A lab notebook works like a data product when it lets another scientifically literate person trace why an experiment was done, how it was carried out, what happened, where the underlying data live, and how the results support the conclusion. That does not make the notebook itself “scientific data” under NIH’s Data Management and Sharing Policy: NIH explicitly excludes laboratory notebooks from that definition. The distinction matters because a notebook should connect and explain research assets, not stand in for every dataset, code file, or sharing obligation.
What makes a lab notebook a useful data product?
Think of the notebook as the navigable record around an experiment. It should link the rationale, methods, samples, observations, data locations, analysis, and conclusions in a way that a reader who has no prior knowledge of the project can follow. NIH describes this as a reproducibility standard for research records: a scientifically literate person should be able to understand the work and the basis for its conclusions from the record. NIH’s Electronic Lab Notebook Policy
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That standard is more useful than treating the notebook as a diary or a place to attach files without context. Each entry should make clear what was planned, what was actually done, what was observed, and what changed during the work. When an observation led to a decision or an analysis step, record the reasoning and point to the relevant data or code.
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- Purpose: State the question or rationale for the experiment and what it is intended to test.
- Method: Record the protocol or procedure used, including deviations that could affect interpretation.
- People and timing: Identify the author and date, and make the sequence of work clear.
- Materials and samples: Use stable identifiers and describe relevant sample characteristics rather than relying on an informal nickname.
- Observations: Separate what was observed from what you infer, and preserve intermediate or descriptive observations that matter to the analysis.
- Data and analysis links: Point to raw and processed data, instrument outputs, analysis methods or code, and any transformations.
- Interpretation: Explain how the results bear on the original question and identify the evidence supporting the conclusion.
Is a lab notebook scientific data under NIH’s sharing policy?
No. NIH’s Final Policy for Data Management and Sharing says scientific data are factual material of sufficient quality to validate and replicate research findings, regardless of whether they support a publication. The policy explicitly excludes laboratory notebooks. It took effect on January 25, 2023, and applies to NIH-funded or NIH-conducted research that generates scientific data.
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This does not mean the notebook is unimportant or that data-management obligations disappear. For research within the policy’s scope, researchers must submit and follow a Data Management and Sharing Plan for the scientific data they generate. The notebook can document where those data are, how they were produced, and how to interpret them; it does not replace the data assets or the plan.
NIH defines metadata as information that makes scientific data interpretable and reusable. Its examples include dates, sample and variable descriptions, methodology, data provenance, transformations, and intermediate or descriptive observations. In other words, a filename alone is not enough context for reuse.
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How should the notebook connect to files, samples, and analysis?
Establish the relationships among records before the lab has accumulated inconsistent names and disconnected files. Use durable identifiers for projects, experiments, samples, and files, and state how they relate in the notebook. A reader should be able to move from an entry to the corresponding sample and data without guessing which similarly named folder or spreadsheet is meant.
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- Use an agreed date format, author identification, and entry structure.
- Define naming conventions for projects, experiments, samples, instruments, and files; document them where lab members can find them.
- Record the location of raw instrument outputs and distinguish them from processed results.
- Link analysis code or methods to the exact input data and note consequential transformations.
- Use tags or indexes to support retrieval, but do not make tags a substitute for descriptive entries.
- Set permissions deliberately so collaborators can access what they need without exposing restricted records.
Harvard Medical School’s ELN guidance recommends planning organization, consistent documentation, naming standards, tags, and appropriate permissions. For data intended for preservation or sharing, DOE guidance also highlights ownership, permitted uses, protection, sharing plans, repository characteristics, and persistent identifiers. DOE guidance on data management
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Paper notebook or electronic lab notebook?
