Scientific computing underwent a structural change when researchers stopped treating computation as a machine they had to own and began treating it as a service they could request. Programmable cloud laboratories propose an analogous transition for physical experimentation. Instead of reserving a local instrument, manually executing a protocol, and later assembling data from several software packages, a researcher specifies an experimental workflow through software. A remote facility receives the samples, schedules the required equipment, executes the procedure under controlled conditions, records machine-readable provenance, and returns results through a digital interface.
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This is more than remote access to an instrument. A genuinely programmable cloud laboratory exposes experimental capabilities as composable operations: dispense, mix, incubate, image, separate, characterize, measure, and analyze. These operations can be parameterized, versioned, validated, scheduled, and repeated. In mature implementations, the experimental protocol becomes an executable object rather than a narrative document interpreted by an individual at the bench.
Commercial platforms such as Emerald Cloud Lab and academic initiatives such as Carnegie Mellon University's cloud laboratory program illustrate the emerging model. Carnegie Mellon described its collaboration with Emerald Cloud Lab as an effort to create a remotely controlled academic laboratory and a platform for AI-driven experimentation. More recently, Nature Methods characterized the university cloud lab as a remote academic facility in which researchers design workflows in advance and those workflows are carried out through staff-operated and robotic automation. These developments do not imply that conventional laboratories are about to disappear. They do indicate that physical experimentation is becoming addressable through software in ways that were previously limited to computation.
The important question is therefore not whether all researchers will soon work without entering a laboratory. They will not. The more useful question is which experimental operations can become reliable, programmable services, and how that change will alter research design, reproducibility, infrastructure, and scientific labor.
What Makes a Laboratory “Programmable”?
The term cloud laboratory is sometimes applied too broadly. Remote desktop access to a microscope, a contract research organization performing a fixed assay, and a robotic laboratory running software-defined protocols are not equivalent systems. They differ in the degree of abstraction, user control, automation, and reproducibility they provide.
| Model | User interaction | Experimental flexibility | Execution model | Typical output |
|---|---|---|---|---|
| Conventional laboratory | Researcher operates locally | High, including improvised work | Human-centered | Notes, files, samples |
| Core facility | User submits samples and request | Moderate | Specialist-operated | Measurements and reports |
| Remote instrument access | User controls a specific device remotely | Device-limited | Direct teleoperation | Instrument data |
| Programmable cloud laboratory | User submits an executable workflow | High within supported operations | Orchestrated staff and automation | Data, metadata, audit trail |
| Self-driving laboratory | System selects and executes subsequent experiments | Goal-directed within a defined search space | Closed-loop automation and AI | Iterative data and decisions |
A programmable laboratory requires at least four properties.
First, it needs a formal experiment representation. A protocol written only as prose is open to interpretation. A programmable protocol must express quantities, timing, equipment requirements, dependencies, acceptable tolerances, branching logic, and failure behavior in a form that a software system can validate and execute.
Second, the laboratory needs an orchestration layer. A multi-step experiment may depend on liquid handlers, incubators, centrifuges, chromatographs, microscopes, plate readers, robotic transport, environmental controls, and human interventions. The orchestration system must convert a logical workflow into a feasible physical schedule while respecting device availability, contamination rules, calibration status, sample stability, and safety constraints.
Third, the physical devices must be controllable and observable. A command such as “incubate at 37 °C for 20 minutes” is insufficient unless the system can identify the correct sample, confirm that the incubator is within specification, record the actual temperature history, detect interruptions, and associate every event with the correct experimental run.
Fourth, results must be returned with sufficient context to permit interpretation and reuse. Raw measurements alone are rarely enough. Researchers need instrument configuration, reagent identity, lot information, calibration records, environmental conditions, protocol version, deviations, timestamps, transformations, and quality-control outcomes.
Programmability is therefore not simply robotics. It is the integration of formal methods, laboratory automation, scheduling, data engineering, quality systems, and scientific domain knowledge.
The Technical Architecture of a Cloud Laboratory
A cloud laboratory can be understood as a cyber-physical computing stack. At the top, researchers express intent. At the bottom, physical devices manipulate matter. Between them are layers that translate, schedule, execute, verify, and record.
Experiment Specification and Protocol Languages
The experiment specification is the interface between scientific reasoning and physical execution. It may appear as a graphical workflow, a domain-specific language, a structured form, an application programming interface, or a higher-level software development kit.
