Week 11 — fermlit dataset engineering

This week delivers the three concrete artifacts below and the supporting learning/branding/opportunity pack. The scientific or commercial outcome follows the preserved source brief, while the acceptance checks make its evidence testable. The career objective is to turn this work into something a scientist, hiring manager or buyer can inspect.

• Scientific/build priority: Normalize supported concentration/yield units, taxonomy identifiers and duplicates while preserving original strings and provenance.
• Public/profile priority: Draft one reporting-pattern figure with corpus-selection and extraction-error caveats.
• Career/relationship/commercial priority: Connect a finding to one founder's published organism/process without attributing a weakness to their company.

Read v6 · Week 11 · Dataset engineering and the reporting finding before Monday. It carries this week's v5 brief unchanged and adds the Welcome analogy, Learn it (▶ core and ＋ deeper sources), Run it (repos with a 20-40 minute exercise), the NotebookLM kit and the mentor check below. Standing v6 rules: resource rules, NotebookLM protocol, chapter anatomy. v6 adds depth only; it does not change a goal, gate, output or the 600-minute cap, so this specification still governs execution.

Act III · Threads: B, M, FL · Next gate: 13

Where this week sits. - ← From: weeks 9-10 (2,000 records, kappa, descriptive figures). - → Feeds: week 13 (HF + Zenodo release), week 14 (Methods: normalisation), week 16 (bioRxiv data availability), week 23 (UniProt/taxonomy skills reused for enzymes), Half 2 Loop Audits ("what does your data look like after normalisation?"). - ⟳ Returns in: the dataset card that makes FermLit citable and reusable (L6).

Goals. - 🎯 This week: normalised, deduplicated export; completeness-over-time result with a planned multiple-testing correction; draft HF dataset card with license. - 🏁 Next milestone: Gate 13. - 🌟 Final: a dataset others download and cite.

Mentor check. "I can explain why kLa limits scale-up, and I can show a dataset card that answers a stranger's questions before they ask."

• Week 09: inspect its actual evidence and review; source/code artifacts must exist before the dependent task begins.
• Week 10: inspect its actual evidence and review; source/code artifacts must exist before the dependent task begins.
• Read commitment, evidence, review and the current root TODAY/PROGRESS/QUESTIONS files.
• Read this week's chapter, v6 · Week 11 · Dataset engineering and the reporting finding, then the standing sections: Part 0 · how to use the document, Part 3 · the scientific thesis, Part 4 · curriculum, Part 8 · the prompt pack and Part 9 · branding and endorsement. The same sections exist in the v5 master, which remains the governing sequence; v6 reproduces them and adds depth. Retained commercial/instrument details come from v4.
• Inspect the actual canonical repository state, available data, existing figures and required human/external inputs. Never assume a previous week succeeded from its folder name.
• Verify current resource links, versions, dataset rights and any live company/venue information before relying on them. Use source catalog; explicitly record inaccessible resources.

The model must turn these into exact physical actions using the observed files and available inputs. Each 90-minute block includes start/stop rituals. All calls, posts, debugging, reading and exercises consume this same capacity.

The source lists more material than necessarily fits. Select exact excerpts for the learning objective and park the remainder; no full-course completion requirement.

Biology/domain unit

Source unit: B11 · Bioreactors III: oxygen transfer and scale-up

Material: NPTEL lectures on kLa, mixing, scale-up

Produce: kLa estimated from IndPenSim DO data

Understanding check: You can say why scale-up fails

During preparation, write one worked example connected to the week's actual task, one exercise with a separate answer key and five questions. Peter supplies answers; score explanation quality and flag misconceptions in QUESTIONS. Numeric/scientific conclusions require actual data or explicitly synthetic teaching values.

AI/scientific-method unit

Source unit: M11 · Bayesian optimization

Material: Frazier, "A Tutorial on Bayesian Optimization" — arxiv.org/abs/1807.02811; BoTorch tutorials — botorch.org

Produce: BO on the simulator's feed profile

Understanding check: You can explain expected improvement

Use the prompt pack: P1 explanation ladder, P2 concept-to-project bridge and P3 quiz. Coding practice belongs within the allocated study/build block. Where the source asks for a favorable scientific result, substitute an honest measured comparison as explained below.

Founder/scientist paper

Select one verified founder/scientific-lead paper relevant to this week's domain. Use P11; save the question, method, numerical result with context, data workflow, three author questions and one connection sentence in founder-paper.md. Keep abstract-only access limitations explicit. On source weeks with no study, prioritize the commercial/submission task and log any skipped digest.

These filenames are outputs to prepare during this week's execution; this scaffold does not claim they already exist. Drafts and measured results have different states. Technical code/data stay in the canonical project repository and are referenced from these coordinating files.

During preparation, future measured tables get column definitions, input requirements and run instructions with PENDING status. During execution, replace pending values only with measured outputs. Do not create realistic-looking fake evidence.

