
See related identifiers for more info regarding Readersourcing 2.0 and its technical documentation. RS_Py is an additional component of the Readersourcing 2.0 ecosystem, providing a fully working implementation of the RSM and TRM models presented in the original paper. These models are encapsulated by the server-side application of Readersourcing 2.0, that is, RS_Server. Developers with a background in the Python programming language can leverage RS_Py to generate and test new simulations of ratings given by readers to a set of publications. They are allowed to alter the internal logic of the models to test new approaches without the need to fork and edit the full implementation of Readersourcing 2.0.
Scholarly Publishing, Peer Review, Crowdsourcing
Scholarly Publishing, Peer Review, Crowdsourcing
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
