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

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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Citizen Science, Experts, and Expertise.

Current World Environment ◽

10.12944/cwe.16.2.05 ◽

2021 ◽

Vol 16 (2) ◽

pp. 378-385

Author(s):

Rebecca Jordan ◽

Amanda Sorensen ◽

Steven Gray

Keyword(s):

Resource Management ◽

Social Learning ◽

Citizen Science ◽

Natural Resource Management ◽

Natural Resource ◽

Learning Process ◽

Knowledge Building ◽

Discursive Practice ◽

Community Members ◽

Community Groups

Interest in citizen science is growing among both scientists and community groups who are interested in creating natural resource management projects. Such projects have the potential to result in social learning, which can further reinforce resource stewardship. Data to study this learning process, however, remain scant. Using transcripts from four different natural resource management projects, we use discourse analysis to investigate the discursive practice between project scientists and community members in the development of models that were used to test ideas and subsequently modified with citizen collected data. We found that only a portion of the discussion focused on knowledge building and that only experts provided challenges to ideas being discussed. Subsequently to these challenges, however, a greater proportion of knowledge co-creation occurred.

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