A Community Science Index

Image: categories and example from the index, including: DECODE, Jupyter Notebooks, Global Open Science Hardware Roadmap, and PreTalx

(AKA Citizen Science)

Cite as:

DOI

10.25815/6pbz-ns09

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

Generation Research. ‘A Community Science Index, 2019. https://doi.org/10.25815/6pbz-ns09.

This is a collaboratively made index of resources to accompany the GenR theme ‘Post-Digital Community Science‘ which ran over May/June 2019. The theme blogposts can all be seen here online.

The index has been organised to represent a number of areas and questions that were felt to be important for researchers looking to organise and plan research projects making use of Community Science. The categories in the index are:

  • projects,
  • collaborative tools and open access,
  • FOSS for open hardware, and
  • spaces.
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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.

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Open Science and Climate Change: A GenR Theme

Time is of the essence when it comes to climate change and many look to Open Science to speed up research and innovation in response to the challenges faced.

The aim of this special theme, as with other Generation Research special topics, is to find example projects and tools that can inspire researcher and show pathways for implementing Open Science and Scholarship practices.

GenR welcomes contributions, collaboration, and suggestions, as: blogposts, repostings, notices, literature, and as contributions to an open pad ‘A Collaborative Listing: Open Science and Climate Change Resources’.

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Making Connections: An Interview with Kerstin Göpfrich of Ring-a-Scientist

Image: Ring-a-Scientist lab videoconference in progress. Courtesy Ring-a-Scientist

Cite as: DOI 10.25815/vdf3-nc44
Göpfrich, Kerstin. Worthington, Simon. ‘Making Connections: An Interview with Kerstin Göpfrich of Ring-a-Scientist’, 2019. https://doi.org/10.25815/vdf3-nc44.

GenR’s editor-in-chief Simon Worthington talks with co-founder Kerstin Göpfrich of Ring-a-Scientist about how the platform for connecting scientists with school students via videoconferencing was started.

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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.

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