FlowRepository: A resource of annotated flow cytometry datasets associated with peer‐reviewed publications
Bibliographic record
Abstract
DATA associated with peer-reviewed manuscripts should be easily available and accessible; however, the rapid expansion of flow cytometry (FCM) applications has outpaced the development of tools for storage, analysis, and data representation (1-3).In addition, data associated with peer-reviewed manuscripts are rarely available publicly and even then are usually not stored with explicit links to experimental metadata, such as data analyses procedures, experimental conditions used, or information about the samples processed.This link between data and metadata is crucial as it facilitates the understanding of analysis approaches and reproducible research.Having datasets linked to figures and summaries through a detailed explanation of the sample processing and analysis pipeline allows other scientists to ask additional questions and build upon the published findings. MIFLOWCYTTo efficiently exchange information, the community must first agree on the kind of information they want to store, although the precise content of documentation will vary by scientific area, study design, the type of data collected, and characteristics of the dataset.In flow cytometry, this need is addressed by the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) (4), which is accepted as a standard by the International Society for Advancement of Cytometry and several journals (e.g., Cytometry A and the Nature Publishing Group journals).However, providing the information specified by MIFlowCyt, including the raw data (FCS files), is often impractical within the context of a peerreviewed manuscript, and searching and retrieving data files of interest across various journals' supplementary information can be cumbersome.Scientific communities in the fields of microarray, proteomics, and sequencing provide public repositories for pub-lished data and are benefiting from reuse and re-exploration of existing data to test new or alternative hypotheses and methods of analysis.Until now, no such repository existed for flow cytometry, constraining open scientific inquiry and progress in the field.To address these needs, we developed a public resource (FlowRepository.org)for authors to deposit their FCM data, provide the MIFlowCyt information, and share annotated datasets upon publication.
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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.031 | 0.032 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.317 | 0.315 |
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".