Zebrahub single cell dataset
Bibliographic record
Abstract
Zebrahub first dataset is a single-cell RNA sequencing atlas a the single embryo resolution of nearly 120671 cells spanning 10 stage: from 0-somite embryos to 10 day larva (bud-, 5-, 10-, 15-, 20-, 30-somite stages, as well as 2-, 3-, 5- and 10-dpf). For each stage, we have at least 4 embryo replicates. We strive to achieve the highest possible quality; in that context, we expect the dataset to evolve to more stages and better data quality. This work is led by the Royer Lab in collaboration with CZ Biohub’s data science and sequencing platforms. It aims at providing a consistent and high-quality single-embryo resolved picture of development leveraging the latest single-cell technologies such as 10X Chromium (standard and HT) for library preparation and novaseq 6000 for sequencing. This dataset was produced in the context of upcoming preprints. In the spirit of open and accelerated science, we make this dataset available ahead of time and we require that redistribution of these data include the full text of the data release policy. <br> <strong>DATA RELEASE POLICY</strong> We aim to make sequence data rapidly and broadly available to the scientific community as a community resource. We intend to publish the work of this project in a timely fashion, and we welcome collaborative interaction on the project and analyses. However, considerable investment was made in generating these data, and we ask that you respect rights of first publication and acknowledgment as outlined in the Toronto agreement (Toronto International Data Release Workshop Authors. Prepublication data sharing. Nature. 2009 Sep 10;461(7261):168-70). By accessing these data, you agree not to publish any articles containing analyses of genes, cell types, or transcriptomic data on a whole atlas, tissue scale, or time point scale prior to the initial publication by the Chan Zuckerberg Biohub. If you wish to make use of restricted data for publication or are interested in collaborating on the analyses of these data, please email us or use the contact form. Redistribution of these data should include the full text of the data use policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.647 | 0.001 |
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; both teacher heads agree on what is shown here.
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".