Job request: 14823
- Organisation:
- Bennett Institute
- Workspace:
- winter-pressures
- ID:
- r5uwxvg7wxwerygp
This page shows the technical details of what happened when the authorised researcher Iain Dillingham requested one or more actions to be run against real patient data within a secure environment.
By cross-referencing the list of jobs with the pipeline section below, you can infer what security level the outputs were written to.
The output security levels are:
-
highly_sensitive
- Researchers can never directly view these outputs
- Researchers can only request code is run against them
-
moderately_sensitive
- Can be viewed by an approved researcher by logging into a highly secure environment
- These are the only outputs that can be requested for public release via a controlled output review service.
Jobs
-
- Job identifier:
-
ospmgfvt4iw6azo2
Pipeline
Show project.yaml
version: "3.0"
expectations:
population_size: 5000
actions:
# Other data
# ----------
# Add actions for other data to this section. Prefix them with a suitable name; place
# scripts in a similarly named sub-directory of the analysis directory; write outputs
# to a similarly named sub-directory of the output directory.
#
# For example, let's call our other data "metrics". We would prefix our actions
# "metrics_"; we would place our scripts in analysis/metrics; we would write outputs
# to output/metrics.
# Appointments data
# -----------------
appointments_generate_dataset_sql:
run: >
sqlrunner:latest
analysis/appointments/dataset_query.sql
--output output/appointments/dataset_long.csv
--dummy-data-file analysis/appointments/dummy_dataset_long.csv
outputs:
highly_sensitive:
dataset: output/appointments/dataset_long.csv
# appointments_generate_dataset:
# run: >
# databuilder:v0
# generate-dataset
# analysis/appointments/dataset_definition.py
# --output output/appointments/dataset_wide.arrow
# outputs:
# highly_sensitive:
# dataset: output/appointments/dataset_wide.arrow
# appointments_get_freq_na_values:
# run: >
# python:latest
# python
# -m analysis.appointments.get_freq_na_values
# needs: [appointments_generate_dataset]
# outputs:
# moderately_sensitive:
# dataset: output/appointments/freq_na_values.csv
# appointments_reshape_dataset:
# run: >
# python:latest
# python
# -m analysis.appointments.reshape_dataset
# needs: [appointments_generate_dataset]
# outputs:
# highly_sensitive:
# dataset: output/appointments/dataset_long.arrow
# appointments_generate_measure:
# run: >
# python:latest
# python
# -m analysis.appointments.generate_measure
# needs: [appointments_reshape_dataset]
# outputs:
# moderately_sensitive:
# measure: output/appointments/measure_median_lead_time_in_days_by_nunique_patient_id.csv
# appointments_generate_deciles_charts:
# run: >
# deciles-charts:v0.0.33
# --input-files output/appointments/measure_*.csv
# --output-dir output/appointments
# config:
# show_outer_percentiles: true
# needs: [appointments_generate_measure]
# outputs:
# moderately_sensitive:
# deciles_charts: output/appointments/deciles_chart_*.png
# deciles_tables: output/appointments/deciles_table_*.csv
Timeline
-
Created:
-
Started:
-
Finished:
-
Runtime: 01:53:52
These timestamps are generated and stored using the UTC timezone on the TPP backend.
Job request
- Status
-
Succeeded
- Backend
- TPP
- Workspace
- winter-pressures
- Requested by
- Iain Dillingham
- Branch
- main
- Force run dependencies
- Yes
- Git commit hash
- 88f76f8
- Requested actions
-
-
run_all
-
Code comparison
Compare the code used in this job request