This page shows the technical details of what happened when authorised researcher esnightingale requested one or more actions to be run against real patient data in the Carehomes project, within a secure environment.
By cross-referencing the indicated Requested Actions with the Pipeline section below, you can infer what
security level various outputs were written to. Outputs
highly_sensitive can never be viewed directly by a
researcher; 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.
Show Hide project.yaml
version: '3.0' expectations: population_size: 1000000 actions: generate_study_population: run: cohortextractor:latest generate_cohort --study-definition study_definition outputs: highly_sensitive: cohort: input.csv # generate_coverage_population: # run: cohortextractor:latest generate_cohort --study-definition study_definition_coverage # outputs: # highly_sensitive: # cohort: input_coverage.csv calc_coverage: needs: [generate_study_population] run: r:latest analysis/calculate_tpp_coverage.R input.csv data/SAPE22DT15_mid_2019_msoa.csv outputs: moderately_sensitive: log: coverage_log.txt rds: tpp_msoa_coverage.rds csv: tpp_msoa_coverage.csv csv2: msoas_in_tpp.csv csv3: msoa_gt_100_cov.csv figure: total_vs_tpp_pop.png prelim: needs: [generate_study_population, calc_coverage] # last argument relates to MSOA TPP coverage >= X% run: r:latest analysis/prelim.R input.csv tpp_msoa_coverage.rds 80 outputs: moderately_sensitive: log: prelim_check_log.txt data_clean: needs: [generate_study_population, calc_coverage] # last argument relates to MSOA TPP coverage >= X% run: r:latest analysis/data_clean.R input.csv tpp_msoa_coverage.rds 80 outputs: moderately_sensitive: log: data_clean_log.txt highly_sensitive: input_clean: input_clean.rds data_check_figs: needs: [data_clean] run: r:latest analysis/data_check_figs.R input_clean.rds data/msoa_shp.rds outputs: moderately_sensitive: figure1: tpp_coverage_msoa.png figure2: tpp_coverage_carehomes.png figure3: tpp_coverage_map.pdf figure4: age_dist.png figure5: infection_death_delays.png figure6: hh_size_dist.png data_setup: needs: [data_clean] # last argument relates to carehome TPP coverage >= X% run: r:latest analysis/data_setup.R input_clean.rds 90 outputs: moderately_sensitive: log: data_setup_log.txt highly_sensitive: comm_prev: community_prevalence.rds analysisdata: analysisdata.rds ch_linelist: ch_linelist.rds ch_agg_long: ch_agg_long.rds descriptive: needs: [data_clean, data_setup] run: r:latest analysis/descriptive.R outputs: moderately_sensitive: report: descriptive.pdf log: log_descriptive.txt data: ch_gp_permsoa.csv run_models: needs: [data_setup] # run: r:latest analysis/run_models.R analysisdata.rds community_prevalence.rds data/msoa_shp.rds 0.4 outputs: moderately_sensitive: output: output_model_run.txt log: log_model_run.txt # figure: model_resids_map.pdf highly_sensitive: fit: fits.rds data: testdata.rds validate_models: needs: [run_models] run: r:latest analysis/validate_models.R fits.rds testdata.rds outputs: moderately_sensitive: output: output_model_val.txt report: test_pred_figs.pdf run_all: needs: [validate_models, descriptive] # In order to be valid this action needs to define a run commmand and # some output. We don't really care what these are but the below seems to # do the trick. run: cohortextractor:latest --version outputs: moderately_sensitive: whatever: project.yaml
State is inferred from the related Jobs.
Timings set to UTC timezone.
- Runtime: 00:09:39