A robust specification must distinguish between scientific parameters and execution parameters. Scientific parameters include concentration, temperature, exposure time, wavelength, strain rate, or flow rate. Execution parameters include the available pipette model, deck position, container type, device driver, transport route, and retry policy. Researchers should ideally define the former without managing every detail of the latter, just as cloud-computing users request storage or compute resources without selecting the physical disk sector or processor core.
However, abstraction cannot remove scientifically relevant detail. A liquid-transfer instruction is affected by fluid viscosity, surface tension, aspiration height, tip geometry, and dead volume. A cloud laboratory must therefore provide abstractions that are convenient without concealing variables that materially affect the result. Poorly designed abstraction can make an experiment easy to submit but difficult to interpret.
Version control is equally important. A protocol should be identified by an immutable version, with changes represented as explicit differences rather than silently edited instructions. Ideally, the protocol, analysis code, calibration state, and machine-readable sample manifest are linked to the resulting dataset.
Compilation and Feasibility Checking
An experiment submitted by a researcher is not necessarily executable as written. The platform must compile the logical protocol into device-level operations and verify that the proposed workflow is physically and operationally feasible.
This stage may check:
- whether requested instruments support the specified ranges and tolerances;
- whether containers, reagents, and sample volumes are mutually compatible;
- whether intermediate products remain stable during anticipated queue times;
- whether sterile, hazardous, or contamination-sensitive operations require isolation;
- whether required calibrations and maintenance states are valid;
- whether the workflow contains ambiguous units, missing dependencies, or unsafe combinations.
The compiler may also expand one high-level instruction into many low-level actions. “Measure an enzyme activity curve” might become a sequence of reagent preparation, dilution, plate layout, timed dispensing, incubation, absorbance acquisition, blank correction, curve fitting, and quality checks.
This resembles compilation in software engineering, but laboratory compilation has a harder constraint: errors can destroy scarce samples, damage equipment, expose personnel to hazards, or create misleading data that appear plausible. Static validation, simulation, and policy enforcement therefore have direct scientific and safety value.
Scheduling and Resource Orchestration
A cloud laboratory is a shared, heterogeneous resource environment. Experimental scheduling is not merely a calendar problem because jobs include precedence constraints, setup times, cleaning requirements, time-sensitive transfers, parallel operations, and uncertain durations.
For a workflow consisting of operations $o_1, o_2, \ldots, o_m$, the total turnaround time can be decomposed as
$
T_{\mathrm{total}} =
T_{\mathrm{queue}} +
T_{\mathrm{setup}} +
T_{\mathrm{execution}} +
T_{\mathrm{transfer}} +
T_{\mathrm{quality}} +
T_{\mathrm{release}},
$
where $T_{\mathrm{queue}}$ is waiting time for shared resources, $T_{\mathrm{setup}}$ covers preparation and equipment changeover, $T_{\mathrm{execution}}$ is active experimental time, $T_{\mathrm{transfer}}$ accounts for movement between stations, $T_{\mathrm{quality}}$ covers automated or manual quality checks, and $T_{\mathrm{release}}$ includes final data packaging and approval.
Reducing instrument idle time alone does not necessarily minimize $T_{\mathrm{total}}$. A scheduler may need to reserve several instruments as a coordinated sequence so that a sample does not wait too long between synthesis and characterization. It may deliberately leave one device underutilized to preserve temporal integrity elsewhere in the workflow.
Advanced schedulers can model experiments as directed acyclic graphs, where nodes represent operations and edges represent sample or information dependencies. The scheduling problem then resembles a resource-constrained project-scheduling problem with additional laboratory-specific constraints. In practice, heuristic optimization is often necessary because exact scheduling becomes computationally expensive as the number of jobs and resources increases.
Device Integration and Interoperability
Most laboratory instruments were not designed as interchangeable network services. They frequently use proprietary file formats, vendor-specific control software, inconsistent identifiers, and incompatible communication protocols. Integration is therefore one of the largest hidden costs in laboratory automation.
Open standards are beginning to address this problem. The SiLA standard defines service-oriented communication approaches for connecting laboratory devices, software, and data systems. The OPC UA Laboratory and Analytical Device Standard, commonly known as LADS, defines an information model for laboratory and analytical instruments using OPC UA. These standards aim to make device capabilities discoverable and communication more consistent across manufacturers.
