Last updated: 2026-10-09
Checks: 6 1
Knit directory: b2ai_survey/
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The following objects were defined in the global environment when these results were created:
| Name | Class | Size |
|---|---|---|
| b2ai_data | tbl_df;tbl;data.frame | 397.5 Kb |
| bridging_participants | tbl_df;tbl;data.frame | 5.5 Kb |
| bridging_summary | tbl_df;tbl;data.frame | 1.5 Kb |
| career_by_dgp | grouped_df;tbl_df;tbl;data.frame | 4.8 Kb |
| career_simple_summary | tbl_df;tbl;data.frame | 1.4 Kb |
| career_summary | tbl_df;tbl;data.frame | 1.7 Kb |
| career_temporal | grouped_df;tbl_df;tbl;data.frame | 7.8 Kb |
| classifyInstitutionType | function | 35.7 Kb |
| completeness | tbl_df;tbl;data.frame | 1.8 Kb |
| concentration_metrics | tbl_df;tbl;data.frame | 1.9 Kb |
| core_colors | character | 984 bytes |
| core_participation | tbl_df;tbl;data.frame | 1.5 Kb |
| core_summary | tbl_df;tbl;data.frame | 1.4 Kb |
| country_counts | tbl_df;tbl;data.frame | 1.5 Kb |
| cross_inst_potential | tbl_df;tbl;data.frame | 2.2 Kb |
| data_path | character | 232 bytes |
| degree_counts | tbl_df;tbl;data.frame | 2.9 Kb |
| degree_data | tbl_df;tbl;data.frame | 56.4 Kb |
| dgp_colors | character | 992 bytes |
| dgp_diversity_long | tbl_df;tbl;data.frame | 1.9 Kb |
| dgp_growth | tbl_df;tbl;data.frame | 3.4 Kb |
| dgp_inst_diversity | tbl_df;tbl;data.frame | 2.2 Kb |
| dgp_participation | tbl_df;tbl;data.frame | 1.5 Kb |
| dgp_summary | tbl_df;tbl;data.frame | 1.2 Kb |
| dgp_temporal | tbl_df;tbl;data.frame | 3.7 Kb |
| expertise_by_dgp | tbl_df;tbl;data.frame | 2 Kb |
| expertise_career | tbl_df;tbl;data.frame | 3 Kb |
| expertise_counts | tbl_df;tbl;data.frame | 5.1 Kb |
| expertise_data | tbl_df;tbl;data.frame | 160.9 Kb |
| expertise_span | tbl_df;tbl;data.frame | 6.3 Kb |
| expertise_span_top | tbl_df;tbl;data.frame | 3.3 Kb |
| inferCareerStage | function | 54.3 Kb |
| inst_counts | tbl_df;tbl;data.frame | 12.2 Kb |
| inst_counts_anon | tbl_df;tbl;data.frame | 10.7 Kb |
| inst_type_ranked | tbl_df;tbl;data.frame | 8.7 Kb |
| inst_type_summary | tbl_df;tbl;data.frame | 1.6 Kb |
| institution_mapping | tbl_df;tbl;data.frame | 23 Kb |
| interdisciplinary_idx | tbl_df;tbl;data.frame | 2.7 Kb |
| overlap_data | tbl_df;tbl;data.frame | 1.4 Kb |
| p_map | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 7.8 Mb |
| p1 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 1.3 Mb |
| p10 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 355.5 Kb |
| p11 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 355.1 Kb |
| p12 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 863.3 Kb |
| p13 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 354.9 Kb |
| p14 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 863.8 Kb |
| p15 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 355.6 Kb |
| p16 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 355.6 Kb |
| p17 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 6.8 Mb |
| p18 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 358.3 Kb |
| p19 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 355.1 Kb |
| p2 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 356.9 Kb |
| p20 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 356.5 Kb |
| p21 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 863.4 Kb |
| p22 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 354.9 Kb |
| p3 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 357.1 Kb |
| p4 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 361.3 Kb |
| p5 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 361.3 Kb |
| p6 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 354.9 Kb |
| p7 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 865.4 Kb |
| p8 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 865.1 Kb |
| p9 | ggplot2::ggplot;ggplot;ggplot2::gg;S7_object;gg | 869.6 Kb |
| raw_data | spec_tbl_df;tbl_df;tbl;data.frame | 257.3 Kb |
| sr_trainee_dgp | tbl_df;tbl;data.frame | 2.3 Kb |
| sr_trainee_long | tbl_df;tbl;data.frame | 1.9 Kb |
| state_counts | tbl_df;tbl;data.frame | 3.7 Kb |
| state_name_to_code | tbl_df;tbl;data.frame | 7.5 Kb |
| summary_metrics | tbl_df;tbl;data.frame | 3.1 Kb |
| summary_stats | tbl_df;tbl;data.frame | 2.1 Kb |
| temporal_data | tbl_df;tbl;data.frame | 2.1 Kb |
| top_degrees | tbl_df;tbl;data.frame | 1.8 Kb |
| top_expertise | tbl_df;tbl;data.frame | 2.7 Kb |
| top_insts | tbl_df;tbl;data.frame | 4 Kb |
| top_insts_anon | tbl_df;tbl;data.frame | 3.7 Kb |
| top10_pct | numeric | 56 bytes |
| top20_pct | numeric | 56 bytes |
| top5_pct | numeric | 56 bytes |
| total_participants | integer | 56 bytes |
| us_data | tbl_df;tbl;data.frame | 109.1 Kb |
| us_map_data | data.frame | 980.5 Kb |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 609d00d | Janani Ravi | 2025-11-20 | Update analysis docs and site, improve onboarding |
| Rmd | 81a7e33 | Janani Ravi | 2025-11-05 | add onboarding analysis, workflowr |
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.
Temporal scope: any aspects that may have temporally evolved.
Career stage classification: still trying to get the best columns for this – but want to gauge their career stage – Trainee/postdoc, early-career (assistant prof), mid-career (associate prof), senior (full prof)? Or based on years since terminal degree?
Success metrics: are we evaluating “successful collaborations” – what are metrics/proxies to consider?
Success metrics: What specific outcomes define “successful collaboration” in B2AI? Are there milestone completion data, deliverable tracking, or quality metrics already being collected? (in addition to our milestone doc)
Career stage refinement: Do we have access to more detailed career information (academic ranks, years of experience) to improve career stage classification?
Longitudinal tracking: Are there plans to track participants over time (tall ask?)? Understanding who stays engaged vs. who leaves would be valuable.
Publication/output data: Maybe we can publication records, GitHub contributions, or dataset generation be linked to this onboarding data? (w/ Sean, Casey’s work?)
Network data: Are there communication logs (email, Slack), meeting attendance, or other interaction data that could reveal actual collaboration patterns?
Survey integration: Maybe link team science evaluation surveys be linked to this onboarding data for richer analysis?
Specific hypotheses: For the commentary paper, what specific team composition questions are most relevant? E.g.:
Privacy considerations: For publication, should all institution names be anonymized, or is it acceptable to identify top contributors?
sessionInfo()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 crayon_1.5.3 later_1.4.8 pillar_1.11.1
[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