Laboratories & chemistry
The result is one record. The investigation explains it.
Laboratory archives can connect chromatograms to an analyst's interpretation, failed specifications to investigations, and method changes to validation results. Reactions that failed can record what a chemist tried next. Volume alone is not the asset. Inputs linked to expert decisions and experimental outcomes can capture reasoning that is difficult to reproduce from a final report.
01Systems you already run
The raw material is already being recorded.
An assessment starts from the software you run today. It asks what those systems hold, never for the records themselves.
- Laboratory information management
- LabWare, LabVantage, STARLIMS, Thermo Fisher SampleManager
- Electronic lab notebooks
- Benchling, Revvity Signals Notebook, IDBS E-WorkBook
- Chromatography data systems
- Waters Empower, Thermo Scientific Chromeleon, Agilent OpenLab
- Quality management
- Veeva Vault QMS, MasterControl, TrackWise
- Instrument data archives
- Waters NuGenesis SDMS, Raw instrument file shares
02Dataset opportunities
Where the learnable data sits.
Each opportunity follows a decision from input to outcome. That chain, not the number of records, is what makes data useful for training and evaluating AI.
01
Instrument output with analyst interpretation
Chromatograms, spectra, and other raw instrument files paired with the analyst's integration, identification, and reported result, plus the reviewer's changes.
- Raw instrument data
- Analyst interpretation
- Second-person review
- Reported result
- Disposition
Why it is hard to reproduce
A reference spectrum alone does not explain an analyst's integration choices. Review corrections and documented justifications connect the instrument output to professional interpretation and the reported result.
AI use cases
- Multimodal training on instrument data with expert labels
- Evaluation of automated integration and identification
- Correction data for analyst-assistant models
02
QC decisions and out-of-specification investigations
Results that failed specification, the phased investigation, the root cause found or ruled out, corrective action, and the final batch disposition.
- OOS result
- Lab investigation
- Root-cause hypothesis
- Retest or full investigation
- CAPA and disposition
Why it is hard to reproduce
An OOS investigation can connect a failed result to hypotheses, evidence, and a documented disposition. Reproducing that chain requires the investigation history, not just the final result.
AI use cases
- Root-cause reasoning benchmarks
- Agent environments for quality investigations
- Failure-case evaluation for lab assistants
03
Method development and validation records
The iterations behind an analytical method: conditions tried, why each one changed, and the validation data that finally passed.
- Analytical goal
- Conditions tried
- Result and diagnosis
- Parameter change
- Validated method
Why it is hard to reproduce
A published method may omit unsuccessful conditions and the reasons for changing them. Records of those iterations can connect experimental choices to validation outcomes.
AI use cases
- Experimental-planning agents
- Multi-step scientific reasoning traces
- Evaluation of method-optimization suggestions
04
Failed experiments and reactions
Notebook entries for syntheses and experiments that failed or underperformed, with conditions, observations, and what the chemist tried next.
- Planned reaction
- Conditions and reagents
- Observed outcome
- Chemist's diagnosis
- Next attempt
Why it is hard to reproduce
A final successful procedure may omit failed attempts. Records of unsuccessful conditions, observations, and the chemist's next decision can help distinguish a tested limit from an untried option.
AI use cases
- Reaction outcome prediction with negative examples
- Retrosynthesis and planning evaluation
- Reinforcement learning environments for experimental design
03Volume is not the test
What is usually not valuable on its own.
Lots of data is not the point. A record of an expert deciding, and of what happened next, is.
Certificates of analysis and final reports alone
The final number without the raw data, interpretation, and review trail is thin signal.
Instrument files with no context
Raw files that cannot be tied to a sample, method, and analyst decision lack the context needed to interpret the result.
Compendial and standard methods
Methods from pharmacopeias and standards bodies are already published, and the text is often copyrighted by its publisher.
Results that repeat the literature
Confirming well-known chemistry adds little unless the conditions or context are unusual.
04Rights and compliance
The sector rules for the dataset.
Rights reviewed before any outreach.
Last reviewed .
How we handle rights and privacy- 01
Client ownership of results
Service and sponsor agreements may assign rights in samples, results, methods, or resulting IP and restrict reuse. Internal QC and operational records can also contain protected client information. Anonymization does not remove contractual or IP restrictions.
- 02
GLP, GMP, and electronic records
FDA 21 CFR Part 11 covers specified electronic records and signatures required by FDA rules or submitted to FDA. Part 58 GLP covers qualifying nonclinical studies supporting FDA applications, not all laboratory work. GMP duties depend on the product and activity; 21 CFR Parts 210 and 211 concern drug manufacturing and finished pharmaceuticals. Review applicability, retention, data integrity, and controls for any licensed copy.
- 03
Export controls
Some chemistry, materials, or process information may be controlled technology under the US Export Administration Regulations (EAR) or defense-related technical data under the International Traffic in Arms Regulations (ITAR). License requirements depend on classification, destination, recipient, end use, and available authorizations or exceptions. Releases to foreign persons can count as exports within the US.
- 04
Health information in clinical labs
HIPAA applies to covered entities and business associates, not every lab holding health information. Use or disclosure of protected health information must be permitted under HIPAA; a sale generally requires authorization acknowledging remuneration, subject to exceptions. HIPAA de-identification uses Safe Harbor or Expert Determination; removing names alone is insufficient. Other applicable privacy laws also require review.
05AI use cases
Training examples and evaluation tasks.
Instrument output linked to analyst decisions gives scientific models labeled interpretation tasks. QC investigations provide root-cause reasoning cases. Failed experiments add tested conditions, observed results, and the chemist's next decision for experimental-planning evaluation.
06Questions
What owners in this sector ask first.
- Which records should a contract lab assess first?
- Inventory instrument files with analyst interpretations, QC investigations, method-development iterations, and failed experiments. Note how samples, decisions, and outcomes connect across the systems.
- Why are failed experiments useful?
- A well-recorded failure shows the conditions tested, the observed result, and how the chemist responded. Those linked records provide negative examples and planning tasks that a final successful procedure omits.
- What makes an investigation useful for AI evaluation?
- The original result, hypotheses tested, supporting evidence, root-cause assessment, and final disposition form a complete reasoning case. Investigation notes explain why one explanation was accepted and another rejected.
- What do we need for the first assessment?
- Describe your instruments and record systems, the methods covered, approximate counts, and whether analyst decisions and experimental outcomes are linked. Start with dataset descriptions.
Find the AI opportunity in your records.
Start with a free assessment of your systems, the decisions recorded, and the outcomes linked to them. You keep ownership.