Interoperability has two levels. Syntactic interoperability allows systems to exchange commands and data. Semantic interoperability ensures that the meaning is preserved. Two instruments may both report a value called “temperature,” yet one may report a set point while another reports a measured chamber temperature. Units, uncertainty, location, sampling interval, and calibration context must be explicit if results are to be combined safely.
A mature cloud laboratory therefore needs more than device drivers. It needs ontologies, unit systems, sample identity models, capability descriptions, and consistent event semantics.
Execution, Observability, and Exception Handling
Physical experiments do not behave like deterministic software functions. Tips clog, emulsions form, samples precipitate, robots lose grip, detectors saturate, reagents degrade, and instruments drift. A cloud laboratory must be designed around exceptions rather than assuming perfect execution.
Observability should include command logs, sensor streams, images, state transitions, alarms, operator interventions, and deviations from the planned workflow. The system should distinguish between at least three outcomes:
- the protocol executed as specified and passed quality checks;
- the protocol executed with documented deviations;
- the protocol did not produce a scientifically valid result.
Automatically returning a numerical file after a failed or compromised run is worse than returning no result. The platform must propagate execution quality into the data product so that downstream analysis can identify censored, suspect, or invalid measurements.
Human operators remain essential in many cloud laboratories because they handle nonstandard materials, replenish consumables, resolve ambiguous failures, perform maintenance, inspect unusual samples, and decide whether recovery is scientifically defensible. Automation changes the location and structure of human involvement; it does not eliminate it.
Data Provenance and Reproducibility
Cloud laboratories have a potential advantage that is more important than labor savings: they can capture experimental provenance by construction. When each action is software-mediated, the platform can automatically record who requested it, which protocol version was used, what device executed it, what materials were consumed, what conditions were measured, and how raw data were transformed.
The FAIR Guiding Principles emphasize that scientific digital assets should be findable, accessible, interoperable, and reusable. Importantly, FAIR practice applies not only to final datasets but also to workflows, algorithms, and contextual metadata. A programmable laboratory can bring these principles closer to the point of data generation rather than attempting to reconstruct metadata during publication.
Yet automated provenance is not automatically complete provenance. If reagent preparation occurred outside the platform, if sample history was entered incorrectly, or if a scientifically important visual observation was never formalized, the digital record may still be incomplete. The quality of provenance depends on the boundaries of the system and the discipline of its inputs.
Why Researchers and Institutions Are Interested
Access Without Full Infrastructure Ownership
Many experiments require equipment that is expensive to purchase, maintain, calibrate, and staff. The acquisition cost is only one component. Facilities also require space, utilities, safety systems, consumables, service contracts, training, and technical expertise. Small companies, early-stage research groups, and institutions in regions with limited infrastructure may be unable to justify this fixed investment.
A cloud laboratory converts part of that fixed infrastructure into an external service. Researchers can potentially access a broader instrument portfolio without owning every device. This resembles the economic logic of shared core facilities, but the programmable interface can reduce coordination overhead and allow workflows to span multiple devices without separate manual bookings.
The access benefit is strongest when the required workflow is supported, samples can be shipped safely, and turnaround time is compatible with the research program. It is weaker when experiments depend on bespoke hardware, immediate observation, field conditions, or frequent improvisation.
Higher Utilization and Operational Specialization
Local academic equipment is often purchased through separate grants and distributed across laboratories. Utilization can be uneven, while maintenance expertise is fragmented. A centralized cloud laboratory can aggregate demand across users, standardize operations, and employ specialists who maintain a smaller number of well-characterized configurations at high utilization.
This centralization can improve consistency, but it also creates dependency on facility capacity. A highly utilized system may produce queueing delays, especially when workflows require scarce instruments or long uninterrupted runs. Service-level agreements must therefore distinguish between nominal execution time and end-to-end turnaround time.
Reproducible Execution
Traditional methods sections often omit details that experienced researchers supply through tacit knowledge. Even a well-written protocol may be implemented differently by different people. Programmable execution can reduce some sources of operator variation by fixing action sequences, timing, device settings, and data-processing steps.
