Citizen science data visualization platform

During nature watch campaigns, citizen scientists are invited to observe and record wildlife timely, which not only strengthens the connection between citizens and nature but also serves as a promising species distribution data source. Species record data collected by citizen scientists via online questionnaire automatically flows into the visualization platform database (after data cleaning and manually periodically check) and turns into intuitive and attractive visualized charts and maps (two types: spatial, spatial and temporal) via Power BI. The platform, with both web and mobile version, provides real-time feedback to citizen scientists’ nature watch efforts, boosting their sense of accomplishment and motivating their future participation in nature watch activities. Moreover, since the platform integrates multiple nature watch campaigns with links to web articles about specific analysis of each campaign, it offers a broad range of biodiversity knowledge and enables “virtual nature watch” for citizens to get to know wildlife in other regions.


A brief timeline of the platform:

  • Jan-Feb 2021: form team, analyze analysis, make blueprint
  • Mar-Jun 2021: develop database and platform
  • Jul-Aug 2021: trial test
  • Sept 2021: go live and promotion
    • A well-designed data-collection questionnaire and automatic data cleaning mechanism to ensure data quality and a manually periodically check (normally once a season) to ensure data reality.
    • Visualization methods selection and aesthetic design with the engagement of citizen scientists.
    • PowerBI technology.
    • Citizen scientist WeChat community operation and maintenance.
    • As a public outreach product, it would never be too much for polishing contents and aesthetic design to make the platform user-friendly and attractive.
    • Engaging users in the planning stage and collecting their thoughts is very helpful for identifying user needs.
    • Questionnaires are needed to be well-designed and citizen scientists are needed to be well-trained before recording data. Otherwise, it’s easy to cause data loss.