Examples of using Distributed data collection in English and their translations into Indonesian
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Purdam(2014) described a distributed data collection about begging in London.
PhotoCity solves the data quality and sampling problems in 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.
Finally, distributed data collection projects are ideally suited for large-scale data collection. .
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. .
Both Purdam(2014) and Windt and Humphreys(2016)have some discussion about the ethical issues in distributed data collection.
In distributed data collection projects, researchers enable participants to contribute new measurements of the world.
In fact,much of quantitative social science already relies on distributed data collection in the form of surveys administered by employees.
Distributed data collection is possible, and in the future it will likely involve technology and passive participation.
Overall, the PhotoCity project shows that sampling anddata quality are not insurmountable problems in distributed data collection.
Further, in distributed data collection, researchers could collect data about people without their consent.
EBird researchers have two main solutions to these data quality issues-solutions that might be helpful in other distributed data collection projects as well.
A classic example of a distributed data collection is eBird, in which hundreds of thousands of volunteers contribute reports about birds they see.
However, eBird, and related projects, indicate that challenges related to sampling anddata quality are concerns for distributed data collection projects.
Further, it shows that distributed data collection projects are not limited to tasks that people are already doing anyway, such as watching birds.
However, eBird, and related projects, indicate that challenges related to sampling anddata quality are concerns for distributed data collection projects.
Further, it shows that distributed data collection projects are not limited to tasks that people are already doing anyway, such as watching birds.
I choose to include it as an open call because it has a contest-like structure andonly the best contributions are used, whereas with distributed data collection, the idea of good and bad contributions is less clear.
In fact, for certain research questions, distributed data collection is better than anything that would realistically be possible with paid data collectors.
Further, in distributed data collection, a scoring system can also be used to focus individuals on providing the most useful input, as was done in PhotoCity.
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.
In human computation and distributed data collection projects, moreover, the best form of quality control comes through redundancy, not through a high bar for participation.
In fact, for certain research questions, distributed data collection is better than anything that would realistically be possible with paid data collectors.
In fact, for certain research questions, distributed data collection is better than anything that would realistically be possible with paid data collectors.
Going forward, I suspect that many distributed data collection projects will begin to make use of capabilities of the mobile phones that are already carried by billions of people around the world.
As the examples below- from ornithology and computer science- show, distributed data collection enables researchers to collect data more frequently and in more places than were possible previously.
As the examples below- from ornithology and computer science- show, distributed data collection enables researchers to collect data more frequently and in more places than were possible previously.