Community

Built in the open. Here's how to take part.

LitFlow is an open research project. You can contribute as a coder, a researcher, a methods reviewer, or an evaluation participant — here is what is currently open.

Calls for participation

What's open right now.

The fastest way to take part. Each call is a concrete opportunity with its own scope.

open

Testers for screening

Researchers running a systematic review, willing to join a methods-evaluation study.

Take part →

Calls above are drafted from the project plan — confirm scope and status before launch.

How to contribute

Code is one way of several.

Non-code contributions count as much as code. Translations follow once the UI is internationalised.

Code

Help build LitFlow — bring your skills to a feature or a fix.

Get in touch →

Tool features

Propose features and shape what LitFlow builds next.

Suggest a feature →

Evaluation data

Share anonymised review data for the methods research.

Email us →
Roadmap

Where things stand.

From ideas we're weighing, to what's in active development, to what's already shipped.

Considering
  • Topic Modeling
  • Dual AI Use
  • OSF / Zenodo Integration
In progress
  • Team Screening
  • Journal Ranking
  • Living Literature Reviews
Released
  • Closed alpha
    live at the University of Münster
Get involved

Have an idea, or want to help build it?

Propose a feature, contribute, or share review data — tell us what you're working on and we'll take it from there.

Open questions

What we're trying to figure out.

Methodological questions LitFlow is actively researching. Each is an invitation, not a settled position.

Q1

Anchoring effects from AI recommendations

Do AI suggestions bias a reviewer's decision, even when shown with a justification and a confidence score? We're designing screening modes to test it.

Q2

Per-criterion reliability across reviewers

How consistently do independent reviewers agree at the level of individual criteria — with and without AI assistance?

Q3

Cross-discipline schema transfer

Can an extraction schema built in one field be reused in another without losing fidelity?

The core team

The people behind it.

The core maintainer team sits at the University of Münster's Department of Information Systems.

HNHead of Project · MaintainerHans-Henning Näscher

Designs the platform, writes the prompts, runs the evaluations, and merges the pull requests.

ORCIDEmailGitHubLinkedInProfile
TSCo-author · MaintainerTimo Strohmann

Co-leads the AI augmentation architecture and the LLM provider abstraction.

JvBPrincipal investigatorJan vom Brocke

Professor at the University of Münster and director of ERCIS. Provides the eDSR methodological framing and steers the research programme.