The effect should not be overstated. Repeating the same flawed protocol with high precision produces reproducible error. Reproducibility of execution is not the same as validity of inference. Cloud laboratories can strengthen procedural consistency, but experimental design, controls, measurement models, and statistical interpretation remain scientific responsibilities.
Asynchronous and Distributed Collaboration
A software-defined experiment can be reviewed by collaborators in different locations before execution. The same workflow can be rerun with modified parameters, shared across teams, or attached to a publication as an executable protocol. Researchers can design an experiment in one time zone while the facility executes it in another.
This asynchronous model is particularly useful for computationally intensive discovery programs in which simulations, machine learning, and experiments form a repeated cycle. A model proposes candidates, the laboratory measures them, and new data update the model. When the physical laboratory can be invoked through software, the computational and experimental components can be connected through a common orchestration system.
Cloud Laboratories and Self-Driving Laboratories Are Not the Same
A programmable cloud laboratory executes experiments selected by a researcher. A self-driving laboratory also participates in selecting what experiment should be performed next. The latter closes the loop between planning, execution, measurement, and learning.
Suppose an experiment explores a parameter vector $\mathbf{x}$, such as composition, temperature, residence time, and catalyst loading. After observing dataset $\mathcal{D}_t$ at iteration $t$, an optimization algorithm may choose the next experiment as
$
\mathbf{x}_{t+1}
=
\operatorname*{arg\,max}_{\mathbf{x}\in\mathcal{X}}
a(\mathbf{x}\mid \mathcal{D}_t),
$
where $\mathcal{X}$ is the feasible experimental domain and $a$ is an acquisition function balancing predicted performance, uncertainty, cost, and risk. Bayesian optimization is one common approach, although active learning, reinforcement learning, evolutionary methods, and mechanistic model-based design may also be used.
The laboratory then executes $\mathbf{x}_{t+1}$, returns a result, and updates $\mathcal{D}_t$. A cloud laboratory can provide the standardized execution environment needed for this loop, but autonomy requires additional components: objective definition, machine-readable constraints, uncertainty estimation, stopping criteria, and governance over what the system is allowed to attempt.
The Coscientist research reported in Nature demonstrated an AI system that combined language-model planning, documentation search, code execution, and experimental automation. Other work has explored knowledge-graph-based orchestration for distributed self-driving laboratories. These systems show that AI agents can interact with experimental infrastructure, but they also expose the importance of bounded permissions and formal safety checks. A language model should not be treated as the final authority on hazardous compatibility, device limits, or regulatory acceptability.
The likely near-term architecture is therefore layered. AI may propose hypotheses and protocols, deterministic software will validate units and constraints, policy engines will enforce safety rules, schedulers will allocate resources, instruments will execute approved actions, and humans will review exceptions and high-consequence decisions.
Where the Model Works Well—and Where It Does Not
Programmable cloud laboratories are best suited to experiments that are modular, repeatable, instrument-addressable, and compatible with standardized sample logistics. High-throughput biology, analytical chemistry, formulation screening, materials characterization, and routine assay development are natural candidates. Experiments with large design spaces also benefit because software can generate parameterized families of runs without manually rewriting each protocol.
They are less suitable when the experiment depends on continuous sensory judgment, custom apparatus, extreme conditions, unstable samples, field context, or rapid improvisation. A geologist examining a newly exposed formation, a physicist aligning a one-off optical system, or a synthetic chemist responding to an unexpected phase change may need direct presence and embodied judgment. Some of these activities may eventually be assisted by telepresence and robotics, but converting them into dependable services is substantially harder than automating plate-based assays.
Exploratory research creates a particular challenge. Early experiments are often performed not only to measure a predefined variable but also to learn what variables matter. Researchers notice texture, sound, odor, vibration, contamination patterns, or unanticipated instrument behavior. These observations may never enter a formal protocol. A remote platform must either capture them through rich sensing and operator annotation or accept that part of the experimental context is lost.
The strongest operating model will therefore be hybrid. Local laboratories will handle novel, delicate, or highly interactive work. Cloud laboratories will handle standardized execution, scale-out studies, routine characterization, and reproducible reruns. Protocols may migrate from local to cloud execution as they mature.
Economics: Service Price Versus Total Cost
Cloud laboratories are sometimes compared with local equipment using only the per-experiment service fee. That comparison is incomplete. The relevant quantity is total cost over the expected research program.
