Scientific rigour is not a philosophical aspiration — it is an operational practice. The reproducibility crisis in science has demonstrated that a significant proportion of published experimental results cannot be replicated by independent laboratories, and the most common root causes are not fraud but process failures: protocols that were not written before execution began and were reconstructed afterwards, controls that were not included because they were considered unnecessary, conditions that were not recorded because they seemed obvious at the time, and data that was analysed with a method chosen after inspecting the results. The structured experiment tracking workflow addresses each of these failure modes: the hypothesis is stated before the experiment is designed; the protocol is written before it is executed; all conditions are recorded contemporaneously; controls are included as a non-negotiable element; and the analysis method is pre-specified. The Electronic Lab Notebook has become the modern standard for this discipline — replacing handwritten lab notebooks with a versioned, searchable, timestamped digital record that supports the data integrity requirements of both academic publication and regulatory submission. This free checklist gives laboratory researchers, R&D scientists, and research teams a structured framework for the full experiment tracking lifecycle.
A specific, testable prediction about the relationship between variables.
Conditions, controls, variables, sample size, and statistical power — before execution.
Written procedure specific enough to enable independent reproduction.
Follow the protocol; document contemporaneously; record all conditions and deviations.
Raw data captured in the ELN or lab notebook; unaltered; with full context.
Per the pre-specified analysis plan; deviations from the plan documented.
Protocol, raw data, processed data, analysis code, and results stored for the required retention period.
Seven phases covering the full experiment lifecycle — from hypothesis formulation through design, protocol writing, execution, analysis, interpretation, and archiving.
The experiment should be designed to falsify the hypothesis, not confirm it. An experimental design that can only produce results consistent with the hypothesis — regardless of what happens — is not a scientific experiment.
The lab notebook rule: if it wasn’t written down, it didn’t happen. Contemporaneous recording — writing observations at the time they are made — is the foundational data integrity practice in laboratory research. Reconstructed records are not original records.
This checklist is available as a free, runnable template in CheckFlow — with hypothesis and design phases as required gates before execution can begin, and a complete experiment archive built automatically as the workflow runs.
Use This Template FreeAn Electronic Lab Notebook (ELN) is a digital platform that replaces the traditional paper lab notebook for recording experimental procedures, observations, and data. ELNs offer significant advantages over paper notebooks: timestamped and versioned entries that cannot be retroactively altered without a visible audit trail; searchable records across all experiments; integration with laboratory instruments for automatic data capture; collaborative access for research teams across locations; and compliance with the data integrity requirements of GLP and regulatory submissions. Major ELN platforms include LabArchives, Benchling, Labfolder, and Agilent’s OpenLab. Most academic institutions and pharmaceutical and biotech companies now consider ELN use a requirement for GLP-compliant or regulatory-submission-quality research.
CheckFlow is not an ELN and does not replace ELN functionality. Rather, CheckFlow’s experiment tracking workflow structures the process that surrounds the ELN entry — ensuring that the hypothesis, design, and protocol are complete before the ELN entry for execution begins, and that the analysis, interpretation, and archiving steps are completed systematically after execution. The CheckFlow record and the ELN record run together as complementary documentation.
The experiment that begins without a written hypothesis and pre-specified analysis plan is the experiment that produces p-hacked results. CheckFlow’s workflow requires Phase 1 (hypothesis) and Phase 2 (design) to be completed before Phase 4 (execution) can begin — enforcing the pre-registration discipline that reproducible research requires.
Template DesignerThe lab notebook entry written from memory three days after the experiment is not a contemporaneous record — it is a reconstruction. CheckFlow’s execution phase is structured for real-time completion during the experiment, with the ELN record and the CheckFlow workflow record running together as complementary documentation.
Powerful ChecklistsJournal submissions, grant reports, regulatory filings, and patent applications all require access to the original experimental record. CheckFlow creates a structured experiment record — hypothesis, design, protocol version, raw data reference, analysis, and interpretation — archived automatically as the experiment runs.
Analytics & ReportingExperiments generate the data that supports patent applications. CheckFlow’s Patent Application Process Checklist covers the full IP protection lifecycle for R&D outputs. See the Patent Application Process Checklist →
Experiment data collection requires an approved data management plan and, for human subjects, ethics approval. CheckFlow’s R&D Data Collection Process Checklist covers both. See the R&D Data Collection Process →
An R&D experiment tracking workflow covers seven phases: hypothesis formulation (specific and falsifiable, variables identified, literature grounded), experimental design (conditions, positive and negative controls, sample size via power analysis, blinding consideration, pre-specified analysis plan), protocol documentation (written before execution, all materials and methods specified, versioned), execution with real-time recording (protocol followed, conditions recorded contemporaneously, observations and deviations documented), data analysis (pre-specified plan, transparent processing, pre-specified outlier policy, statistical assumption verification, analysis code retained), results interpretation (hypothesis assessment, alternative explanations, limitations documented, experiment summary written), and archiving (all records archived in FAIR-compliant storage, retention period noted, data available for peer review).
An Electronic Lab Notebook (ELN) is a digital platform that replaces traditional paper lab notebooks for recording experimental procedures, observations, and data. ELNs offer significant advantages over paper notebooks: timestamped and versioned entries that cannot be retroactively altered; searchable records; integration with laboratory instruments for automatic data capture; collaborative access for research teams; and compliance with data integrity requirements for regulatory submissions. Major ELN platforms include LabArchives, Benchling, Labfolder, and Agilent’s OpenLab. Most academic institutions and pharmaceutical companies now consider ELN use a requirement for GLP-compliant or regulatory-submission-quality research.
Experimental controls are conditions included in an experiment specifically to validate the experimental system and allow proper interpretation of results. A positive control is a condition known to produce the expected positive result — it confirms that the assay, instrument, or measurement system is working correctly. If the positive control fails, a negative result in the experimental condition may be a system failure, not a true negative. A negative control is a condition known to produce no effect — it confirms that the observed result in the experimental condition is not due to contamination, reagent interference, or system artefact. An experiment without appropriate controls is an experiment whose results cannot be unambiguously interpreted.
P-hacking (also called data dredging) is the practice of performing multiple statistical analyses on a dataset until a statistically significant result is found, then reporting only that analysis as if it was the pre-planned analysis. Since statistical significance at p < 0.05 means there is a 5% chance the result occurred by chance, running 20 analyses on the same dataset will produce approximately one “significant” result purely by chance. Pre-specifying the analysis plan — documenting in the ELN or on a registry what statistical tests will be used before the data is analysed — prevents p-hacking by making the analysis binding before the results are seen.
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