Branding: Draft one reporting-pattern figure with corpus-selection and extraction-error caveats.

Opportunity action: Connect a finding to one founder's published organism/process without attributing a weakness to their company.

Use career asset contracts. Publish at most the agreed cadence; one figure and one supported number or admitted failure can support multiple drafts. In long-form weeks, prepare a 1,500–2,500-word article from existing evidence within the build/buffer allocation. In release/demo weeks, prepare a 60-second script: problem, input, command/action, result, limitation and proof link.

Verify individuals, jobs and affiliations at execution time. The GridX list and v4 company map are research seeds, not current facts. Do not reveal named company weaknesses or employer/client confidential information. Keep drafted/sent/replied/booked/held statuses separate. The planning request alone does not authorize external messages or publication; honor explicit authorization in the execution session.

DO time series alone generally do not identify kLa without other information. The study exercise must document identifiability and assumptions or report that estimation is unsupported.

Apply the root scientific evidence contract: provenance; meaningful independent splits; no future-data leakage; baseline comparison; actual denominators/uncertainty; failed cases; human review where required. State whether data are simulated, public experimental, synthetic or partner-provided.

Keep source gates intact, and log scope/time conflicts in review. Missing people, money, access or results must not be replaced with invented evidence. Reduce optional polish first. A specific essential external dependency stays pending while independent preparation continues.

This is an enabling week. Its deliverables feed the next scheduled gate: 13. Complete the acceptance checklist below; do not promote reputation from a draft.

• Monday artifact satisfies: Conversions have dimensions and assumptions; incompatible percent values are not silently converted to g/L.
• Wednesday artifact satisfies: Exploratory versus planned analyses are distinguished before significance claims.
• Friday artifact satisfies: A small reload check reproduces row counts and schema; redistribution terms are justified.
• Study pack is prepared; Peter's actual learning/quiz result is recorded separately.
• Branding and opportunity drafts use supported claims and the correct external-action state.
• Reproduction or document checks are recorded with file/version references.
• This week's special gate, if any, has real evidence or remains explicitly pending.
• Review, root progress/log and the next physical action are updated.

Source-preserving minimum: normalized dataset export.

Operational first step if stalled: open normalization-audit.md, write the required inputs and produce the smallest checkable example in ten minutes. If a more urgent missing gate dependency controls the week, use the source minimum above.

If the full week does not fit, use P14: retain one gate-critical deliverable and one directly supporting study exercise; record what is skipped versus moved. Do not mark the original full gate passed merely because the minimum was completed. After a missed week, reforecast from actual evidence and avoid a second unexamined lost week.

In review, state exactly what exists, what was verified, what Peter can now explain, which external events remain pending, where the canonical code/data live, and the next file/function or draft paragraph to touch. Link each fact to evidence. Carry scientific assumptions, split definitions and rights constraints forward explicitly.

Prepare Week 11 of this LABLOOP execution plan.
Read execution-plan/specs.md, execution-plan/DECISIONS.md,
execution-plan/GATES.md and
execution-plan/weeks/week-11-fermlit-dataset-engineering/specs.md, commitment.md, evidence.md and review.md.
Read prior prerequisite reviews and inspect actual repository/data state.
Use v5 as the governing sequence, the v6 Week 11 chapter
(LABLOOP-Master-Plan-v6.md) as the deeper walkthrough, and v4 only for
retained details.
Produce the full local preparation pack and the exact week-specific files named
in the artifact table. Give me a bounded lesson with exercises/answer key,
daily physical actions, technical runbook or draft, branding drafts and
career/commercial actions. Keep the total to 600 minutes.
Separate prepared artifacts from measured results and my learning completion.
Verify sources and do not invent metrics, citations, papers, people or outcomes.
Do not execute external communications, publication or spending unless the
current session already authorizes the specific action.
Finish by updating evidence/review and TODAY with the next physical action.

v6 reproduces this brief unchanged inside v6 · Week 11 · Dataset engineering and the reporting finding. The following is source planning text, not a claim of completion. Apply the explicit evidence corrections in this specification and DECISIONS.md.

Week 11 · Dataset engineering and the reporting finding

• Goal: FermLit v0.5 as a proper dataset.
• Tue (B11): NPTEL kLa and scale-up; kLa estimate.
• Thu (M11): Frazier + BoTorch; BO on the simulator feed profile.
• Build: [ ] Mon: units normalization (g/L, mg/L, %), organism name normalization (NCBI taxonomy IDs via API), dedup. [ ] Wed: the reporting-completeness analysis as a formal result: which fields are missing, how that changed over 20 years, whether journals differ (with multiple-testing correction from B14 later). [ ] Fri: dataset card draft (HF), license (CC BY 4.0 for the extracted table; papers not redistributed), dataset/ export.
• Sun: L-Sun: "The fermentation literature has a reporting problem, in numbers". L-Wed.
• Only one thing: normalized dataset export.