Planning a Community Science (AKA Citizen Science) Research Project

Cite as:

DOI

10.25815/ktnw-y834

Citation format: The Chicago Manual of Style, 17th Edition

Generation Research. ‘Planning a Community Science (AKA Citizen Science) Research Project’, 2019. https://doi.org/10.25815/ktnw-y834.

Generation Research Dossier #1

The GenR dossier is designed as a conclusion of the initial cluster of articles for the Generation Research theme ‘Post-Digital Community Science‘ which ran over May/June 2019 and is accompanied by a collaboratively built ‘Community Science Index’ of projects and tools.

Intro

The conventional role and partner in a research project would be — a PI, a Co-Investigator, co-authors, a community, partner institution, an SME, or data provider — and their roles are not always fixed and quite often can overlap. Similarly this is the case with how a Community Science project design can shape the roles and types of participation by the public. And as with any module or work package you design for a research programme the goals and activities need to be carefully planned. For this dossier we have commented on six projects using Community Science that have lessons that can be widely applied. Additionally there is a collaboratively built ‘Community Science Index’ with further projects, collaborative tools, and spaces and event formats, etc.

Read More

Interview with Flora Incognita: Innovation in Citizen Science Using Machine Learning

Images: All images courtesy Flora Incognita https://floraincognita.com/de/pressemappe/

Authors
Jana Wäldchen ORCiD 0000-0002-2631-1531
Patrick Mäder ORCiD 0000-0001-6871-2707

Cite as:

DOI

10.25815/xp2t-w456

Citation format: The Chicago Manual of Style, 17th Edition

Wäldchen, Jana & Mäder, Patrick. ‘Interview with Flora Incognita: Innovation in Citizen Science Using Machine Learning’, 2019. https://doi.org/10.25815/xp2t-w456.

An interdisciplinary team has come up with a mobile app for identifying plants based on users taking a photo of the plant on their mobile. The Flora Incognita app applies machine learning to identify plant species in near real-time — flowers, plants, and trees. Simplicity and Innovation are both hard to accomplish but this is where Flora Incognita has excelled and to achieve both deserves a mention. Currently the app suite works with flora in the German Central European region, based on 4,800 species, using 1.7 million images, with a 100,000 images coming from users in 2018 alone. For Citizen Science the enthusiastic engagement of the public with Flora Incognita shows a clear path forward for more widespread uses of machine learning in public participation with science and scholarship, and in knowledge creation.

Read More