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Reproduce the figures, tables, statistical methods, and numerical modelling of selected published papers (examples here).
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Learn, collaborate, and create about science.
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Make our reproductions publically available.
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Learn how to make our own research reproducible.
We will likely spend a lot of time on any single paper, so their selection is very important. Please make suggestions here. Raw data must be available. Reproduction should appear feasible in a couple of months (eight meetings) at most. Probably good to focus on papers with mostly analyses of empirical data (rather than lots of numerical modelling). Please email authors of a paper when we start the reproduction, telling them what we're up to.
Reproductions will be in R markdown, and should be generously commented. We will use the google R style guide. Data manipulations allowed only in R (and other open source software, if required) are permitted. No alteration of original data files allowed.
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A fully reproduced paper would be one that can be reproduced from the raw datasets, which should ideally be available online here.
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An online report of our reproduction, with code.
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Outstanding issues.
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Research ideas.
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Parts of the paper we decided to not reproduce and why.
Information here.
Individuals might like to contribute to a reproduction outside the meeting time, or may not be able to attend the meetings. Anyone can read and contribute to the reproduction on github (look here for instructions about how to; if you do not know what git and Github are, please look at these resources to get you started). For particularly motivated folk that cannot attend the weekly meetings, we may arrange electronic attendance of the weekly meetings, e.g., via skype.
Please use the RREEBES github repository Issues and Wiki for as much communication as possible. There will be no email list.
Students at UZH can gain 1 ECTS for actively participating in the reproduction of two papers and attending at least 20 meetings. Please make an attendance form and bring it to each meeting. This course is BIO633. See more in the Vorlesungsverzeichnis entry.