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Make reproducibility part of the experiment’s design, not a cleanup task after results arrive. Before the first run, define the question, outcomes, controls and replication plan; document how AI receives data and influences decisions; and preserve a traceable record from each sample through its instrument output and analysis. A reproducible computational workflow helps others rerun the analysis, but it does not by itself prove that another laboratory can reproduce the physical experiment.
Start with the experiment, not the AI
Write down what the study is intended to establish before using an AI system to choose conditions or interpret measurements. This gives the team a basis for distinguishing planned tests from exploratory choices made during the campaign. NIH describes scientific rigor as the strict application of the scientific method to ensure unbiased and well-controlled design, methodology, analysis, interpretation and reporting. Its reporting guidance is focused on preclinical research, but the design questions are useful more broadly.
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Specify the question and outcome
- State the research question, hypothesis or decision objective.
- Define the primary outcome and how it will be measured. Identify secondary or exploratory outcomes separately.
- Name the experimental unit: the entity independently assigned to a condition, such as an animal, culture, sample or batch. Distinguish independent experimental replicates from repeated measurements or technical replicates of the same unit.
- Set inclusion and exclusion rules before reviewing results. Record each exclusion and its reason.
Plan conditions, controls and replication
- List the conditions to be compared and the role of each control.
- Set the planned sample size and record its rationale. Report the exact N and how often experiments were performed.
- Choose and document a randomization method where applicable. Use blinding where it is feasible and appropriate, and state who was blinded and at what stage.
- Specify the statistical methods and how they correspond to the experimental unit and outcome.
NIH’s guidance calls for reporting sample-size rationale, randomization, blinding, exclusions, statistical methods, exact N and enough detail to distinguish biological data points from technical replicates. Field-specific reporting checklists or regulatory requirements may add further expectations.
Define what the AI is allowed to do
“AI-driven” can describe very different methods. A system may propose conditions for a person to choose, select the next experiment in an adaptive campaign, control an instrument, process measurements or help interpret results. State which role applies and where human review or intervention enters the process.
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Record the decision path
For each recommendation, retain the information needed to reconstruct what the system knew and what happened next:
- Input data available at the time of the decision, including relevant preprocessing.
- Model or software identity and version, plus settings and parameters that could affect the output.
- The recommendation or action proposed, with a timestamp or run identifier.
- Whether the recommendation was accepted, rejected or changed; who made that decision; and why, where the reason is material.
- The condition actually executed and the resulting measurement, including failed or interrupted runs.
Also document operating boundaries: safety constraints, permitted instrument settings, review requirements and how an operator can override or reject a recommendation. These are practical traceability measures, not a universal published logging standard. NIST’s autonomous-experimentation standards work identifies algorithm/model integration, instrument communication, data and sample management as areas where interoperability is needed; its project page, updated September 11, 2025, describes work in progress rather than a completed universal standard.
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Keep discovery distinct from confirmation
An adaptive system changes what is tested next in response to earlier observations. Preserve that sequence rather than reporting only the final, best-performing condition. Treat conditions identified during optimization as exploratory unless they are evaluated in an appropriate additional study. This is a design safeguard: the cited guidance does not prescribe one validation design for every field or experiment.
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Assign stable identifiers to samples, batches, conditions and runs. Maintain a machine-readable mapping among those identifiers, the protocol version, instrument, acquisition time, operator, raw files and processed outputs. A reader should be able to follow a reported result back through the analysis to the measurement and the material that produced it.
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Capture the physical method
- Record the protocol or SOP version, actual steps performed and any changes from the planned method.
- For critical reagents, record supplier, catalogue details, batch or lot and expiry date where applicable.
- Identify equipment and relevant settings. Record conditions such as temperature and timing, along with the operator and acquisition time where relevant.
- Log deviations, instrument problems, failed runs and other events that could affect interpretation.
- Retain raw data separately from processed data and link both to the run and sample identifiers.
OECD’s Good In Vitro Method Practices (GIVIMP), published December 10, 2018, recommends documenting details needed to reproduce or reconstruct an in vitro method study, including relevant method changes and deviations. Its scope is in vitro methods, including regulatory-use contexts; other disciplines may require different records. A paper notebook can help capture observations and point to electronic files, but it should complement—not replace—digital data management, instrument logs and versioned analysis materials. OECD also recommends cataloguing computer-file references in the notebook and backing up data files.
Make the analysis rerunnable
Physical replication and computational reproducibility are related but different. A second team may be able to rerun the analysis without having the equipment, materials or local conditions needed to repeat the laboratory work. Conversely, a detailed physical protocol does not tell someone how to recreate a computational result if the relevant data, code or model setup is missing.
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A 2021 Nature Methods article on computational reproducibility in life-science machine learning describes three levels. Use them to state clearly what is available and what a reader can do with it:
| Level | What is provided | What it supports |
|---|---|---|
| Bronze | Data, models and code are available. | Access to the main computational artifacts, but not necessarily an installation path or reproducible execution procedure. |
| Silver | Bronze artifacts plus installable dependencies, reproduction instructions and deterministic handling of random components. | A documented attempt to set up and rerun the analysis. |
| Gold | The full analysis is repeatable with a single command. | Automated execution of the analysis workflow. |
These levels describe computational analysis, not whether another laboratory can repeat the physical experiment. For the computational workflow, preserve the data, relevant models and code where sharing is permitted; document dependency installation, execution order, operating-system and resource requirements; and control random behavior where feasible. Automate preprocessing, model execution and generation of tables or figures when practical. A one-command rerun is a useful goal, not a substitute for explaining the physical method.
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Report what was planned, what happened and what can be shared
Make the study’s record complete enough to understand both the intended method and its actual conduct. NIH encourages machine-readable data, repository deposition where available, materials sharing and a statement about software availability. OECD GIVIMP recommends making related documents and method changes available and recording deviations.
- Archive or publish the protocol or SOP, analysis code, relevant model and software versions, and supplementary material.
- Provide the data or a clear access route. If legal, privacy, safety or other constraints limit sharing, say what is restricted and how access may be requested where applicable.
- Report important outcomes, including findings that do not support the preferred conclusion, along with missing or excluded data and reasons for exclusions.
- Describe deviations from the plan and explain how they affect interpretation.
- Keep identifiers and metadata usable across files so that a released dataset can be connected to protocols, runs and analysis outputs.
No single cross-disciplinary standard for AI-driven laboratory experiments is established by the cited guidance. NIST’s page on standards for modular autonomous laboratory ecosystems says the standards effort is ongoing and that a standardized ecosystem for materials R&D does not yet exist. For autonomous-lab infrastructure, useful comparison criteria include sample compatibility, instrument connectivity, data and metadata interoperability, portability of algorithms or models, and the ability to preserve records; these are standards areas, not a certification scheme or product ranking.
Quick Recap
A practical pre-run checklist
- Freeze the plan: document the question, outcomes, experimental unit, conditions, controls, replication and sample-size rationale, randomization, blinding where appropriate, and inclusion or exclusion rules.
- Bound the AI role: specify inputs, preprocessing, model/software version, important settings, decision authority, review points, safety limits and override procedure.
- Set up identifiers: connect sample, batch, condition and run IDs to protocol versions, instruments, operators, raw files and analysis outputs.
- Log every adaptive choice: preserve the observations available at decision time, proposal, acceptance or rejection, executed condition and resulting measurement.
- Prepare the rerun: retain code, data and models where possible; document dependencies and execution order; control stochastic behavior where feasible; automate the workflow when practical.
- Close the record: capture deviations, exclusions, failures and missing data, and state what materials, data, methods and software are available or restricted.
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