Examples of using Distributed data collection in English and their translations into Bulgarian
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Distributed data collection(section 5.4).
Purdam(2014) describes a distributed data collection about begging in London.
Redundancy is an important way to assess the quality of distributed data collection.
In distributed data collection projects, volunteers contribute data about the world.
Bit By Bit- Creating mass collaboration- 5.4 Distributed data collection.
As eBird demonstrates, distributed data collection can be used for scientific research.
Redundancy is an important way to assess the quality of distributed data collection.
Finally, distributed data collection projects are ideally suited for large-scale data collection. .
In fact, much of quantitative social science already relies on distributed data collection using paid staff.
Further, in distributed data collection, researchers could collect data about people without their consent.
PhotoCity solves the data quality andsampling problems in distributed data collection.
Further, in distributed data collection, researchers could collect data about people without their consent.
Both Purdam(2014) and Windt and Humphreys(2016)have some discussion about the ethical issues in distributed data collection.
Finally, distributed data collection projects are ideally suited for large-scale data collection. .
In addition to creating human computation and open call projects,researchers can also create distributed data collection projects.
Open calls and distributed data collection can also raise complex questions about consent and privacy.
In fact, much of quantitative social science already relies on distributed data collection in the form of surveys administered by employees.
In distributed data collection projects, researchers enable participants to contribute new measurements of the world.
The design of PhotoCity solved two problems that often arise in distributed data collection: data validation and sampling.
Further, it shows that distributed data collection projects are not limited to tasks that people are already doing anyway, such as watching birds.
Overall, the PhotoCity project shows that sampling anddata quality are not insurmountable problems in distributed data collection.
A classic example of a distributed data collection is eBird, in which hundreds of thousands of volunteers contribute reports about birds they see.
As I write in Bit by Bit, I think it is helpful to roughly distinguish between three types of mass collaboration projects:human computation, open call, and distributed data collection.
Further, it shows that distributed data collection projects are not limited to tasks that people are already doing anyway, such as watching birds.
EBird researchers have two main solutions to these data quality issues- solutions that might be helpful in other distributed data collection projects as well.
In fact, for certain research questions, distributed data collection is better than anything that would realistically be possible with paid data collectors.
However, eBird, and related projects, indicate that challenges related to sampling anddata quality are concerns for distributed data collection projects.
In fact, for certain research questions, distributed data collection is better than anything that would realistically be possible with paid data collectors.
EBird is a distributed data collection project that solicits information from birders around the world, and it has already received over 260 million bird sightings from 250,000 participants(Kelling, Fink, et al. 2015).
As more projects take advantage of technology that enables de-skilled andpassive participation, distributed data collection projects should dramatically increase in scale, enabling researchers to collect data that were simply off limits in the past.