Digitalisation Needs CS

Digitalisation Needs Citizen Science

Citizen science projects area necessary response to the amount of data in many fields, as well as often the size of the challenges. "While machine learning and computing have been able to take up some of the slack, they are not always adequate replacement for human abilities. After all, our brains have evolved to be extremely good at pattern recognition". The web provides a means of reaching a much larger audience, willing to devote their free time to improve the world. This provides possibility to enable:

• Scale - Coping with extremely large data sets is enabled.

• Minimized error - Multiple independent interactions with the data for error quantitative estimation

• Improved machine learning - Large and powerful training sets for machine learning approaches

• Scope - In the past, collecting large samples of data for research was the most challenging task of any initiative. With today's interconnected world, thousands of people from around the globe can remotely contribute to a study and provide, analyze, or report data that researchers can use. Public participation enables investigations that would not otherwise be possible, ones that push new frontiers in our understanding of our world.

• Bridging gaps - Citizen science bridges gaps by harnessing the power of people who are motivated by curiosity, a desire to advance research, or a concern about environmental conditions in their communities, then connecting them to projects that benefit from their energy and dedication

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