Bridge2AI onboarding analysis
Janani Ravi
2026-10-16
Last updated: 2026-10-16
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Knit directory: b2ai_survey/
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| Rmd | 81a7e33 | Janani Ravi | 2025-11-05 | add onboarding analysis, workflowr |
Overview
This document provides a comprehensive analysis of Bridge2AI (B2AI) consortium onboarding data, examining team composition, temporal evolution, institutional diversity, and participation patterns across data generation projects (DGPs) and cores. The analysis focuses on understanding the structure and dynamics of this large-scale team science initiative.
Data generation projects:
- Voice: Precision public health (B2AI Voice)
- AI-READi: Salutogenesis
- CHoRUS: AI/ML for clinical care
- CM4AI: Functional genomics
Setup and data loading
Dataset dimensions: 511 people × 30 columnsData version: Nov 2025 onboarding form combined with Sept 2026 rosterPeople in both: 313 | Nov 2025 only: 126 | Sept 2026 only: 72 Data preprocessing
Total people after cleaning: 511 People with an onboarding date: 439 Date range: 2022-06-04 to 2025-08-08
Career stage inferred.Career stages identified from: trainee, srKey, and degrees columnsCreated anonymized institution IDs for 84 unique institutionsBasic descriptive statistics
Overall consortium composition
| Metric | Count |
|---|---|
| Total participants | 511 |
| Complete responses | 511 |
| Incomplete responses | 0 |
| Senior key personnel | 85 |
| Trainees (all types) | 100 |
| DGP participants | 289 |
| Core participants | 182 |
| Both DGP and Core | 39 |
| Unique institutions | 84 |
| Unique countries | 5 |
| Multi-DGP participants | 25 |
| Multi-core participants | 45 |
Career stage distribution
| Career stage | Count | Percent |
|---|---|---|
| Mid (established) | 173 | 33.9 |
| Senior (established) | 75 | 14.7 |
| Mid (professional) | 71 | 13.9 |
| Early (staff) | 63 | 12.3 |
| Early (trainee) | 53 | 10.4 |
| Unknown | 43 | 8.4 |
| Early (career) | 33 | 6.5 |
| Career stage | Count | Percent |
|---|---|---|
| Mid | 244 | 47.7 |
| Early | 149 | 29.2 |
| Senior | 75 | 14.7 |
| Unknown | 43 | 8.4 |
DGP and core participation summary
| Component | DGP | Core |
|---|---|---|
| Voice | 75 | NA |
| AI-READi | 86 | NA |
| CHoRUS | 96 | NA |
| CM4AI | 67 | NA |
| Admin | NA | 35 |
| Ethics | NA | 55 |
| Teaming | NA | 47 |
| Standards | NA | 64 |
| Tools | NA | 36 |
| SWD | NA | 34 |
Temporal evolution of the consortium
Onboarding over time
Onboarding by DGP over time
Cumulative growth by component
Career stage evolution over time
Institutional and geographic diversity
Institution type distribution
| Institution type | Participants | Percent |
|---|---|---|
| Academia | 334 | 65.4 |
| Other/Unknown | 55 | 10.8 |
| Healthcare/Clinical | 45 | 8.8 |
| Non-profit research | 45 | 8.8 |
| Government | 30 | 5.9 |
| Industry | 2 | 0.4 |
Top participating institutions (with names)
Top 20 institutions
| Institution | Type | Participants | Percent | Cumulative % |
|---|---|---|---|---|
| University of California | Academia | 55 | 11.3 | 11.3 |
| Oregon Health & Science University | Academia | 24 | 4.9 | 16.3 |
| Sage Bionetworks | Non-profit research | 24 | 4.9 | 21.2 |
| Simon Fraser University | Academia | 20 | 4.1 | 25.4 |
| Yale University | Academia | 20 | 4.1 | 29.5 |
| National Institutes of Health | Government | 19 | 3.9 | 33.4 |
| Johns Hopkins University | Academia | 17 | 3.5 | 36.9 |
| Vanderbilt University Medical Center | Academia | 17 | 3.5 | 40.4 |
| University of Florida | Academia | 15 | 3.1 | 43.5 |
| University of South Florida | Academia | 14 | 2.9 | 46.4 |
| University of California, San Diego | Academia | 13 | 2.7 | 49.1 |
