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The Reproducible Self Publishing toolkit demonstrates how to dynamically include publication-quality data analysis output in most common science communication formats. Data analysis is defined in one and only one place, and styling is applied at the document or output element level. Data and code dependencies are provided or specified, so that both the toolkit itself - as well as your own derivatives - can be reproduced locally and autonomosly by your colleagues, reviewers, students, and everybody else.
transparency, publishing, reexecutable publication, SciPy, self-publishing, reexecutable, PythonTeX, science communication, reproducibility, Python
transparency, publishing, reexecutable publication, SciPy, self-publishing, reexecutable, PythonTeX, science communication, reproducibility, Python
| 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 |
| views | 2 |

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