Experimental methodology¶
Curriculum status
These entries are architecture specifications, not final lesson plans. They define teaching intent, logistics, outputs and scientific boundaries; release-ready settings, worksheets, notebooks and answer keys still require empirical validation.
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Family source cluster: [O6; O7; P5; G9]
MET-01 — From observation to a testable question¶
| Concept / theme | A good practical begins with an observable phenomenon and a question that can be answered by available measurements. |
|---|---|
| Audience / context | School to doctoral/educator. Formal practical, workshop or modular course. Prior modules: FND-01. |
| Logistics | 30 min; 3–6; boards: 0–1; software: None or shared planning document; prepared dataset: Depends on case. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Methodology exercise. Stages: 3–5, 8, 10–12. |
| Spikeling relationship | 7 — Conceptual analogy; 2 — Direct board + GUI |
Learning outcomes.
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Identify and predict the principal behaviour described in a good practical begins with an observable phenomenon and a question that can be answered by available measurements.
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Configure or document convert a free exploration into a focused question, prediction and alternative outcome and record observation, manipulable variable, measurable outcome.
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Measure, calculate or compare convert a free exploration into a focused question, prediction and alternative outcome using an explicit operational rule.
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Interpret the result and state why questions must fit current measurable variables; do not smuggle in unmeasured mechanisms
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Convert a free exploration into a focused question, prediction and alternative outcome.
4. Acquire or inspect observation, manipulable variable, measurable outcome.
5. Produce question–prediction–evidence card.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Questions must fit current measurable variables; do not smuggle in unmeasured mechanisms.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate convert a free exploration into a focused question, prediction and alternative outcome. The reusable output is question–prediction–evidence card.
Sources / connections / priority. Sources: [O6; O7; P5; G9]. Natural follow-ons: MET-02, MET-03. Development priority: Core module. Boundary: Questions must fit current measurable variables; do not smuggle in unmeasured mechanisms.
MET-02 — Operational definitions and measurement¶
| Concept / theme | Terms such as threshold, burst, adaptation and synchrony require explicit measurement rules. |
|---|---|
| Audience / context | Undergraduate to doctoral. Formal practical, workshop or modular course. Prior modules: FND-04, DAT-02. |
| Logistics | 45 min; 3–6; boards: 0–1; software: None or shared planning document; prepared dataset: Depends on case. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Methodology exercise. Stages: 3–5, 8, 10–12. |
| Spikeling relationship | 5 — Recorded-dataset analysis; 7 — Conceptual analogy |
Learning outcomes.
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Identify and predict the principal behaviour described in terms such as threshold, burst, adaptation and synchrony require explicit measurement rules.
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Configure or document define one construct and test alternative operationalisations on a sample trace and record measurement rule, threshold/window and resulting value.
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Measure, calculate or compare define one construct and test alternative operationalisations on a sample trace using an explicit operational rule.
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Interpret the result and state why an operational definition enables consistency but does not exhaust the biological concept
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Define one construct and test alternative operationalisations on a sample trace.
4. Acquire or inspect measurement rule, threshold/window and resulting value.
5. Produce operational-definition table with sensitivity note.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: An operational definition enables consistency but does not exhaust the biological concept.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate define one construct and test alternative operationalisations on a sample trace. The reusable output is operational-definition table with sensitivity note.
Sources / connections / priority. Sources: [O6; O7; P5; G9]. Natural follow-ons: MET-03, DAT-03, EPI-01. Development priority: Core module. Boundary: An operational definition enables consistency but does not exhaust the biological concept.
MET-03 — Protocol design, controls and confounding¶
| Concept / theme | A protocol isolates the variable of interest through controls, standardisation and an analysis plan. |
|---|---|
| Audience / context | Undergraduate to doctoral. Formal practical, workshop or modular course. Prior modules: MET-01, MET-02. |
| Logistics | 60 min; 3–6; boards: 0–1; software: None or shared planning document; prepared dataset: Depends on case. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Methodology exercise. Stages: 3–5, 8, 10–12. |
| Spikeling relationship | 7 — Conceptual analogy; 2 — Direct board + GUI; 3 — Hybrid board + Jupyter |
Learning outcomes.
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Identify and predict the principal behaviour described in a protocol isolates the variable of interest through controls, standardisation and an analysis plan.
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Configure or document design a spikeling experiment with control condition, nuisance variables and planned comparison and record independent/dependent/control variables.
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Measure, calculate or compare design a spikeling experiment with control condition, nuisance variables and planned comparison using an explicit operational rule.
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Interpret the result and state why final values require pilot validation; avoid changing multiple controls unintentionally
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Design a Spikeling experiment with control condition, nuisance variables and planned comparison.
4. Acquire or inspect independent/dependent/control variables.
5. Produce protocol skeleton and design diagram.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Final values require pilot validation; avoid changing multiple controls unintentionally.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate design a spikeling experiment with control condition, nuisance variables and planned comparison. The reusable output is protocol skeleton and design diagram.
Sources / connections / priority. Sources: [O6; O7; P5; G9]. Natural follow-ons: MET-04, STA-03, CMP-09. Development priority: Core module. Boundary: Final values require pilot validation; avoid changing multiple controls unintentionally.
