🌿 EthnoHack 2026

Data Hackathon on Ethnopharmacology & Health

2–8 November 2026 · 100% Online · Discord + Devpost

Official participant registration closes on Octomber 31 at 11:00 PM. Registrations after this time will not be eligible for judging or prizes.

Unlocking traditional knowledge with data, AI, and digital innovation

EthnoHack 2026 is a week-long online hackathon bringing together data scientists, AI researchers, computer scientists, bioinformaticians, pharmacognocists, botanists, historians, anthropologists, pharmacologists, ecologists, clinicians, and students to explore how computational methods can unlock the potential of traditional medicine and ethnopharmacology.

Across four challenge tracks, participants will work with open datasets, scientific literature, biodiversity data, chemical information, and digital health technologies to build innovative solutions addressing real-world challenges in:

  • 🌿 Traditional knowledge 
    • Use NLP and AI to extract medicinal knowledge from historical, folklore texts and ethnobotanical studies. Map plant names across eras, identify therapeutic uses, and connect ancient knowledge to modern pharmacology.
  • 🧪 Natural products and drug discovery
    • Apply machine learning and cheminformatics to identify promising plant-derived compounds for specific disease targets. Build predictive models, knowledge graphs, and screening pipelines.
  • 🌍 Biodiversity and sustainability
    • Leverage geospatial and ecological datasets to map medicinal plant diversity, identify conservation risks, and develop tools for sustainable harvesting.
  • 🩺 Digital health and phytotherapy
    • Design apps, decision-support tools, or recommendation engines that connect phytotherapy evidence to clinical or patient-facing contexts.

The hackathon is organised under the ETHCSTWIN framework and aims to create connections between academia, industry, technology, healthcare, and civil society.

Whether you are an experienced machine-learning researcher or a domain expert with a great research question, you are welcome to participate.

What makes EthnoHack special?

EthnoHack sits at the intersection of several disciplines that are rarely brought together in a single hackathon.

Participants will have the opportunity to combine:

  • Artificial intelligence and machine learning

  • Natural language processing

  • Knowledge graphs

  • Cheminformatics

  • Bioinformatics

  • Geospatial and biodiversity data

  • Ethnobotanical and historical knowledge

  • Natural products chemistry

  • Digital health

  • Scientific research and evidence synthesis

You don't need to be an expert in every field. Interdisciplinary teams are encouraged.

💻 Fully Online

EthnoHack 2026 is 100% online.

Participants can join from anywhere in the world. Most collaboration will happen asynchronously through Discord, with scheduled mentoring, presentations, Q&A sessions, and the final pitch event.

Primary time zone: CET

Platforms: Devpost + Discord -> https://discord.gg/GrQRGfk9YK

Come with a research question, a technical skill, a domain expertise, or simply curiosity. Build something that connects traditional knowledge with the tools of tomorrow.

Requirements

What to Build

Build a working prototype, computational pipeline, model, application, analysis, or digital tool addressing a challenge within one of the four EthnoHack tracks.

Your project should demonstrate how data, computation, AI, or digital technologies can generate useful insights or capabilities related to ethnopharmacology, natural products, biodiversity, sustainability, or health.

Your project may be:

  • A web application

  • A mobile application

  • An AI/NLP system

  • A machine-learning model

  • A knowledge graph

  • A data-analysis pipeline

  • A cheminformatics workflow

  • A geospatial application

  • A visualisation or dashboard

  • A decision-support prototype

  • A research tool

  • An interactive database

  • A computational research project

  • Another digital solution relevant to one of the four tracks

Your project should demonstrate

  1. A clearly defined problem

  2. A computational or data-driven approach

  3. A working prototype or demonstrable result

  4. Evidence supporting your approach

  5. Clear documentation

  6. Potential real-world relevance

Projects do not need to be production-ready. Hackathon prototypes, research prototypes, proof-of-concepts, and experimental systems are welcome.

