Contents

1 Preparing and Submitting a Trio

This guide provides step-by-step instructions for preparing and submitting a Trio object to the BenchHub Datasets. The recommended workflow is to first build a submission bundle with writeSubmission(), review the collected metadata, and then submit it.

The workflow has two common entry points:

library(BenchHub)

1.1 Step 1: GitHub Personal Access Token (PAT)

A GitHub personal access token is needed for uploading custom metrics to a GitHub gist.

  1. Log in to your GitHub account.
  2. Navigate to Settings > Developer settings > Personal access tokens > Tokens (classic).
  3. Click Generate new token.
  4. Select the following scopes:
    • gist (for creating and managing gists)
  5. Click Generate token and copy the token.

1.1.1 Set the PAT in R

Add the token to your environment in R:

Sys.setenv(GITHUB_PAT = "your_personal_access_token")

Replace "your_personal_access_token" with the token you copied.

1.2 Step 2: Build the submission bundle

  1. In R, create a Trio object and ensure it is properly populated with data, supporting evidence, and metrics.
  2. Use writeSubmission() to prepare the Trio submission:
bundle <- writeSubmission(trio)

During the interactive workflow, writeSubmission() may prompt you for:

  • dataset metadata such as the dataType, technology and description
  • task information for the Trio
  • mappings between supporting evidence and tasks
  • metric metadata where needed
  • file preparation details, including whether to verify a Figshare article, and
  • optional submission details if you choose to submit immediately.

1.3 Step 3: Submit the Trio

After reviewing the generated bundle, you can submit it with submitTrioSubmission():

response <- submitTrioSubmission(
  submission = bundle$submission,
  submittedBy = "your.name@example.org"
)

If you prefer a single interactive step, you can also let writeSubmission() submit at the end:

bundle <- writeSubmission(
  trio,
  submittedBy = "your.name@example.org",
  submit = TRUE
)

1.4 Step 4: Verify the submission

Once you upload the Trio, it will be first uploaded in to Submission_Master sheet. After BenchHub team completed the review and approved, the Trio information will update to Dataset, DatasetTask, DatasetEvidence, DatasetTaskMetric and Metric table. Check the BenchHub Datasets to ensure your dataset has been added under the submission tables.

1.5 Step 5: Downloading an existing Trio

You can also download a previously submitted Trio by its dataset ID under Dataset sheet:

trio <- downloadSubmissionTrio("datasetID", cachePath = tempdir())

1.6 Notes

  • Ensure you have an active internet connection during the upload process.
  • If you encounter any issues, check that your Figshare URL and submission details are correctly set.

2 Session Info

sessionInfo()
## R version 4.6.0 RC (2026-04-17 r89917)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] glmnet_4.1-10    Matrix_1.7-5     lubridate_1.9.5  forcats_1.0.1   
##  [5] stringr_1.6.0    dplyr_1.2.1      purrr_1.2.2      readr_2.2.0     
##  [9] tidyr_1.3.2      tibble_3.3.1     ggplot2_4.0.2    tidyverse_2.0.0 
## [13] BenchHub_0.99.12 BiocStyle_2.39.0
## 
## loaded via a namespace (and not attached):
##   [1] Rdpack_2.6.6           gridExtra_2.3          httr2_1.2.2           
##   [4] sandwich_3.1-1         rlang_1.2.0            magrittr_2.0.5        
##   [7] multcomp_1.4-30        otel_0.2.0             compiler_4.6.0        
##  [10] survAUC_1.4-0          vctrs_0.7.3            reshape2_1.4.5        
##  [13] crayon_1.5.3           shape_1.4.6.1          pkgconfig_2.0.3       
##  [16] fastmap_1.2.0          magick_2.9.1           backports_1.5.1       
##  [19] labeling_0.4.3         utf8_1.2.6             ggstance_0.3.7        
##  [22] rmarkdown_2.31         tzdb_0.5.0             bit_4.6.0             
##  [25] tinytex_0.59           xfun_0.57              cachem_1.1.0          
##  [28] jsonlite_2.0.0         tweenr_2.0.3           parallel_4.6.0        
##  [31] broom_1.0.12           cluster_2.1.8.2        R6_2.6.1              
##  [34] bslib_0.10.0           stringi_1.8.7          RColorBrewer_1.1-3    
##  [37] rpart_4.1.27           jquerylib_0.1.4        cellranger_1.1.0      
##  [40] estimability_1.5.1     assertthat_0.2.1       iterators_1.0.14      
##  [43] Rcpp_1.1.1-1           bookdown_0.46          knitr_1.51            
##  [46] zoo_1.8-15             base64enc_0.1-6        parameters_0.28.3     
##  [49] timechange_0.4.0       splines_4.6.0          nnet_7.3-20           
##  [52] tidyselect_1.2.1       rstudioapi_0.18.0      dichromat_2.0-0.1     
##  [55] yaml_2.3.12            codetools_0.2-20       curl_7.0.0            
##  [58] lattice_0.22-9         plyr_1.8.9             withr_3.0.2           
##  [61] bayestestR_0.17.0      S7_0.2.1-1             coda_0.19-4.1         
##  [64] evaluate_1.0.5         marginaleffects_0.32.0 foreign_0.8-91        
##  [67] survival_3.8-6         polyclip_1.10-7        pillar_1.11.1         
##  [70] BiocManager_1.30.27    foreach_1.5.2          checkmate_2.3.4       
##  [73] insight_1.5.0          generics_0.1.4         vroom_1.7.1           
##  [76] hms_1.1.4              scales_1.4.0           xtable_1.8-8          
##  [79] glue_1.8.1             emmeans_2.0.3          Hmisc_5.2-5           
##  [82] tools_4.6.0            data.table_1.18.2.1    fs_2.1.0              
##  [85] mvtnorm_1.3-7          cowplot_1.2.0          grid_4.6.0            
##  [88] rbibutils_2.4.1        datawizard_1.3.0       colorspace_2.1-2      
##  [91] googlesheets4_1.1.2    patchwork_1.3.2        performance_0.16.0    
##  [94] ggforce_0.5.0          htmlTable_2.4.3        googledrive_2.1.2     
##  [97] splitTools_1.0.1       Formula_1.2-5          cli_3.6.6             
## [100] rappdirs_0.3.4         gargle_1.6.1           funkyheatmap_0.5.2    
## [103] gtable_0.3.6           ggcorrplot_0.1.4.1     ggsci_5.0.0           
## [106] sass_0.4.10            digest_0.6.39          ggrepel_0.9.8         
## [109] TH.data_1.1-5          htmlwidgets_1.6.4      farver_2.1.2          
## [112] htmltools_0.5.9        lifecycle_1.0.5        httr_1.4.8            
## [115] bit64_4.8.0            dotwhisker_0.8.4       MASS_7.3-65