Skip to content

Job request: 18168

Organisation:
The London School of Hygiene & Tropical Medicine
Workspace:
openprompt-hrqol
ID:
erqyig4hwsd7gxcr
View repo View project.yaml

This page shows the technical details of what happened when the authorised researcher Alasdair Henderson requested one or more actions to be run against real patient data in the project, within a secure environment.

By cross-referencing the list of jobs with the pipeline section below, you can infer what security level various outputs were written to. Researchers can never directly view outputs marked as highly_sensitive ; they can only request that code runs against them. Outputs marked as moderately_sensitive can be viewed by an approved researcher by logging into a highly secure environment. Only outputs marked as moderately_sensitive can be requested for release to the public, via a controlled output review service.

Jobs

Pipeline

Show project.yaml 
version: '3.0'

expectations:
 population_size: 10000

actions:

  # create_dummy_openprompt_data: 
  #   run: >
  #     r:latest
  #       analysis/create_dummy_openprompt_data.R
  #   outputs: 
  #     moderately_sensitive: 
  #       dummy_openprompt: output/dummy_openprompt.csv.gz

  generate_openprompt_baseline: 
    run: >
      databuilder:v0
        generate-dataset 
        analysis/model_questions/process_baseline.py 
        --output output/openprompt_baseline.csv
        --
        --day=0
    outputs:
      highly_sensitive:
        openprompt_baseline: output/openprompt_baseline.csv

  generate_openprompt_survey1: 
    run: >
      databuilder:v0
        generate-dataset 
        analysis/model_questions/process_research.py 
        --output output/openprompt_survey1.csv
        --
        --day=0
    outputs:
      highly_sensitive:
        openprompt_survey1: output/openprompt_survey1.csv

  generate_openprompt_survey2: 
    run: >
      databuilder:v0
        generate-dataset 
        analysis/model_questions/process_research.py 
        --output output/openprompt_survey2.csv
        --
        --day=30
    outputs:
      highly_sensitive:
        openprompt_survey2: output/openprompt_survey2.csv

  generate_openprompt_survey3: 
    run: >
      databuilder:v0
        generate-dataset 
        analysis/model_questions/process_research.py 
        --output output/openprompt_survey3.csv
        --
        --day=60
    outputs:
      highly_sensitive:
        openprompt_survey3: output/openprompt_survey3.csv

  generate_openprompt_survey4: 
    run: >
      databuilder:v0
        generate-dataset 
        analysis/model_questions/process_research.py 
        --output output/openprompt_survey4.csv
        --
        --day=90
    outputs:
      highly_sensitive:
        openprompt_survey4: output/openprompt_survey4.csv

  combine_openprompt:
    run: >
      r:latest analysis/001_datacombine.R
    needs: [generate_openprompt_baseline, generate_openprompt_survey1, generate_openprompt_survey2, generate_openprompt_survey3, generate_openprompt_survey4]
    outputs: 
      highly_sensitive: 
        openprompt_combined: output/openprompt_raw.csv.gz
      moderately_sensitive:
        openprompt_raw_skim: output/data_properties/op_raw_skim.txt
        openprompt_raw_tab: output/data_properties/op_raw_tabulate.txt
        openprompt_mapped_skim: output/data_properties/op_mapped_skim.txt
        openprompt_mapped_tab: output/data_properties/op_mapped_tabulate.txt
        check_days_after_baseline: output/data_properties/sample_day_lags.pdf
        table1: output/tab1_baseline_description.html

  # generate_openprompt_plus_tpp: 
  #   run: >
  #     databuilder:v0
  #       generate-dataset analysis/dataset_definition_openprompt.py --output output/openprompt_raw_plus_tpp.csv.gz
  #   needs: [create_dummy_openprompt_data]
  #   outputs:
  #     highly_sensitive:
  #       openprompt_tpp_combined: output/openprompt_raw_plus_tpp.csv.gz

  # quick_summ_data:
  #   run: >
  #     r:latest
  #       analysis/010_table1.R
  #   needs: [generate_openprompt_plus_tpp]
  #   outputs:
  #     highly_sensitive:
  #       cleandata: output/cleaned_data.gz.parquet
  #     moderately_sensitive:
  #       table1: output/tab1_baseline_description.html
  #       longcovid_dates: output/longcovid_dates.pdf

Timeline

  • Created:

  • Started:

  • Finished:

  • Runtime: 00:02:04

These timestamps are generated and stored using the UTC timezone on the TPP backend.

Job information

Status
Succeeded
Backend
TPP
Workspace
openprompt-hrqol
Requested by
Alasdair Henderson
Branch
main
Force run dependencies
Yes
Git commit hash
4e29024
Requested actions
  • run_all

Code comparison

Compare the code used in this Job Request