What is Linkr?

Summary

Linkr is an open-source collaborative data science platform designed to exploit hospital data warehouses (primarily in OMOP format, but not exclusively). It combines low-code tools with Python and R programming interfaces, making these data accessible to clinicians, data scientists and data engineers alike.

The problem Linkr tackles

Clinical Data Warehouses (CDWs) bring together huge volumes of data from patient records. Making use of them is hard for three reasons:

Every hospital has its own data model, which hurts interoperability and research reproducibility. Common data models such as OMOP or FHIR exist, but adopting them takes effort.
Manipulating and analysing these data usually requires skills in programming (SQL, Python, R) that clinicians often don’t have.
Existing tools sit at two extremes: programming environments (RStudio, Jupyter) that are powerful but inaccessible to non-developers, and no-code tools (e.g. ATLAS) that are limited in what they let you extend or script.

Linkr is designed to bridge that gap: give non-developer clinicians effective graphical tools, while giving data scientists a real integrated Python + R environment.

Code environments

RStudio, Jupyter

Powerful and flexible, but inaccessible to non-developers.

Linkr’s approach

Low-code

Graphical interfaces for the clinician, a Python + R IDE for the data scientist, in one collaborative tool.

No-code tools

ATLAS (OHDSI)

Accessible to non-developers, but limited in what they let you extend or script.

Who Linkr is for

Linkr is built for an entire chain of roles working around health data:

Healthcare professionals

Explore their department’s data, build cohorts and examine patient trajectories — without writing code.

Data scientists and statisticians

An integrated IDE (Python + R) with direct data access, and the ability to create and share custom analysis plugins.

Data engineers

Schema definition, concept mapping, quality checks, transformation pipelines that run on warehouse data.

Health students

Training on clinical data analysis in a guided environment.

What makes Linkr distinctive is that these four profiles work in the same tool, on the same projects, each at their preferred level of abstraction.

Collaboration as a pillar

Linkr is first and foremost a collaborative platform. The app is organised into workspaces (shared containers at the department, hospital or network level) that contain:

Projects (studies, analyses, dashboards).
Plugins reusable across projects.
A wiki for team documentation.
Shared data and schemas.
A Git integration that lets you version and share everything across centres.

This collaborative model follows a bottom-up approach: each team starts by solving its local problems, then, by sharing scripts, cohorts and plugins through Git, standardisation emerges gradually — rather than being defined upfront. The practical payoff: results arrive faster, and each project enriches the data for the next ones. This is the principle of a cumulative investment, where quality is built up project after project.

“No effort is wasted: data cleaning, semantic alignment, analysis scripts — everything produced for one study feeds into the next.”

What Linkr does

Linkr covers the whole chain, from data preparation to results presentation:

Explore and prepare data

Multi-format data import: CSV, Excel, Parquet, DuckDB, relational databases (via server mode).
Schemas: OMOP CDM as a priority, extensible to custom hospital schemas via schema presets. FHIR support is planned.
Concept mapping: aligning local vocabulary with standard terminologies (SNOMED, LOINC, RxNorm).
Data quality: rules, checks, scoring, run history.
ETL: transformation pipelines from the warehouse to standards (OMOP, analytical formats…).
Data catalogue (HealthDCAT-AP): description of published datasets.

Analyse and present

Visual cohort builder with automatic SQL generation.
Patient-level view: exploration of individual trajectories in a research or monitoring context, with a chronological timeline and customisable widgets.
Analytical datasets in wide format (one row per patient), built via a DAG pipeline of transformations.
Built-in analyses: Table 1, Key Indicator, Plot Builder.
Dashboards made of resizable widgets.
Integrated Python + R IDE for custom analyses.

Plugin editor to build your own analysis or visualisation widgets in Python or R.
Git versioning to share projects, plugins and scripts across centres and collaborate on the same resources.

What makes Linkr different

One tool for every profile. Linkr doesn’t force clinicians to learn Python, nor does it force data scientists to work inside a rigid no-code application. Both work side by side, on the same projects.

Low-code by default, code when needed. Graphical interfaces cover most day-to-day needs. A full IDE is available the moment a use case calls for it.

OMOP first, but not only. OMOP is the best-supported format. Schema presets already let you work with custom hospital schemas, and will eventually support FHIR and other models.

Open source and open. Licensed under GPL-3.0. The code is publicly available and the product can be self-hosted. Everything produced inside the platform (scripts, plugins, studies) can be shared via Git.

Multilingual and clinician-friendly. The interface is available in French and English, and can be extended to other languages. The wording stays accessible to healthcare professionals who are not developers.

Next steps

Understand deployment modes (browser vs. server) and what each mode enables.
Launch Linkr online in two minutes: quickstart.
Install Linkr locally: local install.