A simplified local-cost model is
$
C_{\mathrm{local}}
=
C_{\mathrm{capital}}
+
C_{\mathrm{space}}
+
C_{\mathrm{staff}}
+
C_{\mathrm{service}}
+
C_{\mathrm{consumables}}
+
C_{\mathrm{downtime}},
$
while a simplified cloud-cost model is
$
C_{\mathrm{cloud}}
=
C_{\mathrm{access}}
+
C_{\mathrm{execution}}
+
C_{\mathrm{materials}}
+
C_{\mathrm{shipping}}
+
C_{\mathrm{integration}}
+
C_{\mathrm{delay}}.
$
The last term in each expression matters. Local downtime includes equipment failure, maintenance queues, and unavailable trained staff. Cloud delay includes shipment, external scheduling, data release, and the scientific cost of slower iteration. For some programs, a higher direct service fee may still produce lower total cost by avoiding capital expenditure and staffing. For others, particularly high-volume stable workflows, local automation may be cheaper and faster.
Institutions should also value option flexibility. A cloud service allows a project to test demand before committing to equipment. Conversely, dependence on an external platform can create switching costs if protocols, data models, and validation packages are proprietary.
Security, Intellectual Property, and Regulatory Control
Remote experimentation expands the digital attack surface of the laboratory. Protocols may reveal proprietary formulations, targets, process conditions, or product strategies. Sample identities and analytical results may be commercially sensitive. A cloud laboratory must therefore protect data in transit and at rest, enforce role-based access, separate tenants, maintain audit logs, manage credentials, and define incident-response procedures.
Cybersecurity also has a physical dimension. Unauthorized commands could damage instruments, compromise samples, or create hazardous conditions. Safety cannot rely solely on user authentication. Device-level limits, independent interlocks, approved material registries, and execution sandboxes should constrain what any software client can request.
For regulated research, electronic records and signatures may need to satisfy domain-specific requirements. The US Food and Drug Administration's guidance on 21 CFR Part 11 addresses electronic records and electronic signatures used in FDA-regulated activities. A cloud laboratory serving regulated workflows must demonstrate validated systems, controlled access, auditability, record retention, change control, and appropriate quality governance. A technically sophisticated automation platform is not automatically compliant merely because it records many events.
Contractual questions are equally important. Researchers should establish ownership of protocols, raw data, derived data, method improvements, and operational metadata. They should know whether the provider may use aggregated results to improve scheduling or machine-learning models, and whether samples or residual materials will be retained, returned, or destroyed.
Scientific and Organizational Consequences
Experiments Become Software-Engineering Artifacts
When protocols are executable, laboratory work inherits practices from software engineering: version control, code review, unit-aware validation, reusable modules, automated testing, continuous integration, and dependency management.
A protocol library may contain validated components for dilution, calibration, washing, imaging, or data normalization. Researchers can compose these modules into larger workflows. This reuse can improve reliability, but only if modules clearly state their scientific assumptions and operating limits. A procedure validated for aqueous samples at microliter scale should not silently be generalized to viscous suspensions or volatile solvents.
Researchers Need New Skills
Cloud laboratories do not remove the need for experimental expertise. They shift emphasis toward formalization. Researchers must be able to translate scientific intent into unambiguous operations, define acceptable tolerances, anticipate failure modes, and interpret machine-generated provenance.
This creates demand for hybrid roles: automation scientists, laboratory software engineers, scientific workflow architects, data stewards, and validation specialists. Domain experts will need enough computational literacy to inspect executable protocols, while software teams will need enough laboratory knowledge to understand why seemingly minor execution details matter.
Institutions May Reconsider Laboratory Investment
Universities and companies may begin treating experimental infrastructure as a portfolio of local, shared, and external capabilities. Instead of purchasing every instrument requested by an individual group, an institution could ask whether the need is strategic, high-volume, latency-sensitive, or sufficiently specialized to justify local ownership.
Cloud access can complement core facilities rather than replace them. A local core may provide consultation, method development, and bespoke troubleshooting, while external programmable facilities provide standardized scale. Institutions could also expose their own automated capabilities through internal APIs, creating a private cloud-laboratory model.
