Data sharing ethics toolkit: The Human Cell Atlas
Bibliographic record
Abstract
Striving to build an exhaustive guidebook of the types and properties of human cells, the Human Cell Atlas' (HCA) success relies on the sampling of diverse populations, developmental stages, and tissue types. Its open science philosophy preconizes the rapid, seamless sharing of data - as openly as possible. In light of the scope and ambition of such an international initiative, the HCA Ethics Working Group (EWG) has been working to build a solid foundation to address the complexities of data collection and sharing as part of Atlas development. Indeed, a particular challenge of the HCA is the diversity of sampling scenarios (e.g., living participants, deceased donors, pediatric populations, culturally diverse backgrounds, tissues from various developmental stages, etc.), and associated ethical and legal norms, which vary across countries contributing to the effort. Hence, to the extent possible, the EWG set out to provide harmonised, international and interoperable policies and tools, to guide its research community. This paper provides a high-level overview of the types of challenges and approaches proposed by the EWG.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrityMetaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Open science Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".