- Standardized and electronically available impact measures will help with comparisons across diverse projects and allow scientists to customize evaluations through standard measures, thus improve the ability to collect large scale of quantitative data. 
- Improved tracking technologies can track individuals as they participate in a wide array of other informal science education programs over their lifetime, which provide a picture of user interests and skills over time and thus advances social media research. 

New York University

The researchers examine the future of research process by breaking it down 6 elements: 
- gathering teams/ resources/ partners
- defining research questions
- collecting and managing data
- analyzing and interpreting data
- disseminating results
- evaluating program success and participant outcomes

The future of the citizen-science research process

- accelerate team formation, better locate professional scientists and required resources, and identify participants
- integrated use of networked database, social media, and cyberinfrastructure would form a more formal enterprise and thus enhance the organization of citizen-science related information

gathering teams/ resources/ partners

- emerging technology will improve both top-down (scientist-driven) and bottom-up (community-driven) research approaches 
- Scientist and citizens can have real-time dialogue, which can lead to more creative and convenient discussion of research question. 


defining research questions

- technologies will improve the rate and quality of data collection through location-based, real-item mapping services. 
- novel challenges: “data deluge”: Due to the larger scale of data the emerging technologies generate, improved data management is also required to manage these data.

collecting and managing data

Addressing the challenges posed by analyzing a large amount of data will promote the innovation in statistical analysis and modeling. Thus citizen scientist can examine data more freely. 

analyzing and interpreting data 

- centrally consolidate scientific information across projects, have a better collaborative writing, and create virtual forums and communities -> thus increasing collective capital. 
- Challenge: more susceptible to bias and inaccuracies, so it is important to distinguish scientifically valid information from opinion and/or advocacy. 


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