Users of a citizen science platform for bird data collection differ from other birdwatchers in knowledge and degree of specialization

Data contributed by citizen scientists raise increasing interest in many areas of scientific research. Increasingly, projects rely on information technology such as mobile applications (apps) to facilitate data collection activities by lay people. When developing such smartphone apps, it is essential to account for both the requirements of the scientists interested in acquiring data and the needs of the citizen scientists contributing data. Citizens and participating scientists should therefore ideally work together during the conception, design and testing of mobile applications used in a citizen science project. This will benefit both sides, as both scientists and citizens can bring in their expectations, desires, knowledge, and commitment early on, thereby making better use of the potential of citizen science. Such processes of app co-design are highly transdisciplinary, and thus pose challenges in terms of the diversity of interests, skills, and background knowledge involved.Our &#8220;Nachtlicht-B&#252;HNE&#8221; citizen science project addresses these issues. Its major goal is the development of a co-design process enabling scientists and citizens to jointly develop citizen science projects based on smartphone apps. This includes (1) the conception and development of a mobile application for a specific scientific purpose, (2) the design, planning and organization of field campaigns using the mobile application, and (3) the evaluation of the approach. In Nachtlicht-B&#252;HNE, the co-design approach is developed within the scope of two parallel pilot studies in the environmental and space sciences. Case study 1 deals with the problem of light pollution. Currently, little is known about how much different light source types contribute to emissions from Earth. Within the project, citizens and researchers will develop and use an app to capture information about all types of light sources visible from public streets. Case study 2 focuses on meteors. They are of great scientific interest because their pathways and traces of light can be used to derive dynamic and physical properties of comets and asteroids. Since the surveillance of the sky with cameras is usually incomplete, reports of fireball sightings are important. Within the project, citizens and scientists will create and use the first German-language app that allows reporting meteor sightings.We will share our experiences on how researchers and communities of citizen scientists with backgrounds in the geosciences, space research, the social sciences, computer science and other disciplines work together in the Nachtlicht-B&#252;HNE project to co-design mobile applications. We highlight challenges that arose and present different strategies for co-design that evolved within the project accounting for the specific needs and interests of the communities involved.

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Design of Microplastics Citizen Science Kit

10.5194/egusphere-egu2020-9176 ◽

2020 ◽

Author(s):

Rachael Hughson-Gill

Keyword(s):

Data Collection ◽

Citizen Science ◽

Large Scale ◽

Positive Impact ◽

Research Process ◽

Freshwater Ecosystems ◽

Freshwater Systems ◽

The Public ◽

Plastic Bags ◽

The Impact

Microplastics are an ever-increasing problem. Every river that was tested in a recent study found the presence of microplastics, with 80% of all plastic in the ocean coming from upstream. Despite this, there is little understanding into the abundance of plastic, its characteristics and the full impact that is it having on marine, freshwater ecosystems and wider ecological systems.&#160;Current fresh water monitoring does not consider the fluid dynamics of rivers, is difficult to use and is inaccessible to the wider public. My project will focus on creating a product that allows for the large-scale data collection of microplastic through citizen science. Allowing groups of people to analyse their local natural environment for the presence and abundance of microplastics within the water. This method of data collection could provide information on a scale that is not possible with traditional methods and would allow for the comparison between freshwater systems. This comparison is fundamental to begin to fill the knowledge gaps around the understanding of microplastics.&#160;Inaccessibility of monitoring to the public is not just through tools but also through the current communication of data with research rarely breaking into the public domain. Citizen science offers not just an improvement in understanding but also offers an opportunity for engagement with the public body. Increasing awareness of the impact of habits round plastic through the sharing of monitoring data can generate the much-needed change on both an individual and policy level to address the problem from the source. This method of change through public opinion can be seen to have an effect on freshwater systems through microbeads ban, plastic bags, plastic straws and industrial pollution regulation.&#160;Through the creation of this product a multidisciplinary approach that blends engineering and design practices is implemented. The wholistic approach to creation is something that is fundamental in the success of tools and therefore the success of the research that is implemented through them. A tool such as this whose function is within the public engagement of its use - increased awareness, as well as the outcome of its use - microplastics data, is required to have an engaging user experience as well as data integrity implemented through engineering design.&#160;This project offers an opportunity to show the importance of the design process within research tools to aid the research process and the positive impact that can come from it.

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Participation in Citizen Science for Coastal Water Data Collection: Does Engagement Drive Persistent Contributions

AEA Randomized Controlled Trials ◽

10.1257/rct.4730-1.0 ◽

2019 ◽

Author(s):

Laura Paul

Keyword(s):

Data Collection ◽

Citizen Science ◽

Coastal Water

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Estimating disease vector population size from citizen science data

Journal of The Royal Society Interface ◽

10.1098/rsif.2021.0610 ◽

2021 ◽

Vol 18 (184) ◽

Author(s):

Tam Tran ◽

W. Tanner Porter ◽

Daniel J. Salkeld ◽

Melissa A. Prusinski ◽

Shane T. Jensen ◽

...