What to Submit

Each team must submit the following through Devpost by the final submission deadline:

1. Devpost Project Page

Your project page should include:

  • Project title

  • Selected challenge track

  • Short project description

  • Problem statement

  • Solution/approach

  • Technologies used

  • Data sources

  • Results or prototype demonstration

  • Team members

  • Impact and future development

2. Source Code

Provide a link to a public GitHub repository containing:

  • Source code

  • Data-processing scripts

  • Model code/notebooks where applicable

  • Installation instructions

  • README

  • Dataset references and licences

  • Instructions for reproducing the results where reasonably possible

3. Pitch Video

Submit a maximum 10-minute video explaining:

  • The problem

  • Your motivation

  • Your approach

  • Your technology

  • Your data

  • Your results

  • A demonstration of the project

  • Potential impact

  • Future development

4. Slide Deck

Submit a presentation of maximum 10 slides.

The slides should communicate the problem, methodology, results, demonstration, and potential impact clearly.

Recommended Project Structure

A strong submission will make it easy for judges to understand:

Problem → Data → Method → Prototype → Results → Impact

Teams should clearly identify assumptions, limitations, uncertainty, and any important data-quality issues.

Responsible Research

Projects should respect:

  • Dataset licences and attribution requirements

  • Intellectual property rights

  • Privacy and personal-data protections

  • Indigenous and traditional knowledge considerations

  • Scientific integrity

  • Appropriate interpretation of computational predictions

  • Responsible use of AI

Projects must not present unvalidated computational findings as established medical facts.

Prizes

€8,000+ in prizes
+ other prizes
1st Prize Track 1
€2,000 in cash
1 winner

1st Prize Track 2
€2,000 in cash
1 winner

1st Prize Track 3
€2,000 in cash
1 winner

1st Prize Track 4
€2,000 in cash
1 winner

Best AI Solution
1 winner

Most innovative use of machine learning

Best Student Team
1 winner

Top team composed entirely of students (BSc, MSc, PhD)

Best Scientific Rigor Award
1 winner

Most thorough design and data validation

Most Innovative Application
1 winner

Breakthrough solution to an unexpected real-world problem

Best Community Impact
1 winner

Project with strongest direct benefit to local communities

Best Collaboration Prize
1 winner

Outstanding teamwork across disciplines or institutions

Judges

Nektarios Aligiannis

Nektarios Aligiannis
Scientific Director of Institute for Ethnopharmacological Studies and Phytotherapy (IESP) and Professor at the Department of Pharmacognosy and Natural Products Chemistry (Faculty of Pharmacy, National & Kapodistrian University of Athens (UoA), Greece)

Petros Bouras-Vallianatos

Petros Bouras-Vallianatos
Professor of History of Science at the National and Kapodistrian University of Athens. He is a leading scholar of premodern medicine and pharmacology, with particular expertise in the Byzantine world.

Dimitrios Gunopulos

Dimitrios Gunopulos
Dimitrios Gunopulos got his PhD from Princeton University in 1995. He is currently a Professor and the Chair of the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens.

Antigoni Cheilari

Antigoni Cheilari
Antigoni Cheilari is a pharmacist and postdoctoral researcher at the Laboratory of Pharmacognosy & Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens.

Mehmet Can Ay

Mehmet Can Ay
Fraunhofer Institute for Algorithms and Scientific Computing SCAI Biomedical Data Intelligence

Theodora Manika

Judging Criteria

  • Scientific Relevance
    Does the project address a genuine ethnopharmacology or health challenge?
  • Innovation
    Is the approach novel? Does it use data in a creative way?
  • Technical Implementation
    Quality of code, model or tool — reproducibility and documentation
  • Data Science Quality
    Appropriate methods, validation, handling of uncertainty
  • Feasibility
    Is the solution realistic and scalable beyond the hackathon?
  • Impact
    Potential benefit to conservation, sustainability, drug discovery or public health
  • Presentation
    Clarity of the pitch, slides and demo

Questions? Email the hackathon manager

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