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The presented Open Educational Resources help mitigate common problems that arise when scientific data is used for educational purposes: Often, finding, getting and analyzing data is a time-consuming task. To address this problem, we demonstrate how teachers and their students can use the methods to directly download data. Good analysis scripts begin with code that fetches the data from a repository. This contributes to making research reproducible and supports proper citation of research data. Two examples, one executable in Python, one in a local R installation, will show how to download datafiles from a Dataverse repository, and take first steps in analysing data.
python, Stata, R, Dataverse, SPSS
python, Stata, R, Dataverse, SPSS
| 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 |
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| downloads | 10 |

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