Neither format is universally required by the cited guidance. NIST discusses both bound and electronic notebooks, while Harvard’s guidance says the right choice depends on the science, features needed, lab practices, institutional policy, security, and budget. Paper can be a sensible fit for a workflow that is primarily handwritten; an ELN can improve search and sharing. The decision should turn on whether the format supports the recordkeeping and retrieval the lab actually needs.
| Consideration | Paper notebook | Electronic lab notebook |
|---|---|---|
| Finding past work | Chronological entries and a usable index help retrieve work. | Search and tags can make entries easier to find; organization still matters. |
| Record integrity | Choose and maintain a format consistent with institutional requirements for reliable records. | Assess authorship, timestamps, audit history, versioning, deletion controls, backups, and access controls against applicable policy. |
| Workflow fit | Can suit workflows that rely on handwritten notes; consider how files and instrument outputs will be linked. | Assess science-specific features, instrument outputs, offline needs, collaboration, and integrations. |
| Security and governance | Plan physical access, retention, and custody of records. | Review permissions, data sensitivity, retention, ownership, and access after a researcher leaves. |
| Cost and support | Consider supplies and the effort needed to maintain and retrieve records. | Compare budget, onboarding, training, administration, and vendor documentation. |
| Export and reuse | Plan how records will be preserved and made available under lab policy. | Confirm records can be exported, retrieved, interpreted, and preserved under institutional policy. |
NIST recommends chronological notebooks and indexing work files so data and results can be retrieved. NIST Information Quality Standards In NIH’s intramural research context, the policy adds specific electronic-record safeguards: permanent logs of entries, edits, and deletions; immutable timestamps; authorship; deletion controls; frequent backups; and access controls. NIH’s Electronic Lab Notebook Policy Those are requirements for the NIH Intramural Research Program context, not universal rules for every university or industry lab. NIH also allows specialized systems to serve as the complete research record when they meet its reproducibility standard and applicable IT requirements.
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How to choose an ELN for a lab
Start with requirements and workflows, not a feature list or popularity claim. Harvard recommends matching the system to the type of science and lab practices, while considering institutional policy, security, and budget. Treat record integrity, export, and operational support as core requirements rather than optional conveniences.
- Map the work: List the kinds of experiments, samples, files, analyses, and collaborations the notebook must connect.
- Check governance: Confirm institutional rules for security, permissions, retention, ownership, and any applicable funder requirements.
- Test the record controls: Determine how the system records authorship and changes, handles timestamps and deletions, and supports backup and access management.
- Test retrieval and export: Verify that records and attachments can be found, exported, interpreted, and preserved in the formats and processes your institution allows.
- Plan adoption: Compare cost and ongoing administration, and provide training and documentation so lab members use the conventions consistently.
- Plan offboarding: Decide how project records remain available when a researcher leaves and how copies or transfers comply with institutional policy.
NIH’s ELN policy defines an ELN as a system for creating, storing, retrieving, and sharing electronic research records that replaces paper notebook functions and complies with NIH records-management and security requirements. That definition is specific to the NIH context; other institutions may set different requirements. A system should be evaluated against the lab’s own rules and the records it must produce.
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Where should the notebook end and the dataset begin?
Keep the boundary explicit. The notebook is the explanatory record; raw data, processed data, code, metadata, and repository copies are distinct assets that need their own storage and stewardship. The entry should point to those assets and explain their relationships, not imply that an attached file or the notebook itself is automatically a preservation-ready dataset.
For applicable NIH research, the DMS Plan covers managing, preserving, and sharing scientific data and associated metadata. Sharing is not an instruction to publish every record without restriction: NIH recognizes justified limitations, including legal, ethical, and technical considerations. DOE’s guidance likewise treats privacy, security, permissions, ownership, repository suitability, and persistent identifiers as part of responsible stewardship. The appropriate repository and level of access depend on the data and governing requirements.
How to put a lab notebook data product in place
- Define the trace: Specify how an outsider should follow the path from question and protocol through samples, observations, data, analysis, and conclusion.
- Agree on identifiers and entry conventions: Set naming, dates, authorship, sample IDs, links, and any tags before records diverge across the team.
- Choose the format: Select paper, an ELN, or a domain-specific system based on scientific workflow, security, institutional policy, record controls, cost, and support.
- Separate and link assets: Identify where notebooks, raw and processed data, code, metadata, and repository copies live, and record links and transformations in the notebook.
- Set continuity controls: Establish permissions, backup, retention, export, and researcher-offboarding procedures in line with institutional requirements.
- Review the workflow: Revisit conventions and controls when projects, collaborators, systems, or applicable policies change.
A notebook is a data product in this practical sense when it preserves enough structure and context to make research records navigable and reusable. The measure is not the software or the format; it is whether another person can trace the work and understand how the evidence supports the result.
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