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What the work looks like when the raw material is allowed to stay difficult.
The examples below use synthetic, public or fully anonymised material. They show the technical question, the failure that mattered and the form of the handover. They do not invent client revenue, clinical benefit or performance claims.
Entry 1: Fluorescence objects that merge at high density
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Situation
A fluorescence pipeline detected isolated objects reliably and undercounted dense fields. The pooled overlap score concealed two distinct errors: adjacent objects merged into one mask, while dim objects disappeared.
Work
Cellpose, StarDist and a classical watershed baseline were tested on a stratified annotation set. The set included sparse fields, dense fields, uneven illumination and low-intensity objects. Evaluation separated misses, false detections, merges, splits and boundary disagreement.
Output
- comparison notebook and batch runner;
- error-coded overlays;
- performance by field condition;
- recommended operating parameters;
- measurement table linked to source images and masks;
- annotation plan for the next batch.
Boundary
The example demonstrates research image analysis. It does not support diagnostic interpretation.
Entry 2: A wearable session with four clocks
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Situation
Physiological channels, IMU, laptop events and a participant task log used different clocks. Start markers aligned; later events drifted. A broad interpolation step had also turned a contact-loss interval into a smooth transient.
Work
Timestamp monotonicity, sample counts and shared events were used to estimate offsets and drift. Contact loss, flat lines and motion bursts received separate reason codes. Retained epochs were generated from the corrected time base.
Output
- corrected event table;
- segment-level quality mask;
- drift and missingness report;
- retained-duration summary by session;
- reproducible preprocessing code;
- raw-versus-retained review figure.
Boundary
The output records data fitness for the stated research analysis. It does not infer a clinical state.
Entry 3: Window-level accuracy that vanished by participant
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Situation
A time-series classifier was trained on overlapping windows. Random splitting placed windows from the same participant in training and test data. Performance fell sharply when participants were held out.
Work
The target and leakage routes were mapped first. Scaling, feature selection and tuning were moved inside each training fold. Logistic regression, Elastic Net and a time-series feature baseline were compared under leave-one-participant-out testing. Errors were stratified by participant and signal quality.
Output
- reproducible grouped split;
- baseline and challenger comparison;
- uncertainty across held-out participants;
- confound and negative-control tests;
- failed-case table;
- data-collection recommendation.
Boundary
A lower honest score replaced a higher unusable one. No production claim was made.
Entry 4: A notebook that depended on manual file surgery
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Situation
Instrument exports were renamed, joined in a spreadsheet and loaded into a notebook through absolute paths. The final figure could not be reproduced from a clean checkout.
Work
Input schemas were defined with explicit identifiers, units and failure messages. Raw exports were converted to Parquet, QC tables were generated automatically, and the analysis was split into tested functions. A run manifest recorded input checksums, configuration and software version.
Output
- command-line pipeline;
- schema validation;
- automated QC report;
- tests for known edge cases;
- versioned figures and tables;
- synthetic example dataset;
- handover documentation.
Boundary
The build automated the agreed workflow. Scientific interpretation stayed with the research team.
Entry 5: Detection that failed on degraded recordings
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Situation
An event detector performed well on clean recordings and produced false detections after clipping, bandwidth loss and increased background noise. One average metric hid the failure.
Work
Synthetic degradations were applied separately and in combination. Performance was measured against degradation severity, event amplitude and recording-level quality. Threshold and morphology-based baselines were retained beside the learned detector.
Output
- degradation test suite;
- error curves by failure type;
- operating threshold analysis;
- examples linked to source intervals;
- recommendation for rejection, correction or retraining;
- reusable robustness report.
Boundary
The example supports research and engineering evaluation. Safety-critical operation would require domain-specific assurance beyond this scope.
CTA
Services
A useful example starts with the failure case.
For scoping, send the current output beside the raw input that produced it. A difficult field, failed participant or broken file is more informative than a selected success.