration can require sustained admin effort to keep entity relationships and field mapping aligned.
Building automation that cannot be traced to lifecycle transitions or validated fields
Labguru’s automation is tied to record state transitions, so using it requires enforcing the intended lifecycle logic instead of bypassing manual steps. DataLadder depends on schema-aware workflow graphs with lineage and validation ties, so custom transformations that fall outside the visual editor can weaken reproducibility and provenance.
Assuming all automation layers provide the same API depth for deep record edits
eLABFTW provides an HTTP API for creating and updating notebook entries, but deep edits across connected records can require multiple calls. Benchling and Labguru provide extensible APIs that support automation against entities, metadata, and events, which reduces the number of integration-side workarounds.
Skipping throughput planning for high automation workflows and batch loads
STarLIMS calls out that high automation needs careful rule testing to avoid throughput bottlenecks. JupyterLab also requires careful configuration for throughput and isolation in large deployments, so compute and access patterns must be designed with the platform.
Underestimating integration complexity when legacy systems need custom mappings
LabVantage and LabWare LIMS note that integration effort increases when legacy systems require custom mappings and field alignment. STARLIMS also highlights that integration depth depends on external system fit and interface mapping, so integration tests should include field-level transformations and approvals.
How We Selected and Ranked These Tools
We evaluated Benchling, Labguru, DataLadder, LabVantage, STARLIMS, JupyterLab, LabWare LIMS, eLABFTW, SOPHiA GENETICS, and Archiware P5 using the provided feature and usability scores plus the named capabilities described for integration, automation, data models, and governance. We rated each tool across features, ease of use, and value with features carrying the most weight, while ease of use and value each influence the ranking enough to separate close contenders.
This is editorial criteria-based scoring using the explicit capability statements and the reported overall, feature, ease, and value ratings. Benchling separated from lower-ranked tools because version-controlled sample and protocol entities maintain audit history across linked experiment records, which lifted the features criterion most directly through governed traceability and an extensible API and automation surface.
Frequently Asked Questions About Lab Data Software
How do Benchling and Labguru differ in governed data model design for experiments and samples?
Which tool provides stronger API-driven automation for entity and workflow changes, Benchling or STARLIMS?
What integration approach fits labs that need schema-driven data workflows with lineage, DataLadder or LabVantage?
How do JupyterLab and Archiware P5 handle extensibility when teams need custom operational hooks?
Which platform is better suited for HTTP API-driven lab notebook provisioning, and how does that impact data consistency?
How do LabWare LIMS and LabVantage differ in how admin controls enforce traceability during high-throughput operations?
What security and audit features should be evaluated for regulated labs comparing Benchling and LabVantage?
How should teams plan data migration when moving from ELN-style records to LIMS workflows, LabVantage or LabWare LIMS?
What extensibility tradeoff exists between Labguru workflow automation and DataLadder schema-driven re-execution?
How do SOPHiA GENETICS and Archiware P5 differ when ingestion must normalize data into a controlled schema for downstream analysis?
Conclusion
After evaluating 10 data science analytics, Benchling stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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