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Gamification of participatory science for training and education purposes


Report and lessons learned on The Story of Proteomics (t+30)

This website will be created in three phases, with each phase deepening in the level of information that is presented and participation from the viewer. We are currently working on the Phase I and layer I of this website. The final level will lead to both localised and cyber activities/communities for proteomics with the possibility of a gaming platform where citizens solve real-life clinical challenges in proteomics.

Dissemination and Communication Report

report summarising the dissemination and communication plans and actions

Report on Project Impact

We describe here the criteria of success and how we address the commercialisation of the results of the project (business plan, IP, FTO, sales channels, etc...)

Project final report

Final report to be used for dissemination

MMOS Platform Game engine and client modules

MMOS Platform Game engine and client modules: 100% of clients implemented

Testing and CI beta setup

Testing and CI beta setup

Project Web site

First version of the project Web site to be used in the Project management and first Dissemination activities

Statics of Small Scale Experiments

Deliver statistics on small scale experiment

Searching for OpenAIRE data...


Deep learning is combined with massive-scale citizen science to improve large-scale image classification

Author(s): Devin P Sullivan, Casper F Winsnes, Lovisa Åkesson, Martin Hjelmare, Mikaela Wiking, Rutger Schutten, Linzi Campbell, Hjalti Leifsson, Scott Rhodes, Andie Nordgren, Kevin Smith, Bernard Revaz, Bergur Finnbogason, Attila Szantner, Emma Lundberg
Published in: Nature Biotechnology, 36/9, 2018, Page(s) 820-828, ISSN 1087-0156
Publisher: Nature Publishing Group
DOI: 10.1038/nbt.4225