# Rubric20-Semantic Evaluation Report

Project
CM4AI
D4D File
CM4AI_d4d.yaml
Evaluator Model
claude-sonnet-4-5-20250929
Rubric Type
rubric20-semantic
Temperature
0.0
Evaluation Date
2026-01-12T00:00:00Z
68.0/88.0
77.3% Overall Score · Grade: C+

## Category Performance

Category
1. Structural Completeness
20/21
95.2%
Category
2. Metadata Quality & Content
16/21
76.2%
Category
3. Technical Documentation
18/25
72.0%
Category
4. FAIRness & Accessibility
14/21
66.7%

## Question-Level Assessment

### 1. Structural Completeness

Q1. Field Completeness numeric
4/5
Assessment
Strong field completion with comprehensive content in all major D4D sections. Primary gaps in structured metadata fields (version, publisher, citation) that exist in text but not as structured elements, and hierarchical/governance fields
Evidence Found
Core fields present: id (DOI), title, description, keywords (25), license_and_use_terms, page, creators (15), purposes (3), instances (4), funders, subpopulations (7), collection_mechanisms (4), acquisition_methods (4), preprocessing_strategies (5), cleaning_strategies (3), intended_uses (6), discouraged_uses (3), distribution_formats (5), maintainers, updates, external_resources (12). Missing: version, publisher, citation, conforms_to, format, encoding, variables, is_tabular, resources, parent_datasets, related_datasets, confidentiality_level, regulatory_restrictions
Semantic Analysis
Approximately 85% field completion - excellent coverage of motivation, composition, collection, preprocessing, uses, distribution, and maintenance sections
Q2. Entry Length Adequacy numeric
5/5
Assessment
Excellent narrative field length throughout - all major descriptions exceed 200 characters with rich detail
Evidence Found
description: 1,383 characters (23 lines); purposes[0].description: 430 chars; purposes[1].description: 386 chars; purposes[2].description: 339 chars; tasks descriptions: 200-400 chars each; collection_mechanisms descriptions: 300-600 chars each
Semantic Analysis
All narrative fields substantially exceed minimum length requirement
Q3. Keyword Diversity numeric
5/5
Assessment
Exceptional keyword diversity covering all major dataset dimensions
Evidence Found
keywords: 25 unique keywords spanning methodologies (Cell Maps, AP-MS, SEC-MS, Immunofluorescence, Mass Spectrometry, Perturb-Seq), cell lines (MDA-MB-468, KOLF2.1J, iPSC), disease contexts (Breast Cancer), technologies (CRISPR Perturbation, Deep Learning, Visible Neural Networks), and standards (FAIR Principles, RO-Crate, FAIRSCAPE)
Semantic Analysis
Well above threshold with comprehensive coverage of methods, biological systems, diseases, and standards
Q4. File Enumeration and Type Variety numeric
5/5
Assessment
Highly multimodal dataset with 5 distinct distribution platforms and 4+ data modalities
Evidence Found
distribution_formats: 5 formats - (1) RO-Crate packages with provenance, (2) Mass spec data in MassIVE, (3) Sequence data in NCBI SRA, (4) Hierarchical cell maps in NDEx, (5) Archives in UVA Dataverse. Multiple data types: imaging (IF confocal microscopy), proteomics (AP-MS, SEC-MS), genomics (CRISPR screens, single-cell RNA-seq), network data (hierarchical DAGs)
Semantic Analysis
Excellent file type variety representing multimodal integration of imaging, proteomics, and genomics data
Q5. Data File Size Availability pass_fail
1/1
Assessment
Instance counts and sample dimensions well-documented; file sizes not provided
Evidence Found
instances: 4 documented (2 cell lines, 100 chromatin regulators, 100 metabolic enzymes); subpopulations: 7 conditions; subsets describe: 563 proteins imaged (IF), 17 genes tagged for AP-MS, 72/100 chromatin modifiers in SEC-MS, 11,739 genes in CRISPR atlas, >1,000 complexes detected; no bytes field for file sizes
Semantic Analysis
Pass - dimensional metadata present through instance and subset descriptions

