![]() Language barriers (lack of translations) Balancing researcher time and effort in data research or in training participants Accepting high volume low-quality data or culling for low volume high-quality data Keeping a level of individual privacy or collecting extraneous data for patterns Funding for developing the research or for the tools to facilitate citizen science Rotation of new researchers fail to maintain the inital study's goals Lack of experience with training non-scientists Peer researchers dispute use of public as participants Inadequate training due to restrictive research timelines Poor incorporation of collected data into the existing sytem Collaborating with peers across nations Bridging gaps between academia and the public Increasing the frequency of collections Game elements during science participation. Citizen science games include folding proteins, play brain puzzle challenges, searching for microbes, save a lake, and signalling stroke symptoms. Paticipants aim to complete a challenge, and their activity is monitored as the data Gaming as the means for data collection is also used in citizen science Common elements include score comparisons, leaderboards, work-a-thons, swag, and a storyline Adding game elements to citizen science motivates participants Participants have analysed video samples of microscopic particles for stardust, transcribed historical ship logs, categorized baby sounds, and even classified architectural photographs. Training familiarizes participants with the data and the analysis tools. Participants are trained to sort a vast collection of data into pre-determined categories. Participants have provided information on animals, plants, insects, weather patterns, sounds, traffic, air quality, and even their own lifestyle habits. The observations are collected in a database that is accessible to a interested researchers. Individuals make observations of the real world, from a recording or from a computer simulation. ![]()
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