Overview
Lab notebooks record what was done, when, by whom, with which materials and what happened. Increasingly, the notebook is only one part of a wider record that includes instrument files, analysis scripts, images and spreadsheets spread across laptops, shared drives and cloud storage. Problems appear when these are not linked: a figure cannot be traced back to raw data, a protocol change was never written down, or a departing student's files are unreadable to the next person.
We help individuals and groups design a system that connects them: notebook conventions, a folder hierarchy and file naming scheme, sample and experiment identifiers that appear in both the notebook and the file names, README and metadata files, and version control for code and protocols. For groups moving to electronic lab notebooks (ELNs), we help compare options against your needs and plan a migration.
The aim is data that are findable, accessible, interoperable and reusable (the FAIR principles), at a level of effort a busy lab can sustain. We follow your institution's policies on data retention, security and intellectual property, which take precedence over our recommendations.
Who it’s for and the problems it solves
This is for you if you are…
- PhD students and postdocs whose data and notes are scattered across devices
- Principal investigators setting standards for a new or growing lab
- Research groups choosing or migrating to an electronic lab notebook
- Computational researchers who need a lab-notebook equivalent for code and analyses
- Researchers preparing data for deposit in a repository or to meet funder data policies
Common problems we help with
- You cannot tell which raw file produced a figure in your draft paper
- Notebook entries are incomplete, undated or written up days later from memory
- Files are named final_v3_new.xlsx and live on several laptops
- A lab member left, and nobody can interpret their data
- Your funder or journal now asks for data availability statements and deposited datasets
What’s included — and what isn’t
Included
- Notebook conventions: entry structure, dating, linking to protocols, recording deviations, signing and witnessing where your institution requires it
- Electronic lab notebook selection criteria: templates, sample inventory, integrations, export formats, audit trails, institutional licensing and data location
- Folder hierarchy and file naming conventions using dates (ISO 8601), project and sample identifiers and version numbers
- Identifier schemes that link samples, experiments, notebook entries and data files
- README files and data dictionaries at project and dataset level
- Metadata practices and community standards relevant to your field, where they exist
- Separation of raw, processed and analysis outputs, with raw data kept read-only
- Version control with Git for code, protocols and text; computational notebooks (Jupyter, R Markdown, Quarto) as analysis records
- Backup strategies, such as the 3-2-1 rule, and storage choices aligned with institutional policy
- FAIR-aligned preparation for repository deposit: formats, licenses, persistent identifiers and documentation
Not included
- Backfilling, recreating or altering notebook entries or data after the fact
- Legal advice on intellectual property, patents or data protection law
- Administering institutional IT systems or ELN platforms
- Accessing or handling identifiable personal or patient data outside approved arrangements
What you receive
- Organization guide
- A short written standard for your project or lab covering notebook entries, folder structure, naming, identifiers and backups, with examples.
- Templates
- Notebook entry templates, README and data dictionary templates, and folder skeletons ready to copy, for paper, ELN or file-based systems.
- ELN comparison
- For groups choosing a platform, a comparison of shortlisted options against your requirements, with questions to ask vendors and your IT department.
- Migration plan
- Steps for reorganizing existing files or moving to a new system without losing provenance, including what to leave untouched.
- Training session
- A session for you or your group explaining the system and practicing with real examples.
How it works
Audit current practice
We look at a sample of notebook entries, the folder structure and how data flow from instruments to analysis to figures.
Identify requirements
We collect institutional, funder and journal requirements, and what the lab needs day to day.
Design the system
We draft conventions, identifiers, templates and storage arrangements, and test them against real experiments.
Pilot and adjust
You or a few group members use the system for a few weeks and we refine it based on what was awkward.
Document and train
The final guide and templates are delivered with a training session.
Standards and practices we draw on
- FAIR principles
- Published in Scientific Data in 2016: data should be Findable, Accessible, Interoperable and Reusable, with emphasis on machine-actionable metadata, persistent identifiers, clear licenses and provenance.
- Electronic lab notebooks
- Platforms that offer templates, search, sample tracking, audit trails and integration with instruments and storage. Choice depends on discipline, institutional licensing and where data may be stored.
- File naming
- ISO 8601 dates (YYYY-MM-DD), no spaces or special characters, consistent element order and zero-padded numbers so files sort correctly.
