S3.2 Citizen Science

24 Sep 2018
14:00 - 16:15
Lect. Hall 5

S3.2 Citizen Science

S3.2 _ Citizen science meets informatics: Data science challenges in ecological research with public participation. (Session Chairs: Friederike Klan and Jana Wäldchen)

During the last decade, Citizen Science, i.e. the involvement of laymen in scientific research, has gained great attention, both from the public and within the scientific community. Particularly the life sciences benefit from this development as citizen scientists contribute environmental observations of high resolution, analyze large amounts of ecological data or raise entirely new research questions. In doing so, they help to tackle pressing societal challenges such as loss of biodiversity and climate change.
In addition to the issue of how to engage and empower volunteers, data science aspects are major challenges in Citizen Science projects. This includes the following questions
1.How to make sure that data collected by citizen scientists are useful and relevant for addressing scientific questions?
2.How can data collected by citizen scientists be found, accessed, interpreted and used by others?
3.How to integrate and analyze data collected by the public with other data sources?
4.How to assess and improve the quality and reliability of Citizen Science data?
5.How to increase the credibility of data collected by volunteers and how to acknowledge citizen contributions?
6.How to enable citizen scientists to gain insights from data?

While some of these topics are specific to Citizen Science, they often share challenging aspects of data-intensive science in general (e.g. How to make data findable, accessible, interoperable and re-usable?). However, despite first cross-disciplinary and Citizen Science specific initiatives such as FORCE11 and the FAIR Data Principles [1], the Cost Action “Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe” [2] or first ideas on a EU Citizen Science Gateway for Biodiversity Data [3] where basic data science challenges are jointly discussed and best practices are collected, Citizen Science practitioners often address these core questions in an ad-hoc manner and individually in the context of specific projects.

We believe that the Citizen Science community would greatly benefit from an intensified scholarly exchange on data science topics related to Citizen Science across projects and disciplines. Thus, the objective of this special session is to bring together practitioners in Citizen Science projects as well as “traditional” scientists to discuss basic data science challenges arising in Citizen Science projects, to share best practices and lessons learned as well as to identify next steps towards a regular exchange on these topics and to initiate joint efforts on systematically addressing these challenges.

Keywords: citizen science, data science, data management

Talk 01-09

Crowdsourcing participatory millet variety selection in Hoima, Uganda for climate change adaptation through Triadic Comparisons of Technologies
Tobias Recha, Gloria Otieno and Carlo Fadda

Citizen science and science-policy interface: Towards sustainable forest managements
Ryo Kohsaka, Shuichiro Kajima and Yuta Uchiyama

Flora Capture – An Adaptive Digital Herbarium Using Mobile Devices
David Boho, Michael Rzanny, Jana Wäldchen and Patrick Mäder

Support of Forest Inventory Data Collection by Citizen Scientists
Christian Thiel, Friederike Klan, Carsten Pathe, Christiane Schmullius and Jussi Baade

Mobile app and platform development in citizen science
Ulrike Sturm

Planktonid – Combining in situ imaging, deep learning and citizen science for global plankton research
Rainer Kiko, Svenja Christiansen, Simon-Martin Schröder, Reinhard Koch and Lars Stemmann

sMon – Trend analysis of German biodiversity data
Aletta Bonn, David Eichenberg, Helge Bruelheide, Florian Jansen and Marten Winter

Wikimedia projects as citizen science platforms
Daniel Mietchen

Beyond Data and Quality – Unleashing the Value of Citizen Contributions
Friederike Klan