Cell Maps for Artificial Intelligence - June 2025 Data Release (Beta)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<h3>Description</h3> <p>This dataset is a revision of the June 2025 Data Release of Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org), the Functional Genomics Grand Challenge in the NIH Bridge2AI program. This revision includes adding RGB immunofluorescent images, corrections to ro-crate metadata, and changes to naming conventions. </p> <p> This Beta release includes perturb-seq data in undifferentiated KOLF2.1J iPSCs; SEC-MS data in undifferentiated KOLF2.1J iPSCs, iPSC-derived NPCs, neurons, cardiomyocytes, and treated and untreated MDA-MB468 breast cancer cells; and IF images in MDA-MB-468 breast cancer cells in the presence and absence of chemotherapy (vorinostat and paclitaxel).</p> <h3>External Data Links</h3> <p>Access external data resources related to this dataset:</p> <ul> <li><strong>Sequence Read Archive (SRA) Data: </strong> Embargoed</li> <li><strong>Mass Spectrometry Data (Human iPSCs):</strong> <a href="https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=de876e1d228c4f7ab02f84027894bed7" target="_blank">MassIVE Repository</a></li> <li><strong>Mass Spectrometry Data (Human Cancer Cells):</strong> <a href="https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=ad8b8084f5b14af5bafac70fdd42a577" target="_blank">MassIVE Repository</a></li> </ul> <hr></hr> <h3>Data Governance & Ethics</h3> <ul> <li><strong>Human Subjects:</strong> No</li> <li><strong>De-identified Samples:</strong> Yes</li> <li><strong>FDA Regulated:</strong> No</li> <li><strong>Data Governance Committee:</strong> Jillian Parker (jillianparker@health.ucsd.edu)</li> <li><strong>Ethical Review:</strong> Vardit Ravitsky (ravitskyv@thehastingscenter.org) and Jean-Christophe Belisle-Pipon (jean-christophe_belisle-pipon@sfu.ca)</li> </ul> <h3>Completeness</h3> <p>These data are not yet in completed final form:</p> <ul> <li>Some datasets are under temporary pre-publication embargo</li> <li>Protein-protein interaction (SEC-MS), protein localization (IF imaging), and CRISPRi perturbSeq data interrogate sets of proteins which incompletely overlap</li> <li>Computed cell maps not included in this release</li> </ul> <h3>Maintenance Plan</h3> <ul> <li>Dataset will be regularly updated and augmented through the end of the project in November 2026</li> <li>Updates on a quarterly basis</li> <li>Long term preservation in the University of Virginia Dataverse, supported by committed institutional funds</li> </ul> <h3>Intended Use</h3> <p>This dataset is intended for:</p> <ul> <li>AI-ready datasets to support research in functional genomics</li> <li>AI model training</li> <li>Cellular process analysis</li> <li>Cell architectural changes and interactions in presence of specific disease processes, treatment conditions, or genetic perturbations</li> </ul> <h3>Limitations</h3> <p><strong>Researchers should be aware of inherent limitations:</strong></p> <ul> <li>This is an interim release</li> <li>Does not contain predicted cell maps, which will be added in future releases</li> <li>The current release is most suitable for bioinformatics analysis of the individual datasets</li> <li>Requires domain expertise for meaningful analysis</li> </ul> <h3>Prohibited Uses</h3> <ul> <li><strong>These laboratory data are not to be used in clinical decision-making or in any context involving patient care without appropriate regulatory oversight and approval</strong></li> </ul> <h3>Potential Sources of Bias</h3> <p>Users should be aware of potential biases:</p> <ul> <li>Data in this release was derived from commercially available de-identified human cell lines</li> <li>Does not represent all biological variants which may be seen in the population at large</li> </ul>
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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 it