In vitro blood flow dataset in circular microchannels: High-speed videomicroscopy, micro-piv, and cell-free layer measurements
Bibliographic record
Abstract
This dataset provides micro-particle image velocimetry (µPIV) and high-speed imaging of red blood cell (RBC) suspensions flowing through circular glass microchannels with inner diameters of 25 µm and 50 µm. Suspensions were prepared in phosphate-buffered saline (PBS) and in native plasma at hematocrit levels of 5 %, 10 %, 15 %, and 20 %, covering physiologically relevant concentration ranges. These conditions allow systematic assessment of flow dynamics and the formation of cell-free layers (CFLs) in confined microvascular geometries. The dataset includes raw and processed µPIV image sequences, providing two-dimensional velocity fields of RBC suspensions under varying flow conditions. Complementary high-speed brightfield recordings capture RBC distribution near channel walls, enabling quantification of CFL thickness. Extracted CFL measurements are included alongside the video sequences. Data are organized into a structured folder hierarchy by channel diameter, hematocrit level, and suspending medium. Each folder contains raw image files, processed velocity fields, CFL thickness measurements, and corresponding flow rate and pressure logs. Standardized file naming facilitates straightforward navigation, reproducibility, and integration into external workflows. This resource can be reused by researchers investigating blood rheology, microfluidics, and hemocompatibility to study the effects of hematocrit, shear rate, and suspending medium on microchannel flow. The dataset also provides benchmark data for validating computational models of microcirculation and can support the development of machine learning approaches for flow prediction or RBC core-plasma segmentation. Its combination of raw and processed outputs enables both direct analysis and cross-study integration into broader multi-scale investigations of microvascular transport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".