MIE 2026 Workshop

Dataset Info
Published on
2026-05-26

Variables
35

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 (Email to us)

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.
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)
Event: MIE2026 Workshop
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
Viewing version: v3 (Python)
Created by loki · 2026-05-26 19:24
📦 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
🧾 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)
PYTHON
Version History (detailed)
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
Contact
Marcel Müller
Email
Publisher

Project
MIE2026