| Duke University | Academia | 12 | 2.5 | 51.5 |
| Massachusetts General Hospital | Healthcare/Clinical | 12 | 2.5 | 54.0 |
| University of Colorado | Academia | 12 | 2.5 | 56.5 |
| Nationwide Children’s Hospital | Healthcare/Clinical | 11 | 2.3 | 58.8 |
| University of Virginia | Academia | 11 | 2.3 | 61.0 |
| University of Alabama at Birmingham | Academia | 10 | 2.1 | 63.1 |
| University of Washington | Academia | 10 | 2.1 | 65.2 |
| Stanford University | Academia | 9 | 1.9 | 67.0 |
| Emory University | Academia | 8 | 1.6 | 68.7 |
Concentration metrics
| Metric | Value |
|---|---|
| Total unique institutions (excluding ‘Other’) | 84 institutions |
| Top 5 institutions represent | 29.5% of participants |
| Top 10 institutions represent | 46.4% of participants |
| Top 20 institutions represent | 68.7% of participants |
| Remaining institutions represent | 31.3% of participants |
Top participating institutions (anonymized)
Top 20 anonymized
| Institution ID | Type | Participants | Percent | Cumulative % |
|---|---|---|---|---|
| Academia_60 | Academia | 55 | 11.3 | 11.3 |
| Academia_38 | Academia | 24 | 4.9 | 16.3 |
| Non-profit research_40 | Non-profit research | 24 | 4.9 | 21.2 |
| Academia_44 | Academia | 20 | 4.1 | 25.4 |
| Academia_84 | Academia | 20 | 4.1 | 29.5 |
| Government_33 | Government | 19 | 3.9 | 33.4 |
| Academia_16 | Academia | 17 | 3.5 | 36.9 |
| Academia_80 | Academia | 17 | 3.5 | 40.4 |
| Academia_68 | Academia | 15 | 3.1 | 43.5 |
| Academia_73 | Academia | 14 | 2.9 | 46.4 |
| Academia_63 | Academia | 13 | 2.7 | 49.1 |
| Academia_65 | Academia | 12 | 2.5 | 51.5 |
| Academia_9 | Academia | 12 | 2.5 | 54.0 |
| Healthcare/Clinical_19 | Healthcare/Clinical | 12 | 2.5 | 56.5 |
| Academia_77 | Academia | 11 | 2.3 | 58.8 |
| Healthcare/Clinical_35 | Healthcare/Clinical | 11 | 2.3 | 61.0 |
| Academia_57 | Academia | 10 | 2.1 | 63.1 |
| Academia_78 | Academia | 10 | 2.1 | 65.2 |
| Academia_46 | Academia | 9 | 1.9 | 67.0 |
| Academia_11 | Academia | 8 | 1.6 | 68.7 |
Geographic distribution
| Country | Participants | Percent |
|---|---|---|
| USA | 114 | 87.0 |
| Canada | 12 | 9.2 |
| France | 3 | 2.3 |
| UK | 1 | 0.8 |
| Ukraine | 1 | 0.8 |
Institutional diversity by DGP
| DGP | Participants | Institutions | Inst. types | States | Countries | Participants/Institution |
|---|---|---|---|---|---|---|
| AI-READi | 70 | 18 | 3 | 5 | 1 | 3.9 |
| CHoRUS | 84 | 27 | 5 | 14 | 2 | 3.1 |
| CM4AI | 51 | 18 | 3 | 6 | 2 | 2.8 |
| Multiple | 25 | 19 | 3 | 7 | 2 | 1.3 |
| Voice | 58 | 23 | 4 | 7 | 3 | 2.5 |
Expertise and disciplinary backgrounds
Expertise areas
| Expertise area | Count |
|---|---|
| ML/AI tools and methods | 208 |
| Ethical AI | 154 |
| Data Visualization | 144 |
| Data Integration Technologies | 140 |
| Jupyter Notebooks | 107 |
| Biomedical Informatics | 106 |
| Other Discipline | 106 |
| Project Management | 100 |
| Clinical Standards | 98 |
| Cloud computing expertise | 98 |
| R | 96 |
| Equity in Community Participation | 87 |
| Public Health | 86 |
| Deidentification and Security | 74 |
| Reproducibility | 74 |
| Terminologies | 69 |
| Science of Team Science | 68 |
| Challenges and Evaluation | 66 |
| Genetics | 64 |
| Graph Technologies | 58 |
| Systems Biology | 56 |
| Communications | 55 |
| Neuroscience | 55 |
| Operations | 55 |
| Governance | 54 |
Degree distribution
| Degree | Count |
|---|---|
| PhD | 228 |
| MS | 79 |
| BS | 71 |
| MD | 62 |
| BA | 32 |
| MA | 21 |
| MPH | 15 |
| MBA | 13 |
| Other | 10 |
| JD | 5 |
| BS In Progress | 3 |
| MEng | 3 |
| PhD Candidate | 3 |
| DHA | 2 |
| MEd | 2 |
Team composition analysis
Multi-DGP participation
| Number of DGPs | Participants | Percent | Category |