- Raw data preservation
- Raw instrument output stored read-only, with processing done on copies and every processing step recorded or scripted.
- Version control
- Git for code, protocols and text files, with meaningful commit messages linked to notebook entries.
- Data documentation
- README files describing contents, methods, variables, units and licenses; data dictionaries for tabular data.
Paper, electronic or hybrid?
Paper notebooks are simple and robust at the bench but hard to search and share. Electronic notebooks make entries searchable, link directly to data and support collaboration, but require institutional approval, training and a plan for long-term access and export. Many labs work best with a hybrid: quick bench notes on paper or a tablet, transferred to an ELN the same day with data links. We help you choose based on your field, lab size, regulatory context and institution's supported tools.
A naming scheme that lasts
Good names let files sort sensibly and tell you what they are without opening them. The key is to decide the elements once and use them everywhere, including notebook entries and sample labels.
- 2026-03-14_EXP042_S07_confocal_raw.czi
- Date, experiment ID, sample ID, description, processing stage
- Version numbers only for documents, not for raw data
- A short README in each project folder explaining the scheme
Preparing data for sharing and deposit
Funders and journals increasingly expect data to be deposited in a repository with a persistent identifier. Organization from the start makes this straightforward. We help you choose open file formats, write documentation, pick a license and prepare the dataset, and align it with your data management plan. Repository choice and any restrictions on sensitive data follow your institution's and funder's rules.
What the work can look like
Illustrative example — fictional data
Sample recommendation excerpt for a small imaging lab
A group of six researchers using two confocal microscopes stored images on personal laptops and recorded experiments in paper notebooks with inconsistent sample labels. The details are invented for illustration.
Identifiers
Introduce experiment IDs (EXP + three digits) assigned from a shared register, and sample IDs within each experiment (S01, S02). Write both on tubes, in the notebook entry heading and in every file name.
Storage
Move raw images to the institution's managed research storage in a read-only raw folder per experiment, with processed images and analysis scripts in separate folders. Keep a nightly backup and one off-site copy, following the university's storage policy.
Notebook entries
Adopt a one-page template: aim, protocol link and version, deviations, samples, instrument settings file, results summary and file locations. Complete entries on the day of the experiment. Evaluate two ELNs licensed by the institution during a four-week pilot.
Illustrative example with invented details. Recommendations for your lab depend on your field, institution and data.
What you’ll need to provide
- Examples of current notebook entries (redacted if necessary) and a view of the folder structure
- Description of instruments, file types and analysis software used
- Institutional research data policy, funder requirements and any ELN already licensed
- Number of people who will use the system and their roles
- Constraints on confidential, commercial or personal data
You don’t need everything ready to ask for a quote — tell us what you have and we’ll say what’s missing.
What affects price and turnaround
Price depends on
- Individual researcher or whole research group
- Volume and state of existing notebooks and data to reorganize
- Whether ELN selection, migration planning or group training is included
- Number of instruments, data types and analysis pipelines to cover
Turnaround depends on
- Access to examples of current practice and relevant policies
- Length of the pilot period your group needs
- Time for institutional IT or data stewards to answer questions
Every project is quoted individually. How we price our work.
Questions people ask before starting
Which electronic lab notebook should we use?
Start with what your institution already licenses and supports, since that usually settles data storage, security and cost questions. Then compare fit for your discipline: templates, sample inventory, chemical structure or sequence support, instrument integration, export formats and audit trails. We help you define requirements and run a short pilot rather than recommend a product blindly.
Can you help me fill gaps in my old notebook?
We can help you document what you know now as a dated, clearly labeled retrospective note, which is honest and useful. We will not help backdate entries or recreate records as if they had been written at the time, as that would misrepresent the research record.
I do computational work. Do I need a lab notebook?
Yes, in a form that suits code: version control with descriptive commits, computational notebooks or scripts that regenerate results, and a dated log of decisions, parameter choices and failed approaches. We help you set this up so results can be traced and reproduced.
Does this help with funder data requirements?
Good organization makes data management plans and deposits much easier. We help you prepare documentation, formats and metadata; your plan and repository choice must follow your funder's and institution's current requirements, and your data steward or library is the authority on local policy.
Is our data kept confidential?
Yes. We work from examples and structures rather than full datasets wherever possible, follow your institution's data handling rules, and can sign a confidentiality agreement. We do not need access to identifiable personal data to design an organization system.