Terminology
Definitions for core concepts, abbreviations, performance measures, and domain-specific language used across technical pages.
Measurement services
Testing, evaluation, standards readiness, and technical documentation
Measurement and testing
HKETRI organizes measurement services around problems that require clear definitions, comparable results, documented assumptions, and public-facing technical records.
Service pages may cover benchmark design, prototype assessment, dataset documentation, instrumentation planning, software workflow inspection, and reporting formats for applied engineering studies. Each service should make clear what is being evaluated, what evidence is required, what outputs are produced, and what limitations apply.
Standards readiness
Engineering work becomes more useful when terms, units, procedures, and reporting conventions are clear. HKETRI emphasizes consistent terminology, documented test conditions, traceable references, and careful explanations of what a method does and does not prove.
Method statements
Plain descriptions of method scope, required inputs, equipment or software assumptions, output formats, and uncertainty notes.
Reporting templates
Consistent structures for technical notes, program updates, facility descriptions, and public resource pages.
Data resources
Data resources should make technical work easier to inspect. Pages can include dataset descriptions, source notes, collection conditions, licensing information, quality checks, metadata fields, version information, and responsible use limitations.
For computational and engineering projects, data pages should also describe preprocessing assumptions, measurement units, access conditions, update frequency, and review status so readers can understand how results were produced.
Technical guides
Technical guides translate program knowledge into practical documents. Suitable guide topics include AI evaluation checklists, robotics test scenario planning, sensing system documentation, materials reliability reporting, digital health evaluation notes, and sustainability data interpretation.
Guides should be written for technical readers who need reproducible steps, not promotional summaries. A good guide names the problem, defines the method, lists required inputs, states review limits, and links to relevant program or resource pages.