|---|---|---|---|
| 0 | 222 | 43.4 | No DGP |
| 1 | 264 | 51.7 | Single DGP |
| 2 | 20 | 3.9 | Multiple DGPs |
| 4 | 5 | 1.0 | Multiple DGPs |
Cross-core participation
| Number of Cores | Participants | Percent | Category |
|---|---|---|---|
| 0 | 329 | 64.4 | No Core |
| 1 | 137 | 26.8 | Single Core |
| 2 | 27 | 5.3 | Multiple Cores |
| 3 | 8 | 1.6 | Multiple Cores |
| 5 | 4 | 0.8 | Multiple Cores |
| 6 | 6 | 1.2 | Multiple Cores |
DGP and core overlap
| Participation type | Participants | Percent |
|---|---|---|
| Core only | 143 | 28.0 |
| DGP + Core | 39 | 7.6 |
| DGP only | 250 | 48.9 |
| Other | 79 | 15.5 |
Career stage distribution by DGP
| primary_dgp | n_Early | n_Mid | n_Senior | percent_Early | percent_Mid | percent_Senior |
|---|---|---|---|---|---|---|
| AI-READi | 23 | 28 | 15 | 34.8 | 42.4 | 22.7 |
| CHoRUS | 25 | 41 | 18 | 29.8 | 48.8 | 21.4 |
| CM4AI | 21 | 19 | 10 | 42.0 | 38.0 | 20.0 |
| Multiple | 5 | 14 | 6 | 20.0 | 56.0 | 24.0 |
| Voice | 25 | 27 | 6 | 43.1 | 46.6 | 10.3 |
Senior key personnel and trainees by DGP
| DGP | Total | Sr. Key | Sr. Key % | Trainees | Trainee % |
|---|---|---|---|---|---|
| AI-READi | 70 | 17 | 24.3 | 18 | 25.7 |
| CHoRUS | 84 | 19 | 22.6 | 17 | 20.2 |
| CM4AI | 51 | 11 | 21.6 | 18 | 35.3 |
| Multiple | 25 | 7 | 28.0 | 4 | 16.0 |
| Voice | 59 | 7 | 11.9 | 22 | 37.3 |
Expertise diversity within teams
Expertise diversity by DGP
| DGP | Members | Unique expertise areas | Total expertise tags | Expertise/member | Diversity score |
|---|---|---|---|---|---|
| Multiple | 25 | 47 | 219 | 8.76 | 1.88 |
| CM4AI | 51 | 47 | 293 | 5.75 | 0.92 |
| Voice | 59 | 47 | 320 | 5.42 | 0.80 |
| AI-READi | 70 | 45 | 401 | 5.73 | 0.64 |
| CHoRUS | 84 | 47 | 520 | 6.19 | 0.56 |
Shared expertise across DGPs
| Expertise area | Number of DGPs | Members | DGPs |
|---|---|---|---|
| ML/AI tools and methods | 5 | 125 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Data Integration Technologies | 5 | 91 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Data Visualization | 5 | 89 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Ethical AI | 5 | 88 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Other Discipline | 5 | 73 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Biomedical Informatics | 5 | 70 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Jupyter Notebooks | 5 | 69 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Clinical Standards | 5 | 66 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Cloud computing expertise | 5 | 60 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| R | 5 | 56 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Project Management | 5 | 54 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Deidentification and Security | 5 | 47 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Public Health | 5 | 47 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Reproducibility | 5 | 47 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Equity in Community Participation | 5 | 46 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Neuroscience | 5 | 45 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Other tools | 5 | 42 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Science of Team Science | 5 | 39 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Genetics | 5 | 35 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Terminologies | 5 | 35 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Social Sciences | 5 | 34 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Graph Technologies | 5 | 29 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Other Ethics | 5 | 29 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Challenges and Evaluation | 5 | 28 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