Keyword(s):

Data Collection ◽

Citizen Science ◽

Large Scale ◽

Species Abundance ◽

Natural World ◽

Tick Population ◽

Science Data ◽

Tick Abundance ◽

Vector Population ◽

Public Datasets

Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

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Citizen Sensing: An Action-Orientated Framework for Citizen Science

Frontiers in Communication ◽

10.3389/fcomm.2021.629700 ◽

2021 ◽

Vol 6 ◽

Author(s):

Saskia Coulson ◽

Mel Woods ◽

Keyword(s):

Data Collection ◽

Citizen Science ◽

Social Innovation ◽

Low Cost ◽

Environmental Issues ◽

Environmental Challenges ◽

European Project ◽

Citizen Sensing ◽

The Cross ◽

Future Practice

Citizen Sensing, a correlative of Citizen Science, employs low-cost sensors to evidence local environmental issues and empowers citizens to use the data they collect. Whilst motivations for participation can vary, communities affected by pollution frequently have changemaking as their goal. Social innovation is closely aligned with citizen sensing, however the process of co-creating practices and solutions with citizens who wish to shape their world can be highly complex to design. Therefore, our research articulates an action-orientated framework which emerges from a 2-year pan European project by which follow-on communities may replicate sensing initiatives more easily. The authors examine five studies and explore the cross-cutting principles, phases, stakeholders, methods, and challenges which form this framework. The authors argue that whilst data collection and data awareness are crucial to the citizen sensing process, there are precursory and subsequent stages which are necessary to equip citizens to address complex environmental challenges and take action on them. Therefore, this paper focuses on the stages and methods which are distinctive to citizen sensing. It concludes with recommendations for future practice for citizen sensing and citizen science.

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BioCollect - A modern cloud application for standards-base field data recording

Biodiversity Information Science and Standards ◽

10.3897/biss.2.25439 ◽

2018 ◽

Vol 2 ◽

pp. e25439

Author(s):

Peter Brenton

Keyword(s):

Resource Management ◽

Data Collection ◽

Citizen Science ◽

Natural Resource Management ◽

Natural Resource ◽

Field Data ◽

Ad Hoc ◽

Research Policy ◽

Mobile Apps ◽

Seed Collection

Many organisations running citizen science projects don’t have access to or the knowledge or means to develop databases and apps for their projects. Some are also concerned about long-term data management and also how to make the data that they collect accessible and impactful in terms of scientific research, policy and management outcomes. To solve these issues, the Atlas of Living Australia (ALA) has developed BioCollect. BioCollect is a sophisticated, yet simple to use tool which has been built in collaboration with hundreds of real users who are actively involved in field data capture. It has been developed to support the needs of scientists, ecologists, citizen scientists and natural resource managers in the field-collection and management of biodiversity, ecological and natural resource management (NRM) data. BioCollect is a cloud-based facility hosted by the ALA and also includes associated mobile apps for offline data collection in the field. BioCollect provides form-based structured data collection for: Ad-hoc survey-based records; Method-based systematic structured surveys; and Activity-based projects such as natural resource management intervention projects (eg. revegetation, site restoration, seed collection, weed and pest management, etc.). This session will cover how BioCollect is being used for citizen science in Australia and some of the features of the tool.

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DesPat: Smartphone-based Object Detection for Citizen Science and Urban Surveys

10.31219/osf.io/drs6u ◽

2021 ◽

Author(s):

Christopher Getschmann ◽

Florian Echtler

Keyword(s):

Data Collection ◽

Object Detection ◽

Citizen Science ◽

Urban Areas ◽

Low Cost ◽

Traffic Patterns ◽

Pedestrian Tracking ◽

Privacy Concerns ◽

Wide Range ◽

Pedestrian Traffic

Data acquisition is a central task in research and one of the largest opportunities for citizen science. Especially in urban surveys investigating traffic and people flows, extensive manual labor is required, occasionally augmented by smartphones. We present DesPat, an app designed to turn a wide range of low-cost Android phones into a privacy-respecting camera-based pedestrian tracking tool to automatize data collection. This data can then be used to analyze pedestrian traffic patterns in general, and identify crowd hotspots and bottlenecks, which are particularly relevant in light of the recent COVID-19 pandemic.All image analysis is done locally on the device through a convolutional neural network, thereby avoiding any privacy concerns or legal issues regarding video surveillance. We show example heatmap visualizations from deployments of our prototype in urban areas and compare performance data for a variety of phones to discuss suitability of on-device object detection for our usecase of pedestrian data collection.

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