### 2. Metadata Quality & Content

Q6. Dataset Identification Metadata pass_fail
1/1
Assessment
Excellent identifier coverage with multiple DOIs and valid RRIDs; hosting platforms described but not in publisher field
Evidence Found
id: https://doi.org/10.18130/V3/DXWOS5 (primary DOI); Additional DOIs: 10.18130/V3/B35XWX (V1.4), 10.18130/V3/F3TD5R (V2.1), 10.1101/2024.05.21.589311 (bioRxiv); RRIDs: RRID:CVCL_0419 (MDA-MB-468), RRID:CVCL_B5P3 (KOLF2.1J); Hosting platforms mentioned: UVA Dataverse, MassIVE, NCBI SRA/BioProject, NDEx; no publisher field
Semantic Analysis
Pass - comprehensive persistent identifier coverage; publisher field missing but platforms documented
Q7. Funding and Acknowledgements Completeness numeric
5/5
Assessment
Comprehensive funding documentation with grant numbers, amounts, dates, and complete creator affiliations
Evidence Found
funders: NIH Common Fund Bridge2AI Program with grants 1OT2OD032742-01 and 5U54HG012513-02, Opportunity Number OTA-21-008, FY 2025 funding amounts ($5,289,382), project dates (Sep 2022 - Aug 2026), Frederick Thomas Fund; creators: 15 creators with roles (Contact PI, Co-Investigator) and affiliations (UCSD, SFU, UVA, UAB, UCSF, Stanford, UMontreal, Yale, UT Austin)
Semantic Analysis
Exemplary funding transparency with complete grant details and creator attribution
Q8. Ethical and Privacy Declarations numeric
3/5
Assessment
Ethics oversight well-documented with named reviewers; de-identification described; missing structured fields for DPIA, privacy protections, and re-identification risk (though less critical for cell line data)
Evidence Found
human_subject_research: involves_human_subjects=false, Ethics Module (Vardit Ravitsky, Jean-Christophe Bélisle-Pipon), Data Access Committee (Jillian Parker), Bridge2AI Ethics Working Group, de-identification status described ('cannot be matched to human subject'), special_populations (MDA-MB-468: 51-yo black female, KOLF2.1J: healthy male Northern European); sensitive_elements: de-identified cell lines; Missing: is_deidentified field, data_protection_impacts, participant_privacy, reidentification_risk, informed_consent, participant_compensation
Semantic Analysis
Good ethics documentation appropriate for cell line data; could enhance with structured privacy/DPIA fields
Q9. Access Requirements and Governance Documentation numeric
3/5
Assessment
Strong license documentation with governance oversight mentioned; lacks structured fields for regulatory compliance, confidentiality classification, and governance contact
Evidence Found
license_and_use_terms: Comprehensive CC BY-NC-SA 4.0 with attribution requirements, non-commercial use only, commercial licensing available through UCSD/Stanford/UCSF, Data Access Committee oversight, must cite bioRxiv publication and data DOI; Missing: regulatory_restrictions, confidentiality_level, hipaa_compliant, other_compliance, governance_committee_contact (though Data Access Committee mentioned in text)
Semantic Analysis
License well-specified; governance present but not in structured fields; missing multi-jurisdiction compliance metadata
Q10. Interoperability, Standardization, and Cross-Platform Integration numeric
4/5
Assessment
Excellent standards adoption and integration capability via MuSIC pipeline and FAIRSCAPE; lacks structured conformance declarations
Evidence Found
Standards mentioned: FAIR principles, RO-Crate (Research Object Crate), Schema.org, EVI Evidence Graph Ontology, Gene Ontology, Reactome, PDB, AlphaFold, JSON-Schema, JSON-LD; Integration: MuSIC pipeline integrates multimodal data (IF imaging, AP-MS, SEC-MS, CRISPR screens); FAIRSCAPE framework for AI-readiness; Missing: format, encoding, conforms_to, conforms_to_schema fields (standards described in text but not structured)
Semantic Analysis
Strong interoperability with multiple standards and integration framework; would benefit from structured conforms_to field

