Research & Development Templates

The reproducibility crisis refers to the finding that a significant proportion of published scientific results — across psychology, biomedicine, economics, and other fields — cannot be replicated by independent researchers using the same methods. The causes are substantially process failures rather than fraud: protocols that were not written before execution and reconstructed afterwards, controls that were omitted, conditions that were not recorded, and analysis methods selected after inspecting the data (p-hacking). Structured documentation addresses each of these: recording the hypothesis and experimental design before execution prevents post-hoc hypothesis generation; requiring contemporaneous recording of conditions and deviations during execution produces an accurate record; and pre-specifying the analysis plan before data collection prevents the selection of analysis methods based on which produces a significant result. Research that follows a documented process can be reproduced; research that relies on the researcher's memory of what they did cannot.

IRB (Institutional Review Board in the US) and Research Ethics Committee (REC/IEC in the UK and internationally) approval is the mandatory review of research involving human subjects to ensure participants' rights and welfare are protected. The process involves: submitting a full research protocol (including research design, data collection methods, risk-benefit analysis, and informed consent procedures), supporting documents (consent forms, recruitment materials, investigator CVs and training certifications), and receiving a determination of the review type (Exempt, Expedited, or Full Board — determined by the IRB, not the researcher). Approval must be received before any human subjects data collection begins. Research must be conducted as approved — any changes require an amendment submission. Annual continuing review is required for most approvals. The most important rule: no data collection before approval, no exceptions.

FAIR stands for Findable, Accessible, Interoperable, and Reusable — a set of principles for research data management published in Scientific Data in 2016. Findable: data is assigned a persistent unique identifier and described with rich metadata so it can be located. Accessible: data and metadata can be retrieved using open, standardised protocols. Interoperable: data uses standard vocabularies and formats that allow it to be combined with other datasets. Reusable: data is richly described with provenance and a clear usage licence so others can understand, validate, and build upon it. Funders require FAIR data management because research data produced with public funding represents a scientific asset that should be preserved and made available for reuse — accelerating science and maximising the return on research investment. EU Horizon Europe (the world's largest publicly funded R&D programme at €95.5 billion), the UK Research Councils (UKRI), and the NIH all require FAIR data management plans.

Every CheckFlow template is fully customisable for both contexts. For academic research: align experiment tracking with your institution's ELN (Electronic Lab Notebook) requirements, add funder-specific data management plan steps to the data collection template, and include the institutional ethics submission workflow specific to your IRB. For corporate R&D: adapt the research proposal template to an internal investment case framework, replace academic publication steps with IP protection and technology transfer steps, add the regulatory submission requirements relevant to your industry (FDA, EMA, or other), and align data management with your organisation's data governance policy. For pharmaceutical and medical device R&D specifically: add the GLP and GCP compliance requirements, validation documentation, and the regulatory pathway-specific evidence requirements.