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MIE 2026 Workshop

Published dataset · MIE2026

Published 2026-05-26 35 variables TRE access only 3 code versions
Description

Workshop training application for the MIE 2026 TRACE session. The purpose of this application is to demonstrate the TRACE data access workflow using a synthetic longitudinal clinical dataset. Participants will use the provided data to run example Python/Jupyter analyses and visualize cohort composition, disease activity over time, patient-level trajectories, and laboratory markers.

Available Variables (35)

consent
  • record_id
  • screen_date
  • ic_obtained
  • consent_date
  • ie_all
demographics
  • brthdtc
  • sex
  • ethnic
  • smoking
  • bmi
  • mhcat
diagnosis
  • icd
  • mhstdtc
  • disease_duration
  • disease_behaviour
  • eim_present
visit_status
  • visit_attended
clinical_assesment
  • visit_date
  • symptoms
  • pga
  • current_flare
medication
  • cmtrt
adverse_events
  • aeyn
  • aeterm
lab_results
  • esr_mm_h
  • hb_mg_dl
  • fec_cal
endoscopy_imaging
  • proc_type
  • proc_date
study_completion
  • dscompl
  • dsreas
  • clinical_remission
  • ibd_surgery
  • trt_escalation
  • trt_discontinuation
Analysis Code
Viewing: v3 Python Multi-file Archive
Version v3 (Python)
Created by loki · 2026-05-26 19:24
Download ZIP
Archive contents
  • MIE_workshop_with_readme_file/MIE2026Workshop_DATA_2026-05-18_1112.csv
    data · 56615 bytes
    file
  • MIE_workshop_with_readme_file/MIE2026_fake_IBD_workshop_plots_single_csv.ipynb
    script · 219503 bytes
    script
  • MIE_workshop_with_readme_file/README.md
    documentation · 1801 bytes
    docs
Entry point: MIE_workshop_with_readme_file/MIE2026_fake_IBD_workshop_plots_single_csv.ipynb
Uncompressed size: 277919 bytes
Files: 3
README
# MIE2026 Fake IBD Workshop Plots (Single CSV)

## Overview

This project contains a standalone Python script converted from a Jupyter notebook:

`MIE2026_fake_IBD_workshop_plots_single_csv.py`

The script is intended for generating plots and performing exploratory analysis on a single CSV dataset related to the MIE2026 fake IBD workshop example dataset.

---

## Features

- Loads data from a CSV file
- Performs preprocessing and analysis steps
- Generates plots and visualizations
- Runs as a normal Python script (no notebook required)

---

## Requirements

The script appears to use the following Python libraries:

- `__future__`
- `argparse`
- `matplotlib`
- `numpy`
- `pandas`
- `pathlib`

Install dependencies with pip if needed:

```bash
pip install __future__ argparse matplotlib numpy pandas pathlib
```

---

## Usage

Run the script from the command line:

```bash
python MIE2026_fake_IBD_workshop_plots_single_csv.py
```

If the script expects a CSV file path, update the input file location inside the script or modify the script to accept command-line arguments.

---

## Project Structure

```text
.
├── MIE2026_fake_IBD_workshop_plots_single_csv.py
└── README.md
```

---

## Notes

- This script was automatically converted from a Jupyter notebook.
- Notebook cell outputs and metadata were removed.
- Markdown explanations from the notebook were converted into Python comments where appropriate.

---

## Recommended Improvements

You may want to further improve the script by:

- Adding command-line arguments with `argparse`
- Creating a `requirements.txt`
- Refactoring repeated plotting logic into functions
- Adding logging and error handling
- Saving plots automatically into an output directory

---

## License

Add your preferred license information here.
Version Timeline (by language)
Version History
Version Language Type Relation Author Date
Global v1 (Python v1) Python Single Script Initial Implementation trace20 2026-05-26
Global v2 (Python v2) Python Multi-file Archive Refinement/Bug Fix ← Global v1 mmueller 2026-05-26
Global v3 (Python v3) selected Python Multi-file Archive Refinement/Bug Fix ← Global v2 loki 2026-05-26
Data Access

Data is available only upon formal request and subject to approval.

Approved users receive a secure institute account and work with the data exclusively in our Trusted Research Environment (TRE) via remote desktop.

Request data
Reuse & Usage Terms
  • Data is not downloadable (TRE access only).
  • Approved users receive a personal institute account.
  • Tools available: RStudio, Jupyter, Python, Stata, etc.
  • Data resides in your TRE home directory.
  • Re-use/publication per Data Use Agreement (DUA).
  • No redistribution of the data.
Contact us for the DUA template and details.
Contact
Marcel Müller
Publisher
Email
MIE2026
Project