### 3. Technical Documentation

Q11. Tool and Software Transparency numeric
4/5
Assessment
Software tools comprehensively described throughout documentation with processing details; lacks structured software_and_tools field and annotation quality metrics
Evidence Found
preprocessing_strategies: 5 strategies with tools (node2vec for PPI, Human Protein Atlas deep learning model for imaging, contrastive deep learning, Cytoscape for community detection, GO/Reactome/LLM for annotation); cleaning_strategies: 3 strategies (FAIRSCAPE-CLI for packaging, data standard mapping, integrative modeling pipeline); Software mentioned throughout: MuSIC pipeline, FAIRSCAPE, Cytoscape, HPA model, node2vec; Missing: software_and_tools field, annotation_analyses, machine_annotation_tools, imputation_protocols fields
Semantic Analysis
Strong tool documentation in text form; would benefit from structured software_and_tools field with versions
Q12. Collection Protocol Clarity numeric
4/5
Assessment
Comprehensive collection protocols with technical details and lab attributions; lacks structured fields for collectors, timeframes, and raw data sources
Evidence Found
collection_mechanisms: 4 detailed mechanisms (IF spatial proteomics with automated pipetting robot, AP-MS with endogenous tagging, SEC-MS for proteome-wide mapping, CRISPR screens with lentiviral library); acquisition_methods: 4 detailed methods (confocal microscopy 4-channel, mass spectrometry, single-cell RNA-seq with 10x Genomics 3'HT, MuSIC pipeline); Collectors implied: Lundberg Lab (Stanford), Krogan Lab (UCSF), Mali Lab (UCSD); Missing: data_collectors field, collection_timeframes field, raw_data_sources field (though source cell lines documented)
Semantic Analysis
Excellent protocol documentation; would benefit from structured collector and timeframe fields
Q13. Version History, Maintenance, and Sustainability numeric
4/5
Assessment
Strong version tracking and comprehensive sustainability plan; lacks structured version and version_access fields
Evidence Found
updates: Quarterly release plan through Nov 2026, version history (v0.5 alpha, V1.4 beta March 2025 doi:10.18130/V3/B35XWX, V2.1 beta June 2025 doi:10.18130/V3/F3TD5R), change descriptions (perturb-seq, SEC-MS, IF images, RGB corrections), long-term preservation plan; retention_limit: LibraData (UVA Dataverse) with institutional commitment, NIH data sharing compliance; maintainers: CM4AI Consortium with 8 institutions; Sustainability: persistent identifiers (DOI/ARK), domain repository (Dataverse), institutional funding; Missing: version field, version_access field (though DOIs provide access)
Semantic Analysis
Excellent sustainability with long-term preservation and update schedule; would benefit from version field
Q14. Associated Publications numeric
3/5
Assessment
Publications linked with DOIs and citation required in license terms; lacks structured citation field with formatted string
Evidence Found
external_resources: bioRxiv publication (Clark T, et al. Cell Maps for AI, doi:10.1101/2024.05.21.589311), Perturbation Cell Atlas publication (Nourreddine S, et al, doi:10.1101/2024.11.03.621734, PMCID: PMC11580897); license_and_use_terms requires citation of bioRxiv article and data DOI; Missing: citation field with formatted citation string, citation instructions not structured
Semantic Analysis
Good publication linkage; missing structured citation field (mandatory for Bridge2AI)
Q15. Human Subject Representation numeric
3/5
Assessment
Cell line origins documented with demographic context; not primary human subjects but diversity represented; lacks structured missing data documentation
Evidence Found
instances: 2 human-derived cell lines (MDA-MB-468 from 51-yo black female, KOLF2.1J from healthy male Northern European); subpopulations: 7 conditions (3 MDA-MB-468 treatments, 4 KOLF2.1J differentiation states); human_subject_research.special_populations documents demographic origins; Missing: vulnerable_populations field, is_subpopulation field, missing_data_documentation field
Semantic Analysis
Appropriate for cell line data with demographic context; not direct human subjects

