Experimental units, randomisation, blocking, mixed models, effect sizes, multiplicity and machine-readable data publication.
The biological experimental unit is the smallest independently assigned unit. Multiple leaves from one plant are subsamples, not independent plants. Plants sharing one reservoir may not be independent for a nutrient-solution treatment; reservoir or run can become the true treatment unit.
\nPrimary outcomes are declared before analysis. Secondary chemistry and sensory endpoints are labelled and multiplicity is controlled where inferential claims are made.
\nMeasurement uncertainty and precision terminology are declared separately from biological variation. Repeatability and reproducibility are not treated as synonyms.
\nEvery report links raw data, processed data, analysis code, data dictionary, protocol, deviations and checksums. Metadata follow MIAPPE concepts and FAIR principles.
","text":"In brief Experimental units, randomisation, blocking, mixed models, effect sizes, multiplicity and machine-readable data publication. Experimental unit The biological experimental unit is the smallest independently assigned unit. Multiple leaves from one plant are subsamples, not independent plants. Plants sharing one reservoir may not be independent for a nutrient-solution treatment; reservoir or run can become the true treatment unit. Design controls random assignment to position and treatment; blocking by cycle and relevant spatial factor; blinded sample coding for laboratory and sensory analysis; predefined exclusion and deviation rules; power analysis updated from pilot variance; effect size and confidence interval reported with p-values. Example mixed model STAT-1 y = μ + treatment + position + treatment×cultivar + cycle(random) + ε Example structure; the final model follows the actual experimental unit and design. Multiple outcomes Primary outcomes are declared before analysis. Secondary chemistry and sensory endpoints are labelled and multiplicity is controlled where inferential claims are made. Measurement uncertainty and precision terminology are declared separately from biological variation. Repeatability and reproducibility are not treated as synonyms. [R38] [R46] [R50] Data package Every report links raw data, processed data, analysis code, data dictionary, protocol, deviations and checksums. Metadata follow MIAPPE concepts and FAIR principles. [R23] [R24]","source_ids":["R23","R24","R38","R46","R50"],"visuals":[],"sources":[{"id":"R23","authors":"Papoutsoglou, E. A. et al.","year":2020,"title":"Enabling reusability of plant phenomic datasets with MIAPPE 1.1","publication":"New Phytologist","doi":"10.1111/nph.16544","source_type":"community standard paper","relevance":"Provides a practical metadata structure for investigation, study, biological material and observed variables.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.1111/nph.16544","verified_on":"2026-07-26","verification_status":"OFFICIAL RECORD CHECKED","verified_against":"Official ISO catalogue record"},{"id":"R24","authors":"Wilkinson, Mark D. et al.","year":2016,"title":"The FAIR Guiding Principles for scientific data management and stewardship","publication":"Scientific Data","doi":"10.1038/sdata.2016.18","source_type":"data principles","relevance":"Defines findable, accessible, interoperable and reusable research-data principles.","verification":"Publisher, PubMed, ISO or official proceedings metadata checked 2026-07-26","url":"https://doi.org/10.1038/sdata.2016.18","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R38","authors":"Joint Committee for Guides in Metrology","year":2008,"title":"Evaluation of measurement data — Guide to the expression of uncertainty in measurement","publication":"JCGM 100:2008","doi":"10.59161/JCGM100-2008E","source_type":"international metrology guide","relevance":"Defines the framework for measurement models, standard uncertainty, combined uncertainty and expanded uncertainty.","verification":"Official BIPM/JCGM record checked 2026-07-26","url":"https://doi.org/10.59161/JCGM100-2008E","verified_on":"2026-07-26","verification_status":"DOI METADATA CHECKED","verified_against":"Publisher, DOI landing page, PubMed or official repository where available"},{"id":"R46","authors":"Heckert, N. Alan; Filliben, James J.; Croarkin, C. M.; Hembree, B.; Guthrie, William F.; Tobias, P.; Prinz, J.","year":2002,"title":"NIST/SEMATECH e-Handbook of Statistical Methods","publication":"NIST Handbook 151","doi":null,"url":"https://www.nist.gov/publications/handbook-151-nistsematech-e-handbook-statistical-methods","source_type":"official statistical handbook","relevance":"Supports calibration regression, residual analysis, experimental design and statistical process control.","verification":"Official NIST publication record checked 2026-07-26","verified_on":"2026-07-26","verification_status":"OFFICIAL RECORD CHECKED","verified_against":"Named official institution record"},{"id":"R50","authors":"International Organization for Standardization","year":2025,"title":"ISO 5725-2:2025 Accuracy (trueness and precision) of measurement methods and results — Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method","publication":"ISO","doi":null,"url":"https://www.iso.org/standard/90054.html","source_type":"international standard","relevance":"Supports estimation of repeatability and reproducibility of measurement methods.","verification":"Official ISO record checked 2026-07-26; ISO 5725-2:2025 is the current published edition.","verified_on":"2026-07-26","verification_status":"OFFICIAL RECORD CHECKED","verified_against":"Official ISO catalogue record"}]}