Is crowed-sourced data the best way to collect large-scale datasets?
Best insight from top research papers
Crowd-sourced data presents a valuable method for collecting large-scale datasets efficiently and effectively. It allows for the gathering of substantial amounts of information from a diverse pool of contributors, aiding in the creation of comprehensive datasets for research purposes. Crowd-sourcing enables the validation of assumptions, testing of hypotheses, and verification of claims related to software development processes and social science research. Moreover, crowd-sourced datasets are transparent, reproducible, and can be extended easily, enhancing research quality and reliability. While challenges such as data completeness and timeliness exist, innovative solutions like distributed crowd crawling frameworks have been developed to overcome these obstacles. Overall, crowd-sourced data stands out as a robust and versatile approach for generating large-scale datasets in various research domains.
Answers from top 5 papers
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| Papers (5) | Insight |
|---|---|
| Not addressed in the paper. | |
| Crowd-sourced data collection offers reproducible and agile production of political data, providing comparable results to expert-driven methods but with greater speed and flexibility. | |
| Crowdsourcing system method optimizes large-scale data collection by incentivizing user participation and ensuring maximum data collection, making it an effective approach for gathering extensive datasets. | |
01 Nov 2016 3 Citations | Crowd crawling in Online Social Networks (OSN) is an efficient method for large-scale dataset collection, addressing challenges and providing timely and complete data, as demonstrated in the paper. |
2 Citations | Crowdsourced data from popular open source projects on Github is an effective method for collecting large-scale datasets, as demonstrated in the research paper. |
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