### 4. FAIRness & Accessibility

Q16. Findability (Persistent Links) pass_fail
1/1
Assessment
Excellent findability with multiple persistent identifiers across different systems
Evidence Found
id: https://doi.org/10.18130/V3/DXWOS5; page: https://www.cm4ai.org; Additional DOIs for versions: 10.18130/V3/B35XWX, 10.18130/V3/F3TD5R; ARK identifiers mentioned in FAIRSCAPE framework; RRIDs for cell lines
Semantic Analysis
Pass - comprehensive persistent identifier coverage
Q17. Accessibility (Access Mechanism) numeric
4/5
Assessment
Access mechanisms well-documented via multiple platforms; access tier implied through governance but not explicitly stated
Evidence Found
distribution_formats: 5 platforms with access_urls (Dataverse DOIs, MassIVE, NCBI SRA/BioProject, NDEx, cm4ai.org); license_and_use_terms: CC BY-NC-SA with Data Access Committee oversight; Access tier implied: registration/DUA likely required given Data Access Committee mention; Missing: download_url field, explicit access tier classification
Semantic Analysis
Good access documentation; would benefit from explicit access tier and download_url field
Q18. Reusability, Use Guidance, and Social Impact numeric
4/5
Assessment
Excellent use guidance with clear intended and discouraged uses; social impact implied through purposes and gaps but not formal RAI-aligned analysis
Evidence Found
license_and_use_terms: CC BY-NC-SA with clear terms; intended_uses: 6 uses (AI/ML training, VNN development, genotype-phenotype mapping, drug response prediction, disease mechanisms, AI-readiness model); discouraged_uses: 3 uses (clinical decision-making without validation, use during incomplete release, analysis without domain expertise); addressing_gaps describes transformative potential for AI in genomics; Missing: future_use_impacts field with formal social impact analysis and mitigation strategies
Semantic Analysis
Strong reusability guidance; would benefit from structured future_use_impacts field
Q19. Data Integrity, Provenance Graph, and Quality numeric
4/5
Assessment
Excellent provenance via FAIRSCAPE with full entity-activity-agent tracking; lacks structured was_derived_from and parent_datasets fields but provenance comprehensively described
Evidence Found
Provenance: FAIRSCAPE framework creates RO-Crate packages with datasets, metadata, provenance graphs (W3C PROV-O compatible), software; EVI Evidence Graph Ontology for provenance entailments; end-to-end provenance tracking from source cell lines (ATCC, HipSci) through MuSIC pipeline to hierarchical cell maps; updates: comprehensive update plan with version history; Quality: QC procedures documented for imaging and mass spec; Missing: was_derived_from field, parent_datasets field, missing_data_documentation field, is_data_split field (though provenance described)
Semantic Analysis
Strong provenance graph representation through FAIRSCAPE RO-Crate system with W3C PROV-O alignment
Q20. Bias Documentation and Responsible AI Alignment numeric
1/5
Assessment
Missing explicit bias documentation; dataset has inherent selection and population biases that should be documented using RAI-aligned taxonomy
Evidence Found
No known_biases field present; No future_use_impacts field with fairness analysis; Potential biases not explicitly documented: selection bias (specific cell lines only - MDA-MB-468 and KOLF2.1J), population bias (limited diversity in cell line demographics - one black female donor, one Northern European male donor), representation bias (cancer and iPSC contexts only)
Semantic Analysis
Major gap - no bias documentation despite clear selection and population biases in cell line choices

## Semantic Analysis Summary

### Consistency Checks

Passed: 15
Failed: 0
Warnings: 8

### Issues Detected

missing_structured_fields MEDIUM
Fields: version, software_and_tools, publisher, citation, conforms_to, format, encoding, version_access, was_derived_from
Recommendation:
missing_governance_metadata MEDIUM
Fields: regulatory_restrictions, confidentiality_level, hipaa_compliant, other_compliance, governance_committee_contact
Recommendation:
missing_bias_documentation HIGH
Fields: known_biases
Recommendation:
missing_social_impact MEDIUM
Fields: future_use_impacts
Recommendation:
missing_hierarchical_structure LOW
Fields: parent_datasets, related_datasets, resources
Recommendation:
missing_variable_metadata MEDIUM
Fields: variables, is_tabular, is_data_split
Recommendation:
Generated on 2026-01-12 23:30:33 using Bridge2AI Data Sheets Schema