MET-04 — Calibration, repeatability and uncertainty budget¶
| Concept / theme | Measurement uncertainty arises from command setting, board variability, timing, detection and analysis choices. |
|---|---|
| Audience / context | Advanced undergraduate to master’s. Formal practical, workshop or modular course. Prior modules: EPH-04, EPH-06, STA-01. |
| Logistics | 60 min; 3–6; boards: 0–1; software: None or shared planning document; prepared dataset: Depends on case. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Methodology experiment. Stages: 3–5, 8, 10–12. |
| Spikeling relationship | 2 — Direct board + GUI; 3 — Hybrid board + Jupyter; 5 — Recorded-dataset analysis; 7 — Conceptual analogy |
Learning outcomes.
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Identify and predict the principal behaviour described in measurement uncertainty arises from command setting, board variability, timing, detection and analysis choices.
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Configure or document repeat a reference protocol across runs/boards and build an uncertainty budget and record within-run, between-run and between-board variation.
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Measure, calculate or compare repeat a reference protocol across runs/boards and build an uncertainty budget using an explicit operational rule.
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Interpret the result and state why the exercise characterises the teaching platform; biological variability is absent unless introduced by design
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Repeat a reference protocol across runs/boards and build an uncertainty budget.
4. Acquire or inspect within-run, between-run and between-board variation.
5. Produce repeatability report and uncertainty table.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: The exercise characterises the teaching platform; biological variability is absent unless introduced by design.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate repeat a reference protocol across runs/boards and build an uncertainty budget. The reusable output is repeatability report and uncertainty table.
Sources / connections / priority. Sources: [O6; O7; P5; G9]. Natural follow-ons: MET-06, STA-02. Development priority: High-priority extension. Boundary: The exercise characterises the teaching platform; biological variability is absent unless introduced by design.
MET-05 — Exploratory versus confirmatory analysis¶
| Concept / theme | Exploration generates hypotheses; confirmation fixes outcomes, exclusions and analysis choices before testing. |
|---|---|
| Audience / context | Master’s/doctoral methods. Formal practical, workshop or modular course. Prior modules: DAT-02, MET-03. |
| Logistics | 60 min; 3–6; boards: 0–1; software: None or shared planning document; prepared dataset: Depends on case. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Methodology seminar + analysis. Stages: 3–5, 8, 10–12. |
| Spikeling relationship | 5 — Recorded-dataset analysis; 7 — Conceptual analogy |
Learning outcomes.
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Identify and predict the principal behaviour described in exploration generates hypotheses; confirmation fixes outcomes, exclusions and analysis choices before testing.
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Configure or document analyse one dataset freely, then draft a locked analysis plan for a new dataset and record analysis choices, primary outcome, exclusions.
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Measure, calculate or compare analyse one dataset freely, then draft a locked analysis plan for a new dataset using an explicit operational rule.
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Interpret the result and state why a classroom exercise is not automatically a preregistered study; focus on reasoning and transparency
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Analyse one dataset freely, then draft a locked analysis plan for a new dataset.
4. Acquire or inspect analysis choices, primary outcome, exclusions.
5. Produce exploratory log and confirmatory plan.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: A classroom exercise is not automatically a preregistered study; focus on reasoning and transparency.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate analyse one dataset freely, then draft a locked analysis plan for a new dataset. The reusable output is exploratory log and confirmatory plan.
Sources / connections / priority. Sources: [O6; O7; P5; G9]. Natural follow-ons: STA-06, EPI-03. Development priority: Advanced specialised module. Boundary: A classroom exercise is not automatically a preregistered study; focus on reasoning and transparency.
MET-06 — Reproducible data, code and reporting¶
| Concept / theme | Reusable outputs require structured files, metadata, versioned code, environment information and a clear report. |
|---|---|
| Audience / context | Undergraduate to educator/doctoral. Formal practical, workshop or modular course. Prior modules: DAT-01, DAT-08 or any quantitative module. |
| Logistics | 60 min; 3–6; boards: 0–1; software: None or shared planning document; prepared dataset: Depends on case. Equipment: Standard USB cable; worksheet/protocol card. |
| Mode / stages | Reproducibility exercise. Stages: 3–5, 8, 10–12. |
| Spikeling relationship | 5 — Recorded-dataset analysis; 8 — External computational comparison |
Learning outcomes.
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Identify and predict the principal behaviour described in reusable outputs require structured files, metadata, versioned code, environment information and a clear report.
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Configure or document package one module dataset and notebook for independent rerun and record data, metadata, code, environment and result.
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Measure, calculate or compare package one module dataset and notebook for independent rerun using an explicit operational rule.
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Interpret the result and state why open does not mean undocumented; privacy/licensing and hardware/version provenance remain necessary
Roadmap.
1. Initial prediction or classification.
2. Configure the board, GUI, simulation or dataset and record metadata.
3. Package one module dataset and notebook for independent rerun.
4. Acquire or inspect data, metadata, code, environment and result.
5. Produce reproducibility package and readme.
6. Compare conditions or models and justify the chosen measurement.
7. Answer a limitation question: Open does not mean undocumented; privacy/licensing and hardware/version provenance remain necessary.
Inputs → outputs. Students receive a configuration/protocol prompt, variable definitions and any required starter data. They manipulate package one module dataset and notebook for independent rerun. The reusable output is reproducibility package and readme.
Sources / connections / priority. Sources: [O6; O7; P5; G9]. Natural follow-ons: EPI-04. Development priority: Core module. Boundary: Open does not mean undocumented; privacy/licensing and hardware/version provenance remain necessary.