| Systems Biology | 5 | 28 | AI-READi, CHoRUS, CM4AI, Multiple, Voice |
Collaboration potential metrics
Cross-institutional collaboration potential
| DGP | Participants | Institutions | Inst. types | Possible pairs | Avg. per inst. | Distribution index |
|---|---|---|---|---|---|---|
| AI-READi | 70 | 18 | 3 | 2,415 | 3.89 | 0.26 |
| CHoRUS | 84 | 27 | 5 | 3,486 | 3.11 | 0.32 |
| CM4AI | 51 | 18 | 3 | 1,275 | 2.83 | 0.35 |
| Multiple | 25 | 19 | 3 | 300 | 1.32 | 0.76 |
| Voice | 58 | 23 | 4 | 1,653 | 2.52 | 0.40 |
Cross-DGP collaboration potential
| DGP combination | Number of participants |
|---|---|
| All 4 DGPs | 5 |
| Voice + AI-READi | 5 |
| Voice + CM4AI | 5 |
| AI-READi + CHoRUS | 3 |
| AI-READi + CM4AI | 3 |
| CHoRUS + CM4AI | 3 |
| Voice + CHoRUS | 1 |
Interdisciplinary collaboration index
| DGP | Participants | Inst. types | Diversity index | Sector balance |
|---|---|---|---|---|
| AI-READi | 67 | 3 | 0.6 | 0.15 |
| CHoRUS | 81 | 5 | 1.0 | 0.72 |
| CM4AI | 48 | 3 | 0.6 | 0.17 |
| Multiple | 23 | 3 | 0.6 | 0.61 |
| Voice | 51 | 4 | 0.8 | 0.67 |
Data quality and completeness assessment
Response completion rates
| Field | Completion rate (%) |
|---|---|
| Primary affiliation | 94.9 |
| ORCID | 94.9 |
| Expertise | 94.3 |
| Degrees | 92.8 |
| Department | 64.2 |
| Bio/profile link | 44.8 |
| GitHub handle | 30.1 |
| Country | 25.6 |
| State | 22.1 |
Summary and key findings
Consortium overview metrics
| Metric | Value |
|---|---|
| Total participants onboarded | 511 |
| Unique institutions represented | 84 |
| Institution types represented | 5 |
| Countries represented | 5 |
| US states represented | 23 |
| Data generation projects (DGPs) | 4 (Voice, AI-READi, CHoRUS, CM4AI) |
| Core teams | 6 (Admin, Ethics, Teaming, Standards, Tools, SWD) |
| Senior key personnel | 85 |
| Trainees (all types) | 100 |
| Multi-DGP participants | 25 |
| Multi-core participants | 45 |
| Unique expertise areas reported | 50 |
| Average expertise tags per person | 6.30 |
| Date range of onboarding | 2022-06-04 to 2025-08-08 |
Key observations
Temporal patterns:
- The consortium shows distinct growth phases, with peak onboarding during specific months
- DGPs show different onboarding timelines, likely reflecting project initiation dates
- Career stage composition has evolved over time
Institutional diversity:
- Strong representation from academia, with significant government and non-profit participation
- Top institutions contribute multiple members, but distribution is relatively broad
- Cross-institutional collaboration potential is high within each DGP
Team composition:
- Balanced representation of career stages, from trainees to senior investigators
- Substantial cross-DGP and cross-core participation, facilitating knowledge transfer
- Institution type diversity enables academic-government-industry partnerships
Expertise landscape:
- Broad range of expertise areas covering data science, biomedical research, ethics, and standards
- Shared expertise across DGPs creates natural collaboration bridges
- Interdisciplinary teams with complementary skills
Discussion and future analyses
Data sufficiency for team composition analysis
The current dataset provides:
Sufficient data for:
- ✓ Institutional diversity analysis
- ✓ Geographic distribution assessment
- ✓ Temporal evolution of team size and composition
- ✓ Basic career stage classification (from trainee, srKey, and degrees)
- ✓ Role distribution (senior key vs. trainees vs. other)
- ✓ Expertise area representation
- ✓ Cross-DGP and cross-core collaboration potential
- ✓ Institution type distribution (academia, government, industry, etc.)
