Use FlowRepository to share your clinical data upon study publication
Bibliographic record
Abstract
A fundamental tenet of scientific research is that published results including underlying data should be open to independent validation and refutation. Data sharing encourages collaboration, facilitates quality and reduces redundancy in data production. Authors submitting manuscripts to several journals have already adopted the habit of sharing their underlying flow cytometry data by deposition to FlowRepository-a data repository that is jointly supported by the International Society for Advancement of Cytometry, the International Clinical Cytometry Society and the European Society for Clinical Cell Analysis. De-identification is required for publishing data from clinical studies and we discuss ways to satisfy data sharing requirements and patient privacy requirements simultaneously. Scientific communities in the fields of microarray, proteomics, and sequencing have been benefiting from reuse and re-exploration of data in public repositories for over decade. We believe it is time that clinicians follow suit and that de-identified clinical data also become routinely available along with published cytometry-based findings. © 2016 International Clinical Cytometry Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.639 | 0.489 |
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".