README.md
In edunford/qualify: qualify
qualify
In the social sciences, researchers regularly engage in manual data coding tasks to produce novel data relevant to their specific research topics. However, manual data collection is a messy process that often lacks transparency regarding how these data are generated. We propose an R package, qualify, that allows researchers to easily construct a browser-based application for any manual coding data task. The application, once deployed, can sync seamlessly with cloud-based data resources (such as Azure or AWS) to allow for the construction of a reliable database where all coded information is consistently saved, metadata regarding how variables are coded is retained, and real-time tracking of data quality is reported. The package offers a user-friendly implementation of a sophisticated infrastructure that allows for the easy implementation of best practices in data generation and transparency. Moreover, the application eases the distribution of coding tasks to research assistants.
Development
The project is currently in beta, but we aim to have a working beta version of the package up (functional on Mac OS) by the end of summer 2019.
Usage
Important commands
make build installs js dependencies from npm
make run starts up the R and React servers
edunford/qualify documentation built on Aug. 27, 2019, 9:58 p.m.
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qualify
In the social sciences, researchers regularly engage in manual data coding tasks to produce novel data relevant to their specific research topics. However, manual data collection is a messy process that often lacks transparency regarding how these data are generated. We propose an R package, qualify, that allows researchers to easily construct a browser-based application for any manual coding data task. The application, once deployed, can sync seamlessly with cloud-based data resources (such as Azure or AWS) to allow for the construction of a reliable database where all coded information is consistently saved, metadata regarding how variables are coded is retained, and real-time tracking of data quality is reported. The package offers a user-friendly implementation of a sophisticated infrastructure that allows for the easy implementation of best practices in data generation and transparency. Moreover, the application eases the distribution of coding tasks to research assistants.
Development
The project is currently in beta, but we aim to have a working beta version of the package up (functional on Mac OS) by the end of summer 2019.
Usage
Important commands
make build installs js dependencies from npm
make run starts up the R and React servers
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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