Limited data for:
- ⚠ Precise career stage classification (would benefit from academic rank, years since degree)
- ⚠ Actual collaboration patterns vs. potential (would need network data, co-authorship)
- ⚠ Team effectiveness outcomes (would need performance metrics, deliverables)
- ⚠ Publication-based success metrics (not in onboarding data)
- ⚠ Longitudinal engagement tracking (only onboarding, no off-boarding data)
Recommended additional data for success metrics
To evaluate “successful collaborations” and refine team composition analyses:
- Collaboration network data: (w/ Sean, Jake)
- Co-authorship on publications
- Shared datasets or tools developed
- Joint grant submissions
- Cross-DGP working group participation
- Productivity metrics:
- Publications per team/DGP
- Datasets generated and released
- Software tools developed
- Standards proposed/adopted
- Career tracking:
- Academic rank (for faculty)
- Years since terminal degree
- Current vs. starting career stage
- Career advancement during/after B2AI
- Engagement indicators:
- Meeting attendance patterns
- Contribution frequency (commits, documents, etc.)
- Leadership roles taken over time
- Mentor-mentee relationships
- Team dynamics:
- Team satisfaction surveys (like those by Goldie’s evaluation team)
- Perceived collaboration quality
- Communication effectiveness ratings
- Conflict resolution success
- Impact metrics:
- Citation counts for B2AI-related publications
- Dataset reuse by external researchers
- Tool adoption rates
- Standards implementation
Technical notes
Data sources and methods
Primary data: anonymized B2AI onboarding data combining the Nov 2025 onboarding form and the Sept 2026 roster (file: B2AI_onboarding_combined_anonymized.csv)
Column usage: - Career stage inferred from: trainee, srKey, degrees - DGP participation from: Voice_*, AI-READi_*, CHoRUS_*, CM4AI_* module columns - Core participation from: Admin_core-deprecate, Ethics_core, Teaming_core_depr, Standards_core_depr, Tools_core_deprecate, SWD_core_deprecate - Institution type classified from: primaryAffilName using keyword matching
Limitations
- Onboarding data only: This represents when people joined, not ongoing engagement or departure
- Self-reported: Expertise and other fields rely on participant self-reporting
- Career stage inference: Career stages are inferred from available fields; some may be misclassified
- Institution type classification: Automated classification based on keywords; may have errors
- No outcome data: Cannot assess collaboration success without additional metrics
Session information
Analysis completed: 2026-10-16 13:52:01.941222
Data file: B2AI_onboarding_combined_anonymized.csv
Notes: This analysis provides descriptive summaries and visualizations of B2AI consortium composition and structure. For evaluating team effectiveness and collaboration success, integration with additional data sources (publications, deliverables, surveys) is recommended.
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.6.2
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
time zone: America/Denver
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] kableExtra_1.4.0 knitr_1.51 viridis_0.6.5 viridisLite_0.4.3
[5] ggridges_0.5.7 patchwork_1.3.1 scales_1.4.0 here_1.0.2
[9] lubridate_1.9.5 forcats_1.0.0 stringr_1.6.0 dplyr_1.2.1
[13] purrr_1.2.1 readr_2.2.0 tidyr_1.3.2 tibble_3.3.1
[17] ggplot2_4.0.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] gtable_0.3.6 xfun_0.57 bslib_0.10.0 tzdb_0.5.0
[5] vctrs_0.7.2 tools_4.5.1 generics_0.1.4 parallel_4.5.1
[9] pkgconfig_2.0.3 RColorBrewer_1.1-3 S7_0.2.1 lifecycle_1.0.5
[13] compiler_4.5.1 farver_2.1.2 git2r_0.36.2 textshaping_1.0.1
[17] httpuv_1.6.17 maps_3.4.3 htmltools_0.5.9 sass_0.4.10
[21] yaml_2.3.12 later_1.4.8 pillar_1.11.1 crayon_1.5.3
[25] jquerylib_0.1.4 whisker_0.4.1 cachem_1.1.0 tidyselect_1.2.1
[29] digest_0.6.39 stringi_1.8.7 labeling_0.4.3 rprojroot_2.1.1
[33] fastmap_1.2.0 grid_4.5.1 cli_3.6.5 magrittr_2.0.5
[37] withr_3.0.2 promises_1.5.0 bit64_4.6.0-1 timechange_0.4.0
[41] rmarkdown_2.31 bit_4.6.0 otel_0.2.0 gridExtra_2.3
[45] workflowr_1.7.2 hms_1.1.4 evaluate_1.0.5 rlang_1.2.0
[49] Rcpp_1.1.1 glue_1.8.0 xml2_1.5.2 svglite_2.2.2
[53] rstudioapi_0.17.1 vroom_1.7.1 jsonlite_2.0.0 R6_2.6.1
[57] systemfonts_1.3.2 fs_2.0.1