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How to Evaluate a Cloud Laboratory
A serious evaluation should begin with a representative end-to-end workflow, not a polished demonstration of one instrument. The objective is to determine whether the service can preserve scientific intent across sample intake, execution, exception handling, data return, and reruns.
| Evaluation area | Questions to ask |
|---|---|
| Capability | Which operations, materials, ranges, and tolerances are supported? |
| Protocol model | Are workflows versioned, reviewable, exportable, and machine-readable? |
| Traceability | Are samples, reagents, lots, devices, calibrations, and transformations linked? |
| Quality control | How are failed, deviated, censored, or repeated runs represented? |
| Turnaround | What are typical and worst-case queue, execution, and release times? |
| Interoperability | Can protocols and data be exported through documented formats or APIs? |
| Security | How are tenant isolation, access control, encryption, and incident response handled? |
| Governance | Who owns protocols, improvements, raw data, and residual materials? |
| Compliance | Which quality systems and regulated use cases are formally supported? |
| Continuity | What happens if the provider changes pricing, discontinues a device, or exits the market? |
Researchers should also compare results against a trusted local method. Agreement should be evaluated not only through mean values but also through bias, variance, detection limits, missingness, batch effects, and sensitivity to sample handling. Migration to a cloud laboratory is a method-transfer exercise, not merely a procurement decision.
The Likely Direction of Development
Federated Networks of Specialized Laboratories
No single facility is likely to contain every experimental capability. The more scalable architecture may be a federation of specialized laboratories connected through shared protocol, identity, scheduling, and data standards. A workflow could route synthesis to one site, imaging to another, and high-resolution characterization to a third.
Experiment-as-Code
The most consequential development may be the treatment of experiments as portable, executable research objects. A publication could include not only a methods section but also a versioned protocol, machine-readable materials specification, analysis pipeline, and expected quality criteria.
Portability will remain limited until device capabilities and scientific semantics are standardized. Nevertheless, even partial portability could improve replication. Another laboratory might compile the same high-level protocol against different equipment while preserving the relevant scientific constraints and documenting implementation differences.
AI Agents with Bounded Authority
AI systems will increasingly help translate goals into experimental plans, search protocol libraries, identify missing controls, estimate feasibility, and propose parameter ranges. They may also interpret incoming data and recommend subsequent experiments.
The critical design question is authority. An AI agent should operate within a formally defined envelope: approved materials, validated operations, maximum quantities, permitted devices, cost limits, and review thresholds. High-risk or scientifically consequential actions should require human approval. The more capable the planning system becomes, the more important independent policy enforcement becomes.
Better Digital Twins of Laboratory Operations
Laboratory digital twins can model device state, sample location, workflow progress, and expected timing. More advanced twins may also simulate fluid transfer, thermal history, reaction kinetics, measurement uncertainty, or contamination risk. These models can support feasibility checking, scheduling, fault diagnosis, and protocol optimization before physical execution.
Reproducibility as an Executable Service
Today, replication often requires another group to interpret a paper, obtain materials, reconstruct software, and adapt procedures to local equipment. Programmable laboratories could make some replication studies easier by rerunning archived protocols under controlled conditions or by comparing execution across facilities.
This will not resolve every reproducibility problem. Biological variability, hidden sample history, selective reporting, and inappropriate statistical models remain. But it could isolate one important source of uncertainty: whether the documented procedure was executed consistently.
Conclusion
Researchers will increasingly conduct experiments remotely, but the transition will be selective rather than universal. Routine, modular, data-intensive, and high-throughput workflows are moving fastest because their operations can be represented formally and executed through standardized equipment. Bespoke apparatus, exploratory manipulation, fieldwork, and experiments dependent on continuous human perception will remain locally centered for longer.
The defining innovation is not remote control by itself. It is the emergence of a software layer that converts scientific protocols into validated, schedulable, observable, and reproducible physical operations. Once that layer is reliable, laboratories can be treated as network-accessible research infrastructure, and experiments can participate directly in computational workflows.
The practical future is likely to be hybrid: researchers will develop methods locally, transfer mature procedures to programmable facilities, use cloud laboratories for scale and reproducibility, and connect selected workflows to AI-driven optimization. Institutions will combine local strategic capability with external access rather than choosing one model exclusively.
Programmable cloud laboratories will therefore not make the physical laboratory irrelevant. They will make parts of it addressable through software. That distinction is enough to change how experiments are designed, shared, audited, repeated, and integrated with artificial intelligence—and it may become one of the most important infrastructural shifts in modern research.
